\n Giardia species.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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",isbn:"978-1-83969-012-9",printIsbn:"978-1-83969-011-2",pdfIsbn:"978-1-83969-013-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"3e1efdb1fc8c403c402da09b242496c6",bookSignature:"Dr. Tae-Heung Kim",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9589.jpg",keywords:"Congenital Depigmentation, Albinism, Piebaldism, Dyschromatosis Hereditaria, Vitiligo, Pathogenesis, Phototherapy, Excimer Laser, Excimer Light, Surgical Treatments, Postinflammatory Hypopigmentation, Pityriasis Alba",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 9th 2020",dateEndSecondStepPublish:"November 23rd 2020",dateEndThirdStepPublish:"January 22nd 2021",dateEndFourthStepPublish:"April 12th 2021",dateEndFifthStepPublish:"June 11th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Kim graduated from and acquired a doctoral degree (Ph.D.) at Seoul National University College of Medicine. He is an active member of many international and domestic societies and a former President of the Korean Society for Vitiligo.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"121353",title:"Dr.",name:"Tae-Heung",middleName:null,surname:"Kim",slug:"tae-heung-kim",fullName:"Tae-Heung Kim",profilePictureURL:"https://mts.intechopen.com/storage/users/121353/images/system/121353.png",biography:"Dr. Tae-Heung Kim graduated from and acquired a doctoral degree (PhD) at Seoul National University College of Medicine. He completed an internship and dermatology residency at Seoul National University Hospital.\r\nHe moved to the Department of Dermatology, Gyeongsang National University, and was then promoted to Professor and Chairman of Dermatology.\r\nIn 1996, he did a research sabbatical for two years at the Department of Immunology, University of Texas MD Anderson Cancer Center.\r\nIn 2003, he started private practice as Director of the White-Line Skin Clinic and Research Center, Changwon, Kyungnam.\r\nHe is an active member of many international and domestic societies, and was the President of the Korean Society for Vitiligo (2016–2018).",institutionString:"White-Line Skin Clinic & Research Center",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"184402",firstName:"Romina",lastName:"Rovan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/184402/images/4747_n.jpg",email:"romina.r@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"56706",title:"Giardiasis Epidemiology",doi:"10.5772/intechopen.70338",slug:"giardiasis-epidemiology",body:'\nGiardiasis is the intestinal infection caused by Giardia lamblia (also known as Giardia intestinalis or Giardia duodenalis). G. lamblia is a unicellular eukaryotic protozoan that was first described by Antonie van Leeuwenhoek in 1681—in his own stool sample [1]. For decades, it was considered of uncertain pathogenicity but now is recognized as common causes of diarrheal disease worldwide. Its clinical significance was broadly accepted after many symptomatic cases of giardiasis were diagnosed and reported among visitors to the Soviet Union in the early 1970s. Since then, giardiasis has been reported as responsible for many outbreaks throughout the world.
\nThe epidemiology of giardiasis still is a matter of great discussion. From the original debates around its pathogenicity to the later ones about its speciation and biology, G. lamblia has proven to be an enigmatic and interesting organism [2]. Although giardiasis is currently recognized as one of the main causes of diarrheal disease and a leading cause of death and illness among children under 5 years old in developing countries [3], the long-term impact of pediatric giardiasis remains unclear. Recent cohort studies have confirmed a high prevalence of persistent, subclinical giardiasis and its association with growth shortfalls [4], but such evidence has not been consistently reported in the literature.
\nCommonly, giardiasis prevalence among poor populations is reported as very high, and when the infection became chronic, it has been associated also with malnutrition and cognitive deficits [5]. In developed countries, giardiasis represents the leading cause of traveler’s diarrhea and is frequently reported among citizens that traveled to developing countries and expose themselves to untreated water from lakes, streams, and swimming pools [6–8]. These and other epidemiologic characteristics of giardiasis will be discussed in detail in this chapter based on the classical and latest literature.
\n\n G. lamblia is a parasitic protozoan of the order Retortomonadida that alternates between trophozoites and cysts forms within its life cycle, stages responsible for the clinical illness, and the transmission of the disease, respectively. Under the light microscope, trophozoites appear actively swimming and with its characteristically teardrop (viewed dorsoventrally) or spoon (viewed from the side) shaped, measuring 10–20 μm by 5–15 μm by 2–4 μm, containing four pairs of flagella, two identical nuclei, with a convex dorsum and a ventral disc that acts as a suction cup to facilitate attachment of the organism to the small bowel villi (Figure 1A). On high-quality slides, the parasite movement shows “falling leave mobility” and resembles a human face because of the positions of the median bodies, nuclei, and axonemes. Uncharacteristically Giardia trophozoites lack definable Golgi, peroxisomes, and true mitochondria, but have a menant mitosome. Trophozoites divide by binary fission, and cyst develops as feces dehydrated in transiting to the large bowel.
\n\n Giardia lamblia. Stained trophozoite (A) with its characteristic teardrop shape, binucleate structure, and four pairs of flagella clearly visible. Stained (B) and unstained (C) cysts with its characteristic median (parabasal) bodies and four nuclei. Images are some of the best public-domain light micrographs of Giardia, published by the National Institute of Infectious Diseases of Tokyo, Japan. Images were originally published at http://www.nih.go.jp/niid/ja/kansennohanashi/410-giardia.html.
