Literature addressing the application of microwave technology to seed and weed treatment.
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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:"69587",title:"Microwave Soil Treatment and Plant Growth",doi:"10.5772/intechopen.89684",slug:"microwave-soil-treatment-and-plant-growth",body:'Crop yield gaps are a significant issue for food security and agricultural sustainability. Crop yield gaps are defined as the differences between optimal yield potential and actual crop yield [1]. Yield potential (Yp) is the yield of a crop cultivar when grown in an environment to which it is adapted, with non-limiting water and nutrient supplies, and with pests, weeds, and diseases being effectively controlled [1]. For example, the impact of weeds on crop yield potential has been widely demonstrated [2] and modeled [3, 4, 5]. Noling and Ferris [6] demonstrated that nematodes can reduce alfalfa yields by more than 70%. Similarly, fungi can significantly reduce crop yield potential [7, 8]. The impact of various pathogens on crop yield potential can be demonstrated with some simple models.
According to Noling and Ferris [6], the impact of nematode populations on perennial crops, such as alfalfa, can be described by:
where Yloss is the yield loss, a is the maximum yield loss for the system, b is a population sensitivity parameter for the crop (i.e., damage rate), and N is the nematode population. Therefore, the potential crop yield is described by:
where Yo is the optimal yield.
In a resource limited environment, the rate of population growth is described by:
where °D is the degree days which are suitable for the growth of the pest or pathogen, k is the maximum sustainable population of the pest or pathogen (i.e., the carrying capacity), and r is the base population growth rate. One Degree Day is determined according to some basis temperature (Tb):
Equation (3) can be rearranged to become:
Integrating both sides of Eq. (5) gives:
Therefore, Eq. (6) becomes:
To evaluate the constant of integration (C), it is appropriate to choose a boundary condition on the problem. It is noted that at the start of any study (i.e., when °D = 0 for this study period), the population will have some starting population value “No.” Substituting this into Eq. (7) and setting °D = 0 gives:
or:
Therefore,
Using data from Noling and Ferris [6] as a guide, the population of Meloidogyne hapla nematodes in their study would increase as shown in Figure 1. When these population models are applied to the crop yield model in Eq. (2), the apparent crop yield decline is similar in form to that presented in Noling and Ferris [6], as shown in Figure 2.
Population growth in Meloidogyne hapla nematodes as a function of degree days, based on the initial inoculum of the soil (calculated from Eq. (10)).
Crop yield potential for Alfalfa affected by Meloidogyne hapla nematodes as a function of degree days, based on the initial inoculum of the soil (calculated from Eq. (2)).
Different crops require differing numbers of degree days to reach maturity. For example, maize requires between 800 and 2700 degree days while barley requires between 1290 and 1540 degree days. Using the data presented in Figure 2 to illustrate the importance of the impact of pathogens and pests on crop yield, if a crop requiring 1500 growing degree days to mature is exposed to an initial Meloidogyne hapla nematode population of 1085 individuals kg−1 of soil, the yield potential would be 0.3 at the end of crop maturation; however, if the crop was exposed to an initial population of only 4 individuals kg−1 of soil because of some pre-sowing soil sanitation strategy, the crop yield potential would be approximately 0.7. Therefore, pre-sowing soil sanitation could provide a crop yield increase (compared with untreated soil) of:
Although this may appear to be a significant crop yield increase, the pre-sowing soil sanitation is simply bridging a little more of the crop yield gap by treating the soil to remove crop inhibiting organisms before sowing the crop. In fact, the modeling suggests that the crop growing on the sanitized soil may still not have reached its full crop yield potential.
Many soilborne plant pathogens flourish during the crop growing season and survive between seasons, either in the soil or above-ground, by means of resting structures, such as propagules that are either free or embedded in infected plant debris. Soil sanitation aims to reduce or eliminate the pest population from all sources, thus breaking the continuity of survival in time and space between crops. Soil sanitation (e.g., by fumigation or heating) is a routine procedure in many agricultural systems [9].
Soilborne diseases, plant-parasitic nematodes, and weeds can be devastating, and preplant soil fumigation is commonly relied upon to mitigate the risk of crop loss [10]. Methyl bromide has been widely used for soil sanitation in the past; however, because of its ozone depleting impacts it has been included in the 1987 Montreal Protocol as a substance whose use should be reduced and eventually eliminated. Under the Montreal Protocol exemptions were granted for substances (like Methyl Bromide) where no economic alternative existed [11]. Even so, especially in the Strawberry runner industry, alternative treatments have been investigated and found to be wanting [12, 13]. Most alternative treatments involve other fumigants, such as Metam sodium or chloropicrin [14], or thermal processes, such as solarization or applying steam.
Klose et al. [14] showed that weed seeds and soil pathogens exhibit a logistic dose-response to a commercial soil fumigant formulation of 1,3-dichloropropane (1,3-D; 61%) and chloropicrin (33%). It has been shown elsewhere [15] that a more physically meaningful representation of logistic dose responses can be described by:
where S is the surviving portion of the population, erfc(x) is the Complementary Gaussian Error Function, D is the fumigant dose (μmol kg−1), and a, b and c are constants that are determined experimentally. Equation (11) is based on an underlying normally distributed population susceptibility to some treatment; therefore, the cumulative effect (mortality) in the population becomes the integral of the normal distribution function, which is described by the Gaussian Error Function, and population survival, which is the whole population minus the mortality rate, is therefore described by the Complementary Gaussian Error Function. Therefore, it is anticipated that the crop yield response to varying doses of pre-sowing soil fumigation treatment should also have a Gaussian Error form, as a function of applied pre-sowing fumigant dose.
Growing concern over the use of excessive chemicals in agriculture, with adverse effects on on-farm and off-farm environments, has prompted a search for alternative soil sanitation options. Soil heating has provided some similar pest and pathogen control to chemicals.
The fatal impacts of high temperatures on botanical and zoological specimens have been studied in detail for over a century [16]. In particular, a thoroughly demonstrated empirical relationship between lethal temperature and temperature holding time has been developed by Lepeschkin [17]:
where T is the lethal temperature (°C), and Z is the lethal temperature holding time, in minutes [16]. Individual relationships for different species of plants and pathogens [9, 17, 18] have been developed over time (Figure 3). Ultimately, heat can provide similar lethal effects to chemicals and therefore has been used in soil sanitation processes for some time.
Lethal temperature/time functions for several important pathogenic organisms.
