Development of a novel multi-fidelity meta modeling approach for robust multi-objective optimization of a natural gas-hydrogen/diesel dual fuel engine

Youcef Sehili, Mahfoudh Cerdoun, L. Tarabet, Khaled Loubar, Clément Lacroix
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Abstract

Multi-fidelity modeling (MFM) is an evolving field that matches low-fidelity models (LFM) and high-fidelity models (HFM) to get better solutions with low computational cost. However, improving the duality between accuracy and computational cost remain challenging, particularly for complex problems such as dual fuel engines. This paper contributes to the MF modeling cost-effectiveness improvement by proposing a new approach to solve large-dimensional multi-objective optimization problems. The first step is to build a meta-model based on the LF model, which will be subjected to a comet-governed analysis to detect potential areas where the uncertainty on the LF model is relatively high. Then, a design of experiment (DOE) will be developed based on the results of this analysis to construct an initial HF model. Finally, an iterative loop will be activated to improve the accuracy of the MF model using a well-weighed combination of the details delivered by the LF model correction via the HF model and the HF meta-model. The developed approach is validated on four different mathematical benchmarks with different difficulties, compared with four different MF modeling strategies. This validation shows that the proposed MF modeling is competitive and can produce solutions as accurate as the HF model while reducing significantly the overall computation time by up to 50%. As an engineering application, the operating conditions in a natural gas-hydrogen/diesel dual fuel engine in terms of compression ratio, pilot injection timing, and EGR are optimized. A reduction of 46%, 68%, and 96% was achieved for HC, NOx, and the knocking index, respectively, while an increase in thermal efficiency of about 5% was obtained.
为天然气-氢气-柴油双燃料发动机的稳健多目标优化开发新型多保真元建模方法
多保真度建模(MFM)是一个不断发展的领域,它将低保真度模型(LFM)和高保真度模型(HFM)相匹配,从而以较低的计算成本获得更好的解决方案。然而,提高精确度和计算成本之间的二元性仍然具有挑战性,特别是对于双燃料发动机等复杂问题。本文提出了一种解决大维度多目标优化问题的新方法,有助于提高 MF 建模的成本效益。第一步是在 LF 模型的基础上建立元模型,并对其进行彗星控制分析,以检测 LF 模型不确定性相对较高的潜在区域。然后,根据分析结果制定实验设计(DOE),构建初始高频模型。最后,将启动一个迭代循环,通过高频模型和高频元模型对低频模型修正所提供的细节进行综合权衡,从而提高高频模型的准确性。所开发的方法在四个不同难度的数学基准上进行了验证,并与四个不同的中频建模策略进行了比较。验证结果表明,所提出的 MF 建模具有竞争力,可以生成与高频模型一样精确的解决方案,同时将总体计算时间大幅减少 50%。在工程应用中,对天然气-氢气/柴油双燃料发动机的压缩比、先导喷射正时和 EGR 等运行条件进行了优化。HC、NOx 和爆震指数分别降低了 46%、68% 和 96%,热效率提高了约 5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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