Microscopically, Giardia cysts look oval shaped, measures about 11–14 by 7–10 μm, contains four nuclei (mature cyst)—usually situated at one end—and curved median bodies and linear axonemes (Figure 1B and C). During the process of encystment, which can be observed under the microscope, trophozoites initially become inactive, rounded, and increasingly refractile as encystment begins. Then, nuclear division (but not cytoplasmic) occurs to produce the quadrinucleate infectious cyst. Giardia cysts have a thick hyaline wall that protects them from environmental stressors such as the alkaline environment that characterize the small intestinal, water chlorination, high-altitude, or extreme temperatures such as in boiling water. Also, such strong protection allows cysts to survive in water up to 3 months [9]. Upon excystation in the small bowel, each cyst releases two trophozoites, which continue the life cycle.
\n\n Giardia species are currently classified in six species, which are distinguished based on its morphology and hosts: Gracilinanus agilis (amphibians), G. lamblia (mammals), Giardia muris (rodents), Giardia psittaci, and Giardia ardeae (both mainly in birds) (Table 1) [2]. Previously, many more species of Giardia were listed based only on microscopic and epidemiological criteria as well, but using molecular tests such as polymerase chain reaction (PCR), the list was shortened. Later on, genotyping studies confirmed that the species of Giardia could be classified in eight genetic groups (A–H): Groups A and B, which are found in humans and many domestic animals and wildlife [10], and the host-specific groups C–H. Among the latest ones, groups C and D infect dogs, cats, coyotes, and wolves [11]; group E infects cattle, sheep, goats, pigs, water buffaloes, and mouflons [12]; group F infects cats [12]; group G infects rats [12]; and group H infects marine animals [13]. This classification could be of great clinical value given that in addition to many genetic differences found, these genotypes exhibit several relevant biologic differences also. In example, genotype B seems to be more pathogenic than genotype A in humans, although they appear to grow slower [14]. Contradictory, the genetics of parasites of the genus Giardia is still poorly understood. Furthermore, recent evidence has cast doubt the classical view of an asexually replicating organism in favor of the occurrence of meiosis and genetic exchange. Such game breaker discovery demanded that the whole population genetics of Giardia should be reevaluated to take into account the effect of recombination on G. lamblia species [14].
\nSpecies | \nHosts | \nMorphology | \n
---|---|---|
\n G. agilis\n | \nAmphibians | \nLonger and slender than G. lamblia, with a teardrop-shaped median body | \n
\n G. ardeae\n | \nHerons and other birds | \nSimilar to G. lamblia | \n
\n G. lamblia\n | \nMammals including humans, dogs, and some wild species | \nTeardrop shaped with claw-shaped median bodies | \n
\n G. microti\n | \nRodents, voles, and muskrats | \nSimilar to G. lamblia | \n
\n G. muris\n | \nRodents | \nShorter and rounder than G. lamblia, with small rounded median body | \n
\n G. psittaci\n | \nPsittacine birds | \nSimilar to G. lamblia | \n
Life cycle begins with the infection by the ingestion of the cyst. Then the excystation continues, which starts at the stomach triggered by the exposure of the cyst to the gastric acid, the presence of bile and trypsin in the duodenum and/or the alkaline, protease-rich milieu, duodenum [2]. Excystation ends at the proximal small intestine where the emerging parasites (excyzoites) quickly transform into trophozoites that attach to the intestinal epithelial cells using the adhesive disc. The adhesive disc is essential for attachment and appears to play a major role in the virulence of Giardia [15]. Several disc-associated proteins have been identified using proteomics [16], and it is clear that the disc is an advanced cytoskeletal structure [17]. At the jejunum, the trophozoites start to encyst forming the wall that enables the parasite to survive outside the host for several weeks in cold water. This process is triggered by a particular composition of biliary secretions, possibly by a deprivation of cholesterol [18]. Regulatory factors are encystation-specific transcription factors, chromatin remodeling enzymes, and posttranslational modifications, which vary their expression in correlation with the variation of antigens on the parasite surface [19]. Finally, trophozoites and cysts are released with the stool, with cysts continuing the transmission of the disease when ingested by another host.
\nReservoir hosts include humans, as well as a variety of animals, including cats, dogs, dairy cattle, beavers, and other farm, wild, and domesticated animals such as horses, pigs, cows, chinchillas, alpacas, lemurs, sheep, guinea pigs, monkeys, goats, and rats [20]. However, among all these animal hosts, only beavers, dogs, and humans have been implicated as a source of infection in different waterborne epidemics and outbreaks of giardiasis in humans. Additionally, it is important to highlight the key role of “reverse zoonotic transmission” (zooanthroponotic) in the epidemiology of Giardia infections, which means that humans have been identified as the source of infection for beavers, muskrats, and coyotes in the Unites States and Canada [21–23], muskoxen in the Canadian arctic [24], nonhuman primates (gorillas) and painted dogs in Africa [25], marsupials in Australia, house mice in remote islands [26], and marine mammals (seals) in various parts of the world [27].
\nEven in developed countries such as the United States, it is common to isolate Giardia cysts in the water reservoirs and unfiltered water supplies of major cities, the water supply that is not filtrated [28].
\n\n G. lamblia has been consistently reported as one of the most common pathogens worldwide [29]. Due to high endemicity among humans, and domestic and wildlife animals, it is considered of public health and veterinary health importance [12]. Symptomatic infections have been reported by millions in Asia, Africa, and Latin America by the World Health Organization, which have estimated that it causes 183 million (confidence interval of 95%, 130–262 million) cases of giardiasis [30].