It has been demonstrated that steam soil treatment is as effective as some soil fumigants at reducing pre-sowing soil pathogen loads [19]; however, if the steam is applied to the surface of the soil (i.e., not injected), effective treatment is shallow compared with conventional soil fumigation techniques. This is due to limitations of heat being transferred from the steam into the soil. The governing equation for heat transfer from a hot fluid (air, water or steam) with a temperature of Tf into a solid, such as soil, with an initial temperature of Ts, is expressed as:
where q is the heat flow (W), A is the cross sectional area through which the heat passes (m2), and h is the convective heat flow coefficient of the soil’s surface [20]. When studying thermodynamic processes, temperatures are usually expressed in absolute (Kelvin) values.
The convective heat flow coefficient depends on a number of other parameters and conditions [21]. For example, the convective heat flow coefficient for a vertical surface where natural convection achieves turbulent fluid flow conditions over the surface is given by [21]:
where k is the thermal conductivity of the heating fluid (W m−1 K−1), Pr is the Prandtl number, and L is the characteristic length of the object being heated (m).
The Rayleigh number (RaL) in Eq. (14) is also based on a complex relationship between temperature and the physical properties of the fluid. It is given by [21]:
where g is the acceleration due to gravity; β is the thermal expansion coefficient of the fluid; υ is the kinematic viscosity of the fluid medium; α is the thermal diffusivity of the fluid medium; and L is the characteristic length of the surface.
Finally, the Prandtl number used in Eq. (14) is a relationship between the fluid’s viscous and thermal diffusion rates given by [21]:
where v is the kinematic viscosity (m2 s−1) and α is the thermal diffusivity (m2 s−1).
Close examination of these equations shows that the convective heat transfer coefficient is dependent on the temperature differential between the fluid and the surface of the soil (see Figure 4) and the apparent surface area of the heat transfer interface. Injecting the steam into the soil through hollow tines effectively increases the surface area of the heat transfer interface between the cool soil and hot steam.
Convective heat transfer coefficient (h) for air as a function of temperature differential between an object and the air.
Semi-commercial steam soil sanitation systems have been in operation for some time [13, 19]. They are functional, though their application is limited, because they are energy expensive and difficult to use due to their large and heavy operation systems. Soil heat treatment may be better achieved through direct heating of the soil.
Microwaves are non-ionizing electromagnetic waves (Figure 5) with a frequency of about 300 MHz to 300 GHz and the wavelength range of 1 m to 1 mm [23]. Biological and agricultural systems are electro-chemical in nature [24] and a mixture of organic and dipole molecules, i.e., H2O, arranged in different geometries [25, 26].
The electromagnetic spectrum (adapted from [22]).
Interest in the study of the interactions of ultra-high frequency electromagnetic energy with complex biological system dates back to the nineteenth century [27]. The interactions of microwave energy with living systems are characterized at atomic, molecular, cellular and subcellular level [24].
The basic consideration in measuring the influence of microwave irradiation on living systems is the determination of the induced electromagnetic field and its spatial distribution. The bio-effects of microwave treatments can be described solely by differences in temperature profile between microwave and conventionally heated systems [28]. The energy of microwave photon at 2.45 GHz is 0.0016 eV [29]. This is not enough energy to break the structure of organic molecules [30]. The basic interactive mechanism of microwave energy with biological system/materials is inducing torsion on polar molecules, i.e., H2O, Proteins and DNA, by induced electric field [31]. Oscillations in this torsion occur 2.45 billion times/second for 2.45 GHz waves. These oscillations manifest as internal kinetic energy in the material, which is heat.
Microwave (electromagnetic) heating has major advantages over conventional heating techniques. Some of these include: rapid volumetric heating as opposed to surface heating only, precise control, rapid start up and shut down [32], and in the case of soil, having a lighter apparatus than a steam generator to avoid soil compaction issues.
Many of the earlier experiments on plant material focused on the effect of radio frequencies [33] on seeds [27]. In many cases, exposure to low energy densities resulted in increased germination and vigor of the emerging seedlings [34, 35]; however, exposure to higher energy densities usually resulted in seed death [27, 36, 37].
Davis et al. [38, 39] were among the first to study the lethal effects of microwave heating on weed seeds. They treated seeds, with and without any soil, in a microwave oven and showed that seed damage was mostly influenced by a combination of seed moisture content and the energy absorbed in each seed. In addition, they suggested that both the specific mass and specific volume of the seeds were strongly related to a seed’s susceptibility to damage by microwave fields. The association between the seed’s volume and its susceptibility to microwave treatment may be linked to the “radar cross-section” [40] presented by seeds to propagating microwaves. Large radar cross-sections allow the seeds to intercept, and therefore absorb, more microwave energy.
Ferriss [8] conducted experiments on soil samples with moisture contents between 7 and 37% (wet/dry-weight) and showed that treatment in a microwave oven for 150 seconds eliminated populations of Pythium, Fusarium and all nematode species, except Heterodera glycines in the soil samples. Compared with autoclaving or Methyl bromide (MB) treatment, he found that microwave treatments released less nutrient into the soil solution but had less effect on soil prokaryotes and resulted in less recolonization of the soil by Fusarium and other fungi after treatment. Similar observations were made by Mattner and Brodie [41] during a preliminary experiment in soils growing strawberry runners at Toolangi, Victoria.
Speir et al. [42] examined the effect of microwave energy on low fertility soil (100 randomly selected cores at a depth of 50 mm), microbial biomass, nitrogen, phosphorus, and phosphatase activity. They reported that an increase in microwave treatment duration (90 seconds) dramatically increased the nitrogen level in the soil by a factor of approximately 10 times (106 μg N g–1) compared with untreated soil (9–10 μg N g–1), but available phosphorus concentration declined as treatment time increased. Furthermore, relevant to soil productivity, Gibson et al. [43], demonstrated that shoot and root growth of birch (Betula pendula) significantly increased in microwave irradiated soil. Their experiment evaluated the effect of microwave treatment of soil supplemented with two mycorrhizas on birch seedlings. Shoot growth progressively increased with irradiation duration, with the highest dry shoot weight of 84 mg coinciding with the highest irradiation duration (of 120 seconds) compared to non-irradiated soil which resulted in 25 mg of growth. This result was achieved with no mycorrhizal supplementation. In addition, a recent study reported that microwave (915 MHz; different power × duration) soil treatment increased the dissolved organic carbon (+1.6-fold compared with the control), inorganic phosphorus (+1.2-fold compared with the control), and nitrate content in soil [44]. In addition, they grew the pregerminated seeds of Medicago truncatula Gaertn. in microwave treated soil and found that its dry biomass accumulation significantly increased in response to soil heating (75–80°C), compared with the untreated control soils.
