发表机构
Institute of Turbomachinery, Xi’an Jiaotong University（西安交通大学涡轮机研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多保真度代理模型在涡轮优化后期因低保真度样本干扰而性能下降的问题，提出集成加权多保真度代理模型（EMFS）及MSFO算法，结合DBSCAN检测并构建局部单保真度代理，经GE-E3叶片和端壁冷却设计验证有效性。
AI 中文摘要
为了高效解决涡轮设计优化问题，常采用基于代理模型的优化（SBO）算法。为进一步降低涡轮设计成本，研究者提出了基于多保真度代理模型（MFS）的优化方法，该方法在代理建模和优化过程中，利用大量低保真度（LF）但廉价的样本，来扩充少量昂贵的高保真度（HF）样本。然而，根据我们的观察，基于MFS的优化有时仅在优化过程早期具有更好的收敛速度，但最终解的质量却不如仅使用高保真度样本的单保真度代理模型（SFS）优化。其原因可解释如下：随着优化过程中HF样本的增加，LF样本可能产生负面影响，从而误导优化搜索。为解决上述问题，提出了一种集成加权多保真度代理模型（EMFS）。具体而言，使用基于密度的噪声应用空间聚类（DBSCAN）来检测MFS无法构建更精确代理模型的区域，并在该区域构建局部SFS。然后，通过将MFS和SFS以自适应权重结合，构建EMFS，用于指导优化过程。相关算法被命名为多保真度和单保真度代理融合优化，即MSFO。通过在GE-E3叶片优化和涡轮端壁气膜冷却布局设计上的测试，所提出的MSFO的有效性得到了充分验证。
英文摘要
To solve the turbine design optimization problems efficiently, surrogate-based optimization (SBO) algorithms are frequently used. To further reduce the cost of turbine design, the multi-fidelity surrogate (MFS) based optimization is proposed by the researchers, who resort to augmenting the small number of expensive high-fidelity (HF) samples by a large portion of low-fidelity (LF) but cheap samples in surrogate modeling and optimization process. Nonetheless, according to our observations, the MFS based optimization sometimes can only have better convergence rate at the early stage of optimization process, but yielding worse final solution than the single-fidelity surrogate (SFS) based optimization that uses high-fidelity samples alone. The reason behind can be explained as follows. With the increase of HF samples in the optimization process, the LF samples can cause negative effect and therefore misleading the optimization search. To address the above issue, an ensemble weighted multi-fidelity surrogate (EMFS) is proposed. Specifically, the density-based spatial clustering of applications with noise (DBSCAN) is used to detect the region where the MFS cannot build a more accurate surrogate, and a local SFS is built there. Then, an EMFS is built by combining the MFS and SFS with adaptive weights, which is used to guide the optimization process. The related algorithm is named as multi- and single-fidelity surrogate fused optimization, i.e., MSFO. Through tests on GE-E3 blade optimization and the film cooling layout design of a turbine endwall, the effectiveness of proposed MSFO is well demonstrated.
CommentsJournal author manuscript; 12 pages, 13 figures. Related conference article: DOI 10.1115/GT2023-104237
Journal refJournal of Turbomachinery 146(4), 041011 (2024)