\nWorldwide, the incidence of Giardiasis has been estimated in 2.8 × 108 cases per year [31]. However, several epidemiological studies have reported that such rates could be significantly underestimated, with giardiasis prevalence rates ranging from 10 to 20% of the general population [32], from 10 to 50% in developing countries [33, 34], and from 2 to 5% in developed countries [35, 36]. This could be explained by the large fraction of asymptomatic carriers, which regardless of the absence of symptoms also contribute to the transmission of the diseases.
\nGiardiasis is a ubiquitous disease so it occurred across broad epidemiological contexts and with a broad range of distributions. On one side, in most developed countries such as the United Kingdom [37] and Germany [8], Giardia is mostly reported as a rare disease affecting travelers. Furthermore, in the countries such as the Unites States, where Giardia is continually under surveillance, Giardia has higher incidences (incidence rate ratios, 1.2–1.5) in counties with higher private well reliance compared to counties with lower well reliance [38]. On the other side, in most developing countries, Giardia has been associated with poor health hygiene, poor toilet training, overcrowding, and low socioeconomic status [39]. Furthermore, due to the high prevalence of Giardia among children, and its higher exposure to dogs in poor setting, it is believed that Giardia has developed zoonotic transmission. This possibility has been reported concordantly by genotyping and molecular studies from Mexico [40], Jamaica [41], and Cambodia [42].
\nThe high prevalence of Giardia among children raised a major concern about their long-term impacts, which currently have been well documented and reported as more worrisome due to the association between persistent Giardia and children’s growth [43]. Giardia infections can be detected repetitively in over 40% of children suggesting that persistent infections are common and associated with a damage of the intestinal permeability, which—independently of diarrhea—can lead to stunted growth. In fact, according to the results from the MAL-ED birth cohort study, the persistence of Giardia before 6 months of age was associated with a −0.29 (95% CI, −0.53 to −0.05) deficit in weight-for-age z score and −0.29 (95% CI, −0.64 to 0.07) deficit in length-for-age z score at 2 years [44].
\nThe Giardia cysts are overall highly infectious, and as few as 10 cysts can cause an infection in an individual. Giardiasis prevalence rates have been reported consistently as high among young children from developing countries, with high rates of repeated infection even within the first year of life. However, many developed countries have many regions with endemic giardiasis or regular outbreaks. At these countries giardiasis outbreaks are particularly common during the summer months (likely due to recreational swimming exposure) or throughout the year around day-cares and nurseries, infecting children under 5 years old—and their caregivers—the most [45]. In fact, an investigation of 242 outbreaks, affecting 41,000 persons, reported that most outbreaks resulted from waterborne (74.8%), foodborne (15.7%), person-to-person (2.5%), and animal contact (1.2%) transmission, with waterborne outbreaks been that largest ones in terms of number of cases per outbreak [46].
\nSurveillance data cases have shown that giardiasis infects populations with a bimodal age distribution, peaking at ages 0–9 years and 45–49 years, without gender preferences [35], and within areas that are endemic, giardiasis commonly shows a seasonal pattern, with most cases occurring in the summer months due to a recent history of drinking untreated surface water and a history of swimming in a lake or pond or swimming in any natural body of fresh water [47]. Other risk factors that have been reported as associated to giardiasis in endemic areas include living in areas that use at-risk tap water (i.e., filtered or unfiltered surface water [48, 49] or unfiltered shallow well water [48]) or in rural areas [49].
\nOne of the most common mechanisms of transmission of Giardia infections is a waterborne transmission but also can be transmitted by fecal-oral transmission with contaminated food or direct fecal-oral contact among family members, person-to-person contact, and sexual transmission (oral-anal contact). Although it is unclear which ones are clinically the most important, there is a common understanding about the populations at high risk of giardiasis, which include:
adults that work in child-care organizations or day-care centers [52];
institutionalized individuals [53];
men who have sex with men [54];
immunocompromised individuals (chronic variable immunodeficiency, hypogammaglobulinemia, HIV, immunosuppressed individuals, cystic fibrosis, and others) [55, 56]; and
international travelers or any subject (hikers, campers, sportsman’s adventures, and others) exposed to drinking untreated water from lakes, streams, and swimming pools [57].
Waterborne transmission is recognized as the most common transmission, with numerously documented outbreaks throughout the world [46, 58]. This includes the consumption of contaminated water from pools, rivers, or lakes, as well as from contaminated drinking water, either unpurified or inadequately purified. There have been multiple documented cases of cysts in the municipal water supply here in the United States, although such scenarios do not account for the vast majority of infections [35].
\nFoodborne transmission of G. lamblia is much less common than waterborne transmission, but there are many ways food can be fecally contaminated. For example, street food and any food prepared with the unclean hands of an infected subject could easily transmit giardiasis given a few cysts necessary to transmit the disease. Ingestion of 100 or more cysts is required to ensure infection in humans, but as few as 10 cysts have proven to be enough to infect a volunteer [59].
\nFecal-oral transmission is also a significant mechanism of transmission and is the one responsible for the outbreaks in day-cares and nurseries. These outbreaks reflect the close contact between young children, who are significantly more likely to pass the parasite fecal-orally at day-cares than at home. For example, in the Netherlands, where around half of preschool children are cared for in day-care centers, a mean of 2.5 days a week, children at day-care centers are twice as likely to test positive to G. lamblia as their home-care counterparts [45], infecting around 4.2% of them [60].
\nSexual transmission of Giardia is now a very well described form of oral-anal transmission and fecal-oral transmission among men who have sex with men. Currently, there exist a large body of publications that have led to improving our understanding of giardiasis as a sexually transmitted infection. According to these studies, prevalence rates of giardiasis among men who have sex with men range from 2 to 30% [61]. Although giardiasis is not a major cause of AIDS-associated diarrhea, the prevalence of giardiasis, as well as the chronicity of symptoms, is greater in patients with AIDS, especially in developing countries [62]. Overall, every immunocompromised group, like AIDS patients, is recognized as more susceptible to the development of chronic giardiasis [63].
\nTo sum up, it is really important that healthcare providers consider Giardia as a differential diagnosis among high-risk populations that match giardiasis epidemiology, and if patients tested positive, it is really important that they provide patients with appropriate therapy and follow-up, as well as proper counseling to increase treatment compliance rate. And in the case of men who have sex with men, also encourage partner notification, and teach them strategies for preventing the transmission of this disease, including the discussion of the risk of enteric infections after oral-anal sexual contact.
\nAerodynamic heating has been a major study issue for a long time now and many investigations have been conducted by researchers in order to achieve high-quality simulation results, both on the theoretical and the numerical level. Experimental studies of the above phenomenon are rare, as conducting such research is prohibitively expensive.
\nAerodynamic heating is a phenomenon worth studying as its significance lies in two major scientific fields. At first, aerodynamic heating is the most significant parameter of the emitted infrared radiation from aircraft, a fact that is of direct interest in military studies. According to statistics, the majority of aerial vehicles downed in modern warfare was due to weapons based on detecting Infrared signature level of those vehicles. Their percentage is as high as 89% of total aircraft shot downs between 1967 and 1993, according to Thompson J. et al. [1]. Apart from the emitted infrared radiation, the phenomenon of aerodynamic heating has to be taken into consideration when designing aerospace vehicles that fly at speeds comparable to that of sound. Ultrasonic aircraft and aircraft that re-enter the atmosphere can develop temperatures high enough that have to be carefully considered when designing the aircraft, in order to avoid unpleasant situations.
\nAerodynamic heating is a phenomenon that takes place mostly due to heat from the warm part of the boundary layer to the body’s surface. However, when approaching the speed of sound, aerodynamic heating phenomena are enhanced by the radiation emitted from molecules that are being decomposed and ionized due to the temperature rising behind the shockwave. At speeds greater than 5000 m/sec, the emitted radiation becomes significant and plays a measurable role in the phenomenon. Finally, another way that aerodynamic heating takes place concerns aircraft that fly in the higher atmospheric layers, where the distance between the air molecules is comparable to the aerial vehicle dimensions. However, the present study is focused only on the convection phenomena that lead to aerodynamic heating, as the speeds examined do not exceed Mach numbers of 2.
\nAs mentioned above, the significance of aerodynamic heating is a major issue in both the preliminary design of aerial vehicles and the calculation of the infrared radiation emitted from the vehicle in flight. Apart from experimental methods, which may offer reliable results but require enormous budgets, numerous simulations have been carried out via means of computational mechanics. Computational mechanics simulations approach the problem mainly through inviscid-viscous methods thus promising reduced computational time. AEROHEAT and INCHES are two of the simpler methods to approach the phenomenon of aerodynamic heating [2]. Both methods use the axisymmetric analog concept that allows axisymmetric boundary layer techniques to be applied to three-dimensional flows, provided that the surface streamlines are known. AEROHEAT calculates approximate surface streamlines based solely on the body geometry. INCHES uses an approximate expression for the scale factor in the windward and leeward planes, which describes the spreading of surface streamlines. An empirical relation then generates circumferential heating rates.
\nDRA Farnborough developed the SAPPHIRE software that is capable of calculating the surface temperatures and the characteristics of the flow field for a given geometry and defined flight characteristics. The above software is a series of codes that use PHOENICS [3] as the main CFD solver. The main advantage of CFD techniques is that aerodynamic heating can be related to other phenomena, as well, such as conjugating heat transfer and providing more detailed results. In order for SAPPHIRE to provide reliable results on the thermal trace, aerodynamic heating of the body has to be calculated with an error that does not exceed 5% [4]. For this purpose, a PHOENICS-based code was developed that has the possibility to validate results with available experimental data. The purpose of the present work is to make the model more time efficient and more accurate, producing results closer to the available experimental data.
\nAccording to finite volume or codes with FEM, attempts have been made, with acceptable results, by means of conjugating CFD and FEM. Murakami et al. [5] used CFD coding for the Navier-Stokes equations to calculate the outer flow, while the finite element method was used to perform the thermal analysis via equations describing unsteady heat conduction for a 3D field. The results were impressive, as they were very close to the experimental data measured in a wind tunnel experiment. Lu Jianwei and Wang Qiang [6] in order to calculate the emitted IR radiation suggested a division of the computational grid into two regions: the fluid and the solid. The fluid region is modeled by the Navier-Stokes equations, while turbulence is modeled by the RNG k-ε model. In the solid region, the energy transfer equation is used. In a similar study for calculating the emitted IR radiation, Marlene Johansson and Mats Dalenbring [7] developed the SIGGE software. SIGGE uses inputs of temperature, pressure, and species distribution that have been roughly estimated beforehand, together with the grid of the geometry being studied. The Navier-Stokes equations are solved by the VOLSOL solver, while turbulence is described by the k-ε model.