Since then there has been ongoing research interest in microwave soil treatment and weed management. Table 1 lists a subset of the papers that have been published on these and related topics. The consensus from these studies is that: microwave treatment can kill plants; moderate microwave treatment can break dormancy in some hard-seeded species; and high energy microwave treatment can sanitize soil.
Paper title | Reference |
---|---|
Douglas- fir tree seed germination enhancement using microwave energy | [45] |
Microwave processing of tree seeds | [46] |
Increasing legume seed-germination by VHF and microwave dielectric heating | [47] |
Effects of low-level microwave radiation on germination and growth rate in corn seeds | [48] |
Effects of microwave energy on the strophiole, seed coat and germination of acacia seeds | [35] |
The effect of microwave-energy on germination and dormancy of wild oat seeds | [49] |
The effect of externally applied electrostatic fields, microwave radiation and electric currents on plants and other organisms, with special reference to weed control | [50] |
Control of field weeds by microwave radiation | [51] |
Effect of microwave irradiation on germination and initial growth of mustard seeds | [52] |
Inhibition of weed seed germination by microwaves | [53] |
A possibility of correction of vital processes in plant cell with microwave radiation | [54] |
Microwave irradiation of seeds and selected fungal spores | [7] |
Response surface models to describe the effects and phytotoxic thresholds of microwave treatments on barley seed germination and vigor | [55] |
Energy efficient soil disinfestation by microwaves | [56] |
Microwave effects on germination and growth of radish (Raphanus sativus L.) seedlings | [57] |
Report on the development of microwave system for sterilization of weed seeds: stage I – feasibility | [58] |
Design, construction and preliminary tests of a microwave prototype for weed control | [59] |
Thermal effects of microwave energy in agricultural soil radiation | [60] |
Influence of low-frequency and microwave electromagnetic fields on seeds | [61] |
An improved microwave weed killer | [62] |
Observations on the potential of microwaves for weed control | [63] |
Plant response to microwaves at 2.45 GHz. | [64] |
Germination inhibition of undesirable seed in the soil using microwave radiation | [65] |
Effect of microwave radiation on seed mortality of rubber vine (Cryptostegia grandiflora R.Br.), parthenium (Parthenium hysterophorus L.) and bellyache bush (Jatropha gossypiifolia L.) | [36] |
Effects of microwave treatment on growth, photosynthetic pigments and some metabolites of wheat | [66] |
Microwave seed treatment reduces hardseededness in Stylosanthes seabrana and promotes redistribution of cellular water as studied by NMR relaxation measurements | [67] |
Effect of microwave fields on the germination period and shoot growth rate of some seeds | [68] |
Germination of Chenopodium album in Response to Microwave Plasma Treatment | [69] |
Work conditions for microwave applicators designed to eliminate undesired vegetation in a field | [70] |
Literature addressing the application of microwave technology to seed and weed treatment.
Typically, responses of weed seeds and soil biota are both energy and depth dependent, because of the absorption of microwave energy with soil depth. The relationships between applied microwave energy and seed or biota survival at different depths are given by:
where Ψ is the microwave energy density at the soil surface (J cm−2), d is the depth in the soil (m) and a, b, c, and f are constants to be determined experimentally. This is illustrated by the relationships for weed seeds and bacteria in (Figures 6 and 7).
Response of multiple species of weed seeds as a function of applied microwave energy and soil depth [71].
Response of soil bacteria as a function of applied microwave energy and soil depth [71].
Unlike in the case of chemical soil fumigants, microwave soil treatment does not sterilize the soil. Although there is a general reduction in soil bacteria after microwave treatment (Figure 7), Khan et al. [72] demonstrated that immediately after microwave soil treatments, the relative abundance of Firmicutes increased while the relative abundance of Proteobacteria decreased significantly. They also showed that the relative abundances of beneficial soil microbes (Micromonosporaceae, Kaistobacter and Bacillus) were significantly higher, as soils recovered from high heating intensities induced by microwave soil treatment, compared with untreated soils.
There is also considerable evidence that microwave soil treatment releases more nitrogen sources in the soil for the crop growth [73]. This may be due to the resilience of nitrifying bacteria and archaea to microwave soil heating. Khan et al. [72] showed that microwave soil treatment did not significantly affect ammonia oxidizing bacteria or ammonia oxidizing archaea. Vela et al. [74] also demonstrated that nitrifying bacteria in the soil were resilient to 40 kJ cm−2 of microwave energy at the soil surface; which is 70 times higher than the energy densities used during experimental work undertaken by the current authors.
Fully replicated pot and field plot experiments have been undertaken over an extended period of time by the authors to better understand the impact of pre-sowing microwave soil treatment on crop growth. In all cases, the experiments had at least 5 experimental replicates and in many cases, they used 10 experimental replicates. Experiments were undertaken to explore the effect of pre-sowing microwave soil treatments on plant growth and yield of wheat (Triticum spp.), rice (Oryza sativa), maize (Zea mays), canola (Brassica napus), processing tomatoes and strawberry runners. In most cases the potted experiments were repeated two or three times and in some cases the field experiments were also repeated. Microwave energy was applied to the soil in pots or in situ using a trailer mounted microwave prototype system with 4 individual 2 kW microwave generators (see Figure 8).
Prototype 4 by 2 kW microwave weed killer in a strawberry runner field at Toolangi, Victoria.
The crops were planted within hours of the microwave treatment, once the soil had returned to ambient temperature. Plant growth rate, final plant height, and crop yield showed significant increases with increasing microwave energy (Table 2). In the potted trials and in one wheat field trial, hand weeded controls were included in the experiments to determine whether crop growth response was simply due to less weed competition.
