Large Eddy Simulations of Turbulent and Buoyant Flows in Urban and Complex Terrain Areas Using the Aeolus Model

Gowardhan, Akshay; McGuffin, Dana L.; Lucas, D. D.; Neuscamman, Stephanie; Alvarez, Otto; Glascoe, L

doi:10.3390/atmos12091107

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…The aim of this work is to illustrate the potential of the SDM in modeling nuclear fallout microphysics, so we test the numerical technique in a zero‐dimensional setup that can be extended to a spatially resolving model in future work. High‐altitude detonations, like the airbursts considered here, do not include complexities such as ground interactions so that the post‐detonation cloud is spherical, symmetric, well‐mixed (Gowardhan et al., 2021), and modeling a homogeneous cloud in zero‐dimensions is justified.…”

Section: Fallout Super‐droplet Methodsmentioning

confidence: 99%

Super‐Droplet Method to Simulate Lagrangian Microphysics of Nuclear Fallout in a Homogeneous Cloud

et al. 2022

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Nuclear detonations produce hazardous local and global particles or fallout. Predicting fallout size, chemical components, and location is necessary to inform officials and determine immediate guidance for the public. However, existing nuclear detonation fallout models prescribe the particle size distributions based on limited observations. In this work, we apply the super‐droplet method, which is a numerical modeling technique developed for cloud microphysics, to simulate size distributions of particles in a mushroom cloud formed post‐detonation of a nuclear device. We model fallout formation and evolution with homogeneous nucleation and condensation of a single species and a Monte Carlo coagulation algorithm. We verify the numerical methods representing coagulation and condensation processes against analytical test problems. Additionally, we explore several scenarios for the integral system mass and yield in equivalent kilotons (kt) of TNT (trinitrotoluene). The fallout size distribution median diameter dpg follows a scaling law based on the integral system mass mv0 kg and yield Y kt: dpg=0.647mv00.609/Y0.436 ${d}_{pg}=0.647{m}_{v0}^{0.609}/{Y}^{0.436}$ nm. We test the effect of cloud turbulence, enhanced nucleation and growth, and vapor volatility with a sensitivity study. The range in median diameter predictions for simulations of historical tests performed over the Pacific encompass the measurements of particles sampled from the cloud caps. Predicted median particle size ranges up to 217, 123, 86, and 35 nm for historical tests with yields of 0.2, 0.7, 2, and 10 Mt, respectively. This work can be expanded in many different directions to build a more predictive model for fallout formation.

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Section: Fallout Super‐droplet Methodsmentioning

confidence: 99%

Super‐Droplet Method to Simulate Lagrangian Microphysics of Nuclear Fallout in a Homogeneous Cloud

et al. 2022

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“…The temporal evolution of the total turbulent kinetic energy over the domain (not shown here) reveals that a 30‐min spin‐up time is sufficient for a fully developed turbulence flow to reach a quasi‐steady state. In fact, many LES studies have applied a spin‐up time of 1.5 hr or less (e.g., Bera & Prabha, 2019; Bonekamp et al., 2020; Catalano & Moeng, 2010; Gasch et al., 2020; Gowardhan et al., 2021; Klose & Shao, 2013; Ma & Liu, 2019; Slater et al., 2020; Stawiarski et al., 2015; Yin et al., 2021). Here, we analyze the simulation outputs between 1200 and 1800 LT to fully avoid any misleading result from outputs during a spin‐up period.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Synergistic Effect of Surface Thermal Heterogeneity in Phase With Topography on Deep Moist Convection

Kang,

Ryu

2024

JGR Atmospheres

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Using large eddy simulation, we investigate the combined effect of terrain and surface sensible heat flux (SHF) heterogeneity on the development of afternoon deep moist convection (DMC). We implement an analytically derived, two‐dimensional terrain and SHF variations transformed from a κ−3 (where κ is the wavenumber) spectrum spanning wavelengths from 32 to 0.2 km. By separately coupling multiscale terrain with a homogeneous SHF field and the multiscale SHF field with flat terrain, we discern the individual impacts of these κ−3‐spectrum forcings on DMC. Our specific forcing configuration demonstrates that the multiscale terrain had a greater influence on DMC development compared to the multiscale SHF field. While the solely surface SHF heterogeneity forcing results in a wider pool of high relative humidity above the boundary layer, its significance is relatively lower in the mountainous terrain cases due to the shorter interaction time between highly buoyant thermals and the surrounding environment. However, when the multiscale terrain and SHF field are synchronized, DMC develops rapidly within a time frame of 4.5 hr, which is facilitated by enhanced surface buoyancy fluxes, the presence of highly buoyant thermals, and the persistence of mesoscale structures such as near‐surface convergence and mesoscale updrafts. Our study highlights the importance of the synergistic effects between multiscale terrain and surface SHF heterogeneity in DMC development. Additionally, our multiscale analyses of atmospheric variables reveal distinct atmospheric regimes between the pre‐storm and DMC periods. These findings contribute to a better understanding of the complex dynamics involved in the formation of afternoon DMC.

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“…The data is obtained from simulations and later post-processed to make it adequate for machine learning training. Given large-scale winds as an inflow boundary condition, the computational fluid dynamics (CFD) code Aeolus 34 uses hundreds of millions of grid cells to simulate fluid flow and material transport in complex, three-dimensional environments at high resolution, accounting for turbulence from structures, terrain features, and obstacles and predicting deposition on the ground and other surfaces. For demonstration purposes, megapixel deposition images were obtained by processing the output of Aeolus simulations, which were run using a resolution of cells, each cell representing 5 m 5 m 5 m. Within Aeolus, pollutant concentration and deposition values are calculated by releasing and transporting Lagrangian particles of specified masses and sizes within the flow field.…”

Section: Introductionmentioning

confidence: 99%

Predicting wind-driven spatial deposition through simulated color images using deep autoencoders

Fernández-Godino

Lucas

Kong

2023

Sci Rep

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For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93.

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Large Eddy Simulations of Turbulent and Buoyant Flows in Urban and Complex Terrain Areas Using the Aeolus Model

Cited by 8 publications

References 20 publications

Super‐Droplet Method to Simulate Lagrangian Microphysics of Nuclear Fallout in a Homogeneous Cloud

Super‐Droplet Method to Simulate Lagrangian Microphysics of Nuclear Fallout in a Homogeneous Cloud

Synergistic Effect of Surface Thermal Heterogeneity in Phase With Topography on Deep Moist Convection

Predicting wind-driven spatial deposition through simulated color images using deep autoencoders

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