Qing Wang
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Coupled Fire-Cloud Simulations Reveal Attenuation and Self-Intensification Mechanisms of Pyrocumulonimbus Clouds
Sheide Chammas
Matthias Ihme
Yi-fan Chen
John Anderson
Tapio Schneider
Cenk Gazen
Jeff Parker
(2025)
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Pyrocumulonimbus (pyroCb) firestorms, which inject climate-altering aerosols into the stratosphere, represent a growing and unpredictable hazard in an era of increasing wildfire activity. The complex fire-atmosphere feedbacks that govern their behavior, however, are poorly understood, limiting forecasting capabilities. Here we use high-fidelity, fully coupled simulations to dissect the competing mechanisms controlling pyroCb evolution. We show that fuel moisture primarily acts as an energy sink that attenuates fire intensity and suppresses pyroCb development, rather than serving as a significant moisture source for the cloud itself. Conversely, we identify a potent positive feedback loop, the Self-Amplifying Fire-induced Recirculation (SAFIR) mechanism, where precipitation-induced downdrafts intensify the parent fire. Our simulations reveal that the prevalence of either attenuation or SAFIR-driven intensification is dictated by ambient conditions, particularly wind speed. These findings provide a new mechanistic framework for understanding pyroCb behavior, offering a critical step toward improved prediction of these extreme events.
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With the increasing severity and frequency of large wildfires, there is a critical need for improved modeling capabilities to inform mitigation plans for fire management and risk mitigation. In particular, high-fidelity modeling tools are needed to provide reliable predictions of fire-spread behavior to support scientific inquiry and fire-risk assessment, as well as landscape
management at an early stage. However, because of the computational complexity, physics-
based models are largely limited to simulating a few conditions. We present a high-fidelity
simulation framework that takes advantage of emerging programming paradigms, novel
computing hardware architecture, and ensemble calculations for simulating large-scale wildfires scenarios at affordable cost, thereby enabling the parametric study and statistical analysis of wildfires scenarios under consideration of changing environmental conditions, ignition probabilities, and vegetation and fuel-moisture regimes. We discuss details of the simulation framework that is based on TensorFlow and the utility of ensemble simulations to examine fire- spread behavior in the presence of coupled wind-slope conditions that remain an outstanding scientific challenge for fire-spread predictions.
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CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields
Jianxun Wang
Pan Du
Meet Hemant Parikh
Xiantao Fan
Xinyang Liu
Yi-fan Chen
Physics Review Fluids (2025)
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Eddy-resolving turbulence simulations require stochastic inflow conditions that accurately replicate the complex, multi-scale structures of turbulence. Traditional recycling-based methods rely on
computationally expensive precursor simulations, while existing synthetic inflow generators often
fail to reproduce realistic coherent structures of turbulence. Recent advances in deep learning (DL)
have opened new possibilities for inflow turbulence generation, yet many DL-based methods rely on
deterministic, autoregressive frameworks prone to error accumulation, resulting in poor robustness
for long-term predictions. In this work, we present CoNFiLD-inlet, a novel DL-based inflow turbulence generator that integrates diffusion models with a conditional neural field (CNF)-encoded
latent space to produce realistic, stochastic inflow turbulence. By parameterizing inflow conditions
using Reynolds numbers, CoNFiLD-inlet generalizes effectively across a wide range of Reynolds
numbers (Reτ between 103 and 104) without requiring retraining or parameter tuning. Comprehensive validation through a priori and a posteriori tests in Direct Numerical Simulation (DNS)
and Wall-Modeled Large Eddy Simulation (WMLES) demonstrates its high fidelity, robustness,
and scalability, positioning it as an efficient and versatile solution for inflow turbulence synthesis.
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Wildfires pose serious threats to society, environment, and ecosystems as they can disrupt, damage, and destroy infrastructure, services, and properties. To examine the complex interaction of wildfires, arising from strong coupling precession between combustion, atmospheric flow, heat-transfer, topography, and fuel properties, we present a simulation framework that integrates a high-fidelity ML-enabled simulations framework for wildfire predictions with a sampling technique to perform high-resolution ensemble simulations of large-scale wildfire scenarios. The simulation results are compared to existing experimental data for fire acceleration, mean rate of spread, and fireline intensity. Strong coupling between key compounding parameters (wind speed and slope) are observed for fire spread and intermittency. Scaling relations are derived and presented to delineate regimes associated with plume-driven and convection-driven fire spread.
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Wildland fires in regions of complex terrain are often associated with extreme fire behavior.
Understanding the interaction between complex terrain and atmospheric flows that results in these extreme conditions is thus an important endeavor. In particular, sloped terrain can lead to highly dynamic wind patterns, thereby enhancing turbulence which in turn directly affect all modes of heat transfer, resulting in more severe fire behavior. Thus, by leveraging a recently developed physics-based solver Swirl-LM based on TensorFlow and running on Tensor Processing Units, we investigate extreme fire behavior in complex terrain in a prescribed fire scenario. We discuss how thermal instabilities contribute to turbulence generation, and how coupled fire-atmosphere interactions generate a circulation of the convective smoke column.
Comparison with experimental data allows us to validate our numerical models and opens pathways for simulations of extreme fires in complex terrain at affordable computational cost.