Microwave treatment | Control | Hand weeded | Microwave energy (J cm−2) | LSD (P = 0.05) | Change from hand weeded/control (%) | ||
---|---|---|---|---|---|---|---|
136 | 318 | 545 | |||||
Pot trials | |||||||
Canola pod yield (g pot−1) | 0.27a | 0.56a | 0.36a | 1.25b | 1.95c | 0.55 | 250% |
Wheat grain yield (g pot−1) | 0.66a | 0.67a | 0.68a | 0.75a | 1.25b | 0.3 | 87% |
Rice grain yield (g pot−1) | 40.0a | 41.3a | 43.3a | 59.0ab | 64.0b | 18.9 | 55% |
Maize (g pot−1) | 5.3a | 6.6a | __ | 10.3ab | 12.8b | 4.8 | 92% |
Field trials | |||||||
Rice (t ha−1) – Dookie Year 1 (2015/2016) | 7.5a | __ | __ | __ | 10.1b | 2 | 35 |
Rice (t ha−1) – Dookie Year 2 – (2016/2017) - crop was cold affected at panicle initiation | 2.1a | __ | __ | __ | 3.9b | 1.3 | 84 |
Rice (t ha−1) – Old Coree – (2016/2017) | 7.7a | __ | __ | __ | 9.1b | 1.2 | 19 |
Wheat (t ha−1) | 5.7a | 6.6ab | __ | __ | 7.8b | 1.4 | 18 |
Tomato (t ha−1) | 64.1a | 65.2a | __ | __ | 89.6b | 24.7 | 37 |
Strawberry runner production (daughter plants m−2) | 6970a | __ | __ | __ | 8445b | 670 | 21 |
Summary of pot and field trial crop yields in response to microwave soil treatment.
Means with different superscript letters (i.e. a, b, c etc) are statistically different from one another at a probability of 0.05.
Pre-sowing microwave soil treatment was found to have significant beneficial effects on subsequent crop growth. Most crops showed a typical Gaussian Error Function response to increasing microwave soil treatment dosage (Figure 6), as would be expected if the pre-sowing soil treatment were acting as a soil fumigant (Figure 9).
Canola pod yield response to increasing microwave treatment.
Pre-sowing microwave soil treatment acts as a soil sanitation technology and results in significant increases in crop yield, as would be expected from other soil sanitation techniques. Microwave treatment has some major advantages over other soil sanitation techniques in that it is purely thermal in nature and allows immediate access to the site once the soil has cooled to ambient temperatures. Unlike, other thermal treatment systems, such as steam treatment, microwave systems can be light and highly controllable, reducing other impacts on the soil such as compaction.
Also, unlike other soil sanitation techniques, it is evident that microwave treatment does not sterilize the soil, but favors beneficial species of soil biota making more nutrients available for better plant growth. From these perspectives, microwave soil treatment may become an important pre-sowing soil sanitation technology for high-value cropping systems, allowing agricultural systems to better bridge the crop yield gap.
Researchers and engineering practitioners are attentive to understanding the behavior of structures under the effects of various loading patterns and conditions, to enhance their lifetime performance. Wind forces can threaten the safety of structures if their effects are underestimated; therefore, it is crucial to properly simulate and assess wind effects on civil engineering structures in order to achieve optimal and resilient designs that can maintain accessibility and functionality after natural disasters. Due to climate change and its consequences, the patterns of extreme winds and hurricane occurrence have been altered [1, 2, 3]. As a result, wind loads are becoming important in the analysis and design of buildings, especially in hurricane active regions. To put it into perspective, in most parts of the United States, especially in the east coast and the southern region, hurricanes and severe windstorms hit and bring widespread damage to buildings and other types of structures. The associated losses are estimated in billion dollars. The normalized hurricane-induced damage in the United States, between 1900 and 2005 (106 years of record), was estimated at about $10 billion (normalized to 2005 USD) [4]. Damage records totaling $265 billion were set by hurricanes Maria, Harvey, and Irma [5].
Due to the population growth, coastal zones are being more and more concentrated with residential buildings. These buildings are mostly light and low-rise, constructed from wooden materials, with different aerodynamic performance compared to high-rise buildings and residential homes. The American Society of Civil Engineers (ASCE) design standard defines a low-rise building to have an average roof height that is less than its lateral dimension; however the building should not exceed 18.3 m [6]. The majority of failures in low-rise buildings are reported because of strong wind effects on their envelope and specifically on roof panels [7]. Figure 1(a) shows a total failure of a low-rise building induced by hurricane Sandy in New York in 2012 [8]. The building envelope experienced significant loads from hurricane winds and lost its load path connections. In other scenarios, once part of a roof is breached during high winds, it facilitates the penetration of rainwater which can be harmful to interior properties and may cause serious problems to the building and loss of contents. Figure 1(b) shows severe roof damage during Hurricane Katrina in Lake Charles, New Orleans, in 2005 [9].
Hurricane-induced damage: (a) complete collapse of a residential home induced by hurricane Sandy, New York, 2012 [8] and (b) severe roof damage by hurricane Rita in Lake Charles, in 2005 [9].
Examination of post-disaster surveys indicates initiation of damage through failure of roof components under extreme wind events. Earlier studies confirm the presence of extreme negative pressures at corners, ridges, and leading edges of roofs. The performance of roofs in low-rise buildings can differ significantly during a windstorm according to the shape of roof and its dimension. For instance, large roofs in industrial buildings may behave differently, compared to those of small roofs in a single-family low-rise building which can lead to different damage patterns to the building envelope [10, 11, 12]. In large roofs, the correlations among pressures acting at different roof locations are usually low [13]. In large roofs of light metal industrial buildings, leading edge failure usually occurs due to poor attachment of metal sheathing in areas that are exposed to uplift wind forces. This weakness eventuates to progressive peeling of the roof membrane causing further damage to the whole integrity of the building envelope.
The components and claddings in small roofs are usually exposed to damage during windstorms, due to local fluctuating negative pressures (uplift effects) due to flow separation, especially at roof edges and corners. Figure 2 represents wind flow around a residential building [13]. The flow separates at sharp edges and re-attaches again in a fluctuating manner within the separation zones at a distance that is called separation bubble length, leading to uplift forces on the roof surface. The stagnation point is also specified in the windward wall, where the along-wind velocity is zero. Figure 3 shows homes damaged by Hurricane Andrew in 1992 as a result of low pressures on the roof; and as a result, the shingles and sheathings were blown off due to high uplift forces. Referring to Figure 2, now it is shown that the separation bubble effects and the flow detachment are the main causes of these damage patterns of roof coverings which are a representation of roof areas under uplift forces. To fully understand windstorm effects on low-rise and residential buildings, it is essential to replicate the physics by experimental and computational methods. There are two important requirements: (1) correct reproduction of the main characteristic in the atmospheric boundary layer (ABL) and (2) aerodynamic testing at proper scales.
Fluctuating flow separation and re-attachment (adapted from Ref. [14]).
Homes damaged by hurricane Andrew in 1992 [15].
The variation of the mean velocity profile with height can be different over different terrain conditions depending on the friction effects from the earth’s surface and the value of roughness length. Figure 4 shows a schematic of different mean wind profiles over various topographical conditions of a dense urban area, suburban terrain, and over sea surfaces. In Figure 4, higher velocity is anticipated in lower altitudes on sea surfaces than the gradient wind in a dense city center.
