用代理模型与贝叶斯校准，提升燃烧模拟中燃料烧蚀率预测的可靠性。
UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration
- 构建高斯过程与分层多尺度代理模型，以加速复杂燃烧模拟。
- 模型误差低于15%，仅需远场输入即可准确预测多尺度边界量。
- 发现默认参数偏小，需上调以更好匹配实验数据，适合燃烧仿真研究者。
本文旨在展示并解决在2D板式燃烧器直接数值模拟（DNS）这类复杂物理仿真中进行完整不确定性量化分析所面临的挑战。该不确定性量化框架包括数据驱动代理模型的构建、参数不确定性向燃料烧蚀率（主要关注量）的传播，以及利用实验数据对升华潜热和化学反应温度指数进行贝叶斯校准。基于拉丁超立方采样生成的64组模拟结果，构建了高斯过程（GP）与分层多尺度代理模型（HMS）。交叉验证表明HMS在预测性能上更优，仅需少量远场输入即可实现对多尺度边界量的预测，误差小于15%。后续贝叶斯校准显示，原始DNS中使用的默认参数应调高，才能更好地匹配实验观测结果。本研究强调了代理模型选择与参数校准在复杂燃烧系统中燃料烧蚀率预测不确定性量化中的关键作用。
原文摘要 · Abstract (English)
The goal of this paper is to demonstrate and address challenges related to all aspects of performing a complete uncertainty quantification analysis of a complicated physics-based simulation like a 2D slab burner direct numerical simulation (DNS). The UQ framework includes the development of data-driven surrogate models, propagation of parametric uncertainties to the fuel regression rate--the primary quantity of interest--and Bayesian calibration of the latent heat of sublimation and a chemical reaction temperature exponent using experimental data. Two surrogate models, a Gaussian Process (GP) and a Hierarchical Multiscale Surrogate (HMS) were constructed using an ensemble of 64 simulations generated via Latin Hypercube sampling. HMS is superior for prediction demonstrated by cross-validation and able to achieve an error < 15% when predicting multiscale boundary quantities just from a few far field inputs. Subsequent Bayesian calibration of chemical kinetics and fuel response parameters against experimental observations showed that the default values used in the DNS should be higher to better match measurements. This study highlights the importance of surrogate model selection and parameter calibration in quantifying uncertainty in predictions of fuel regression rates in complex combustion systems.
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