发表机构
Institute of Turbomachinery, Xi’an Jiaotong University（西安交通大学叶轮机械研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对涡轮机械多保真度优化中序贯采样导致最终解劣于单保真度的问题，提出MSFO算法，结合全局多保真度代理与局部单保真度代理，在基准函数及涡轮气动与冷却优化中表现更优。
AI 中文摘要
涡轮机械设计优化涉及昂贵的黑箱问题。样本高效的多保真度优化（MFO）提供了一种有效的解决方案。通过利用多保真度代理模型（MFS），MFO算法可以在低保真度样本的辅助下，使用较少的高保真度样本来建立精确的代理模型。然而，当MFS用于序贯采样优化时，观察到最终由单保真度优化（SFO）获得的最优解优于MFO，尽管MFO在早期阶段表现更好。这归因于样本均匀且嵌套分布的假设，而在使用序贯添加策略时该假设是不正确的。为了解决这些问题，我们提出了一种称为多单保真度优化（MSFO）的新算法，以克服传统MFO流程的局限性。在MSFO的代理模型建立中，我们使用基于密度的噪声应用空间聚类（DBSCAN）方法来检测低保真度样本不再有效的局部区域。将全局MFS与仅使用高保真度样本构建的局部单保真度代理模型相结合，建立一个集成模型，这提高了算法对误导性低保真度数据的抗干扰能力。MSFO算法的有效性首先在数值基准函数上得到验证。然后，该算法被用于优化涡轮的气动外形和涡轮端壁的薄膜冷却布局设计。在此，高保真度样本来源为细网格CFD模拟，而低保真度样本来源为相同模拟在较粗网格上的运行结果。结果表明，我们的MSFO算法明显优于传统的SFO和MFO过程，且具有更高的鲁棒性。
英文摘要
Turbomachinery design optimization involves expensive black-box problems. Sample-efficient multi-fidelity optimization (MFO) offers an efficient solution. By utilizing multi-fidelity surrogates (MFS), the MFO algorithm can use fewer high-fidelity samples aided by low-fidelity samples to establish an accurate surrogate model. However, when MFS is used in sequential sampling optimization, it has been observed that the final optimal solution obtained by single-fidelity optimization (SFO) is better than that of MFO, even though MFO performs better at the early stages. This can be attributed to the assumption of an even and nested distribution of samples, which is incorrect when using a sequential adding strategy. To address these issues, we propose a novel algorithm called multi-single-fidelity optimization (MSFO) to overcome the limitations of the conventional MFO procedures. In the surrogate establishment of MSFO, we use the density-based spatial clustering of applications with noise (DBSCAN) method to detect local areas where low-fidelity samples are no longer effective. A combination of both global MFS and local single-fidelity surrogate model, built using high-fidelity samples alone, is used to establish an ensemble, which improves the anti-interference ability of the algorithm against misleading low-fidelity data. The effectiveness of the MSFO algorithm is verified first on numerical benchmark functions. Then, the algorithm is used to optimize the aerodynamic profile of a turbine and the film cooling layout design of a turbine endwall. Here, high-fidelity sample sources are obtained from fine-mesh CFD simulations, whereas low-fidelity sample sources are obtained from the same simulations run on a coarser mesh. The results demonstrate that our MSFO algorithm performs significantly better than the conventional SFO and MFO processes, with a higher level of robustness.
CommentsConference author manuscript; 10 pages, 12 figures. Related journal article: DOI 10.1115/1.4064228
Journal refProceedings of ASME Turbo Expo 2023, Volume 13D, V13DT34A023 (2023)