Mean wind speed profiles over different terrains according to Davenport’s power law profiles (adapted from Ref. [16]).
After recording time series of wind velocity in the lab or in the field, the turbulence spectrum can be obtained accordingly. For the validation of the turbulence spectrum, theoretical spectra are usually used. The Kaimal spectrum is one of the widely used spectra, which is defined as follows [17]:
in which f is nU/z. One can obtain the spectrum, Suu, in the along-wind direction by considering A to be105 and B to be 33 [14, 18]. For the lateral and vertical spectra, different values for the parameters A and B are suggested [14, 18].
The Engineering Science Data Unit (ESDU) spectrum is proposed based on a new von Karman spectrum, covering the full frequency range, as follows [19]:
For more details regarding the ESDU spectrum and definition of different terms, readers are referred to Ref. [19]. The nondimensional cross-spectrum of u-component is defined in Ref. [20]:
where
Davenport:
Maeda and Makino:
where
The integral length scale of turbulence, Lux, is a measure of the size of the largest eddy in a turbulent flow [18]. Having the time history of along-wind velocity component at any height, Lux can be calculated using the approach described in Ref. [22]:
where ū is the standard deviation of the along-wind velocity component and E(f) is the power spectral density. Studies show that the integral length scale of turbulence may decrease in the flow direction, due to the fact that larger eddies will usually dissipate energy into smaller eddies [23]. According to actual measurements, as the terrain roughness decreases, Lux increases with the height above ground [18]. To quantify these changes, the integral length scale formulation suggested by ESDU is defined as follows [19]:
And Counihan formulation used by Refs. [24, 25]:
Bluff body aerodynamics, and in particular fluctuating pressures on low-rise buildings immersed in turbulent flows, are associated with the complex spatial and temporal nature of winds [26]. This complexity mainly comes from the transient nature of incident turbulent winds, and the fluctuating flow pattern in the separation bubble. The flow in the separated shear layer is associated with fluctuations in the velocity field leading to the evolution of instabilities. The flow physics are dependent on upstream turbulence intensity, integral length scale, as well as Reynolds number. The later makes it difficult to scale up loads based on pressure and force coefficients as the process can be highly nonlinear, which is the case, for example, when full-scale pressure coefficients do not meet those from small-scale aerodynamic testing (Figure 5). Not only free stream turbulence impacts the flow pattern around bluff bodies, but also it can impact the thickness and length of the wake, hence significantly altering aerodynamic pressures.
Minimum pressures at building corner (adapted from Ref. [14]).
In order to propose mitigation alternatives to minimize damages induced by windstorms to low-rise buildings, it is vital to understand how peak loads and spatial correlation of pressures are developed. As a first step to understand this mechanism, a true simulation of flow characteristics in accordance to real full-scale winds is necessary. There are common and valuable resources for the physical investigation of wind effects on structures, including small-scale wind tunnel testing, large-scale testing an open-jet laboratory, and full-scale field measurements.
According to Ref. [27], at relatively large-scale wind tunnel models, it is very difficult to model the full turbulence spectrum, and only the high-frequency end is matched [28]. For instance, as described in Ref. [29], more than 50% discrepancies in wind tunnel aerodynamic measurements are realized from six reputable centers for roof corner pressure coefficients and peak wind-induced bending moment in structural frames of low-rise building models. Therefore, selecting an appropriate testing protocol, including model scale ratio, for physical testing to minimize discrepancies in aerodynamic loads is essential. This can be achieved by considering constraints on laboratory testing that limits producing the large-scale turbulence and the inherent issues with limited integral length scale [30].
The literature raises questions regarding the adequacy of predicting full-scale pressures on low-rise buildings tested in flows that lack the large-scale turbulence. For instance, although a good agreement was observed between a wind tunnel testing on a generic low-rise building and full-scale data, discrepancies were shown in reproducing the largest of peak pressure near roof edges [31]. Figure 5 shows minimum pressure coefficients at a building corner and eave level for the full-scale Texas Tech University (TTU) experimental building, along with wind tunnel measurements [14]. The local peak pressures are weaker in wind tunnel testing than those at full-scale. For instance, at 65° wind direction angle, the wind tunnel reproduced minimum pressure coefficient of −4.3, while the full-scale field measurement is −6.8, and at 250° wind direction angle, wind tunnel shows −2.2, while the full-scale data shows −5.3. Therefore, there would be a major doubt on estimating the correct wind loads for design purposes based on wind tunnel testing. To describe this mismatch, first we need to define the concept of the energy cascade in a flow.
As depicted in Figure 6, the structure of a turbulent wind flow is constituted from a combination of large eddies and small eddies. In physical space, the large eddies are broken into smaller and smaller eddies with different spectral energy contents in various frequency ranges. In conventional wind tunnel testing, it can be challenging to appropriately reproduce low-frequency turbulence, which overwhelmingly contributes to the integral length scale and intensity of fluctuations. This leads to significant disparity among the wind tunnel flows and the target full-scale field flow conditions. As observed in Figure 5, this mismatch affected the local vorticity at edges and corners of a low-rise building model tested in a wind tunnel and resulted in local pressures weaker than those at full-scale. To alleviate these issues and to replicate the ABL flow characteristics for aerodynamics of buildings, advanced research in computational and experimental methods is essential.
Energy cascade in a turbulent flow (adapted from Ref. [32]).
In recent years, computational fluid dynamics (CFD) simulations have witnessed a spread use and applications as a potential tool in aerodynamic investigations of buildings. However, by considering the constraints of experimental testing in wind tunnels that limit producing the low-frequency large-scale turbulence and the inherent issues with limited integral length scale, implementing appropriate turbulence closure in CFD and developing a proper inlet transient velocity may alleviate the issues with experimental measurements in wind engineering. In CFD, the scale is not an immediate issue, as a full-scale model of the structure can be modeled and tested under various extreme wind scenarios. The simulation can be repeated to yield the same results any time. Even large-scale problems, such as simulating an urban area with condensed high-rise buildings for pollutant dispersion studies can be performed in CFD [33]; this can be challenging in laboratory testing due to scale issues.
