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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace12040341