Population and climate of cities of interest.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-signs-new-contract-with-cepiec-china-for-distribution-of-open-access-books-20210319",title:"IntechOpen Signs New Contract with CEPIEC, China for Distribution of Open Access Books"},{slug:"150-million-downloads-and-counting-20210316",title:"150 Million Downloads and Counting"},{slug:"intechopen-secures-indefinite-content-preservation-with-clockss-20210309",title:"IntechOpen Secures Indefinite Content Preservation with CLOCKSS"},{slug:"intechopen-expands-to-all-global-amazon-channels-with-full-catalog-of-books-20210308",title:"IntechOpen Expands to All Global Amazon Channels with Full Catalog of Books"},{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"}]},book:{item:{type:"book",id:"1360",leadTitle:null,fullTitle:"Mechanisms in Parkinson's Disease - Models and Treatments",title:"Mechanisms in Parkinson's Disease",subtitle:"Models and Treatments",reviewType:"peer-reviewed",abstract:"Parkinson's disease (PD) results primarily from the death of dopaminergic neurons in the substantia nigra. Current PD medications treat symptoms; none halt or retard dopaminergic neuron degeneration. The main obstacle to developing neuroprotective therapies is a limited understanding of the key molecular mechanisms that provoke neurodegeneration. The discovery of PD genes has led to the hypothesis that misfolding of proteins and dysfunction of the ubiquitin-proteasome pathway are pivotal to PD pathogenesis. Previously implicated culprits in PD neurodegeneration, mitochondrial dysfunction, and oxidative stress may also act in part by causing the accumulation of misfolded proteins, in addition to producing other deleterious events in dopaminergic neurons. Neurotoxin-based models have been important in elucidating the molecular cascade of cell death in dopaminergic neurons. PD models based on the manipulation of PD genes should prove valuable in elucidating important aspects of the disease, such as selective vulnerability of substantia nigra dopaminergic neurons to the degenerative process.",isbn:null,printIsbn:"978-953-307-876-2",pdfIsbn:"978-953-51-6741-9",doi:"10.5772/1826",price:159,priceEur:175,priceUsd:205,slug:"mechanisms-in-parkinson-s-disease-models-and-treatments",numberOfPages:606,isOpenForSubmission:!1,isInWos:1,hash:"823c4dc5acbf952ba3723cae01f7f67a",bookSignature:"Juliana Dushanova",publishedDate:"February 8th 2012",coverURL:"https://cdn.intechopen.com/books/images_new/1360.jpg",numberOfDownloads:48878,numberOfWosCitations:24,numberOfCrossrefCitations:8,numberOfDimensionsCitations:25,hasAltmetrics:0,numberOfTotalCitations:57,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 14th 2010",dateEndSecondStepPublish:"November 11th 2010",dateEndThirdStepPublish:"March 18th 2011",dateEndFourthStepPublish:"April 17th 2011",dateEndFifthStepPublish:"June 16th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,editors:[{id:"36845",title:"Dr.",name:"Juliana",middleName:null,surname:"Dushanova",slug:"juliana-dushanova",fullName:"Juliana Dushanova",profilePictureURL:"https://mts.intechopen.com/storage/users/36845/images/1775_n.jpg",biography:"Dr. Dushanova’s research interests are in motor neurophysiology, pathophysiology of Parkinson’s disease and in the development of approaches for diagnostics. Her works span human and non-human primate research, computational modeling and simulations. She received her MS degree from Sofia University, Bulgaria, Predoctoral fellow by Prof. Pfurtscheller at Department of Medical Informatics, Ludwig Boltzmann Institute for Medical Informatics and Neuroinformatics, Technical University Graz, Austria, PhD from Institute of Neurobiology, Bulgarian Academy of Sciences and studied neurophysiology under Prof. J.P. Donoghue in the Neuroscience Department of Brown University, RI USA. Assoc. Prof. J. 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Stawicki and Michael S. Firstenberg",coverURL:"https://cdn.intechopen.com/books/images_new/7447.jpg",editedByType:"Edited by",editors:[{id:"181694",title:"Dr.",name:"Stanislaw P.",surname:"Stawicki",slug:"stanislaw-p.-stawicki",fullName:"Stanislaw P. 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The concentration of population (Pop.) leads to a specialization of labor, allowing individuals to concentrate their efforts into fields where they have a particular aptitude. This inevitably leads to the rise of some type of market economy in which one trades upon the skills possessed to fulfill needs in areas outside of one’s chosen field of endeavor. Urbanization historically has led to greater overall prosperity in the long term [3, 4, 5, 6]. However, immediate consequences are more varied and lead to the “known evils” of city life: poverty, slums, an uneven distribution of resources, and a marked decline in public health [7].
\nThe historical trend toward urbanization is continuing and accelerating into the present. The world population has grown dramatically in the past 75 years and has become increasingly urbanized. The total population of the planet grew by 148% between 1960 and 2017 and by 42% in the roughly quarter century between 1990 and 2017 [8]. During that same quarter century period, the urban population of the planet grew at almost double the rate of the overall population, increasing by 83% between 1990 and 2017 [9]. In 1990, 43% of the world’s population lived in urban centers compared to 54% of a larger population in 2017, an increase of 1.9 billion people occupying the world’s cities [8, 9].
\nThe link between urbanization and the decline of public health has been weakened by advances in basic sanitation and the developments in modern medicine. There is now no discernable difference in life expectancy and infant mortality between urban and rural areas in developed countries, and metrics now favor the urban population in many developing nations [4, 6, 10]. However, the unequal distribution of resources still persists in urban centers, especially with regard to inequalities in the cost and quality of housing. The modern age has added energy to the list of resources whose availability is uneven and prosperity related [11, 12, 13]. This chapter will present a model for alleviating these systemic inequalities through the incorporation of electric microgrids directly into the planning and construction of new urban developments.
\nThe United States Department of Energy defines a microgrid as “A group of interconnected loads and distributed energy resources that act as a single controllable entity with respect to the grid. A microgrid can connect and disconnect from the grid to enable it to operate in both a grid-connected or island mode” [14]. A model is developed wherein a trigenerating, combined cycle electrical generating system is integrated into the design and construction of a combined residential (Res.) and commercial (Com.) development project. The term combined cycle indicates that steam produced as exhaust from a fossil fuel-powered turbine operates an additional steam turbine in order to increase efficiency. It is referred to as “trigenerating” because the waste heat from the combined cycle is then used to provide heat, hot water, and air conditioning (AC) to buildings on the microgrid, further increasing efficiency. The model also incorporates renewable energy sources, solar panels and wind turbines, in the building structures.
\nIt will be shown that an integrated development is economically and environmentally sustainable and is also profitable. The integrated development will be modeled in several cities around the world which were selected in order to present a representative cross section of both environmental conditions and levels of national economic development. In developed countries, the implementation of the methodology presented will alleviate the strain on the now-aging electrical grids that accelerated urban development is causing. In less developed countries, its adoption will add to often inadequate supply. Local conditions of cost, revenue, and environment are incorporated into each model.
\nThe following cities were selected for inclusion in this study: Cairo, Egypt; Lagos, Nigeria; Shanghai, China; Mumbai, India; London, England; New York City (NYC), United States; and Mexico City, Mexico. These cities were chosen for the following reasons. They all are considered “megacities” as defined by the United Nations, with populations greater than 10 million [15]. As seen in Table 1, they have all had major population increase over the past 20 years [16]. Table 1 also shows their ranking by population globally (WPR) and with respect to their respective continents (CPR; North America, NA; South America, SA) [17, 18]. Additionally, Table 1 shows that they are located in varied Köppen-Geiger (K-G) climate zones, a fact that affects heating and air-conditioning loads and cycles [19]. The definition of the K-G climate zones is given in Table 2 [20].
\nCity | \nContinent | \nPop. | \nPop. | \nChange | \nWPR | \nCPR | \nK-G class | \n
---|---|---|---|---|---|---|---|
Cairo | \nAfrica | \n9,900,000 | \n18,800,000 | \n89.9% | \n8 | \n1 | \nBwh | \n
Lagos | \nAfrica | \n4,800,000 | \n12,200,000 | \n154.2% | \n21 | \n2 | \nAw | \n
Shanghai | \nAsia | \n8,600,000 | \n23,500,000 | \n173.3% | \n3 | \n1 | \nCfa | \n
Mumbai | \nAsia | \n12,400,000 | \n19,300,000 | \n55.7% | \n6 | \n3 | \nAw | \n
London | \nEurope | \n6,800,000 | \n8,700,000 | \n28.0% | \n38 | \n3 | \nCfb | \n
Mexico City | \nNA | \n15,600,000 | \n21,300,000 | \n36.5% | \n4 | \n1 | \nCwb | \n
NYC | \nNA | \n16,100,000 | \n18,600,000 | \n15.5% | \n9 | \n2 | \nCfa | \n
Sao Paolo | \nSA | \n14,800,000 | \n20,900,000 | \n41.2% | \n5 | \n2 | \nCfa | \n
Population and climate of cities of interest.
Main climates | \nPrecipitation | \nTemperature | \n|||||
---|---|---|---|---|---|---|---|
A | \nEquatorial | \nW | \nDesert | \nh | \nHot arid | \nc | \nCool summer | \n
B | \nArid | \nS | \nSteppe | \nk | \nCold arid | \nd | \nExtremely continental | \n
C | \nWarm | \nf | \nFully humid | \na | \nHot summer | \nF | \nPolar frost | \n
D | \nSnow | \ns | \nSummer dry | \nb | \nWarm summer | \nT | \nPolar tundra | \n
E | \nPolar | \nw | \nWinter dry | \n\n | \n | \n | \n |
Köppen-Geiger climate classification.
The cities vary greatly in their wealth and development. This impacts the reliability of the electrical supply and the availability of affordable housing. Table 3 presents the state of economic development in the nations in which these cities are located, as measured by the United Nations Human Development Index (HDI) [21] as well as the percentage of the national population living in poverty (NP%) [22]. The percentage of any given city’s population living in slums is not presented in a self-consistent manner. The United Nations defines a slum household as “a group of individuals living under the same roof in an urban area who lack one or more of the following: Durable housing of a permanent nature that protects against extreme climate conditions; sufficient living space which means not more than three people sharing the same room; easy access to safe water in sufficient amounts at an affordable price; access to adequate sanitation in the form of a private or public toilet shared by a reasonable number of people and security of tenure that prevents forced evictions” [23]. The world organizations do not keep data on such a granular level, and national data might not report poverty in terms of locality. As the purpose of this study is to use sustainable development to improve living conditions, the state of local housing quality is of prime interest. Therefore, it was deemed appropriate to use non-internally consistent data for local slum conditions in Table 3 with data on the percentage of the population living in slum conditions for each city (LSC%) which was obtained from the following sources: Cairo [24], Lagos [25], Mumbai [26], London [27], Mexico City [28], New York City [29], and Sao Paolo [30]. There is no measure or recognition of slum conditions in Shanghai.
\nCity | \nCountry | \nHDI Index | \nNP (%) | \nLCS (%) | \n
---|---|---|---|---|
Cairo | \nEgypt | \n0.696 | \n25% | \n10.6% | \n
Lagos | \nNigeria | \n0.532 | \n46% | \n66% | \n
Shanghai | \nChina | \n0.752 | \n4.6% | \nN/A | \n
Mumbai | \nIndia | \n0.640 | \n22% | \n41.3% | \n
London | \nUnited Kingdom | \n0.922 | \nN/A | \n27% | \n
Mexico City | \nMexico | \n0.774 | \n52.3% | \n40% | \n
New York City | \nUnited States | \n0.924 | \nN/A | \n20% | \n
Sao Paolo | \nBrazil | \n0.759 | \n8.9% | \n19% | \n
State of economic development for cities of interest.