CFD is gaining popularity within the wind engineering community along with the rise of computational power. Nowadays, CFD is commonly used to address wind engineering problems such as pollutant dispersion, wind comfort for pedestrians, aerodynamic loads on structures, or effects of bridge scour [34, 35, 36, 37]. CFD-based numerical simulations will eventually complement the existing experimental practices for a number of wind engineering applications [38, 39, 40]. In most cases, numerical approaches are less time-consuming than experiments, and detailed information at higher resolution can be retrieved for scaled models from numerical simulations. In few earlier studies, the accuracy of analyzing bluff bodies with CFD has been questioned [41, 42, 43]. The reason behind inaccuracies was detachment of shear layer at sharp edges of bluff bodies. Detachment of shear layer makes the overall flow in the domain more responsive to local behaviors. The local effects are influenced by turbulence intensity and turbulence length scales of the incoming flow [36, 44]. Inaccurate replication of incoming turbulence properties in earlier studies was considered a reason for discrepancies in results. In Ref. [45], careful replication of horizontal turbulence properties at roof height of low-rise buildings was declared important. Few earlier studies focused on comparing surface pressures from numerical simulations with experiments and full-scale measurements. Good agreement was found among different data sources for mean pressure coefficient, while differences were found for fluctuating pressure coefficient [46].
Large eddy simulation (LES) can yield better results than turbulence closures that are based on Reynolds-Averaged Navier-Stokes (RANS), however, for higher cost of computations. The accuracy of solution of any wind engineering problem with CFD depends on the precise simulation of wind flow. A number of studies have indicated better performance of LES turbulence model for predicting mean and instantaneous flow field around bluff bodies [42, 47]. The concept of LES involves resolving the large scales in fluid flow and modeling the small scales. This approach is theoretically suitable for wind engineering applications as normally large scales are responsible for forces of interest [42]. Earlier applications of LES involving treatment of flows at low-Reynolds number yielded satisfactory results. Simply, the use of LES does not guarantee meaningful and accurate results. For flows with higher turbulence, results become more sensitive to the quality of the model [42]. Modeling of small-scale turbulence has gone through stages of improvement over the years. Sub-grid scale modeling remains the commonly used modeling technique. To yield accurate results, maintaining proper inflow boundary condition (IBC) is fundamental. Three methods are identified for generating IBC, and they are [48] (a) precursor database, (b) recycling method, and (c) turbulence synthesizing. The first two methods are computationally demanding; the third method is promising [49].
Maintaining horizontal homogeneity in the computational domain is another challenge in CFD simulations. Horizontally homogeneous boundary layer refers to the absence of artificial acceleration near the ground or stream-wise gradients in vertical profiles of mean velocity and turbulence intensity [50]. One may run steady-state simulation until it reaches convergence and monitors the vertical profiles of velocity and turbulence intensity at different locations in the domain. In case of LES, the mean value should be taken from the velocity time history for monitoring the vertical profiles. Achieving horizontal homogeneity ensures that the inlet, approach and incident flow are the same and eventually provide results with higher accuracy [50]. In several previously conducted studies, maintaining a consistent profile of mean wind speed and turbulent kinetic energy was an issue with different turbulence closure models. Significant near wall flow acceleration was found to cause unwanted change in mean wind speed and turbulent kinetic energy in simulation conducted in [51]. Additionally, issues in maintaining a consistent profile for turbulent kinetic energy were observed in [52, 53]. For accurate CFD results, maintaining consistent vertical profiles throughout the domain is important. Minor change in the profiles can create significant changes in the flow field. For flow around buildings, the importance of retaining the vertical flow profiles was stressed in Refs. [54, 50].
In Section 2, the main characteristics of ABL wind were presented. One of the main parts of any wind engineering study is to appropriately reproduce the wind characteristics in a controlled manner, to examine the response of a structure in the scope of a certain wind event. This means that first the wind flow characteristics should be simulated following an acceptable protocol and following that wind-induced pressures and loads on the surfaces of a building can be obtained by aerodynamic testing, according to the laws of similitude [55]. In order to satisfy these requirements, there are some tools used for ABL processes, including wind tunnels and open-jet facilities [56].
For several decades, wind tunnel modeling has been widely used as a technique to estimate wind-induced pressures and loads on buildings. Figure 7 shows a view of a wind tunnel at the University of Western Ontario and a 1:100 scale low-rise building model. The arrangement and height of passive roughness elements are designed to reproduce wind flow over an open-terrain exposure with z0 = 0.01 m [57]. This test case was selected benchmark for validation and comparison with other computational and experimental measurements. For accurate estimation of aerodynamic forces on buildings, proper replication of wind speed, turbulence intensity profiles, and spectral characteristics is essential [58]. Matching the spectral content of real wind flow over the entire frequency range of interest has been a major challenge in laboratory testing [30]. Duplication of the entire range of spectral content requires equality of Reynolds number. In traditional wind tunnels, small-scale turbulence can be generated. For cases where incident flow contains only small-scale turbulence, the vortices are shed downstream before attaining maturity or before creation of maximum peak pressure. The increase in large-scale turbulence content in incident flow permits vortices to attain maturity, and as a result higher peak pressures on building models are obtained [59]. The low-frequency part on the turbulence spectrum corresponds to large-scale turbulence content of the incoming flow.
A view of a wind tunnel at the University of Western Ontario: (a) 1:100 low-rise building model and the roughness element arrangement for an open-terrain exposure simulating z0 = 0.01 m and (b) a closer view of the test model instrumented with pressure taps [57].
The gap between small and large wavelengths of velocity fluctuations (frequency domain), for real atmospheric flows, is larger than that in wind tunnel flows. It is challenging to duplicate both small and large scales of turbulence in wind tunnels due to the absence of Reynolds number equality [59]. Moreover, the neutral atmospheric boundary layer is scaled down in the order of 1:100 to 1:500 in wind tunnels. If low-rise buildings are scaled down in a similar proportion, additional problems may be encountered. The issues with too small test models are (a) inability to modeling structural details accurately, (b) lack of aerodynamic surface pressures at higher resolution, and (c) interference effects of measuring devices [59, 60]. In practice, larger test models with scales in the order of 1:50 are used to minimize these issues. This leads to mismatch in scaling ratio of the model and the generated boundary layer, which is responsible for difference in turbulence spectra in experiments and full-scale situation. The difference in turbulence spectra is considered to be a primary reason for the large variation in aerodynamic pressures on low-rise buildings for different wind tunnel experiments [60].
