In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization. A suite of computational fluid dynamics (CFD) simulations geared toward chemical process equipment modeling has been developed and validated with experimental results from the literature. Various regression-based active learning strategies are explored with these CFD simulators in-the-loop under the constraints of a limited function evaluation budget. Specifically, five different sampling strategies and five regression techniques are compared, considering a set of four test cases of industrial significance and varying complexity. Gaussian process regression was observed to have a consistently good performance for these applications. The present quantitative study outlines the pros and cons of the different available techniques and highlights the best practices for their adoption. The test cases and tools are available with an open-source license to ensure reproducibility and engage the wider research community in contributing to both the CFD models and developing and benchmarking new improved algorithms tailored to this field.

中文翻译：

---

过程设备的数据驱动替代建模和基准测试

在化学过程工程中，复杂系统的替代模型通常是领域探索、设计参数敏感性分析和优化任务所必需的。已经开发了一套面向化学工艺设备建模的计算流体动力学 (CFD) 模拟，并使用文献中的实验结果进行了验证。在有限功能评估预算的约束下，使用这些 CFD 模拟器在环中探索了各种基于回归的主动学习策略。具体而言，比较了五种不同的抽样策略和五种回归技术，考虑到一组具有工业意义和不同复杂性的四个测试用例。观察到高斯过程回归对这些应用程序具有始终如一的良好性能。本定量研究概述了不同可用技术的优缺点，并重点介绍了采用这些技术的最佳实践。测试用例和工具具有开源许可证，以确保可重复性并让更广泛的研究社区参与为 CFD 模型做出贡献，并为该领域量身定制的新改进算法的开发和基准测试。