The data on electrical distribution and reliability shown in Table 4 correlates strongly with the economic prosperity of the country wherein that city is located, as well as the age of the supporting infrastructure. The National Access to Electricity for 2016 (NAE) [31] and the National Average Blackout Days per Month (BD/M) [32] are strong indicators of development. Both the National Quality of Electricity Supply [33] and the National Average Interruption Frequency Index [34] are reported using the Reliability of Supply and Transparency of Tariff Index, a scale which “encompasses quantitative data on the duration and frequency of power outages as well as qualitative information on how utilities and regulators handle power outages and how tariffs and tariff changes are communicated to customers” [35]. A score of 8 is the highest possible on this scale. The measurement of power transmission and distribution losses (PD/T) is presented as an indicator of the existing strain on the local distribution networks [36].
\nCity | \nCountry | \nNAE | \nBD/M | \nQES | \nAIF | \nPD/T | \n
---|---|---|---|---|---|---|
Cairo | \nEgypt | \n100% | \n1.8 | \n5 | \n3 | \n14% | \n
Lagos | \nNigeria | \n59.3% | \n32.8 | \n1.8 | \n0 | \n16% | \n
Shanghai | \nChina | \n100% | \n0.1 | \n3.9 | \n6 | \n5% | \n
Mumbai | \nIndia | \n84.5% | \n13.8 | \n3.1 | \n7 | \n19% | \n
London | \nUnited Kingdom | \n100% | \n0 | \n6.7 | \n6 | \n12% | \n
Mexico City | \nMexico | \n100% | \n1.6 | \n4.1 | \n7 | \n14% | \n
NYC | \nUnited States | \n100% | \n0 | \n6.3 | \n7.2 | \n6% | \n
Sao Paolo | \nBrazil | \n100% | \n1.6 | \n5 | \n5.4 | \n16% | \n
Quality of electrical supply at the national level.
A comparison of Tables 3 and 4 shows a strong correlation between the National HDI Index and the quality of electricity distribution as measured by both the Quality of Electric Supply Index and the Average Interruption Frequency Index. The state of the electricity distribution grids servicing the cities cited in this work fit into three categories: insufficiently maintained and planned (Cairo [37] and Lagos [38]), extensive but aging (London [39], Mexico City [40], New York City [41], and Sao Paolo [42]), and relatively new and robust (Mumbai [43] and Shanghai [44]). The categorization broadly mirrors HDI in the nations in which the selected cities are located. China and India are rapidly modernizing from an underdeveloped base and can build or expand a modern, robust grid from scratch. The United States and United Kingdom, and, to a lesser extent, Mexico and Brazil, have long established industrial economies, meaning that increasing rate of urbanization is a straining and extensive, but aging, infrastructure. Egypt and Nigeria are underdeveloped countries relying on insufficient base infrastructure.
\nGrowing metropolitan areas require greater local power generation capacity in order to meet growing local needs and to maintain balance in the national distribution grids. However, the fact that this energy is needed in already congested cities presents an economic problem. Reliable energy is necessary for sustained growth, but the real estate needed for additional power production facilities is also needed for further housing and commercial uses. The use of land for power production addresses a potentially catastrophic future problem, while development for residential and commercial use produces profits for developers and increased tax bases for the municipality. Barring direct government intervention, the latter is the predominantly preferred course of action.
\nBoth needs can be simultaneously addressed through integrated development. The following sections will outline how such a development might be structured as well as the economic and ecological return produced. Although the definition of a microgrid [14] seems straightforward, this definition relies largely on self-classification and makes actual quantification difficult. The data available at microgridprojects.com, a trade-related site that is partially based upon self-reporting, illustrates the elasticity of the definition [45]. A majority of microgrids are located in remote, undeveloped areas or on distant islands, places where connecting to the distribution grid is economically unviable or even physically impossible, making local generation the only possible choice. A prime example of this is the fact that 816 MW of the total 844 MW generated in remote areas of Asia is generated by the Russia Far-East Microgrid Portfolio, a conglomeration of 82 generating station serving remote and isolated communities in Siberia which could, in fact, be considered a proper power distribution grid in its own right. Also, the municipal adoptions of microgrids in North America are illustrative of the inherent idiosyncrasies. Of 114.3 total MW generated in this sector, 104 are generated by the New Jersey Transit microgrid. The fact that the energy used to run this large commuter rail system is generated independent of the grid is energy and efficiency neutral, since the State of New Jersey could have just as easily compelled public utilities to add equal capacity for this necessary service. Additionally, with respect to the reported data, the United States military has committed, for strategic and ecological reasons, to make all domestic military bases energy self-sufficient [46]. Although the adoption of microgrid power consumption by military bases does alleviate the strain on the distribution grid at present, the relief is singular and finite and does not address the future strains which will occur due to increased population densification. In fact, only two reported microgrids in the data set addressed residential users in congested areas. Both are located in Kings County, New York, Brevoort Cogeneration Microgrid, and New York Affordable Housing Microgrid. Both are retrofits, with the structures not optimized to take advantage of the benefits of a microgrid.
\nIt is posited that an integrated, holistic approach to real estate development using multiple technologies in buildings designed to maximize their use is not only socially responsible but also economically viable. Inclusion of the microgrid from the outset would allow buildings within the development to utilize the maximum amount of energy. Therefore, it is proposed that a future development be designed around a grid-connected microgrid capable of island-mode operation as follows:
A model is presented which incorporates a microgrid utilizing renewable energy assets into a development consisting of three 32-story residential towers and one 57-story commercial tower. The scale of the development is in keeping with current large-scale developments. The model is run in each of the eight cities of interest using local data on environmental conditions, construction costs (exclusive of real estate purchase), and income levels based upon local rents and power rates. Analyses of the benefits of including the microgrid, as opposed to the same scale conventional development, are performed on three levels:
Actual hourly electrical usage data and building specifications of one 32-story residential tower and one 57-story commercial tower located in New York City were generously provided by GridMarket LLC, New York City, NY. The dimensions of each tower, given in square feet (sq. ft.) and number of apartments (apts.), are given in Table 5. Rentable area for the entire four-tower development is calculated, per industry standard, at 50% of total square footage, with the remaining 50% designated for hallways, stairwell, elevator shafts, and other such types of general-use areas.
\n\n | Built | \nES | \nLot area | \nInternal area (sq. ft) | \nFloors | \nRetail (sq. ft) | \nOffice (sq. ft) | \nRes. (sq. ft) | \nApts. | \n
---|---|---|---|---|---|---|---|---|---|
Res. | \n1916 | \n94 | \n80,333 | \n938,324 | \n32 | \n26,805 | \n0 | \n879,019 | \n608 | \n
Comm. | \n1972 | \n165 | \n127,966 | \n2,689,635 | \n57 | \n216,912 | \n2,319,046 | \n0 | \n0 | \n
Specifications for buildings to be used in the model (provided by GridMarket LLC).
A 2 × 1 (two gas turbines 7.9 MW powering 1 MW steam turbine) was the optimal configuration for the cogeneration plant. This would be supplemented by power provided by 10,500 solar panels and 295 1 kW vertical drum-type wind turbines. The number of solar panels was estimated by covering the entire roof area of the four proposed buildings with standard 77 inch by 39 inch panels, while the number of wind turbines was estimated by placing a turbine every 10 feet around the periphery of each roof. It is recognized that whole-roof coverage with solar panels is impracticable; however, the estimate is valid because some amount of appropriately facing surface area would be available for additional panels. Also, it is assumed for these calculations that the buildings will be boring rectangles. As this is neither likely nor desirable, setbacks will create additional space for more wind turbines. Energy storage devices will be included in the design from the outset in order to balance generated power between times of low load and high load.
\nPublically available commercial data was used to estimate all generating capabilities for gas turbines, steam turbines, and wind turbines as follows: two 7.9 kW gas turbines operating at 30.6% efficiency [47] driving a single 750 kW gas turbine [48] raising the total efficiency to 50.2% and 1 kW wind turbines [49]. All power generation was calculated on an hourly basis and balanced with the hourly load as much as possible. Renewable energy sources were given precedence. Annual average daily data for wind speed at 50 meters aboveground [50], sunrise and sunset [51], and average solar irradiance in kWh/m2 [50] were obtained for each city of interest. As the wind speed and irradiance data were daily averages, they were applied for all 24 hr in each given day. Sunrise and sunset data were used to “turn on” and “turn off” the solar component of the system.
\nThere are multiple methods for determining the efficiency of trigeneration systems [52, 53, 54, 55]. For this study, general estimates based upon these methods will be used. The fast-start capability of modern turbines was utilized to estimate cogeneration outputs with one gas turbine operating at all times. If hourly load minus available renewables exceeded the capacity of one gas turbine, the second turbine was started. If the hourly load still exceeded the capacity of both gas turbines, the steam turbine was included. Solar generation was calculated on an hourly basis by multiplying the irradiance by the total panel area (total roof area) at a 15% conversion efficiency and a 75% transmission efficiency. For wind energy, the manufacturer’s power generation curve was used [49]. The power curve, with a cut-in at 6 miles per hour of wind speed, was applied to the hourly average wind speed to determine the kW delivered by the posited 295 turbines. Usable waste heat from cogeneration (as well as input fuel needs) was calculated on an hourly basis. Input energy in kW was calculated as hourly output divided by hourly efficiency of the cogeneration set, 30.6% for gas-only generation and 50.2% for combined generation. Gross waste heat was obtained by subtracting this number from generated power (Eq. 1):
\nUsable waste heat was calculated by obtaining the ideal thermodynamic efficiency of the system (Eq. 2) [56] and multiplying this by the results of Eq. 1 (Eq. 3):
\nThe annual volume of natural gas (NG) required to run the cogeneration system, measured in industry standard cubic feet (ft3), was calculated by summing the hourly energy input, (kWh/hr) divided by hourly efficiency of cogeneration and converted to cubic feet of gas (Eq. 4):
\nDays | \nCairo | \nLagos | \nShanghai | \nMumbai | \nLondon | \nMexico City | \nNYC | \nSao Paolo | \n
---|---|---|---|---|---|---|---|---|
AC | \n206 | \n365 | \n103 | \n365 | \n0 | \n110 | \n98 | \n245 | \n
Heat | \n63 | \n0 | \n107 | \n0 | \n199 | \n107 | \n178 | \n0 | \n
None | \n96 | \n0 | \n155 | \n0 | \n166 | \n148 | \n89 | \n120 | \n
Annual climate control needs for cities of interest.
Building performance in terms of ENERGY STAR rating (EGR) was not modified. Usage was normalized to environmental conditions in each of the given cities as follows. Daily average high and low temperatures for obtained for each city [57]. Hourly temperatures were calculated using a linear regression each day of the year in each city, starting from the daily low for the day at 1:00 a.m. up to the daily high for the day at 12:00 noon and going back to the daily low again at 12:00 midnight. It was assumed that, on any given day, air conditioning (AC) would be required at or above a daily high of 80°F, and heat would be needed at a daily low of 50°F
In order to estimate the base electric usage for the proposed development, the hourly base building usage data provided by GridMarket was increased by 30% for each day for each city that air conditioning was assumed to be needed. (Given that the New York City data is actual usage, days that the data indicated that air conditioning would be needed in both New York and any other given city were not modified.) This provided a reference point for the estimated electrical load for the proposed development being connected to the regional/national power grid. Since inclusion of the microgrid would essentially eliminate electricity usage for air conditioning, daily usage data for the development with an included microgrid was reduced by 30% assuming that air conditioning increases daily load by 30% (Table 6).
