Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis

Valero, Miguel Ángel; Jofre, L.; Torres, Ricardo

doi:10.1016/j.envsoft.2021.105050

Cited by 31 publications

(15 citation statements)

References 58 publications

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“…Jiang et al (2021) proposed a fire spread model based on the heterogeneous Cellular Automata model for large-scale complex wildland-urban interface (WUI) areas using thermal principles and empirical statistics. Valero et al (2021) enabled uncertainty quantification in wildfire CFD models using multi-fidelity strategies.…”

Section: Wildfire Modelingmentioning

confidence: 99%

Wildfire and evacuation simulation: An overview of research, development, and practice

Beyki¹,

Santiago²,

Laím³

et al. 2022

Advances in Forest Fire Research 2022

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Wildfires have been growing dramatically over the past decades due to climate changes, global warming, droughts and forest landscape, and vegetation changes. Increasing losses due to wildfires have been a significant concern; therefore, protective measures are being taken to mitigate the risk of health hazards and even deaths. This paper assesses and reviews the approaches to fire spread and propagation simulation to predict the fire spread characteristics. These simulations are essential to determine the evacuation necessity and requirement. The assessment of evacuation triggers is investigated according to the available literature and studies on simulation and computational fluid dynamic (CFD) modeling of wildfire and fire spread are reviewed. In addition, the modeling and simulation of evacuation decisions, residents' evacuation, traffic patterns, and the evacuation trigger models are presented and discussed. All mentioned simulations are modeled on different scales, i. e. microscale, mesoscale and macroscale, based on the desired criteria assessment of each case study. These approaches are distinguished basically by their ability to model an area of certain extent, i. e. scale, and their accuracy in attaining the desired, analysis, data and prediction, i. e. detail level, and selecting one approach over the others is always a trade-off between these two criteria.

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Section: Wildfire Modelingmentioning

confidence: 99%

Wildfire and evacuation simulation: An overview of research, development, and practice

Beyki¹,

Santiago²,

Laím³

et al. 2022

Advances in Forest Fire Research 2022

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“…It is important to note that, over the past years, multifidelity (MF) strategies to reduce the cost of the outer-loop problem by combining the accuracy of high-fidelity (HF) models, e.g., DNS and LES, with the speedup achieved by low-fidelity (LF) representations, e.g., RANS, DES and analytical functions/correlations, have been extensively developed [20][21][22][23][24] and applied to propagate uncertainty in large-scale multiphysics turbulent flows [25][26][27][28] and optimization problems [29][30][31][32]. As demonstrated in [12], the SGD approach of this study may be extended to incorporate MF aerodynamic models, a direction we leave for a future study.…”

Section: B Sources Of Uncertaintymentioning

confidence: 99%

Rapid Aerodynamic Shape Optimization Under Parametric and Turbulence Model Uncertainty: A Stochastic Gradient Approach

Jofre,

Doostan

2021

Preprint

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Aerodynamic optimization is ubiquitous in the design of most engineering systems interacting with fluids. A common approach is to optimize a performance function -subject to some constraints -defined by a choice of an aerodynamic model, e.g., turbulence RANS model, and at nominal operating conditions. Practical experience indicates that such a deterministic, i.e., single-point, approach may result in considerably sub-optimal designs when the adopted aerodynamic model does not lead to accurate flow predictions or when the actual operating conditions differ from those considered in the design. One approach to address this shortcoming is to consider an average or robust design, wherein the statistical moments of the performance function, given the uncertainty in the operating conditions and the aerodynamic model, is optimized. However, when the number of uncertain inputs is large or the performance function exhibits significant variability, an accurate evaluation of these moments may require a large number of forward and/or adjoint solves, at each iteration of a gradient-based scheme. This, in turn, renders the design of complex aerodynamic systems computationally expensive, if not infeasible. To tackle this difficulty, we consider a variant of the stochastic gradient descent method where, in each optimization iteration, a stochastic approximation of the objective, constraints, and their gradients are generated. This is done via a small number of forward/adjoint solves corresponding to random selections of the uncertain parameters and aerodynamic model. The methodology is applied to the robust optimization of the standard NACA-0012 airfoil in a low-Mach-number turbulent flow regime and subject to parametric and turbulence model uncertainty. With a cost that is a small factor larger than that of the deterministic approach, the stochastic gradient approach significantly improves the performance (mean and variance) of the aerodynamic design for a wide range of operating conditions and turbulence models.

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“…The paper (Valero et al, 2021) gives the application results of multi-accuracy approaches to the problems of forecasting the spread of fires in nature. The proposed methodology can also be used to analyze the uncertainty in other relevant parameters of fire behavior, such as heat transfer parameters, fuel consumption, and smoke generation.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Fires in Natural Ecosystems of the Ukrainian Roztochchiya by Wildland Fire Dynamics Simulator Model

Popovych¹,

Gapalo²,

Tovarianskyi³

2023

Ecol. Eng. Environ. Technol.

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Fires in natural ecosystems require a comprehensive approach due to the dependence of these processes on many factors -climatic conditions, moisture content of combustible material, type of ignition source, soil temperature regime, availability of possibilities and tools for extinguishing, presence of fire-fighting obstacles, organization of localization and elimination. The investigation of natural fires today takes into account (developed and effectively used) computer models, which are based on numerical methods of the physics of combustion of substances and materials. In the presented work, a study of the fire at the site was carried out, which included the growth of grass and other components of the phytocenosis, particularly shrubs. The research was carried out taking into account the environmental conditions and the combustible material's physical and chemical properties. In general, the fire simulation lasted 180 seconds. Rapid burning stopped 66 s from the beginning of ignition, and after that, single burning and smoldering of the studied area were observed. It was established that the maximum flame temperature was more than 1250 °C, which was observed in 33 s within the limits of burning grass in stacks. The maximum power from the fire of the studied area is reached approximately at the 65 th second and was 209650 kW/m 3 , and starting from the 66 th second, it was decreasing. It should be noted that the species composition of the pyrogenic succession is depleted, and on the site of the fire, there is a scattering of plants and a spontaneous arrangement in the studied area. The predominance of Asteraceae in the pyrogenic succession is a rather positive phenomenon because they are the most widespread family of the flora of Ukraine and have great practical applications and are used as medicinal, food, fodder, honey, oil, and decorative species.

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Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis

Cited by 31 publications

References 58 publications

Wildfire and evacuation simulation: An overview of research, development, and practice

Wildfire and evacuation simulation: An overview of research, development, and practice

Rapid Aerodynamic Shape Optimization Under Parametric and Turbulence Model Uncertainty: A Stochastic Gradient Approach

Investigation of Fires in Natural Ecosystems of the Ukrainian Roztochchiya by Wildland Fire Dynamics Simulator Model

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