Problem
Research questions and friction points this paper is trying to address.
Turbomachinery Design Optimization
Multi-fidelity Optimization
Single-fidelity Optimization
Sequential Sampling
A Novel Multi-fidelity Surrogate for Turbomachinery Design Optimization
🤖 AI Summary
针对涡轮机械设计优化中的昂贵黑箱问题，提出了一种新的多单保真度优化算法（MSFO），通过结合全局多保真度代理模型和局部单保真度模型来改善传统多保真度优化方法的局限性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-single-fidelity optimization (MSFO)
density-based spatial clustering of applications with noise (DBSCAN)
global multi-fidelity surrogates (MFS)
local single-fidelity surrogate model
anti-interference ability
🔎 Similar Papers
No similar papers found.