\nHourly heat usage for both hot water and building heat was assumed to remain constant across all cites and climates since the heat capacity of water is constant and the amount of hot water required on a daily basis would be independent of location or climate. Also, as the configuration (and hence the volume) of the buildings was identical in all cities, and the need for heating is temperature dependent, the amount of heat required to provide building heat on an hourly basis would also be constant. Hourly heat requirements for both needs were therefore calculated based upon the New York City data and applied to all cities. The total annual energy use breakdown for New York is available from the United States Energy Information Agency as follows: electricity, 27.2%; heating, 55.8%; and hot water, 17.0% [58].
\nTotal actual electrical usage for the four-tower development was converted to BTUs and divided by 0.272 (27.2%) to give total energy usage (Eq. 5):
\nThis number was then multiplied by 0.17 (17%) to provide annual hot water usage and divided by 8760 hr/year to arrive at an hourly hot water usage of 4.4 × 107 BTU/hr. for hot water (Eq. 6). This was applied to every hour of the year in all cities:
\nHourly structural heating power was calculated at 0.558 (55.8%) of total energy divided by actual hours of heat usage in New York. This number of 2.96 × 108 BTU/hour was then applied to each city for each hour (Eqs. 7–8):
\nUsable hourly waste heat (Eq. 3) was initially applied as needed for climate control. Hourly heat transfer needs were calculated at 80% of available waste heat for building heating, with 80% efficiency being the average efficiency of a standard heat exchanger [59]. Hourly heat transfer needs were calculated at 120% of available waste heat for air-conditioning needs, with 120% being the average efficiency of a two-stage absorption chiller [60]. Remaining heat, on an hourly basis, was then applied to provide hot water, again at 80% efficiency.
\nInfrastructure impact is defined by the degree that the implementation of an integrated development model would relieve strain on the local power distribution grid. As can be seen in Table 7, this is highly correlated to air-conditioning needs as shown in Table 6. This is the expected result as heat provided from the trigeneration plant to the absorption chillers replaces electrical load for air conditioning. Mumbai and Lagos, cities which essentially require air conditioning year-round, had the highest reduction in load, while London, which essentially requires no air conditioning, saw no reduction in load.
\nCity | \nNo microgrid (kWh/year) | \nMicrogrid (kWh/year) | \nSaved | \nMicrogrid production (kWh/year) | \nHours off-grid | \nSurplus/deficit (kWh/year) | \nSurplus/deficit | \n
---|---|---|---|---|---|---|---|
Cairo | \n1.96E+08 | \n1.75E+08 | \n10.6% | \n1.73E+08 | \n48.0% | \n(2,274,872) | \n−1% | \n
Lagos | \n2.05E+08 | \n1.60E+08 | \n21.8% | \n1.75E+08 | \n61.8% | \n14,415,926 | \n9% | \n
Shanghai | \n1.83E+08 | \n1.80E+08 | \n1.2% | \n1.76E+08 | \n44.0% | \n(4,663,087) | \n−3% | \n
Mumbai | \n2.16E+08 | \n1.69E+08 | \n21.6% | \n1.77E+08 | \n57.7% | \n8,210,049 | \n5% | \n
London | \n1.81E+08 | \n1.81E+08 | \n0.0% | \n1.72E+08 | \n41.2% | \n(9,295,666) | \n−5% | \n
Mexico City | \n1.90E+08 | \n1.77E+08 | \n7.0% | \n1.73E+08 | \n48.3% | \n(3,766,257) | \n−2% | \n
NYC | \n1.81E+08 | \n1.63E+08 | \n10.0% | \n1.71E+08 | \n55.8% | \n8,533,701 | \n5% | \n
Sao Paolo | \n2.12E+08 | \n1.71E+08 | \n19.6% | \n1.76E+08 | \n51.8% | \n5,855,500 | \n3% | \n
Load and production comparison (selected cities).
The microgrid must be grid-connected for both safety and regulatory reasons in an urban environment. To be effective, the system must not add additional load to the grid but must also be balanced in order to protect the local grid infrastructure; it should not push power onto the distribution grid at any point. In case of emergency, such as a blackout condition, the microgrid should also be able to disconnect from the local power grid and provide all needed services in island mode. Table 7 indicates that the proposed model succeeds in this respect. The incorporated power generating systems produce surplus electricity on an hourly basis between 40% and 60% of the time, depending upon the city (“% hours off-grid”). Excess energy produced in hours of low load can be stored in incorporated batteries to meet demand in hours of high load, producing a system that is completely grid-neutral throughout the year. The annual difference between electricity usage and production in each city as shown in Table 7, be it positive or negative, is small and can be corrected in the local design phase.
\nThe cost of constructing the proposed development buildings was calculated using local average construction cost data and applying it to the total square footage found in Table 5 (Table 9-“Cost-Less Microgrid”) [61]. The cost of integrating the microgrid is calculated on average prices in the United States (Table 8) [62]. An internally consistent data set containing all cities of interest for this metric was not found. It is assumed that the cost would be fixed since the capital components required are not locally produced. The cost of batteries, absorption chillers, and heat exchangers was not included, as it is assumed that these costs would be balanced by the deletion of HVAC equipment, cooling towers, boilers, and hot water heaters. Cost differential is presented in Table 9.
\nCogeneration | \n$895 | \nPer kW | \n15,660 | \nMW | \n$14,015,700 | \n
---|---|---|---|---|---|
Solar panels | \n$2434 | \nPer kW | \n2100 | \nWatts | \n$5,111,400 | \n
Wind turbines | \n$1630 | \nPer kW | \n292 | \nTurbines | \n$475,960 | \n
\n | \n | \n | \n |
Microgrid cost.
City | \nRes. twrs | \nCom. tws | \nTotal sq. ft. | \nPer sq. ft. res. | \nPer sq. ft. com. | \nCost-less microgrid | \nCost-with microgrid | \n
---|---|---|---|---|---|---|---|
Cairo | \n3 | \n1 | \n5,504,607 | \n$31.13 | \n$35.94 | \n$184,295,560 | \n$203,898,620 | \n
Lagos | \n3 | \n1 | \n5,504,607 | \n$30.00 | \n$32.00 | \n$170,517,480 | \n$190,120,540 | \n
Shanghai | \n3 | \n1 | \n5,504,607 | \n$21.23 | \n$33.82 | \n$150,725,311 | \n$170,328,371 | \n
Mumbai | \n3 | \n1 | \n5,504,607 | \n$18.96 | \n$20.09 | \n$107,406,636 | \n$127,009,696 | \n
London | \n3 | \n1 | \n5,504,607 | \n$112.63 | \n$120.56 | \n$641,312,692 | \n$660,915,752 | \n
Mexico City | \n3 | \n1 | \n5,504,607 | \n$52.41 | \n$22.36 | \n$207,672,921 | \n$227,275,981 | \n
NYC | \n3 | \n1 | \n5,504,607 | \n$285.32 | \n$534.00 | \n$2,239,432,901 | \n$2,259,035,961 | \n
Sao Paolo | \n3 | \n1 | \n5,504,607 | \n$18.11 | \n$42.17 | \n$164,401,051 | \n$184,004,111 | \n
Construction cost comparison of proposed development: incorporating vs. not incorporating a microgrid.
Revenue is calculated on local monthly rental rates per square foot of rental. As previously mentioned, rentable space is calculated at 50% of available floor space. Internally consistent residential rental rates were found [63]. However, internally consistent rates for residential, commercial, and retail were only found for New York City [64, 65]. Therefore, commercial rates for other cities are calculated at the ratio of those rates to residential rates for New York. As the microgrid will also be a revenue source, local electricity rates are included and applied to the revenue for each city [66]. Total annual revenue from rents and electrical for each city, as well as the rates used, is given in Table 10. Table 11 then estimates the gross time to repay the initial investment, with “Cost-Less Microgrid” in Table 9 divided by the sums of all rentals in Table 10 to determine the number of years to repay the development if built conventionally and the “Cost-With Microgrid” divided by the sum of all rentals plus electricity revenue in Table 10 used. Operating and real estate costs were not considered in the gross time to repay, but it can be assumed that these costs will be identical in both scenarios in any given city.
\nCity | \nRes./sq. ft./month | \nOffice/sq. ft./month | \nRetail/sq. ft./month | \nElectricity/kWh | \nRes. ent/year | \nOffice rent/year | \nRetail rent/year | \nElectricity/year | \n
---|---|---|---|---|---|---|---|---|
Cairo | \n$1.11 | \n$2.35 | \n$17.51 | \n$0.02 | \n$17,562,800 | \n$32,725,167 | \n$31,235,468 | \n$3,501,974 | \n
Lagos | \n$1.12 | \n$2.37 | \n$17.67 | \n$0.08 | \n$17,721,023 | \n$33,019,989 | \n$31,516,869 | \n$3,207,401 | \n
Shanghai | \n$1.07 | \n$2.27 | \n$16.88 | \n$0.09 | \n$16,929,906 | \n$31,545,882 | \n$30,109,866 | \n$3,607,725 | \n
Mumbai | \n$0.60 | \n$1.27 | \n$9.46 | \n$0.07 | \n$9,493,405 | \n$17,689,280 | \n$16,884,037 | \n$3,382,754 | \n
London | \n$2.40 | \n$5.09 | \n$37.86 | \n$0.22 | \n$37,973,621 | \n$70,757,118 | \n$67,536,148 | \n$3,617,546 | \n
Mexico City | \n$0.49 | \n$1.04 | \n$7.73 | \n$0.08 | \n$7,752,948 | \n$14,446,245 | \n$13,788,630 | \n$3,536,433 | \n
NYC | \n$3.45 | \n$7.31 | \n$54.42 | \n$0.18 | \n$54,587,080 | \n$101,713,358 | \n$97,083,212 | \n$3,425,333 | \n
Sao Paolo | \n$0.53 | \n$1.12 | \n$8.36 | \n$0.19 | \n$8,385,841 | \n$15,625,530 | \n$14,914,233 | \n$3,412,530 | \n
Estimated rental and electrical rates and annual revenues per source.
City | \nOil | \nNG | \nCoal | \nNuclear | \nHydroelectric | \nNon-hydroelectric renewables | \n
---|---|---|---|---|---|---|
Cairo | \n44.67% | \n50.72% | \n0.47% | \n0.00% | \n3.49% | \n0.65% | \n
Lagos | \n42.12% | \n28.25% | \n21.80% | \n0.82% | \n5.87% | \n1.14% | \n
Shanghai | \n18.95% | \n6.20% | \n61.83% | \n1.58% | \n8.62% | \n2.82% | \n
Mumbai | \n29.38% | \n6.23% | \n56.91% | \n1.18% | \n4.03% | \n2.27% | \n
London | \n38.89% | \n36.70% | \n5.83% | \n8.63% | \n0.65% | \n9.31% | \n
Mexico City | \n44.41% | \n43.20% | \n5.26% | \n1.28% | \n3.63% | \n2.21% | \n
NYC | \n0.00% | \n44.00% | \n1.00% | \n31.00% | \n19.00% | \n5.00% | \n
Sao Paolo | \n46.61% | \n11.06% | \n5.55% | \n1.21% | \n29.19% | \n6.38% | \n
Electric power generation source fuels.
