Uncertainty quantification of reacting fluids interacting with porous media using a hybrid physics-based and data-driven approach
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| Format: | Preprint |
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2025
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| _version_ | 1866912629433303040 |
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| author | Behnoudfar, Diba Niemeyer, Kyle E. |
| author_facet | Behnoudfar, Diba Niemeyer, Kyle E. |
| contents | Accurately simulating coupled physical processes under uncertainty is essential for reliable modeling and design in performance-critical applications such as combustion systems. Ablative heat shield design, as a specific example of this class, involves modeling multi-physics interactions between reacting flows and a porous material. Repeatedly evaluating these models to quantify parametric uncertainties would be prohibitively computationally expensive. In this work, we combine physics-based modeling using a single-domain approach with data-driven reduced-order modeling to quantify uncertainty via the operator inference method. The detailed physics-based simulations reproduce the measured surface temperature of an object exposed to high-enthalpy flow in a plasma wind tunnel experiment within 5%. We further use the model to demonstrate the effect of complex flow situations on the dynamic interactions between the porous heat shield material and the surrounding gas. The parametric reduced-order model, built on physics-based simulation data, successfully captures variations in quantities of interest resulting from changes in the permeability and heat transfer coefficient of the porous material in two separate studies: solid fuel combustion and emission of buoyant reacting plumes in quiescent air and ablation in a wind tunnel. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03649 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncertainty quantification of reacting fluids interacting with porous media using a hybrid physics-based and data-driven approach Behnoudfar, Diba Niemeyer, Kyle E. Computational Physics Accurately simulating coupled physical processes under uncertainty is essential for reliable modeling and design in performance-critical applications such as combustion systems. Ablative heat shield design, as a specific example of this class, involves modeling multi-physics interactions between reacting flows and a porous material. Repeatedly evaluating these models to quantify parametric uncertainties would be prohibitively computationally expensive. In this work, we combine physics-based modeling using a single-domain approach with data-driven reduced-order modeling to quantify uncertainty via the operator inference method. The detailed physics-based simulations reproduce the measured surface temperature of an object exposed to high-enthalpy flow in a plasma wind tunnel experiment within 5%. We further use the model to demonstrate the effect of complex flow situations on the dynamic interactions between the porous heat shield material and the surrounding gas. The parametric reduced-order model, built on physics-based simulation data, successfully captures variations in quantities of interest resulting from changes in the permeability and heat transfer coefficient of the porous material in two separate studies: solid fuel combustion and emission of buoyant reacting plumes in quiescent air and ablation in a wind tunnel. |
| title | Uncertainty quantification of reacting fluids interacting with porous media using a hybrid physics-based and data-driven approach |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2510.03649 |