Efficient Adaptive Robust Aerodynamic Design Optimization Considering Uncertain Inflow Variations for a Diffusion Airfoil Across All Operating Incidences

The random fluctuations in inlet flow represent a common uncertainty in aero-engine compressors, necessitating the control of its effects through blade optimization design. To account for the impact of inlet flow fluctuations on performance in blade design optimization, an efficient multi-objective...

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Published in:	Aerospace Vol. 12; no. 4; p. 341
Main Authors:	Guo, Zhengtao, Bao, Lei, Li, Chaolong, Gao, Xianzhong, Chu, Wuli
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.04.2025
Subjects:	Accuracy adaptive robust aerodynamic design optimization Aerodynamics Aircraft Airfoils Approximation Business metrics compressor blade Compressors Costs Data analysis Design optimization Efficiency Fluctuations Gaussian process Genetic algorithms Inlet flow inlet flow variations Inlets Methods Multiple objective analysis Polynomials Robust design Signal processing Simulation sparse polynomial chaos Statistical analysis uncertainty quantification Massachusetts
ISSN:	2226-4310, 2226-4310
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Abstract	The random fluctuations in inlet flow represent a common uncertainty in aero-engine compressors, necessitating the control of its effects through blade optimization design. To account for the impact of inlet flow fluctuations on performance in blade design optimization, an efficient multi-objective adaptive robust aerodynamic design optimization (ARADO) method is proposed. The optimization method employs a novel sparse polynomial chaos expansion (PCE) and the advanced noisy Gaussian process regression (NGPR) technique is used to establish an initial stochastic surrogate model (SSM) containing statistical moments of aerodynamic performance. By introducing advanced sparse signal processing concepts, the sparce PCE significantly enhances the efficiency of acquiring each training sample for SSM. During the optimization process, the initial SSM autonomously updates based on historical optimization data, without requiring high precision across the entire design space. Compared to traditional model-based aerodynamic robust optimizations, the proposed ARADO method exhibits a faster convergence speed and achieves a superior average level of the optimal solution set. It also better balances various optimization objectives, concentrating the space distribution of optimal solutions closer to the average level. Ultimately, the ARADO is applied to the aerodynamic robust design of a high-load compressor airfoil across all operating incidences. The optimization results enhance aerodynamic performance while reducing performance diversity, thus aligning more closely with practical engineering requirements. Through data analysis of the optimal solutions, robust design guidelines for blade aerodynamic shapes are obtained, along with insights into the flow mechanisms that enhance aerodynamic robustness.
AbstractList	The random fluctuations in inlet flow represent a common uncertainty in aero-engine compressors, necessitating the control of its effects through blade optimization design. To account for the impact of inlet flow fluctuations on performance in blade design optimization, an efficient multi-objective adaptive robust aerodynamic design optimization (ARADO) method is proposed. The optimization method employs a novel sparse polynomial chaos expansion (PCE) and the advanced noisy Gaussian process regression (NGPR) technique is used to establish an initial stochastic surrogate model (SSM) containing statistical moments of aerodynamic performance. By introducing advanced sparse signal processing concepts, the sparce PCE significantly enhances the efficiency of acquiring each training sample for SSM. During the optimization process, the initial SSM autonomously updates based on historical optimization data, without requiring high precision across the entire design space. Compared to traditional model-based aerodynamic robust optimizations, the proposed ARADO method exhibits a faster convergence speed and achieves a superior average level of the optimal solution set. It also better balances various optimization objectives, concentrating the space distribution of optimal solutions closer to the average level. Ultimately, the ARADO is applied to the aerodynamic robust design of a high-load compressor airfoil across all operating incidences. The optimization results enhance aerodynamic performance while reducing performance diversity, thus aligning more closely with practical engineering requirements. Through data analysis of the optimal solutions, robust design guidelines for blade aerodynamic shapes are obtained, along with insights into the flow mechanisms that enhance aerodynamic robustness.
Audience	Academic
Author	Chu, Wuli Bao, Lei Li, Chaolong Gao, Xianzhong Guo, Zhengtao
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SubjectTerms	Accuracy adaptive robust aerodynamic design optimization Aerodynamics Aircraft Airfoils Approximation Business metrics compressor blade Compressors Costs Data analysis Design optimization Efficiency Fluctuations Gaussian process Genetic algorithms Inlet flow inlet flow variations Inlets Methods Multiple objective analysis Polynomials Robust design Signal processing Simulation sparse polynomial chaos Statistical analysis uncertainty quantification
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Title	Efficient Adaptive Robust Aerodynamic Design Optimization Considering Uncertain Inflow Variations for a Diffusion Airfoil Across All Operating Incidences
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