From Table 11, it can be seen that the gross time to repay initial investment is lower when the microgrid is present. This has positive sociological implications. Since repayment time is shorter, long-term revenue will be higher, making the proposed development model economically profitable and therefore feasible. This has an additional advantage; the charging of premium rents is not economically required due to the lower repayment time of a microgrid inclusive development. The enhanced revenue stream and lowered operating costs associated with building a development around this model would also make affordable housing economically viable, serving to include those who are often left behind and displaced when a neighborhood is redeveloped.
\nBuildings generate greenhouse gases indirectly by consuming electricity produced from various fuels and directly generate such gases through the production of heat and hot water. Table 12 presents the breakdown of fuels used to generate electricity for the local power grid in each of the cities of interest [67]. Table 13 shows the greenhouse gas emissions for each of those sources per kWh [68]. Table 14 presents the percentage of energy derived from renewable sources incorporated into the microgrid in each city of interest. Table 15 contains data on natural gas usage for the development both with and without inclusion of the microgrid. In both cases, annual hot water needs are calculated according to Eq. 7 multiplied by 8760 hr/year, and heating needs are calculated by Eq. 8 multiplied by the number of heating hours estimated in each city from Table 6. For the traditional version of the development, it is assumed that these needs will be supplied by burning natural gas, although less environmentally friendly fuel oil could also be used. When the microgrid is present, heat and hot water needs are met first by trigeneration waste heat, and any unmet needs are met by the same natural gas feed that would fuel the gas turbines. Finally, Table 16 compares the calculated greenhouse gas emissions between the two scenarios with data from Tables 8, 13, 14 and 15.
\nCity | \nOil | \nNG | \nCoal | \nNuclear | \nHydroelectric | \nNonhydroelectric renewables | \n
---|---|---|---|---|---|---|
Cairo | \n44.67% | \n50.72% | \n0.47% | \n0.00% | \n3.49% | \n0.65% | \n
Lagos | \n42.12% | \n28.25% | \n21.80% | \n0.82% | \n5.87% | \n1.14% | \n
Shanghai | \n18.95% | \n6.20% | \n61.83% | \n1.58% | \n8.62% | \n2.82% | \n
Mumbai | \n29.38% | \n6.23% | \n56.91% | \n1.18% | \n4.03% | \n2.27% | \n
London | \n38.89% | \n36.70% | \n5.83% | \n8.63% | \n0.65% | \n9.31% | \n
Mexico City | \n44.41% | \n43.20% | \n5.26% | \n1.28% | \n3.63% | \n2.21% | \n
NYC | \n0.00% | \n44.00% | \n1.00% | \n31.00% | \n19.00% | \n5.00% | \n
Sao Paolo | \n46.61% | \n11.06% | \n5.55% | \n1.21% | \n29.19% | \n6.38% | \n
Electric power generation source fuels.
Lbs/BTU | \nCoal | \nOil | \nNG | \nSolar | \nHydroelectric | \nNuclear | \nWind | \n
---|---|---|---|---|---|---|---|
CO2 | \n2.15E-04 | \n1.61E-04 | \n1.17E-04 | \n2.89E-05 | \n1.54E-05 | \n7.70E-06 | \n7.10E-06 | \n
SO2 | \n2.59E-06 | \n1.12E-06 | \n7.00E-09 | \n
Greenhouse gas emissions per source fuel.
KwH/year | \nCairo | \nLagos | \nShanghai | \nMumbai | \nLondon | \nMexico City | \nNYC | \nSao Paolo | \n
---|---|---|---|---|---|---|---|---|
Total | \n1.73E+08 | \n1.75E+08 | \n1.76E+08 | \n1.77E+08 | \n1.72E+08 | \n1.73E+08 | \n1.71E+08 | \n1.76E+08 | \n
Wind | \n2.47E+06 | \n7.62E+05 | \n2.17E+06 | \n1.74E+06 | \n2.06E+06 | \n1.70E+06 | \n2.26E+06 | \n2.01E+06 | \n
Solar | \n2.89E+07 | \n3.35E+07 | \n3.18E+07 | \n3.48E+07 | \n2.76E+07 | \n2.99E+07 | \n2.81E+07 | \n3.32E+07 | \n
Percent renewable power generation on microgrid.
\n | On national power grid | \nTrigenerating microgrid | \n\n | |||||||
---|---|---|---|---|---|---|---|---|---|---|
\n | Cubic feet NG annually | \nCubic feet NG annually | \nDifference | \n|||||||
City | \nHot water | \nHeat | \nAC | \nSUM | \nHot water | \nHeat | \nAC | \nTurbine | \nSUM | \n\n |
Cairo | \n1.0E+07 | \n4.3E+09 | \n0 | \n4.4E+09 | \n0.0E+00 | \n2.7E+09 | \n0.0 | \n1.5E+10 | \n1.8E+10 | \n|
Lagos | \n1.0E+07 | \n0.0E+00 | \n0 | \n1.0E+07 | \n0.0E+00 | \n0.0E+00 | \n0.0 | \n1.5E+10 | \n1.5E+10 | \n|
Shanghai | \n1.0E+07 | \n7.4E+09 | \n0 | \n7.4E+09 | \n0.0E+00 | \n4.5E+09 | \n0.0 | \n1.5E+10 | \n2.0E+10 | \n|
Mumbai | \n1.0E+07 | \n0.0E+00 | \n0 | \n1.0E+07 | \n0.0E+00 | \n0.0E+00 | \n0.0 | \n1.5E+10 | \n1.5E+10 | \n|
London | \n1.0E+07 | \n1.4E+10 | \n0 | \n1.4E+10 | \n1.0E+07 | \n9.5E+09 | \n0.0 | \n1.3E+10 | \n2.2E+10 | \n|
Mexico City | \n1.0E+07 | \n7.3E+09 | \n0 | \n7.3E+09 | \n0.0E+00 | \n4.6E+09 | \n0.0 | \n1.5E+10 | \n2.0E+10 | \n|
NYC | \n1.0E+07 | \n1.2E+10 | \n0 | \n1.2E+10 | \n1.0E+07 | \n7.8E+09 | \n0.0 | \n1.5E+10 | \n2.3E+10 | \n|
Sao Paolo | \n1.0E+07 | \n0.0E+00 | \n0 | \n1.0E+07 | \n0.0E+00 | \n0.0E+00 | \n0.0 | \n1.5E+10 | \n1.5E+10 | \n
NG consumption: with microgrid compared to without microgrid.
Tons/year | \nOn power grid | \nMicrogrid | \nReduction (%) | \n|||
---|---|---|---|---|---|---|
City | \nCO2 | \nSO2 | \nCO2 | \nSO2 | \nCO2 | \nSO2 | \n
Cairo | \n9.84E+04 | \n4.80E+02 | \n4.46E+04 | \n2.67E+00 | \n54.64% | \n99.44% | \n
Lagos | \n7.54E+04 | \n5.25E+02 | \n2.81E+04 | \n1.68E+00 | \n62.80% | \n99.68% | \n
Shanghai | \n1.12E+05 | \n6.56E+02 | \n5.58E+04 | \n3.34E+00 | \n50.08% | \n99.49% | \n
Mumbai | \n7.93E+04 | \n6.73E+02 | \n2.81E+04 | \n1.68E+00 | \n64.55% | \n99.75% | \n
London | \n1.49E+05 | \n4.93E+02 | \n8.58E+04 | \n5.14E+00 | \n42.53% | \n98.96% | \n
Mexico City | \n1.14E+05 | \n4.70E+02 | \n5.62E+04 | \n3.36E+00 | \n50.88% | \n99.28% | \n
NYC | \n1.41E+05 | \n8.04E+02 | \n7.56E+04 | \n4.52E+00 | \n46.21% | \n99.44% | \n
Sao Paolo | \n7.81E+04 | \n5.01E+02 | \n2.82E+04 | \n1.69E+00 | \n63.87% | \n99.66% | \n
Comparison of greenhouse emissions for development: without vs. with microgrid.
These results are significant. As seen in Table 15, a development incorporating a microgrid uses over 10 times the natural gas in all cases than the identical development drawing power from the local distribution grid. However, Table 16 definitively shows that the use of a microgrid would greatly reduce the greenhouse gas emissions from the development, with approximately half of the CO2 and virtually all SO2 emissions eliminated. By incorporating trigeneration from the outset, all upstream emissions from electricity generation are eliminated. Additionally, the use of waste heat in the building systems eliminates emissions from the production of hot water, halves the emissions from building heat, and also eliminates any emissions from air conditioning (bearing in mind that, in a conventional arrangement, air-conditioning emissions would be included in electricity generation emissions). Finally, Table 14 shows that incorporating maximal renewable assets by design accounts for roughly 20% of the electricity production which, at 50% generator efficiency, amounts to a 40% drop in potential greenhouse gas production through electricity generation.
\nUrbanization of populations is occurring at an accelerating pace worldwide, and, in all countries, the increasing densification of population is putting a strain on the pre-existing infrastructure. Depending on the state of national economic development, that infrastructure could be robust, aging, or nonexistent, but was not designed to support the increasing strain. Additionally, this seismic population shift requires housing and employment opportunities in relatively small geographic areas. While growth has always bought opportunity, that opportunity was never immediate or evenly distributed. Hence, slum populations are increasing, and both housing and economic opportunity are increasingly scarce.
\nHistory has shown that economies cannot be managed, but it is the job of the government to “promote the general welfare” [69]. At present, various local, national, and international entities are promoting the general welfare through establishing programs to create sociological and environmentally sustainable opportunity. These incentives recognize the existence of a need which can be addressed by a new model of urban growth, one that is economically advantageous and sociologically and environmentally sound, such as the design model proposed herein. The work presented develops a new model for urban development, a model which incorporates a myriad of mature technologies into a real estate development at the design stage. The model bases the development around a self-contained microgrid using trigeneration of power where, at the first stage, fossil fuel-powered turbines produce electricity and heat which, at the second stage, powers a steam turbine to produce more electricity. The third stage of trigeneration is to use the remaining exhaust heat to provide building heat, hot water, and air conditioning. The system is supplemented by renewable solar and wind power, with the buildings designed from the outset to maximize such assets. Modern power storage assets are included in the design to balance the load between times of high usage and low usage.
\nThe study demonstrates that the proposed model succeeds in meeting all sustainability requirements. It is more profitable than constructing the same development on the national power grid. This is vital since economic sustainability is a sine qua non for any urban development. It balances load and generation capability on a large scale, allowing the construction of large numbers of buildings to accommodate increasing populations with essentially no impact on the existing power distribution infrastructure. It is also environmentally sustainable, producing fewer emissions than traditional developments on the same scale.
\nThese conclusions point to the viability and the economic and environmental desirability of proceeding with urban development under the model herein presented and also lead to a sociological conclusion. Cities are historically built by the poor striving to make a better life for themselves and their families. In developed countries, the consequence of real estate development is too often to push such people out of their homes and further to the fringes. In developing countries, such people are often not even considered, relegated to living in shanty towns. The economic and environmental advantages of this development model present an opportunity to promote the general welfare of all. Environmental financial incentives, coupled with increased profitability, will allow for the maintenance of exceptional living conditions at comparatively low rents. Since renewability and regeneration are incorporated into the building design, heat and hot water, so necessary for everyday life, will be readily available. Lower rents are economically possible since all these usual living expenses are being provided for by the same source. Most importantly, the model is scalable and variable and power is fungible. A commercial tower was included in this study to both provide an economic focus point to start the development and to provide jobs to the people living in the residential towers because real-world data was made available. The model could be applied to any combination of residential, commercial, retail, or industrial spaces providing centers for human advancement, with both jobs and housing provided at an economically and environmentally favorable rate.
