用于超临界燃烧中真实流体热力学性质神经预测的热力学信息输入重新参数化
Thermodynamics-Informed Input Reparameterization for Neural Prediction of Real-Fluid Thermodynamic Properties in Supercritical Combustion
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中文总结 AI 辅助
研究超临界燃烧模拟中真实流体热力学性质评估成本高的问题，提出TAIR策略，通过用目标匹配的热力学坐标替换原始焓坐标，降低了预测温度、密度和压缩系数的RMSE，证明收益源于热力学匹配的输入设计。
中文摘要 AI 辅助
在超临界燃烧模拟中，真实流体热力学性质评估成本高昂。在基于焓的压力校正公式中，封闭项通过焓 - 温度反演和重复的真实流体状态方程评估从求解器状态（h，p，Y）计算温度T、密度ρ和压缩系数ψ。神经网络代理可提供固定成本推理，但从（h，p，Y）到（T，ρ，ψ）的直接映射必须捕捉焓 - 温度关系和非理想状态方程响应，这是个复杂的回归问题。本文引入一种热力学信息输入重新参数化策略TAIR，用目标匹配的热力学坐标替换每个属性网络的原始焓坐标。该方法通过超临界甲烷 - 氧气逆流火焰数据评估，相比原始输入基线和目标不一致的交叉重新参数化控制，TAIR分别将T、ρ和ψ的留出RMSE降低约1.5、2.0和7.5倍。对于增强热力学包络内未见过的应变率火焰，相应倍数为3.6、14.5和6.0。目标不一致的控制效果更差，表明收益源于热力学匹配的输入设计而非一般预处理。
英文摘要
Real-fluid thermodynamic property evaluation is a major computational cost in supercritical combustion simulations. In the enthalpy-based pressure-correction formulation, the closure evaluates temperature T, density $ρ$, and compressibility coefficient $ψ$ from the solver state (h,p,Y) through enthalpy-temperature inversion and repeated real-fluid equation-of-state evaluations. Neural-network surrogates offer fixed-cost inference, but direct mapping from (h,p,Y) to $(T,ρ,ψ)$ must capture the enthalpy-temperature relation and non-ideal equation-of-state response, resulting in a complex regression problem. This work introduces a thermodynamics-informed input reparameterization strategy, termed target-aligned input reparameterization (TAIR). TAIR replaces the raw enthalpy coordinate of each property network with a target-matched thermodynamic coordinate: the temperature network uses a temperature estimate obtained by inverting a constant-$c_p$ ideal-gas mixture enthalpy approximation, whereas the density and compressibility networks use an ideal-gas density estimate. These algebraic transformations use only solver-available variables and species constants, guiding the networks to learn real-fluid departures from ideal-gas baselines rather than reconstructing the full closure from raw enthalpy. The method is assessed using supercritical methane-oxygen counterflow flame data against a raw-input baseline and target-inconsistent cross-reparameterization controls. TAIR reduces held-out RMSE by factors of about 1.5, 2.0, and 7.5 for T, $ρ$, and $ψ$, respectively. For an unseen strain-rate flame within the augmented thermodynamic envelope, the corresponding factors are 3.6, 14.5, and 6.0. The target-inconsistent controls perform worse, indicating that the gains arise from thermodynamically matched input design rather than generic preprocessing.
发表机构
- State Key Laboratory of Turbulence and Complex Systems, College of Engineering, Peking University（湍流与复杂系统国家重点实验室，北京大学工学院）
- AI for Science Institute (AISI)（科学智能研究院）
机构由 AI 辅助整理，请以论文原文为准。