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Background. Wildfire research uses ensemble methods to analyze fire behaviors and assess
uncertainties. Nonetheless, current research methods are either confined to simple models
or complex simulations with limits. Modern computing tools could allow for efficient, high-
fidelity ensemble simulations. Aims. This study proposes a high-fidelity ensemble wildfire
simulation framework for studying wildfire behavior, ML tasks, fire-risk assessment, and
uncertainty analysis. Methods. In this research, we present a simulation framework that
integrates the Swirl-Fire large-eddy simulation tool for wildfire predictions with the Vizier
optimization platform for automated run-time management of ensemble simulations and
large-scale batch processing. All simulations are executed on tensor-processing units to
enhance computational efficiency. Key results. A dataset of 117 simulations is created,
each with 1.35 billion mesh points. The simulations are compared to existing experimental
data and show good agreement in terms of fire rate of spread. Computations are done for
fire acceleration, mean rate of spread, and fireline intensity. Conclusions. Strong coupling
between these 2 parameters are observed for the fire spread and intermittency. A critical
Froude number that delineates fires from plume-driven to convection-driven is identified and
confirmed with literature observations. Implications. The ensemble simulation framework
is efficient in facilitating parametric wildfire studies.
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Uncertainty quantification in coupled wildfire-atmosphere simulations at scale
Paul Schwerdtner
Frederick Law
Cenk Gazen
Yi-fan Chen
Matthias Ihme
Benjamin Peherstorfer
PNAS Nexus (2024)
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Uncertainties in wildfire simulations pose a major challenge for making decisions about fire management, mitigation, and evacuations. However, ensemble calculations to quantify uncertainties are prohibitively expensive with high-fidelity models that are needed to capture today’s ever more intense and severe wildfires. This work shows that surrogate models trained on related data enable scaling multi-fidelity uncertainty quantification to high-fidelity wildfire simulations of unprecedented scale with billions of degrees of freedom. The key insight is that correlation is all that matters while bias is irrelevant for speeding up uncertainty quantification when surrogate models are combined with high-fidelity models in multi-fidelity approaches. This allows the surrogate models to be trained on abundantly available or cheaply generated related data samples that can be strongly biased as long as they are correlated to predictions of high-fidelity simulations. Numerical results with scenarios of the Tubbs 2017 wildfire demonstrate that surrogate models trained on related data make multi-fidelity uncertainty quantification in large-scale wildfire simulations practical by reducing the training time by several orders of magnitude from three months to under three hours and predicting the burned area at least twice as accurately compared to using high-fidelity simulations alone for a fixed computational budget. More generally, the results suggest that leveraging related data can greatly extend the scope of surrogate modeling, potentially benefiting other fields that require uncertainty quantification in computationally expensive high-fidelity simulations.
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Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations
Anudhyan Boral
James Lottes
Yi-fan Chen
John Anderson
Fei Sha
Advances in Neural Information Processing Systems (NeurIPS) 36 (2023)
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We introduce a data-driven learning framework that assimilates two powerful ideas: ideal large-eddy-simulation (LES) from turbulence closure modeling and neural stochastic differential equations (SDE) for modeling stochastic dynamical systems. ideal LES identifies the optimal reduced-order flow fields of the large-scale features by marginalizing out the effect of small-scales in stochastic turbulent trajectories. However, ideal LES is analytically intractable. In our work, we use a latent neural SDE to model the evolution of the stochastic process and a pair of encoder and decoder for transforming between the latent space and the desired optimal flow field. This stands in sharp contrast to other types of neural parameterization of the closure models where each trajectory is treated as a deterministic realization of the dynamics. We show the effectiveness of our approach (niLES – neural ideal LES) on a challenging chaotic dynamical systems: Kolmogorov flow at a Reynolds number of 20,000. Compared to prior works, our method is also able to handle non-uniform geometries and unstructured meshes. In particular, niLES leads to more accurate long term statistics, and is stable even when rolling out to long horizons.
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A TensorFlow Simulation Framework for Scientific Computing of Fluid Flows on Tensor Processing Units
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A computational fluid dynamics (CFD) simulation framework for predicting complex flows is developed on the Tensor Processing Unit (TPU) platform. The TPU architecture is featured with accelerated performance of dense matrix multiplication, large high bandwidth memory, and a fast inter-chip interconnect, which makes it appealing for scientific high performance computing (HPC). This CFD framework is implemented with the finite difference method on a collocated structured mesh, and it uses the graph-based TensorFlow as the programming library. The accuracy and speed of this framework is studied both numerically and analytically. In particular, the impact of machine precision on the mesh convergence is discussed in detail, which provides a guideline for the simulation framework to achieve the desired rate of convergence. Additionally, a linear weak scaling and a super-linear strong scaling is observed up to a full TPU v3 pod with 2048 cores. The algorithm and implementation are validated with canonical 2D and 3D Taylor Green vortex simulations. To demonstrate the capability for simulating turbulent flows, verification simulations are conducted for two configurations, namely the decaying homogeneous isotropic turbulence and a turbulent planar jet. Both simulations show good statistical agreement with reference solutions.
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This work presents a high-fidelity simulation framework for modeling large-scale wildfire scenarios that take into consideration realistic topographies, atmospheric conditions, turbulence/fire interaction, and flow dynamics. With the overall goal of enabling large-scale ensemble simulations and the integration of the simulation results into machine-learning applications, this modeling framework has been implemented into the TensorFlow programming environment. To demonstrate the capability of this simulation framework in predicting large-scale fires, we performed high-resolution simulations of a realistic wildfire scenario that is representative of the 2017 Tubbs fire.
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