Several experiments have been conducted on scaled low-rise building models and heliostats over the past few decades. Large variation in tests has been attributed to difference in Reynolds number, turbulence spectrum, geometric scaling ratio, etc. While studying the influence of turbulence characteristics on peak wind loads on heliostats, wind tunnel tests were performed, the turbulence intensity and size of the largest vortices had a noticeable effect on peak pressures, compared to other parameters Reynolds number [61]. For solar panels, peak pressures in the wind tunnel were underestimated compared to full-scale data [62]. Geometric scaling is found to be a primary source of inconsistent results in wind tunnels with similar mean flow condition [60]. It was recommended to correctly model the high-frequency end of spectrum in order to obtain acceptable mean pressure coefficients. However, for accurate mean and peak pressures, the importance of replicating the entire turbulence spectra in large-scale testing was highlighted [27]. The size of the wind tunnel was held responsible for mismatch in the low-frequency end of the spectrum. High-frequency vortices are responsible for creating the flow pattern around bluff bodies, whereas low-frequency large eddies have higher influence on aerodynamic peak loads [63]. To conclude, in the case of low-rise buildings, it has been always a challenge, in wind tunnel testing, to properly simulate wind effects due to the lack of capability in turbulence modeling [56]. As a result, other concepts and tools such as open-jet testing were devised in recent years.
As part of developing ABL simulation capabilities, a small open-jet facility was built at the Windstorm Impact, Science and Engineering (WISE) research lab, Louisiana State University (LSU) (Figure 8). The concept of open-jet testing is that unlike wind tunnels, the flow has no physical boundaries which has two main advantages: (i) larger eddies can be produced, leading to higher peak pressure coefficients, similar to those at full scale, and (ii) minimum blockage can be achieved. The aim was to physically simulate hurricane wind flows with similar characteristics to those of open and suburban terrain. Small-scale models of low-rise buildings were tested to examine how the turbulence structure of the approaching flow, scale issue, and open-jet exit proximity effect can influence the flow pattern on low-rise buildings and alter the separation bubble length on the roof surface. Specifically, the aim was to understand how these parameters affect the values of peak fluctuating external pressures on the roof surface [58, 64]. With an adjustable turbulence producing mechanism, different wind profiles are physically simulated. In addition, this lab has cobra probes, load cells, laser displacement sensors, and a 256-channel pressure scanning system (Figure 9).
The concept of open-jet testing: (a) test model located at an optimal distance from the blowers’ exit and (b) 15-fan small open jet at LSU.
LSU WISE small open-jet hurricane simulator (with adjustable turbulence and profile production mechanism): (1) general view of testing setup, (2) section model test specimen, (3) cobra probes for measuring 3-component wind velocities, (4) ZOC23b miniature pressure scanner, and (5) lap top computer with software for data collection and processing.
A facility capable of testing low-rise buildings at full-scale would be ideal, if the artificial flow is also at full scale, which is difficult to achieve. A 1:1 scale flow that mimics real hurricane characteristics at full-scale would need giant blowers located at a distance that is significantly far than what a feasible facility can afford. Artificial wind contains significant high-frequency turbulence with limitations on the large-size vortices that make scaling buildings unavoidable, if we were to replicate correct physics. There are some testing capabilities that can engulf full-scale residential homes; however, the flow characteristics raises important questions about their similarity to those at full scale. This said, scaling residential homes is essential to maintain correct physics, and at the same time large-scale testing (not full-scale) will lead to improved Reynolds number. Large-scale wind testing went through several phases before reaching the present stage [63]. A multidisciplinary LSU research team from Civil and Environmental Engineering, Mechanical Engineering, Coast and Environment, Louisiana Sea Grant, Geography and Anthropology, Construction Management, and Sociology collaborated on a project titled “Hurricane Flow Generation at High Reynolds Number for Testing Energy and Coastal Infrastructure” that was awarded by the Louisiana Board of Regents to build Phase 1 of a large wind and rain testing facility (Figure 10). Phase 1 permits generating wind flows at a relatively high Reynolds number over a test section of 4 m × 4 m. These capabilities enable executing wind engineering experiments at relatively large scales. Moreover, the large open-jet facility has a potential for conducting destructive testing on models built from true construction materials. Blockage is minim, as per the concept of open-jet testing [65]. This state-of-the-art facility can generate realistic hurricane wind turbulence by replicating the entire frequency range of the velocity spectrum.
LSU WISE large testing facility (with a test section of 4 m × 4 m).
The large LSU WISE open-jet facility enables researchers to test their research ideas; to expand knowledge leading to innovations and discovery in science, hurricane engineering, and materials and structure disciplines; and to build the more resilient and sustainable infrastructure. The facility will enable scientists and researchers to test potential mitigation and restoration solutions, both natural (e.g., vegetation) and artificial. Potential applications include, but are not limited to, wind turbines, solar panels, residential homes, large roofs, high-rise buildings, transportation infrastructure, power transmission lines, etc. Testing at this facility can provide knowledge useful for homeowners and insurance companies to deal more effectively with windstorms, for example, to fine tune design codes and give coastal residents options for making their dwellings more storm-resistant. The goal is to build new structures and retrofit existing ones in innovative ways to balance resilience with sustainability, to better protect people, to enhance safety, and to reduce the huge cost of rebuilding after windstorms. In addition, the facility offers tremendous education value to k-12, undergraduate, and graduate students at a flagship state university, designated as a land-grant, sea-grant, and space-grant institution. This will broadly impact the wind/structural engineering research and education field and facilitate effective investments in the infrastructure industry that will result in more resilient and sustainable communities and contribute to economic growth and improve the quality of life.
The LSU research team aspires to match the spectral content of real wind using large-scale open-jet testing and CFD simulations in their quest of accurate estimation of peak pressures on building surfaces under wind. The goal is precise estimation of peak pressures on buildings through the generation of large- and small-scale turbulence via open-jet testing as well as advanced CFD simulations. Extreme negative pressures near ridges, corners, and leading edges of roofs are governed dominantly by wind turbulence and Reynolds number, among other factors. Both small- and large-scale turbulence vortices are responsible for peak pressures and can influence separation in the shear layer. This demands for precise replication of wind speed profile, turbulence intensity profiles, and spectral characteristics. Replication of the true physics requires higher Reynolds number which is difficult to achieve in wind tunnels. In traditional wind testing, it is challenging to create large-scale turbulence. An increase in large-scale turbulence content in incident flow allows vortices to attain maturity, and as a result higher peak pressures can be reproduced. A fundamental research objective, however, is to address the challenge of replicating real wind turbulence experimentally and computationally. Resolving the scaling issue by investigating larger test models at higher Reynolds number is another highlight of our research at the LSU WISE lab.
The velocity was measured at different heights in the open-jet facility with cobra probes to obtain the mean velocity and turbulence intensity profiles. Figure 11(i) shows the comparison of experimental mean velocity profiles from LSU open jet and TPU wind tunnel with theoretical wind profiles measured for open terrain condition (
Flow characteristics: (i) mean velocity and (ii) turbulence intensity.
