A High-resolution Large-eddy Simulation Framework for Wildland Fire Predictions using TensorFlow Article Swipe
Qing Wang
,
Matthias Ihme
,
Rod Linn
,
Yifan Chen
,
Vivian Yang
,
Fei Sha
,
Craig B. Clements
,
Jenna S. McDanold
,
John Anderson
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.05141
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.05141
As the impact of wildfires has become increasingly more severe over the last decades, there is continued pressure for improvements in our ability to predict wildland fire behavior over a wide range of conditions. One approach towards this goal is through coupled fire/atmosphere modeling tools. While significant progress has been made on advancing their physical fidelity, existing modeling tools have not taken full advantage of emerging programming paradigms and computing architectures to enable high-resolution wildfire simulations. By addressing this gap, this work presents a new wildfire simulation framework that enables landscape-scale wildfire simulations with physical representation of the combustion at affordable computational cost. This is achieved by developing a coupled fire/atmosphere model in the TensorFlow programming paradigm, which enables highly efficient and scalable computations on Tensor Processing Unit (TPU) hardware architecture. To validate this simulation framework and demonstrate its efficiency, simulations of the prescribed fire experiment FireFlux II (Clements et al., 2019) are performed. By considering a parametric study on the mesh resolution, we show that the global quantities such as volumetric heat release and fire-spread rate are insensitive to the horizontal mesh resolution within a range between 0.5 m and 2 m, which is sufficient for predicting fire intermittency and dynamic fire properties associated with fine-scale turbulent structures in the atmospheric boundary layer.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2212.05141
- https://arxiv.org/pdf/2212.05141
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311406892
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4311406892Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2212.05141Digital Object Identifier
- Title
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A High-resolution Large-eddy Simulation Framework for Wildland Fire Predictions using TensorFlowWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-09Full publication date if available
- Authors
-
Qing 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
-
https://arxiv.org/abs/2212.05141Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2212.05141Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2212.05141Direct OA link when available
- Concepts
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Computer science, Scalability, Large eddy simulation, Range (aeronautics), Environmental science, Computational science, Meteorology, Aerospace engineering, Simulation, Turbulence, Engineering, Physics, DatabaseTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.decades, | 13 |
| abstract_inverted_index.emerging | 65 |
| abstract_inverted_index.existing | 56 |
| abstract_inverted_index.hardware | 129 |
| abstract_inverted_index.modeling | 43, 57 |
| abstract_inverted_index.physical | 54, 94 |
| abstract_inverted_index.presents | 82 |
| abstract_inverted_index.pressure | 17 |
| abstract_inverted_index.progress | 47 |
| abstract_inverted_index.scalable | 122 |
| abstract_inverted_index.validate | 132 |
| abstract_inverted_index.wildfire | 74, 85, 91 |
| abstract_inverted_index.wildland | 25 |
| abstract_inverted_index.(Clements | 148 |
| abstract_inverted_index.advancing | 52 |
| abstract_inverted_index.advantage | 63 |
| abstract_inverted_index.computing | 69 |
| abstract_inverted_index.continued | 16 |
| abstract_inverted_index.efficient | 120 |
| abstract_inverted_index.fidelity, | 55 |
| abstract_inverted_index.framework | 87, 135 |
| abstract_inverted_index.paradigm, | 116 |
| abstract_inverted_index.paradigms | 67 |
| abstract_inverted_index.turbulent | 207 |
| abstract_inverted_index.wildfires | 4 |
| abstract_inverted_index.Processing | 126 |
| abstract_inverted_index.TensorFlow | 114 |
| abstract_inverted_index.addressing | 77 |
| abstract_inverted_index.affordable | 100 |
| abstract_inverted_index.associated | 204 |
| abstract_inverted_index.combustion | 98 |
| abstract_inverted_index.developing | 107 |
| abstract_inverted_index.experiment | 145 |
| abstract_inverted_index.fine-scale | 206 |
| abstract_inverted_index.horizontal | 181 |
| abstract_inverted_index.parametric | 157 |
| abstract_inverted_index.performed. | 153 |
| abstract_inverted_index.predicting | 197 |
| abstract_inverted_index.prescribed | 143 |
| abstract_inverted_index.properties | 203 |
| abstract_inverted_index.quantities | 168 |
| abstract_inverted_index.resolution | 183 |
| abstract_inverted_index.simulation | 86, 134 |
| abstract_inverted_index.structures | 208 |
| abstract_inverted_index.sufficient | 195 |
| abstract_inverted_index.volumetric | 171 |
| abstract_inverted_index.atmospheric | 211 |
| abstract_inverted_index.conditions. | 33 |
| abstract_inverted_index.considering | 155 |
| abstract_inverted_index.demonstrate | 137 |
| abstract_inverted_index.efficiency, | 139 |
| abstract_inverted_index.fire-spread | 175 |
| abstract_inverted_index.insensitive | 178 |
| abstract_inverted_index.programming | 66, 115 |
| abstract_inverted_index.resolution, | 162 |
| abstract_inverted_index.significant | 46 |
| abstract_inverted_index.simulations | 92, 140 |
| abstract_inverted_index.computations | 123 |
| abstract_inverted_index.improvements | 19 |
| abstract_inverted_index.increasingly | 7 |
| abstract_inverted_index.simulations. | 75 |
| abstract_inverted_index.architecture. | 130 |
| abstract_inverted_index.architectures | 70 |
| abstract_inverted_index.computational | 101 |
| abstract_inverted_index.intermittency | 199 |
| abstract_inverted_index.representation | 95 |
| abstract_inverted_index.fire/atmosphere | 42, 110 |
| abstract_inverted_index.high-resolution | 73 |
| abstract_inverted_index.landscape-scale | 90 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |