In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computational cost associated with engine design optimization. However, such methods still suffer from drawbacks. One main disadvantage is that the default machine learning (ML) hyperparameters are often severely suboptimal for a given problem. This has often been addressed by manually trying out different hyperparameter settings, but this solution is ineffective in case of a high-dimensional hyperparameter space. Besides this problem, the amount of data needed for training is also not known a priori. In response to these issues that need to be addressed, the present work describes and validates an automated active learning approach, AutoML-GA, for surrogate-based optimization of internal combustion engines. In this approach, a Bayesian optimization technique is used to find the best machine learning hyperparameters based on an initial dataset obtained from a small number of CFD simulations. Subsequently, a genetic algorithm is employed to locate the design optimum on the ML surrogate surface. In the vicinity of the design optimum, the solution is refined by repeatedly running CFD simulations at the projected optima and adding the newly obtained data to the training dataset. It is demonstrated that AutoML-GA leads to a better optimum with a lower number of CFD simulations, compared to the use of default hyperparameters. The proposed framework offers the advantage of being a more hands-off approach that can be readily utilized by researchers and engineers in industry who do not have extensive machine learning expertise.

近年来，使用基于机器学习的替代模型进行计算流体动力学 (CFD) 模拟已成为降低与发动机设计优化相关的计算成本的有前途的技术。然而，这样的方法仍然存在缺陷。一个主要缺点是默认的机器学习 (ML) 超参数对于给定问题通常严重次优。这通常通过手动尝试不同的超参数设置来解决，但这种解决方案在高维超参数空间的情况下是无效的。除了这个问题，训练所需的数据量也不是先验的。针对这些需要解决的问题，目前的工作描述并验证了一种自动化主动学习方法 AutoML-GA，用于基于代理的内燃机优化。在这种方法中，贝叶斯优化技术用于基于从少量 CFD 模拟中获得的初始数据集来寻找最佳机器学习超参数。随后，采用遗传算法在 ML 代理曲面上定位设计最优值。在设计最优值附近，通过在预计最优值处重复运行 CFD 模拟并将新获得的数据添加到训练数据集来改进解决方案。事实证明，与使用默认超参数相比，AutoML-GA 以较少的 CFD 模拟次数获得了更好的优化。