\nApart from CFD methods, attempts have been made to simulate phenomena that involve aerodynamic heating via other methods as well. Rodrigo C. Palharini and Wilson F. N. Santos [8] used the Direct Simulation Monte Carlo (DSMC) method to simulate a two-dimensional steady supersonic flow in an orthogonal cavity. Main interests of this study were the characteristics of the aerodynamic surface, such as heat transfer coefficients, etc. Another method introduced by Georgia Tech Research Institute [9] named GTSIG (Georgia Tech Signature Inviscid Model) involves the calculation of temperature distribution by creating thermal meshes and partial differential equations. Finally, Christopher J. Riley et al., in their study [2], developed a 3D technique without viscous calculations that has the ability to calculate the degree of surface heating. Surface streamlines are calculated from the inviscid solution and the axisymmetric analog. Then, they are used in combination with an equation for convective heat transfer, to calculate the total surface heat transfer. The results of this method are satisfying as they are close to experimental data and the results of both the Navier-Stokes and the VSL equation solutions.
\nIn order to describe the phenomena under investigation, use is made of the conservation equations describing a three-dimensional, single phase, turbulent, steady state flow. The above equations are formulated in such a manner as to incorporate the physical modeling of the case study under the following assumptions:
One phase, steady-state flow
Compressible fluid
Newtonian fluid
Absence of chemical reaction
It is also clear that because of the general geometries considered, the equations must be modified in a way to be suitable for use in a body-fitted coordinate (BFC) system.
\nAssuming that \n
Furthermore,
\nAssume a non-orthogonal coordinate system, defined as \n
where Uj are the Cartesian components and the unit tangent vectors alongside the three directions are:
\nwhere \n
In order to obtain the resolute [10] of the momentum equation in each of the coordinate directions, we form the dot product of tangent unit vectors with the momentum equation (Eq. (1)). The momentum equation, considering the assumptions and the physics of this simulation, is derived as follows:
\nwhere
\nIn order to obtain the resolute of the momentum equation in each of the coordinate directions, the dot product of the tangent unit vector and the above momentum equation is formed:
\nwhere \n
From vector calculus, using the identity \n
Also,
\nThus,
\nForming the dot product of the above equation and (Eq. (7)), we have:
\nAlso, for a scalar variable φn:
\nand
\nUsing Eqs. (11)–(13), we can write (Eq. (6)), as follows:
\nand
\nFrom Eqs. (1) and (3), the partial derivative in the direction ξn is:
\nWith inverse
\nwhere Gn,j the elements of the inverse matrix of the elements gn,j.
\nSince the case study is for steady flow, the conservation of mass equation is hence simplified in the following form for the Cartesian coordinate system:
\nUsing body-fitted coordinates, the continuity equation must be solved in terms of the velocity components along the coordinate directions. These components, un, would be \n
Velocity components in terms of the resolutes are
\nwhere
\nwhere
\nand
\nUsing Eqs. (1) and (18), the following expression for the velocity is derived:
\nwhere
\nthe vector used to obtain the resolutes (projections) of the velocity vector \n
From Eqs. (1), (16), and (20):
\nFinally, the continuity equation can be written as \n
In order to evaluate the use of different turbulence models in the cases under consideration, a parametric study was accomplished by trying three “popular” turbulence models: (i) standard k-ε model, (ii) RNG k-ε model, and (iii) k-ω model. The k-ε model simulation was chosen in order to reproduce the original simulation accomplished by Steve Rooks [4]. In order to take it one step further and include the effect of the swirling of the flow, another simulation was performed using the RNG k-ω model. Last but not least, the k-ω turbulence model was used as well, as preliminary studies proved its suitability for wall-bounded flows.
\nBy generalizing Eq. (14), the appropriate equation for any scalar variable can be provided [11]:
\nwhere
\nand φ a general scalar variable like H (enthalpy), k, ε, etc. Regarding k (turbulence kinetic energy) and ε (turbulence dissipation rate):
\nwhere ui, uj the velocity components in the direction of the Cartesian coordinates xi, xj with i = 1,2,3 and j = 1,2,3 [10]. It must also be noted that the effective exchange coefficients for k, ε are as follows:
\nwhere μl is the laminar viscosity; C1, C2, and Cμ are turbulence model constants; and σk,t and σε,t are the turbulence Prandtl numbers for k, ε [10].
\nIn total seven differential equations are solved, namely, the continuity equation, the three momentum equations, the enthalpy equation, and the two equations for the turbulence variables.
\nSpecial attention was given to the treatment of the near-wall region, as it is of high importance for the type of flows studied and the 2-equation models used to simulate turbulence do not, in general, produce results of high accuracy in near-wall regions. The near-wall region was treated via a log-law wall function that also played a major role in deciding the computational grid as well as ensuring computational economy since there is no need to solve throughout the viscous sub-layer. The use of this method was introduced by Launder and Spalding [12], and the formula used is:
\nwhere
\nU: the absolute velocity parallel to the wall in the first node of the grid
\nUτ: the friction velocity
\nk: the Von Karman constant
\nE: the surface’s roughness parameter
\nY+: the dimensionless distance from the wall
\nThe above function is applied to nodes whose dimensionless distance from the wall is between 30 and 130. In this region of the boundary layer, the effects of turbulence and viscosity are equally important.
\nBecause of the selection of body-fitted coordinates to design the grid of the problem, the velocity must be broken down to its components and transferred from the Cartesian coordinate system to the BFC used in the present study.