\nThe author would like to acknowledge the assistance of the following people:
Mr. Richard Denis—
Mr. Oisín O’Brien—
Mr. William Kenworthey—
Mr. Anthony Alduino—
Mr. Bradford Sussman-Gonzalez—
This work is dedicated to the memory of Mr. Peter Tymus, formerly of Turner Constructions, Inc. and Long Island University, Brooklyn, NY. Mr. Tymus was a gentleman in the truest sense of the word, and his vision and sharing of knowledge were the inspiration for this work.
\nDetonative combustion is a potential propulsion method for aerospace systems, offering high efficiency and low mechanical complexity. In comparison, deflagration is generally considered easier to control and has therefore dominated both experimental and real world engine applications. Research into detonation engines has been limited due to the lack of the necessary tools required to design and analyse such systems [1, 2]. As such, practical development of detonation engines, notably the pulsed detonation engine (PDE) and the rotating or rotational detonation engine (RDE), has been limited [3]. Nevertheless, the application of detonation engines for propulsion is very promising, already proving to be compact, whilst providing highly efficient thrust generation [3, 4, 5, 6, 7]. This supersonic thrust could be utilised independently as a rocket engine, or as part of a gas turbine system. Interest in the development of RDE technology has grown and the challenges of utilising a more thermodynamically-efficient cycle have become better understood [8, 9].
\nCombustion can occur at both subsonic and supersonic velocities, known as deflagration and detonation, respectively. Deflagration is typified by a regular flame, which propagates at less than the speed of sound. The heat release may be used to expel the resulting products, generating thrust. Deflagration has been used in a broad range of applications to produce power. However, in theory, deflagration lacks the thermodynamic efficiency of a detonation system, which is a system where combustion is initiated suddenly and “propagates utilising most, if not all, of the heat from combustion in an incredibly rapid shock wave” [10]. The heat generated by the exothermic chemical reaction sustains the shock wave. The concept of using detonation as a propulsion source has been proposed since the 1840s [11], but no substantial work had been completed until the 1950s when the development of models and concepts for a more lightweight and compact engine began [12]. The mechanisms that drive the detonation engine were not well understood at that time, so much of the research over the following decades was centred on the theoretical development of the engine.
\nAs the name implies, the pulse detonation engine (PDE) has been proposed for propulsion using detonations [12, 13]. In a PDE, a detonation chamber is filled with a fuel/oxidiser mixture, which is subsequently detonated. The accelerating detonation propels the exhaust from the chamber, thereby generating thrust. The chamber is then re-primed with fresh reactants, and re-detonated. With sufficiently high cycle speeds, large amounts of thrust may be generated in a small engine [14, 15]. This type of engine has been found to be particularly efficient [3, 16, 17].
\nDevelopment of the concept of a rotating detonation engine (RDE) began as a result of further work into detonative propulsion. This engine type is characterised by one or more detonation waves contained within an open-ended annular chamber. A fuel/oxidiser mixture is fed into one end of the chamber, and the detonation wave consumes these reactants azimuthally, expelling reactants from the open end of the annulus. In some literature, this type of engine may also be referred to as a continuous detonation wave engine (CDWE) or a spin detonation engine [6].
\nEarly research into rotating detonations was conducted in the 1950s [18], with attempts to document the structure of detonation shock waves, including those in spinning detonations, with further developments through the 1960s [1]. Subsequent research has been conducted into the effects of geometry, rotation characteristics, spiralling of the wave, and other variables [6, 19, 20, 21, 22]. Another advancement in general detonation research is improvements in deflagration to detonation transitions (DDTs), leading to a greater understanding of the consumption of fuel in the chamber [23, 24, 25]. Further work has developed prototype RDEs to measure the thrust of small-scale units as a baseline for larger model behaviour, utilising the results from experimental work to verify theoretical results, and to generate new results [26, 27, 28, 29, 30].
\nIn this review, several aspects of RDEs will be examined, starting with a brief comparison of RDEs and PDEs. This will be followed by further exploration into RDE operation, and methods of analysing RDEs, both experimentally and with numerical modelling. Finally, there will be an overview of areas still requiring further work.
\nThe majority of gas turbines that operate with a deflagration follow the Brayton (B) cycle: an isobaric (constant pressure) process, as shown in Figure 1 [31]. In contrast, a detonation is almost isochoric (constant volume) and may be modelled with the Humphrey (H) cycle, or, preferably, with the Fickett-Jacobs (FJ) cycle, which models detonation [3, 31]. The H cycle assumes that combustion occurs in a fixed volume, resulting in a pressure spike as the products expand. Differentiation between the H and FJ cycles in Figure 1 can be seen through the state changes of 2–\n
Thermodynamic cycles: Humphrey, Brayton, and Fickett-Jacobs. Adapted from Wolański [
where \n
Fuel | \nBrayton (%) | \nHumphrey (%) | \nFickett-Jacobs (%) | \n
---|---|---|---|
Hydrogen (H2) | \n36.9 | \n54.3 | \n59.3 | \n
Methane (CH4) | \n31.4 | \n50.5 | \n53.2 | \n
Acetylene (C2H2) | \n36.9 | \n54.1 | \n61.4 | \n
Calculated thermodynamic efficiencies for various fuels under different thermodynamic cycles [26].
In a PDE, such as that shown in Figure 2, a detonation chamber is filled with a fuel/oxidiser mixture and then ignited. The deflagration of the reactants accelerates, and through a deflagration-to-detonation transition (DDT), generates a shock wave. The products are accelerated from the end of the chamber, carried by the detonation front, generating thrust [30, 31]. For each cycle, the chamber must be purged and then refilled with fresh fuel/oxidiser mixture and then detonated again, limiting the maximum practical frequency of operation to an order of 100 Hz [32]. This results in poor efficiency when scaled to high thrust levels as the discontinuous thrust cycles may not be fast enough to approximate the continuity required for propulsion purposes [32, 33, 34, 35]. In some designs, it is also necessary to purge the chamber with an inert gas due to some residual combustion products remaining stagnant in the detonation chamber that interfere with the next detonation cycle. This process further restricts the operating frequency to approximately 50 Hz [3, 16].
\nLabelled schematic of a PDE. Adapted from [
In order to provide a more compact device, obstacles may be placed in the chamber to accelerate the DDT, but these reduce the specific impulse (\n
An RDE, such as the one shown as a cutaway in Figure 3, consists of an annular combustion chamber, into which fuel and oxidiser, either premixed or non-premixed, are fed through a series of orifices [3, 26, 36]. Each fuel/oxidiser mix requires a slightly different orifice geometry for optimal operation, so some devices have an adjustable injector plate [37, 38].
\nCross-section of a typical rotating detonation engine [
A detonation wave is initiated in the chamber, most commonly utilising a high speed flame that undergoes DDT by the time it enters the chamber [39, 40]. As this wave propagates around the chamber, it consumes the fuel, generating a high pressure zone behind it. This zone expands, and due to the geometric constraints, exits the chamber, generating thrust [35, 41]. An example of a CFD representation of the propagating wave can be seen in Figure 4 [42]. Behind the wave, fresh fuel enters the chamber at a constant rate, priming that section of the chamber for the wave to continue on the next revolution, thus making a self-sustaining wave as long as fresh mixture is supplied [35, 43]. The detonation waves generally propagate close to the Chapman-Jouguet velocity (discussed in Section 3.2) for each fuel type (typically 1500–2500 m s−1), so the effective operational frequency of current RDEs is approximately 1–10 kHz. Frequency is dependent on the chamber geometry, fuel, and thermal and frictional losses [31, 44]. The result is quasi-continuous thrust that approximates a continuous thrust through high frequency rotations, suitable for both direct propulsion applications and in the combustor of a gas turbine [31, 32, 45].
\n3D model of the detonation wave propagation in an RDE [
Important areas of RDE research include determining the wave characteristics, geometric constraints, the effects of pressure on the injection characteristics, determining fuel flow properties, and examining the geometry and structure of the detonation wave [3, 4, 30, 31, 41, 42, 44]. Additionally, there has been research into potential applications of detonation engines in which an RDE may be applied, such as air-breathing vehicles and gas turbines [46]. Despite a growing body of work on RDEs, there are still large gaps in current understanding that restrict practical application. Notably, optimising the system for wave stability, ensuring reliable detonation initiation, and ensuring the RDE does not overheat, are significant challenges facing engine development prior to commercial applications. Further development in this area would allow an engine to operate reliably over extended durations, with well-designed chamber and fuel supply.
\nMost experimental RDEs are geometrically similar in design, consisting of an annulus made up of coaxial cylinders [5, 38, 47]. The chamber width, characterised by \n
There is reasonable consistency across published designs in the methods of initiating detonation waves in the RDE. Detonator tubes, in which a high-speed flame is encouraged to transition from deflagration to detonation, have been regularly and reliably used [26, 31, 32, 39, 49, 51]. It has been shown that the success of the detonation tube makes it an excellent initiator, producing a self-sustaining rotating detonation 95% of the time [26].
\nLike all jet-thrust reaction-based engines, the exhaust from a RDE may be channelled through a nozzle to increase thrust. Outlet and nozzle designs have varied across different RDEs. Many have not attached any nozzle, whilst some have chosen to utilise an aerospike [30, 31, 52]. The use of an aerospike increases performance through higher expansion area ratios, although the increased surface area results in higher heat flux and thus a loss of efficiency from the additional heat transfer [53]. Aerospikes may be directly attached to the end of the reaction chamber [31]. A diverging nozzle was found to increase the specific impulse, although the thrust increase was small, and for angles greater than 10°, the increase with angle was negligible [53]. None have made use of converging or converging-diverging nozzles, because the exhaust is typically flowing at supersonic velocities and thus could be choked through the converging cross-section. This would result in a loss of energy that would decrease the overall efficiency of the system.
\nA typical RDE, 90.2 mm in diameter, has been tested on a thrust sled [54]. It produced a thrust of 680 N using 176 g s−1 of C2H4/O2 propellant at an equivalence ratio of 1.48 [54]. As can be seen from Table 2, this is well below that required for typical supersonic flight applications. The specific impulse (\n
Engine | \nApplication | \nThrust | \nThrust to weight | \n
---|---|---|---|
Rolls-Royce Olympus 593 | \nBAC Concorde | \n38,000 lb (169,000 N) | \n5.4:1 [55] | \n
Teledyne CAE J402 | \nMcDonnell Douglas Harpoon | \n660 lb (2900 N) | \n6.5:1 [56] | \n
Pratt and Whitney F135 | \nLockheed Martin F-35 | \n191,300 N | \n11.47:1 (dry) [57] | \n
General Electric F414-400 | \nBoeing F/A-18E/F | \n98,000 N | \n9:1 [58] | \n
Experimental RDE | \nNone | \n680 N | \n3.47:1* [54] | \n
Thrusts and applications of various engines.
This is the thrust to weight ratio calculated using a pre-weight load cell system.
