【Applied Energy最新原创论文】用于无人机的小型轴流压气机的不确定性量化与鲁棒优化

AEii国际应用能源 · 公众号 · · 2023-10-16 18:30

正文

原文信息：

Robust optimization and uncertainty quantification of a micro axial compressor for unmanned aerial vehicles

原文链接：

https://www.sciencedirect.com/science/article/pii/S0306261923011911

Highlights

(1) 基于真实压气机的几何数据建立几何不确定性模型。

(2) 建立了针对微型燃气轮机压气机的不确定性量化和鲁棒性优化的框架。

(3) 采用SOM神经网络一定程度可视化和验证了不确定变量和性能响应之间的相关性。

(4) 构建的SOM-RBF-NSGA-III模型成功地实现了压气机多目标鲁棒优化。

(5) 对压气机在三种工况下的气动性能同时进行了鲁棒性优化。

摘要

轴流压气机在制造和运行过程中容易受到不确定性的影响，使得效率降低和性能分散。然而，由于几何结构和内部流动的复杂性，压气机的不确定性量化和鲁棒性优化设计仍然具有挑战性。本研究提出了一种用于微型轴流压气机的不确定性量化和鲁棒性优化的方法。对于设计点和两个非设计点（近失速点和近堵点），分别针对十个几何不确定性进行分析。主要目标是优化多工况下压气机的气动鲁棒性能，采用基于稀疏网格的概率配置方法来传播和量化这些不确定性，再利用构建的代理模型和多目标遗传算法来进行鲁棒性优化设计。

结果表明，最优的压气机构型在整个特性线上实现了平均气动性能和气动鲁棒性能的双重提升，且在设计工况展现出了相比于非设计工况更高的性能提升。在设计工况，最优构型的平均等熵效率和压比分别提高了0.6%和0.5%，等熵效率、压比和质量流量的标准差分别降低了32.4%、41.2%，和25.1%。该优化设计的框架被证明是可行且高效的，可应用于叶轮机械的气动鲁棒优化。后续将尝试把这个框架应用到燃气轮机生命周期的不同方面，对更高维的不确定性进行建模和分析。

更多关于"unmanned aerial vehicles"的研究请见：

https://www.sciencedirect.com/search?qs=unmanned%20aerial%20vehicles&pub=applied%20energy

Abstr act

Axial compressors are susceptible to uncertainties during their manufacturing and operation, resulting in reduced efficiency and performance dispersion. However, uncertainty quantification and robust design of compressors remains challenging due to the complexity of structure and internal flow. In this study, an automated framework for uncertainty quantification and robustness optimization of micro axial compressors is proposed. Ten geometrical uncertainties are propagated for the nominal design point and two off-design points, i. e., near stall and choke conditions, respectively. The main objective of this paper is to optimize the aerodynamic robustness performance at these operating points. The sparse grid-based probabilistic collocation method is used to propagate these uncertainties, and a multi-objective genetic algorithm is employed to perform robust optimization based on a novel constructed surrogate model.

The results show that the optimal configuration achieves an improvement in aerodynamic robustness and mean performance across the entire characteristic map, with greater improvement at the design working point than at the off-design points. At the design working point, the mean isentropic efficiency and pressure ratio of the optimal configuration increase by 0.6% and 0.5%, respectively, while the standard deviation of isentropic efficiency, pressure ratio, and mass flow rate decreases by 32.4%, 41.2%, and 25.1%, respectively. This optimization framework proves to be both feasible and efficient and can be applied to aerodynamic robust optimization of turbomachinery. In the future, we will apply this framework to different aspects of the gas turbine life cycle to model and analyze uncertainties of larger orders of magnitude.

Keywords

Micro transonic compressor

Geometric uncertainties

Uncertainty quantification

Surrogate model

Aerodynamic robustness optimization

Multiple working points

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