\nAt entrance, a uniform flow is applied of Mach 2 speed. The rate of flow is consistently calculated for each control volume. The kinetic energy of turbulence and its rate of dissipation are considered constant with values of 21.78 m2/s2 (10% of the mean velocity squared) and 2.765*105 m2/s3 (assuming turbulence viscosity 1000 times the laminar value), respectively (\nFigure 1\n).
\nThe plane of inlet boundary conditions specification.
The outlet boundary conditions describe the fact that there is no alteration in the rate of mass flow, as there is neither mass creation nor destruction. As for the turbulence kinetic energy, turbulence dissipation rate, and the enthalpy, Neumann conditions prevail, suggesting there is no alteration of values regarding these properties along the boundary (\nFigure 2\n). Since the boundary conditions must be transported in the BFC system, the Jacobian of the transformation used is:
\nThe plane of outlet boundary conditions specification.
and the general form of the functions:
\nFinally, the boundary condition at the northern boundary is prescribed external pressure. Like the boundary conditions at the outlet, the kinetic energy of turbulence, its dissipation rate, and the enthalpy are described by Neumann boundary conditions, implying absence of factors capable of altering their values along the northern boundary.
\nThe study for grid independence came down to the same results as the one proposed by Steve Rooks [4]. The computational grid used is one with dimensions 48*45*1 and has been proved to lead to grid-independent results [4]. Briefly, the grid divides the computational field in three regions along the x-axis and three along the y-axis, as one can see in the following figure (\nFigure 3\n).
\nThe computational grid.
Along the y-axis the three sub-regions correspond to the area below the lip, the area of the lip, and finally the area above the lip. A most important role in the dimensions of the grid played the fact that a denser or a sparser grid would result in improper use of the wall functions, as it would affect the dimensionless distance Y+.
\nIn order to solve the equations, the finite-volume method is employed. As for regular grids, the finite-volume equations can be derived by integrating over a control volume in a system with body-fitted coordinates. The use is made of the Gauss theorem that transforms a volume integral to a surface one:
\nwhere \n
Any dependent variable’s partial differential equation is represented by a coupled set of algebraic equations of the following form:
\nwhere the Ai’s represent the influence of convection and diffusion, Sφ is the source term, and P refers to the control volume under consideration. Σi indicates the summation over the neighboring nodes. All the above equations are derived through work that cannot be presented here due to limited space [10].
\nThe numerical simulations in this work are realized by the fluid dynamics package PHOENICS. The SIMPLEST algorithm is used in the iterative procedure, whereas the equations are solved via the finite-volume method. The momentum equations are solved using the initial estimate of the pressure field, using velocities that do satisfy momentum but not in the general continuity. For each cell, the equation of (inlet-outlet) is formed, and an equation of pressure correction is solved where the coefficients are A (d(vel))/dp and the sources are the errors in continuity. Afterwards, the pressure field is corrected alongside with the velocity field. The iterative procedure continues until the errors in the equations of momentum and continuity are acceptable.
\nIn order to improve convergence and eliminate great fluctuations in values of variables in consecutive iterations that could lead to the divergence of the solution, relaxation techniques are used. In the present study, two techniques are used: the linear relaxation and the false time-step relaxation. Linear relaxation is used for the variables of pressure, enthalpy, turbulence kinetic energy, and turbulence dissipation rate as it is more suitable for scalar variables in the following form: φnew = φold + α(φnew − φold). In the case of the vector variables of U1 and V1, false time-step relaxation is used as it is more suitable for the velocity variables in the following form ρVp/δt(Φ* − Φρ) = β(Φ* − Φρ) where VP is the cell volume, Φ* the previous iteration value of variable Φ, Φρ the value of φ in the last iteration, and δtfalse the false time step, generally of the order of the cell residence time.
\nIn the original coding, the Hybrid discretization scheme was used. However, in order to investigate further the most efficient way to simulate the phenomenon, runs were also carried out using the upwind discretization scheme that, according to literature, is more efficient when describing directional flows but introduces more “false-diffusion” errors.
\nIn order to improve the simulation results both in terms of convergence speed and agreement with the experimental data, numerous modifications were made to the original coding. These modifications include the calculation of the diffusion coefficient by means of the harmonic mean and not the numerical mean, and the calculation of reference kinematic viscosity for laminar flow was accomplished via Sutherland’s law. Moreover, the initial values of the variables for enthalpy and velocity were not chosen randomly, as in the original coding but were calculated based on the simulation’s data so as to help the convergence procedure with more “realistic” initial variable values. At the northern boundary, a boundary condition that suggests constant mass flow was used so as to ignore the effects of diffusion. Finally, in opposition to the initial coding, where false time-step relaxation was used for the variables of enthalpy, turbulence kinetic energy, and turbulence dissipation rate, in the modified version, for these variables, linear under-relaxation was used.
\nIn terms of this present research, many simulations were carried out, and results of the most relevant eight of them were brought into comparison in order to decide which method is more efficient in simulating the phenomenon studied by Markley et al. [13]. The first simulation carried out in terms of this research was the reproduction of the simulation carried out by Steve Rooks [4] in an up-to-date version of the PHOENICS. The other seven include the modified original version, described in the above paragraph, although no alteration in either the turbulence model or the discretization scheme was made. The other six were the combinations of the three turbulence models (k-ε, RNG k-ε, and k-ω) with the two discretization schemes (Hybrid and Upwind) studied in this research paper.