RDEs have been found to be successfully operable with a range of gaseous fuels including hydrogen, acetylene and butane, as well as various jet fuels [30, 31]. Air, pure oxygen, and oxygen-enriched air have all be used as oxidisers [31]. Each of these has a variety of advantages and disadvantages, in both performance characteristics, and ease of obtaining, transporting, and storing the oxidiser. Particular difficulty is noted in the transport of gases such as H2 and O2 due to the high risk regarding transportation and significant compression of these chemical species [59]. In the case of transporting liquid fuels such as LH2 and LOx cryogenic units are also required, adding to the already challenging process. The performance characteristics for several of these fuel types will be discussed further in Section 4.4.
\nThe detonation wave velocity in operational H2/air RDEs has been found to be on the order of 1000 m s−1 [30, 39]. In these RDEs, the operational frequencies are on the order of 4000 Hz, which produces quasi-continuous thrust [3, 32]. As wave speed is a key factor in the development of thrust, stable waves with high speeds are ideal for propulsion purposes. Stable detonation waves have reached maximum speeds in the range of 1500–2000 m s−1 in most designs using a H2/air or H2/O2 fuel/oxidiser combination (more commonly the former), suggesting that there is open research into whether there is upper limit for detonation wave speed, and subsequently the thrust that may be produced [3, 22, 26, 60]. However, at very high frequencies (19–20 kHz), there may be multiple waves rotating around the annulus [60, 61, 62]. Multiple wave modes of propagation appear to be affected by fuel/oxidant equivalence ratio as well as total mass flow rate through the system. The high frequencies are a result of multiple waves travelling at approximately the same speed as the normal single wave. This phenomenon has the potential to provide more continuous thrust, though the higher frequency may limit \n
There are several methods of recording data from an operating RDE. Thrust generated may be measured with a thrust plate, and the flow rates of fuel and oxidiser may be measured or controlled within the supply lines [30]. The details of the shock may be recorded with pressure sensors attached to the chamber head, and external cameras [30]. Pressure sensors record the increased pressure generated by the shock, and by using multiple sensors, the detonation wave propagation velocity may be determined. A high-speed camera may be set up to capture the operation of the engine, allowing various parameters to be recorded, including the detonation wave propagation velocity, although this method is limited by spatial resolution, as the channel width can be quite small [30, 39]. A camera may also be used to image from the side, if the outer surface of the annulus is made of a transparent material [63]. Additionally, OH* chemiluminescence may be used to detect, record, and analyse the detonation waves in UV-transparent optically-accessible RDEs [64, 65]. These radicals are indicative of the reaction zone, and so, by analysis of their chemiluminescence, the structure of the detonation can be inferred. Often this detection is done through a quartz side window integrated into the RDE [63]. Peak intensity of the OH* chemiluminescence indicates the location of the detonation front, and so the effects of varying factors such as equivalence ratio and chamber geometries can be documented. Images are often phase-averaged and can by “unwrapped” for comparison to equivalent two-dimensional, “linearised”, simulations and designs.
\nThe structure of shock waves in gases was examined in detail by Voitsekhovskii in 1969, including those of shock waves in spinning detonations [66]. These examinations resulted in the first diagram of the structure of a spinning shock wave, and the identification of a number of features, which are identified from the computational model of an RDE shown in Figure 5 [32]. This model used premixed hydrogen/air as the fuel/oxidiser mixture and has been “unwrapped” into two-dimensions (this approach is described in Section 5.1). Feature A is the primary detonation front; Feature B is an oblique shock wave that propagates from the top of the detonation wave; Feature C is a slip line between the freshly detonated products and older products from the previous cycle; Feature D is a secondary shock wave; Feature E is a mixing region between the fresh premixture and the product gases, where deflagration may occur [67]; Feature F is the region where the injector nozzles are blocked; and Feature G is the unreacted premixture.
\nPressure contour indicating the cell structure of a detonation wave in an RDE with a premixed supply, taken from a computational modelling study [
In both Figure 5b and Figure 8c (Section 4.3) the detonation cell structure can be seen, with high pressure zones outlining each cell. These lines of high pressure contain triple points, where the transverse and oblique shocks meet the Mach stem of the detonation wave [68, 69]. The concentrated pressure at these triple points is the point of maximum energy release, and the subsequent pressure spike when two triple points collide generates new detonation cells [68, 70]. While this generation is the main reason behind the propagation of detonation waves, the triple points still require further investigation as to the effects they have on the overall characteristics of a detonation wave [70]. The direction of these triple points can be seen as the white lines in Figure 8c with trailing high pressure zones forming the walls of the detonation cells. As the detonation cell width is defined by the geometry of the system and the chemical composition of the detonating fuel, it seems that the triple point velocity and direction must also directly relate to these factors, although limited research has been done to formally connect these points.
\nIn an RDE, the detonation wave remains attached to the base of the annulus, as illustrated in Figure 5b and in Figure 6 [3, 6, 71]. This is due to the continuous fuel/oxidant supply [3, 71], as a premixture or allowed to mix in the chamber ahead of the detonation wave [32, 39]. There is also some evidence that stable, lifted waves may also be possible if there is insufficient mixing between the fuel and oxidant [27, 44]. The propagating detonation wave combusts the reactants [32, 39] which generates a region of extremely high pressure immediately behind the wave. This pressure is on the order of 15–30 times higher than the pressure ahead of the detonation, preventing flow through the injectors [3]. The high pressure zone expands in a Prandtl–Meyer fan, allowing fresh fuel and oxidiser to enter the chamber [35]. This expansion propels the mixed products axially along the engine, generating thrust. In addition to the primary shock, an oblique shock and a secondary attached shock are also generated (Features B and D in Figure 5a).
\nDiagram showing the general structure of the detonation in an unwrapped RDE [
At the interface between the premixed reactants and the combustion products, there is a significant difference between the conditions of the unburnt fuel/oxidiser mixture and the products. This causes some deflagration along the slip line, as shown in Figure 6, generating Kelvin-Helmholz instabilities, which vary the detonation propagation velocity [3, 22, 72, 73]. This decrease in the propagation velocity results in an increase in the pressure, disturbing the oncoming shock wave and forcing the sonic flow directly behind the shock wave to undergo supersonic flow acceleration [74]. As shown in Figure 6 there is a section of injector flow blockage that occurs as the wave passes the fuel array. The high pressure front from the shock wave causes stagnation of the injector flow, or even back-flow which, if not handled, could cause catastrophic failure of the system [3, 6, 36]. This back-flow is a strong reason as to why the fuel and oxidants should not be premixed in practical systems or experimental investigations as it can result in flashback.
\nThe Chapman-Jouguet (CJ) condition can be defined as the requirements for the leading shock of a detonation to not be weakened by the rarefactions of the upstream detonation products [75]. This sonic plane then acts to allow the supersonic expansion of the detonated gases to occur without disturbance by rarefactions downstream of the flow [75]. The CJ condition can be used to approximate the detonation velocities in three-dimensional models but is better suited to a one dimensional analysis with an infinitesimally thin detonation front [76]. Despite this, it is used in most instances of numerical modelling as a guide as to whether the wave is performing as expected for the given parameters of the RDE [4, 6, 27, 31, 32, 42, 75, 77]. Chapman and Jouguet’s theory only applies to kinetic energy, disregarding the chemical energy of the reacting species, and hence, the Zel’Dovich-von Neumann-Doring (ZND) model is used as a more complete representation of the shock, taking into account the finite chemical reaction area directly upstream of the leading shock [3, 21, 45, 75, 78, 79, 80].
\nThere are two methods which may be used to initiate the detonative shock in an RDE—directly in the chamber, or indirectly via a high speed flame in a deflagration to detonation transition (DDT) tube [26, 31, 39, 49, 51]. These tubes are very similar in structure to a PDE. Directly initiating the detonation in the chamber via commercial spark plugs has been found to be generally unreliable, with only a 40% success rate for shock initiation when using CH4 in O2 [26]. Particular difficulty is noted in ensuring the detonation travels in the desired direction [26, 32]. In contrast, indirect initiation via a DDT tube has had a 95% success rate for the same fuel/oxidant combination [26, 31]. The indirect method involves using a detonator tube that can be set up in any orientation relative to the chamber, although tangential is favoured for initiating the detonation direction. Initiation is then caused by a small volume of a highly detonative mixture being ignited by spark plugs before DDT occurs, thus initiating the RDE. Perpendicular initiation can also be used, but this often results in the development of two detonation waves that rotate around the chamber in opposite directions [31]. Collision of these opposing waves usually destabilises the system as the waves weaken and reflect back in the direction of origin [31]. Desired direction also appears to be affected by initial total pressure and ignition distribution around the fuel plenum [27, 81]. For a desired single wave direction and propagation, tangential initiation is the most suitable method. Although slightly less compact due to the initiator tube, this may be reduced by placing obstacles in the tube to accelerate the DDT, or by using a more detonative fuel than that used in the primary process [31, 48, 62, 82, 83]. Using an initiator tube, however, may produce small wavelets ahead of the main detonation front, which, if present, reduce the detonation propagation velocity by up to 60% [84]. Once the main detonation is running, the interface between the initiator tube and main chamber must be closed off prior to the shock completing a revolution of the chamber [84]. Additionally, there may be a slight delay, on the order of milliseconds, between the detonation exiting the DDT tube and the commencement of full RDE operation in order to purge the spent reactants from the DDT process [85]. This delay seems to only be transient with no large effects on shock structure or stability, and the excess products are expelled along with the rest of the exhaust [85].
\nThree-dimensional modelling has shown that increasing the width of the channel—whilst maintaining the equivalence ratio, injection pressure, chamber length, and injector configuration—increases the detonation velocity, but the transverse shock wave ceases to be aligned with the radial direction [22, 27, 86]. As can be seen in Figure 7, the point of contact with the inner wall begins to lead the detonation wave as the channel width increases [22]. This phenomenon generates reflected shocks from the outer annulus wall, which may produce instabilities in the primary shock. It has been suggested through qualitative observation, however, that the effect of upstream reflected shocks on the shock structure may only be minimal [39, 87]. Once the channel becomes sufficiently wide, as shown in Figure 7c, the shock wave detaches from the inner wall, briefly forming a horseshoe shape against the outer wall [22]. This allows significant amounts of fuel to pass through the engine without combusting, and produces large instabilities and fragmentation in the detonation wave, which causes the structure to collapse [22]. These lead to a significant loss of performance, and secondary detonations in the exhaust [22]. It has been noted that increasing the channel width also results in increased variance of \n
Schematic of three different RDE designs showing the effect of varying the channel width on detonation structure. Arrows show detonation wave propagation direction. The red line is detonation wave, indicative only. Based on research from [22]. (a) Narrow channel, (b) mid-sized channel, and (c) wide channel.
It has been found that using a fuel-rich mixture produces stable waves with high detonation velocity and efficiency [80, 88]. Higher mass flow rates have also been attributed to increasing the chance of a stable wave being formed [6, 89]. Additionally, it has been shown that the equivalence ratio has a strong influence on the effectiveness of detonation and the stability of the system [80]. Detailed investigation has shown that the stability of the system is improved with increased equivalence ratio, but indicated a maximum equivalence ratio of 1.27, before the detonation wave became short-lived and transient, which is unsuitable for practical purposes [60]. Whether this is a universal limit, or a limit of that particular investigation is unclear, and requires further research. Furthermore, the findings indicated that lower equivalence ratio influences the number of wave fronts produced, with stoichiometric seeming to be a transition point to a stable one wave propagation mode [60, 86, 90]. It is interesting to note that for lean mixtures, the initial channel pressure needs to be higher for a stable detonation to propagate [88].
