过程设备的数据驱动代理建模和基准测试

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2020-03-13 DOI:10.1017/dce.2020.8
G. Gonçalves, A. Batchvarov, Yuyi Liu, Yuxin Liu, L. Mason, Indranil Pan, O. Matar
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引用次数: 6

摘要

摘要在化工过程工程中,复杂系统的代理模型常常用于领域探索、设计参数敏感性分析和优化等任务。一套面向化工过程设备建模的计算流体动力学(CFD)模拟已经开发出来,并通过文献中的实验结果进行了验证。在有限的功能评估预算约束下,利用CFD仿真器在环上探索了各种基于回归的主动学习策略。具体来说,考虑到一组具有不同工业意义和复杂性的四个测试用例,比较了五种不同的抽样策略和五种回归技术。观察到高斯过程回归在这些应用中具有一致的良好性能。目前的定量研究概述了不同可用技术的优点和缺点,并强调了采用这些技术的最佳实践。测试用例和工具可以通过开源许可获得,以确保可重复性,并吸引更广泛的研究社区为CFD模型做出贡献,并为该领域量身定制的新改进算法开发和基准测试。此处提供的建议可用于有兴趣为流体系统设计或优化建立计算成本低廉的替代模型的工程师。研究人员可以使用这些测试用例来测试和测试针对这类问题的主动学习的新算法。为了支持派生工作,提供了一个带有工具和脚本的开源库。
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Data-driven surrogate modeling and benchmarking for process equipment
Abstract 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. Impact Statement The recommendations provided here can be used for engineers interested in building computationally inexpensive surrogate models for fluid systems for design or optimization purposes. The test cases can be used by researchers to test and benchmark new algorithms for active learning for this class of problems. An open-source library with tools and scripts has been provided in order to support derived work.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
期刊最新文献
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