\nIn order to examine the validity of the results of the simulation, a comparison had to be made with the available experimental data. The available experimental data are for temperature, as that was the main point of interest at the experiment conducted by NACA [13], measured behind the lip, and high enough so as the flow would be unaffected by boundary layer phenomena. As a result, measurements were taken along the line Y = 18 and behind the region dictated by cell X = 20.
\nThe first simulation carried out in terms of this research was the reproduction of the simulation carried out in [4] using our modified version of the PHOENICS code, so as comparison can be carried out. The modifications made to the original coding that led to improved results, both in terms of agreement with the experimental data and in terms of decreased convergence time, include the calculation of the diffusion coefficient by means of the harmonic mean rather than the numerical mean and the calculation of reference kinematic viscosity for laminar flow that was accomplished via Sutherland’s law. Moreover, the initial values of the variables for enthalpy and velocity were not chosen randomly, as in the original coding, but were calculated based on the simulation’s data so as to help the convergence procedure with more “realistic” initial variable values. At the northern boundary, a boundary condition that suggests constant mass flow was used so as to ignore the effects of diffusion. Finally, different to the initial coding, where false time-step relaxation was used for the variables of enthalpy, turbulence kinetic energy, and turbulence dissipation rate, in the modified version, for these variables, linear relaxation was used.
\nFor this initial modified version, no alterations were made as far as the turbulence modeling and the discretization scheme are concerned. The initial modeling with k-ε turbulence modeling and the Hybrid discretization scheme were used. As a result of the above changes, the convergence of the coding was faster by 48.33% as convergence was reached at 5427 iterations instead of 10,503 for the same level of errors remaining, and there were even slight improvements in the agreement with the experimental results as well.
\nThe following diagram depicts the distribution of temperature along the x-axis above the airfoil at the height of the line Y = 18, as mentioned above (\nFigure 4\n).
\nComparison diagram for temperature distribution along the x-axis.
As seen in the above diagram, the two simulations differ only slightly, and it can be noted that the modified version offers a marginal 0.04% better accuracy along with the faster convergence. More precisely, at the point where the temperature drop is noticed, which is the lip region, the accuracy is improved by 0.015%, whereas after the threshold of 5.3 cm, which is behind the lip region, the accuracy is improved by 0.27%.
\nLikewise, the diagrams depicting the main velocity and density distribution along the x-axis slightly differ from the diagrams of the initial simulation since the mean fault is 0.5847 and 0.9%, respectively (\nFigures 5\n and \n6\n).
\nComparison diagram for velocity distribution along the x-axis.
Comparison diagram for density distribution along the x-axis.
Regarding the distributions of temperature, velocity and density along the Y-axis, defined by the line X = 20 (\nFigures 7\n–\n9\n), no major difference is noticeable in their main part, as one can notice from \nFigures 7\n–\n9\n. The mean errors are 0.7, 3.06, and 0.9%, respectively. Yet, reaching the northern boundary, the results from the modified simulation differ from the initial one, as they do not alter the value they have settled in following the exit of the boundary layer. In the initial simulation, an increase can be spotted for the variables of temperature and density and a decrease in the value of velocity. The increase in the temperature value along with the decrease in velocity can be explained from the fact that enthalpy is assumed constant. However, in the modified simulation, the steady mass flow boundary condition neglects diffusion phenomena, and therefore there is stability in this variable. This approaches the nature of the original experiment, as no factors able to alter the values of these variables exist near the northern boundary.
\nComparison diagram for temperature distribution along the y-axis.
Velocity distribution along Y-axis.
Density distribution along Y-axis.
\n\nFigures 10\n–\n13\n present the contour plots for temperature, density, and velocity distributions as well as the vector diagram of the velocity vectors.
\nContour plot of the temperature distribution on the computational field for the modified simulation (k-ε hybrid).
Contour plot of the velocity distribution on the computational field for the modified simulation (k-ε hybrid).
Contour plot with the density distribution on the computational field for the modified simulation (k-ε hybrid).
Velocity vectors zoomed in the area of interest depicting the recirculations.
In order to investigate further the problem and any possible aspects that may improve the quality of the results produced, a parametric study was conducted. The parameters studied were both the turbulence model and the discretization scheme. Simulations were conducted using all three different turbulence models: k-ε, RNG k-ε, and k-ω. Each of the above turbulence models was used in combination with either the Hybrid discretization scheme or the Upwind.
\nIn total, six different simulations were performed. The results were compared to the available experimental data in order to evaluate their accuracy. The schematic of the comparison can be seen in the diagram that follows (\nFigure 14\n).
\nComparison diagram of parametric simulations and experimental data.
The simulation results can also be seen in numbers in the following table, where the mean error is calculated (\nTable 1\n).
\n\n | k-ε Hybrid | \nk-ε Upwind | \nRNG k-ε Hybrid | \nRNG k-ε Upwind | \nk-ω Hybrid | \nk-ω Upwind | \n
---|---|---|---|---|---|---|
Overall mean error (%) | \n4.9529 | \n3.5055 | \n4.9729 | \n3.5741 | \n5.034 | \n3.3418 | \n
Overall mean error calculation.
As one can see, the most successful simulation in terms of the overall mean error is the one engaging the k-ω turbulence model in combination with the Upwind discretization scheme. If we take a closer look, it is obvious that it is the only simulation that seems to approach the steep temperature descent in the 4–5 cm region. In that region, all simulations fail to keep the mean error below 5%; however, as one can see in the following table, the k-ω Upwind simulation achieves a mean error of 5.6274% (\nTable 2\n).