\nThe wave propagation velocity varies with the fuel/oxidiser combination. A variety of mixtures have been tested in a detonation tube of an RDE, with their wave propagation velocities and wavefront pressures shown in Table 3, which is indicative of their varying performance in an RDE. It should be noted that the pressure, energy and specific impulse in Table 3 are determined with a detonation tube, and provide a numerical comparison between each fuel/oxidiser combination. Hydrogen/oxygen mixes have been ideal for modelling purposes due to the simple chemistry involved, and are often used in experimental work due to the predictable behaviour. Additionally, the high detonation propagation velocity and wavefront pressure of hydrogen makes it a suitable fuel for real applications. Another common fuel choice is methane, due to the satisfactory propagation velocity and specific impulse in testing [31]. As mentioned in Section 2, the theoretical \n
Fuel mixture | \nDetonation speed (m s−1) | \nWavefront pressure (atm) | \n\n\n | \n\n\n | \n
---|---|---|---|---|
Hydrogen/oxygen | \n2836 | \n18.5 | \n8.43 | \n289.39 | \n
Hydrogen/air | \n1964 | \n15.5 | \n3.48 | \n200.41 | \n
Ethylene/oxygen | \n2382 | \n31.9 | \n5.23 | \n243.06 | \n
Ethylene/air | \n1821 | \n18.2 | \n2.85 | \n185.82 | \n
Ethane/oxygen | \n2257 | \n29.0 | \n4.87 | \n230.31 | \n
Ethane/air | \n1710 | \n15.8 | \n2.49 | \n174.49 | \n
Propane/oxygen | \n2354 | \n34.2 | \n5.18 | \n240.20 | \n
Propane/air | \n1797 | \n17.5 | \n2.80 | \n183.37 | \n
Fuels, wave propagation velocities and pressures, heat of combustion (\n
Transportability of fuel, and maintenance of fuel lines, are deciding factors in determining which fuels can be used. These issues are especially important for aerospace applications. Gases such as H2 and O2 are particularly volatile and reactive, hence can be difficult to transport in the large quantities needed for use in an RDE. Therefore, gaseous fuels and non-air oxidisers are challenging and largely unsuitable for real world applications [5]. However, H2 does have a high heat of combustion that is not matched by liquid hydrocarbon fuels. Jet fuel, kerosene, octane and other long-chain hydrocarbons provide a practical alternative to the H2/O2 mixture though. High volumetric energy density as a result of liquid state, as well as greater ease of transportability makes these hydrocarbons a more feasible fuel choice.
\nThere are several issues regarding fuel choice that deserve further discussion. In particular, the use of cryogenic fuels for cooling the engine is a beneficial approach, increasing thermal efficiency, as well as reducing the thermal load on other components such as mounting systems [3]. Another advantage is a higher volumetric energy density that comes from the compression of normally gaseous fuel sources. Testing of liquid oxygen (LOx) and gaseous or liquid hydrogen (GH2/LH2) fuel/oxidant systems for viability has been performed, but implementation in real world scenarios is challenging [92, 93]. Liquid hydrocarbons require further investigation to demonstrate their effectiveness in producing thrust through detonation [30], particularly because of the need for flash vapourisation to avoid multiphase effects in the mixing process [30, 51].
\nAn axial fuel injection process through a circumferential orifice plate was consistent across most simulations and real world models as an injection scheme [5, 6, 22, 26, 30, 32, 36, 38, 39, 41, 42, 42, 52, 61, 62, 82, 86, 88, 92, 94, 95, 96, 97, 98, 99]. Further research is required into fuel blockage effects due to the high pressure of the shock wave, with particular emphasis on the effects of increasing fuel pressure to alleviate blockage and increase overall engine performance [100]. In the majority of numerical and physical models, such as Figure 3, fuel and oxidiser are injected through an orifice place around the annulus, allowing them to continually feed the propagating detonation wave. Typically, the fuel and oxidiser are fed in separately, and allowed to mix in the chamber [26]. This design is also used in most numerical models, although some have used premixed fuel/oxidiser as a simplified boundary condition. Almost all physical designs have been built without a premixed fuel/oxidant injection scheme due to concerns with flashback [99]. In a premixed design, the shock wave may propagate into the injection plenum, carrying with it the reaction front. With sufficient pressure though, typically 2.3–3 times the chamber pressure, this can be avoided [32].
\nInvestigation into flow characteristics of a turbulent inflow have shown that there are specific zones within the chamber which favour different forms of combustion: some zones favour deflagration, and others favour detonation [101]. The larger deflagration zones created reduce the thermodynamic efficiency of the engine, indicating that fuel flowrate influences the reliability of an RDE [101]. It has been suggested that high inlet velocities generate incomplete combustion and hot spots, reducing detonation wave stability and reducing system efficiency, although further research is required [102]. As indicated in Section 3.3, the introduction of instabilities in the flow profile can decrease the efficiency of the engine as well as disrupt the detonation wave itself. Further findings indicate that increasing the fuel injection area, particularly by increasing the number of orifices, results in more efficient pressure gain [86, 97, 99, 103]. This produces a larger expansion wave of the previous combustion reactants, generating higher thrust, without disrupting the flow-field characteristics [98]. However, with lower fuel injection velocities comes an increased risk of flashback. There is, therefore, some optimal fuel injection area for operation which requires further work to verify [98]. Finally, the pressure ratio between the inlets and the engine outlet also has an effect on the \n
Existing RDEs tend to be relatively small, and therefore may need to be scaled up, or arranged in parallel, to produce thrust required for practical applications, such as those listed in Table 2. One method of scaling RDEs is to run multiple identical devices in parallel, in a similar manner to that used to run multiple PDEs [34, 104]. However, this would require more complex plumbing, increasing the weight of the overall system, and thus decreasing the thrust-to-weight ratio. However, this solution has not been explored in any depth and its viability is unknown.
\nIn order to make larger RDEs, in-depth research into the geometry of the combustion chamber is required. A number of relationships between the critical detonation wave height and the various dimensions have been identified [27, 30]. Detonation structure, as described in Section 3.1 is composed of small diamond shaped detonation cells that make up the front. The widths of these cells are dependent on the energy of the detonation (related to the fuel in use) as well as the available geometry for detonation. In this way, the equivalence ratio can be a large determining factor [30, 105, 106]. Critical minimum fill height is the minimum mixture height required for a detonation wave to propagate through a given fuel/oxidiser mixture. It has been found that the critical minimum fill height, \n
and the minimum channel width, \n
Finally, the minimum axial length of an RDE, \n
although lengths under 2–3 times the minimum result in reduced efficiency due to incomplete combustion [27]. However, in simulations, it has been suggested that for low inlet-nozzle pressure ratios the wave the wave height grew with the chamber length, reducing the \n
Geometric parameters of an RDE. The red area is the area filled by the fuel/oxidiser mix in which the detonation propagates. (a) Top view, (b) side view, and (c) detonation cell width adapted from [79].
There is not yet any theoretical data for \n
Modelling a large-scale RDE presents a challenge due to increasing computational requirements with increasing size, so limited work has been done in this area. Nevertheless, a larger scale experimental RDE has been demonstrated [5]. This RDE had an outer chamber diameter of 406 mm, and a channel width of 25 mm, and an air inlet slit that could be varied across the range 2–15 mm [5]. It produced a consistent thrust of 6 kN with a combined fuel/oxidiser flow rate of 7.5 kg s−1, whilst also producing an \n
The design used in most simulations and experimental work is a coaxial cylinder structure [3, 27, 31, 35]. This simple geometry is advantageous for both modelling and manufacturing. Design variations including using nozzles, aerospikes such as that shown in Figure 9, or an entirely hollow cylinder, have been utilised in several RDE designs [5, 52].
\nExample of an aerospike nozzle configuration [
Alternative chamber geometries have been largely limited to adjustments in the diameters of the chamber [4, 42], including with different sized engines [15, 31, 39, 54]. Other work has been conducted on a single RDE with interchangeable outer wall sections [22, 30]. As noted in Section 2 and Section 3, both of these factors influence the stability and the performance of RDEs. The effect of varying the length of the chamber on the detonation propagation has been investigated, which led to the previously mentioned requirement that the chamber be at least twice, and preferably four to six times, the fuel fill height [4, 96].
\nHollow RDEs, dubbed “centrebodiless” designs, have been tested with two different designs [50, 61]. One design was identical to a conventional RDE 100 mm across, but the inner cylinder terminated parallel to the fuel/oxidiser injectors [61]. In this design, tested with 169.7 g s−1 of CH4/O2 at an equivalence ratio of 1.154, it was found that the detonation was unstable [61]. The fuel and oxidiser were free to move into the space usually occupied by the centre body, and thus insufficiently mixed to sustain a stable detonation [61]. However, when the same geometry was tested with 253.3 g s−1 of CH4/O2 at an equivalence ratio of 0.665, the mixture became sufficiently mixed to sustain a stable four-wave detonation structure [61]. Another design was completely hollow, allowing oxygen-enriched air to be pumped through the centre of the chamber, and fuel was supplied around the edge [50]. In this design, stable detonations, operating at \n
The attachment of turbines to RDEs has been proposed [8, 9, 31, 32, 45]. It has also been noted that there is a secondary shock propagating from the detonation, which exits the outlet of the chamber [32]. However, turbine blades are sensitive to shocks. As such, the effect of the secondary shocks on the blades of potential turbines must be investigated. It is worth noting that an experimental PDE array has been tested with an attached turbine, in the form of an automotive turbocharger [31]. In that case, a buffer chamber was inserted between the PDE and the turbine [31], and such a technology may be suitable for RDEs.
\nComputational fluid dynamics (CFD) modelling is a powerful tool for the analysis of rotating detonations prior to, or in tandem with, experimental systems. The majority of numerical studies have aimed to provide in-depth understanding and details of the detonation structure [22, 41, 62, 67, 72, 94, 107, 108] or assess the physical and modelling factors influencing performance [32, 67, 73, 109].
\nComputational models of the azimuthal detonations in RDEs may use full three-dimensional geometries [20, 22, 67, 94, 95, 107, 110] or simplified, two-dimensional geometries [6, 32, 41, 43, 62, 72, 73, 108, 109, 111, 112, 113, 114]. The former, higher-fidelity, approach can incorporate complex geometric and flow features, although require \n
Two-dimensional modelling of RDEs assumes that the flowfield along the centre of the channel is representative of shock and deflagration structure across the entire width. Consequently, this inherently assumes slip-wall conditions and that the detonation-front is normal to the two-dimensional geometry. In the unwrapped two-dimensional geometry, all fuel is injected axially from one edge (the bottom edge in Figure 5a [32]) and is exhausted through the opposite edge (the top edge in Figure 5a) [6, 32, 72, 111]. It therefore follows that all exhaust products must leave the domain axially, due to conversation of angular momentum. This was confirmed in early two-dimensional modelling, which found that the density-averaged azimuthal velocity was less than 3% of the axial velocity [41]. Such a criterion could be extended to assessing whether a three-dimensional model, at some fixed radius within the channel, could be treated as an unwrapped planar domain.
\nDetonation wave curvature, imperfect mixing, three-dimensional turbulent structures and transverse shocks are features reported in three-dimensional computational modelling [22, 67, 79, 94, 107] and experimental studies [62]. These features arise from the effects of channel size [22], discrete injectors [79] and interactions between transverse waves and walls [62, 79]. These features are inherently three-dimensional and cannot be captured using planar, periodic models, and require more complex computational geometries.