The vertical profile plot for turbulence intensities shows that the LSU open-jet facility has approximately 20% turbulence intensity at reference height. Both mean velocity and turbulence intensity profiles in Figure 11 shows that LSU open-jet facility is capable of replicating open terrain near-ground ABL flow.
A scaled (1:13) cubic building model was tested at the LSU WISE large open-jet facility. The primary objective of this task was to compare surface pressure coefficients those obtained by wind tunnel testing on a smaller scale (1:100) model. Wind tunnel measurements are obtained from the published dataset of Tokyo Polytechnic University (TPU). The building model was instrumented with several pressure taps to capture surface pressures. A total of 64 taps were distributed on roof, same as the TPU wind tunnel model. Pressure taps were connected to Scanivalve pressure scanners through appropriate tubing. Two cobra probes were used to monitor upstream velocity at roof height [58]. The following equation was used to compute the pressure coefficient.
The time history of pressure coefficients, Cp(t), was obtained from the pressure time history, p(t), recorded using pressure scanners. The static pressure ps was considered reference for the pressure transducers. In addition, base line pressures were collected before and after each experiment. Once the time history of pressure coefficients was obtained, statistical analysis was done to obtain mean, minimum, and root mean square (rms) values. Measurements from LSU open-jet and TPU wind tunnel were processed the same way. The maximum and minimum values were obtained using MATLAB functions with a probabilistic approach described in Ref. [67]. This approach was considered, to account for the highly fluctuating wind flow, to yield a more stable estimator of peak values.
Sample of the findings of the experiment and comparison with TPU results is shown in Figure 12. The distribution of pressure coefficients obtained by open jet testing is symmetric like what is observed in the TPU wind tunnel testing. Since the model in open jet was tested at a higher Reynolds number, higher values of peak pressure coefficients are realized. Higher suction was observed near the zone of flow separation on the roof. Stronger suction for open-jet testing was found due to higher Reynolds number in open-jet and the presence of larger-scale turbulence compared to the wind tunnel. This difference in Reynolds number leads to difference in formation of flow separation zone, stagnation point on windward face, and the reattachment length. The difference between full-scale and reduced-scale wind tunnel tests is owed to similar reasons.
Minimum pressure coefficients on roof: (a) LSU open-jet (b) TPU wind tunnel (wind from bottom left corner).
On the computational side, the k- SST turbulence model was employed for improved mean pressure prediction near the flow separated region. An advanced approach is ongoing that employs large eddy simulation (LES) to generate accurate mean and peak pressures. Figure 13 shows a sample of high-quality mesh and CFD simulations in OpenFOAM.
With high-quality mesh and potential turbulence closure, CFD can provide continuous flow information: (a) 3D view of the computational grid, (b) meshing arrangement along the longitudinal section over a cube, and (c) velocity contour, after simulations in OpenFOAM.
In order to alleviate the challenges and shortcomings involved within the experimental tests in boundary-layer wind tunnels, in recent years, CFD was considered as an effective tool for the simulation of wind effects on civil engineering structures. However, it is necessary that the numerical CFD model would be capable of generating turbulence in a flow with certain spectral contents and eventually to reproduce peak pressures on building surfaces. The objective of this research is therefore to provide a basis for the development of recommendations and guidelines on using a CFD LES model that enables appropriate simulation of turbulence spectra of ABL inflow and reproducing the peak wind pressures on the roof of low-rise buildings. Figure 14 represents a schematic of the tools used by Aly and Gol Zaroudi [49] to simulate peak wind loads on a benchmark full-scale building from the Texas Tech University (TTU) in an open-terrain field. The details, advantages, and disadvantages of each tool are discussed in Aly and Gol Zaroudi [49].
The research tools employed to reproduce peak wind pressures on the roof of a benchmark low-rise building from the Texas Tech University (TTU) in an open-terrain field.
Considering the current rapid improvements in developing high-speed processors that can run in parallel on high-performance computing (HPC) clusters and devising new digital storage devices with huge capacities, CFD is becoming a promising tool in wind engineering applications. However, it is still a challenge for proper simulation of turbulence according to ABL wind characteristics and accurately reproducing peak pressures on low-rise buildings, even with supercomputers [40]. Aly and Gol Zaroudi [49], therefore, attempt to address some of the challenges in experimental and numerical simulations for aerodynamic testing of low-rise buildings, to reproduce realistic peak pressures. The study focused on wind flow processes in CFD with an objective to mimic full-scale pressures on low-rise building. The study implemented CFD with LES on a scale of 1:1 building. After a proximity experiment was executed in CFD-LES, a location of the test building from the inflow boundary was recommended, different from existing guidelines (RANS-based, e.g., COST and AIJ).The inflow boundary proximity showed significant influence on pressure correlation and the reproduction of peak pressures. The CFD LES turbulence closure showed its capabilities to reproduce peak loads that can mimic field data owing to the ability of creating inflow with enhanced spectral contents at 1:1 scale [49].
This chapter described the main characteristics of ABL winds, as well as some available tools for aerodynamic testing. Earlier studies confirm the presence of extreme negative pressures near ridges, corners, and leading edges of roofs in wind events. Turbulence (small- and large-scale) is responsible for large peak negative pressures and separation in the shear layer. This demands for precise replication of wind speed profile, turbulence intensity profiles, and spectral characteristics. Replication of true physics requires equality of Reynolds number which is not possible in wind tunnels. In traditional wind tunnels, only small-scale turbulence can be generated. An increase in large-scale turbulence content in incident flow allows vortices to attain maturity, and as a result higher peak pressures are obtained. The challenge of properly simulating wind effects on low-rise buildings is related to the lack of capability in turbulence modeling at a reasonably large scale and its limitation in reproducing the low-frequency part of the ABL turbulence spectrum. As a result, advances in aerodynamic testing employing modern tools such as open-jet testing for large- and full-scale testing were devised in recent years. Resolving the scaling issue by studying larger models at higher Reynolds number is another highlight of recent advances in aerodynamic testing. A large-scale cubic building model was tested in LSU open-jet facility at higher Reynolds number, and pressure coefficients were compared with those from wind tunnel testing. The results reveal the importance of large-scale testing at higher Reynolds numbers to obtain realistic peak pressures. Furthermore, CFD with appropriate turbulence closure was widely implemented recently for full-scale studies of wind effects on civil engineering structures. However, adopting proper inlet transient velocity is very crucial to correctly simulate ABL wind characteristics.
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