A high-resolution large-eddy simulation framework for wildland fire predictions using TensorFlow Article Swipe
Mengqing Wang
,
Matthias Ihme
,
Rod Linn
,
Yifan Chen
,
Vivian Yang
,
Fei Sha
,
Craig B. Clements
,
Jenna S. McDanold
,
John Anderson
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1071/wf22225
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1071/wf22225
Background Wildfires are becoming more severe, so we need improved tools to predict them over a wide range of conditions and scales. One approach towards this goal entails the use of coupled fire/atmosphere modelling tools. Although significant progress has been made in advancing their physical fidelity, existing tools have not taken full advantage of emerging programming paradigms and computing architectures to enable high-resolution wildfire simulations. Aims The aim of this study was to present a new framework that enables landscape-scale wildfire simulations with physical representation of combustion at an affordable cost. Methods We developed a coupled fire/atmosphere simulation framework using TensorFlow, which enables efficient and scalable computations on Tensor Processing Units. Key results Simulation results for a prescribed fire were compared with experimental data. Predicted fire behavior and statistical analysis for fire spread rate, scar area, and intermittency showed overall reasonable agreement. Scalability analysis was performed, showing close to linear scaling. Conclusions While mesh refinement was shown to have less impact on global quantities, such as fire scar area and spread rate, it benefits predictions of intermittent fire behavior, buoyancy-driven dynamics, and small-scale turbulent motion. Implications This new simulation framework is efficient in capturing both global quantities and unsteady dynamics of wildfires at high spatial resolutions.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1071/wf22225
- https://www.publish.csiro.au/wf/pdf/WF22225
- OA Status
- hybrid
- Cited By
- 4
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387718042
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387718042Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1071/wf22225Digital Object Identifier
- Title
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A high-resolution large-eddy simulation framework for wildland fire predictions using TensorFlowWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-17Full publication date if available
- Authors
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Mengqing Wang, Matthias Ihme, Rod Linn, Yifan Chen, Vivian Yang, Fei Sha, Craig B. Clements, Jenna S. McDanold, John AndersonList of authors in order
- Landing page
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https://doi.org/10.1071/wf22225Publisher landing page
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https://www.publish.csiro.au/wf/pdf/WF22225Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://www.publish.csiro.au/wf/pdf/WF22225Direct OA link when available
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Computer science, Scalability, Large eddy simulation, Fire Dynamics Simulator, Scaling, Environmental science, Meteorology, Simulation, Aerospace engineering, Computational fluid dynamics, Turbulence, Engineering, Mathematics, Physics, Geometry, DatabaseTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 1, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | International Journal of Wildland Fire |
| primary_location.landing_page_url | https://doi.org/10.1071/wf22225 |
| publication_date | 2023-10-17 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2530960585, https://openalex.org/W2915052705, https://openalex.org/W2043347079, https://openalex.org/W3118915980, https://openalex.org/W1976638764, https://openalex.org/W2942386344, https://openalex.org/W2005089486, https://openalex.org/W3034728704, https://openalex.org/W2021513946, https://openalex.org/W2167477614, https://openalex.org/W3205566854, https://openalex.org/W3187377802, https://openalex.org/W1955806497, https://openalex.org/W3190062760, https://openalex.org/W2165553433, https://openalex.org/W2003988906, https://openalex.org/W2083789510, https://openalex.org/W2099722563, https://openalex.org/W2067375228, https://openalex.org/W2960710220, https://openalex.org/W1483839007, https://openalex.org/W4233149348, https://openalex.org/W2179285023, https://openalex.org/W2898801287, https://openalex.org/W2886009597, https://openalex.org/W3094623573, https://openalex.org/W2059564548, https://openalex.org/W2117659426, https://openalex.org/W2156061745, https://openalex.org/W2145445755, https://openalex.org/W2097086408, https://openalex.org/W1998525406, https://openalex.org/W2791997448, https://openalex.org/W1997126380, https://openalex.org/W4306878043, https://openalex.org/W1975474747, https://openalex.org/W4226276902, https://openalex.org/W4313142543, https://openalex.org/W2401580071, https://openalex.org/W2165977355, https://openalex.org/W4210684252, https://openalex.org/W3098105755, https://openalex.org/W3102779861, https://openalex.org/W3104803445 |
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