学习 HyChem 模型的机器学习方法
Machine Learning Approaches to Learn HyChem Models
AI总结：
本文提出用随机梯度下降训练 HyChem 化学动力学模型，使其覆盖高温与低温燃烧区间；在 F-24 上达到遗传算法同等精度，成本降低1000倍且参数改动更小。
摘要：
HyChem 方法最近被提出用于对真实多组分燃料的高温燃烧进行建模。该方法将燃料热解和氧化热解的集总反应步骤，与所得热解产物氧化的详细化学机理相结合。然而，由于低温化学比高温化学更具燃料特异性，该方法在负温度系数（NTC）区间内通常与实验数据存在显著差异。本文提出一种机器学习方法，用于学习能够同时覆盖高温和低温区间的 HyChem 模型。具体而言，我们利用覆盖广泛温度和当量比范围的点火延迟时间实验数据集开发 HyChem 模型。化学动力学模型被视为神经网络模型，随后采用为深度学习开发的随机梯度下降（SGD）技术进行训练。我们在为 F-24（一种衍生自 Jet-A 的燃料）学习 HyChem 模型时演示了该方法，并将结果与先前采用遗传算法的工作进行比较。结果表明，SGD 方法能够取得与遗传算法相当的模型性能，但计算成本降低了1000倍。此外，借助 SGD 中的正则化，SGD 方法对动力学参数原始值的改变远小于遗传算法，因此更有可能保留机理含义。最后，我们的方法建立在开源软件包之上，可用于内燃机模拟中化学动力学模型的开发和优化。
英文摘要：
The HyChem approach has recently been proposed for modeling high-temperature combustion of real, multi-component fuels. The approach combines lumped reaction steps for fuel thermal and oxidative pyrolysis with detailed chemistry for the oxidation of the resulting pyrolysis products. However, the approach usually shows substantial discrepancies with experimental data within the Negative Temperature Coefficient (NTC) regime, as the low-temperature chemistry is more fuel-specific than high-temperature chemistry. This paper proposes a machine learning approach to learn the HyChem models that can cover both high-temperature and low-temperature regimes. Specifically, we develop a HyChem model using the experimental datasets of ignition delay times covering a wide range of temperatures and equivalence ratios. The chemical kinetic model is treated as a neural network model, and we then employ stochastic gradient descent (SGD), a technique that was developed for deep learning, for the training. We demonstrate the approach in learning the HyChem model for F-24, which is a Jet-A derived fuel, and compare the results with previous work employing genetic algorithms. The results show that the SGD approach can achieve comparable model performance with genetic algorithms but the computational cost is reduced by 1000 times. In addition, with regularization in SGD, the SGD approach changes the kinetic parameters from their original values much less than genetic algorithm and is thus more likely to retrain mechanistic meanings. Finally, our approach is built upon open-source packages and can be applied to the development and optimization of chemical kinetic models for internal combustion engine simulations.
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