\nFuel/oxidiser inlets may be modelled as simple points, lines, surfaces or complex, discrete injectors. The latter may be treated as a series of inlets in two-dimensional models, assuming upstream micro-mixing [109, 112]. Differences in the injector configuration can lead to differences in detonation pressure [112], or lifted flame behaviour in the event of poor mixing in a partially premixed system [109]. The study which observed the latter phenomenon, however, was undertaken using the Euler equations, which may affect the fidelity of modelled mixing (discussed later in this section), and a simplified induction parameter model (described in Section 5.4) [109], although this has also been observed experimentally in C2H2-fuelled RDEs [115].
\nInlet boundary conditions in premixed models, are often defined by inlet throat-to-nozzle-exit ratios. These, and the set upstream pressure, control whether the inlets are blocked, subsonic or choked and are chosen to range from 0.1–0.2 [6, 109, 110, 112], although ranges as large as 0.07–0.3 have shown little effect on \n
Rotating detonation engines have often been numerically modelled using the compressible Euler Equations [6, 20, 32, 41, 43, 62, 72, 95, 108, 110, 111, 112]. The Euler equations conserve momentum, mass and energy, but do not account for viscosity, following the assumption that the detonation structure dominates viscous dissipation. Viscous effects may, however, be incorporated into numerical studies of RDEs through the use Reynolds-averaged Navier Stokes (RANS) modelling [107, 113], LES, LES-RANS hybrids such as [improved] delayed detached eddy simulations (IDDES) [67, 94], or DNS [22]. Of these approaches, Euler, IDDES and DNS studies [22, 41, 67] have all been able to capture Kelvin-Helmholtz instabilities in the unreacted/reacted and the post-shock mixing layers (see Figure 5a as an example), using sufficiently small element sizing in both two- and three-dimensional models.
\nThe grid required to resolve large structures in RDE mixing layers is dependent on the size of the geometry. Elements of 200 μm have been shown to predict shear layer instabilities using either Euler equations or IDDES in an RDE with a mid-channel diameter of 90 mm [67] and an \n
Both viscosity and species diffusion have been stated as critical features in non-premixed models of RDEs, promoting the use of IDDES or LES in modelling studies [67]. In contrast, a negligible dependence of detonation velocity or \n
Although the Euler equations cannot account for viscous effects, such as wall shear-stress and heat transfer, these have a small, but non-negligible, effect (\n
Incorporating viscosity and thermal wall-effects into IDDES simulations requires significant computational resources. One such study required a computational mesh of \n
The majority of numerical RDEs works to date targeted H2/air and H2/O2 systems [6, 20, 22, 41, 62, 72, 73, 79, 94, 95, 111, 112, 118, 121, 122], given their relatively simple chemistry in comparison with both small and large hydrocarbons. Nevertheless, limited data are also available for linearised CH4/air and C2H4/air systems [114].
\nThe simplest approach to describe the chemistry is that of a one-step irreversible reaction [6, 43, 62, 95, 108, 109]. This assumption has been widely used to numerically investigate various aspects of fully premixed canonical RDE cases and useful insights have been gained [6, 32, 95]. However, it is well known that such a simplification is not able to accurately quantify many detonation responses of interest (e.g. upstream deflagration phenomena [109], triple shocks structure [79, 116]), mainly due to the sensitive Arrhenius nature of the reaction rate to temperature variations. Also, the use of
Simplified approaches to chemical kinetics may employ a one-step reversible reaction [20, 62] or a two-step mechanism [22, 41] to describe the chemistry within a system. In particular, for the one-step case, the forward reaction rate is calculated using the classical Arrhenius equation with the reaction rate constants tuned from a reference case while the backward reaction rate is calculated from the assumption of local chemical equilibrium [20, 62]. This approach has been validated against detailed chemistry for a 1D model [20]. For canonical 2D premixed RDEs, a one-step reversible reaction is not able to accurately capture the post-detonation temperature while it is able to predict both the experimental pressure and velocity fields [20]. In addition, it was also found that this approach can be successfully implemented to describe stratification effects in three-dimensional non-premixed RDE systems [62].
\nFor the one-step case, a number of two- and three-dimensional premixed RDE simulations employ an induction-time parameter model (IPM) to compute the chemical source terms [6, 32, 43, 109]. The IPM has shown reasonable accuracy for the prediction of detonation wave propagation in premixed systems [108], as the induction time is derived from the same configuration as the CJ wave-speed [116]. In addition, it is computationally inexpensive as a global induction parameter allows for release of energy over a finite period of time. Nevertheless, the IPM lacks the flexibility to accurately describe the physics occurring in more realistic non-premixed systems [94]. The thermodynamic properties of the single product species employed in this model are dependent upon the equivalence ratio of the fuel/air mixture. Therefore, this approach cannot easily handle the spatially varying local equivalence ratio occurring in a non-premixed system [116]. This model also lacks the capability to capture the low-pressure heat release and the change in equilibrium chemistry of post-detonation products. Finally, this method requires
Finite-rate kinetics and the associated kinetic mechanisms are needed to capture complex phenomena such as near-limit propagation leading to quenching of the detonation wave [116]. This is mainly because the use of a one-step reaction precludes the influence of chain-branching-termination mechanisms that are invariably multi-step in nature. In this regard, an advanced approach is the induction-length model, which concerns determining the induction length for adiabatic propagation and using it to estimate global detonation parameters such as the cell size of steady propagation and the wave curvature at quenching [116]. This study showed that at least a four-step mechanism is required to achieve acceptable predictions in CJ detonation.
\nModels of RDEs using H2/air, H2/O2, CH4/air and C2H4/air mixtures have employed detailed chemistry and simplified configurations [68, 72, 73, 79, 111, 112, 114, 118, 122], although only limited studies are available in comparison with simplified (one- or two-step) chemistry, given the relatively large computational expense required and the current computational resources. A set of 8–9 chemical species and 18–21 elementary reactions are generally employed for H2 systems [72, 112], while 21–22 species and 34–38 reactions are used for simple hydrocarbons systems [114]. These studies highlighted that the use of detailed chemistry is needed to accurately predict the energy-release pattern in RDEs and complex characteristics, including re-ignition, number of triple points and transverse waves [68].
\nA linearised model may be constructed to simulate the operation of an RDE [79, 124]. These models, shown in Figure 10, are known as linearised model detonation engines (LMDEs). In this model, fuel is fed into the chamber, and a transverse shock wave propagates through it. This occurs in much the same manner as in an RDE. However, the chamber is rectangular, and so the detonation only makes a single pass through the chamber [79, 124]. Both computer models and practical experiments have been run in three different modes, all using fresh supplies [79, 125]:
The chamber is pre-filled with premixed fuel/oxidiser, and then the detonation is initiated.
The chamber is pre-filled with an inert gas, then premixed fuel/oxidiser is injected and the detonation is initiated simultaneously.
The chamber is pre-filled with oxidiser, then fuel is injected and the detonation is initiated simultaneously.
An example linearised model detonation engine [
LMDEs have been used to characterise the detonation process, by allowing both sides of the chamber to be imaged through quartz walls, or the density field imaged through the use of the Schlieren technique [79, 126]. It has been found that the critical fill height of an LMDE is about \n
Computer modelling of an LMDE indicated that the propagation of a detonation wave was not affected by the turbulence caused by in-chamber mixing of fuel and oxidiser [118]. However, the presence of this turbulence did cause the reaction zone to trail the detonation wave [118]. A model of an LMDE was also used to test the result of applying different back pressures, such as might occur if a nozzle or a turbine was attached to an RDE [114]. This indicated that increased back pressure also increased the detonability of the fuel mixture, but also restricted the acceleration of the products, which, in some cases, led to the production of tertiary shock waves to sufficiently compress the flow to match the exit plane conditions [114]. However, as noted previously in Section 2, nozzles have very limited benefit [53], and, as noted in Section 4 the effect of secondary and tertiary shocks on a turbine may be problem.
\nRotating detonation engines have the potential to provide a significantly more efficient combustion cycle than deflagration-based engines. The application of this technology to turbines promises to increase the thermodynamic efficiency of these engines to previously unattainable levels. Additionally, RDEs as a standalone engine hold significant promise for both air-breathing and air-independent rocket propulsion. However, there exists a large body of research and development work still-to-be undertaken, including:
Nozzles have been shown to have limited benefit to the thrust generated by RDEs. However, varying the angles of the walls of an RDE, either independently or together, may simulate the effect of a nozzle to provide a slight benefit to performance. It remains unknown what effect such modifications to the conventional cylinder might have.
Comparisons of thrust to weight ratios between experimental RDEs and conventional rocket engines show similar values, indicating that an RDE could represent a method of propulsion in space. This has not been widely explored as an option, and would benefit from experimental work in vacuum conditions or microgravity conditions.
It has been suggested that there may be a maximum equivalence ratio at which an RDE will operate, but further investigation is required to determine if this is a universal limit, and identify ways to lower the limit.
Triple points appear to have significant effect on the propagation of the detonation wave but little work has been done on determining the constraints, besides chemical composition, on the formation of stable and consistent triple points as well as the effect of those parameters on other characteristics of the triple points such as peak pressure and propagation direction. Findings would be beneficial in terms of properly defining the parameters that affect \n
Very few studies have provided a mathematical relationship between the detonation cell width and the geometry requirements of the chamber. More supporting work to help refine and verify or dispute the relationships that have been established needs to be done, so that in the future, specialised design needs can be catered for through knowing the geometry and cell width of fuel types.
Varying the channel width has been noted to affect the stability of the detonation wave in an RDE. As such, this is likely to affect the performance of such devices. Further research is required to determine what the optimal width would be for different design requirements.
It is established that RDE chambers need to be at least twice as long as the fuel fill height, and increasing the length four to six times the fill height improves the efficiency. However, depending on the ratio of inlet pressure to nozzle pressure, such a length increase may also result in reduced \n
So-called “centrebodiless” designs have been explored, and proposed for use in afterburners. However, they have not been modelled or tested with heated high velocity air, as would be typically found at the outlet of a conventional jet engine, so their potential performance remains unknown.
It has been demonstrated that the thrust produced by RDEs scales non-linearly with engine size, but they are not yet approaching the size required to replace most existing gas turbines. It remains unknown if an RDE can be scaled up sufficiently to provide the thrust levels offered by contemporary gas turbine engines.
It has been suggested that a turbine could be attached to an RDE. However, the effects of the various shocks on a turbine have not been explored. In particular, the oblique shock (Feature B in Figure 5a) has been shown to propagate out of the chamber, and is likely to have significant effect on the viability of using a turbine.
The invsicid Euler equations have been demonstrated to over-predict deflagration in three-dimensional computational models of premixed RDEs, even with the use of detailed chemistry. Their validity in non-premixed RDE configurations, with deflagration upstream of the detonation and the potential to produce lifted detonation waves, still requires rigorous assessment.
Viscous and thermal wall-effects in RDEs have significant effect on RDE performance characteristics, and may be essential in accurately reproducing experimentally measured values. Understanding of the appropriate numerical modelling approaches of these effects, however, is still immature, owing to the computational resources required for sufficiently fine resolution of near-wall grids.
The computationally predicted wave-speeds and plenum pressures in RDEs are significantly different to those measured experimentally. It has been proposed that this could be partially due to baroclinic vorticity, resulting from interactions between detonation waves, fresh reactants, deflagration reaction-zones and post-combustion products, although this is yet to be analysed in detail in either full RDEs or linearised models.
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