Problem
Research questions and friction points this paper is trying to address.
turbine design optimization
multi-fidelity surrogate
single-fidelity surrogate
convergence rate
A Novel Multi-fidelity Surrogate for Efficient Turbine Design Optimization
🤖 AI Summary
为解决涡轮设计优化问题，提出了一种基于密度的多保真度集成代理模型（EMFS），通过结合高、低保真度样本并自适应加权，提高了优化效率和准确性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
ensemble weighted multi-fidelity surrogate
DBSCAN
adaptive weights
multi- and single-fidelity surrogate fused optimization
MSFO
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