\n\n | k-ε Hybrid | \nk-ε Upwind | \nRNG k-ε Hybrid | \nRNG k-ε Upwind | \nk-ω Hybrid | \nk-ω Upwind | \n
---|---|---|---|---|---|---|
Mean error in region 4–5 cm (%) | \n8.42 | \n6.763 | \n8.4331 | \n6.8564 | \n8.0097 | \n5.6274 | \n
Mean error calculation in the 4–5 cm region.
The difficulty in simulating realistically the behind the lip region is possibly due to heat conduction phenomena. In the simulations performed, no solution is run for conjugate heat transfer, and given that the airfoil geometry is very thin, heat conduction taking place on the edge of the lip is speculated to be one among the causes that lead to higher calculated temperatures than the experimental data.
\nWhen examining the region behind the lip, where the flow presents a more stabilized form than the one when encountering the lip, the turbulence model that achieves better accuracy is the RNG k-ε model with the Upwind discretization scheme. This can be seen in both the diagram and the table that follow (\nFigure 15\n and \nTable 3\n).
\nComparison diagram between simulation performed and experimental data in the behind the lip region.
\n | k-ε Hybrid | \nk-ε Upwind | \nRNG k-ε Hybrid | \nRNG k-ε Upwind | \nk-ω Hybrid | \nk-ω Upwind | \n
---|---|---|---|---|---|---|
Mean error in behind the lip region (%) | \n6.5465 | \n0.3348 | \n6.6621 | \n0.6050 | \n8.0915 | \n1.9930 | \n
Mean error calculation in behind the lip region.
It must be noted that in all simulations performed, the discretization scheme that produced the best results was the Upwind scheme. That would be justified by the fact that the flow is basically unidirectional and is characterized by high velocity. As a result, the diffusion phenomena are of very low intensity, with the convective phenomena being the main terms in the discretization equation. In both k-ω and k-ε simulations ran, the benefits from altering the initial code were still valid.
\nThe calculation of aerodynamic heating phenomena includes many aspects that can interfere with both the speed of convergence but the accuracy of the results as well. Apart from the choice of proper initial value and other numeric factors, attention has to be paid to the flow field characteristics in order to decide the suitability of the turbulence model and the discretization scheme that will be used in order to achieve better results. Despite the fact that improvements for the 4–5 cm region were also suggested here, future research could focus on simulating the lip region, as this study’s main interest lied in the behind the lip region. In order to achieve that, high-order discretization schemes could be used, like the Van Leer scheme as well as high-order turbulence models, like the large eddy simulation (LES) and the direct numerical simulation (DNS) or any combinations.
\nHeat transfer and pressure measurement on a 5-inch hemispherical concave nose at a Mach number of 2.0 is investigated via the use of PHOENICS to evaluate aerodynamic heating phenomena. In continuation of [4], the code is modified in order to achieve better results both in terms of faster convergence but also in terms of more accurate results as well. Finite-volume method is used, while turbulence models and discretization schemes are studied as parameters in order to optimize results. Before altering the above mentioned parameters, attempts were made to improve the speed of convergence. Altering the values of input variables in order to be closer to the problem considered as well as choosing relaxation methods suitable for each variable type, scalar or vector, along with a few other changes described above, managed to improve the convergence speed by up 48.33%. Moreover, an improvement in boundary conditions of the northern boundary so as to ignore dissipation phenomena corrected the results of all variables at the northern region of the computational field, as their values were stabilized, as expected. In the parametric study, the main observation was that the discretization scheme plays a major role in the quality of the results, as the flow is unidirectional and is characterized by high speed of 2.0 Mach. As a result, dissipation phenomena are not comparable to convection, which plays the major role. The choice of the Upwind discretization scheme seems to be more accurate, as it considers the fact that the flow is unidirectional and provides results improved by more than 1.5% for given turbulence model on the whole computational field. That improvement can go up to 6% when discussing regions behind the lip, where the flow has been stabilized.
\nThe parametric study regarding the turbulence model produced interesting results, as well. In order to evaluate solely the impact of the turbulence model, the discretization scheme used was the Hybrid, as in the original. As it appears, there is not only one fitting turbulence model for this simulation, as their suitability changes with regions. When studying the flow field in the lip region (4–5 cm), the k-ω turbulence model gave the best results, improved by 0.5% when compared to the original simulation performed with k-ε turbulence model. The improvement introduced by the use of the k-ω model in that area is due to the fact that this model can calculate better the effect of the created vortices. When both k-ω turbulence model and Upwind discretization scheme were implied, the total improvement in that area of interest went up to almost 3% (5.6274% discrepancy from the experimental data, compared to 8.42%). However, the study showed that behind the lip (x > 5.2), where flow has been stabilized, the use of the RNG k-ε turbulence model combined with the Upwind discretization scheme improved the mean error by more than 6% (0.3348% compared to 6.5465).
\nWe would first like to thank our colleagues in DRA Farnborough, who performed the original step in this modeling effort. Their inspired research was the basis of this current research paper. Moreover, we would also like to thank Dr. Mike Malin, Technical Support Manager of CHAM Ltd., whose assistance was truly helpful to go over technicalities that appeared along the way of this present research.
\nPHOENICS is licensed by CHAM Ltd., London, UK. The present model may become available from the present authors.
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
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Creative Commons Attribution 3.0 Unported (CC BY 3.0) | \n\t\t\t\n\t\t\t 5 October 2011 (2011-10-05) \n\t\t\t | \n\t\t\tCurrently | \n\t\t
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