A CFD-based methodology for aerodynamic-aeroacoustic shape optimization of airfoils

Ricks, Nathan; Tsirikoglou, Panagiotis; Contino, Francesco; Ghorbaniasl, Ghader

doi:10.2514/6.2020-1729

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Although a thinner airfoil is expected to be more silent, the second constraint (T hickness ≤ T hickness 0 ) limits the thickness of the airfoil to avoid being thicker than the benchmark airfoil. This is required to ensure structural capabilities/resistance in the final geometry [16]. The third constraint min(y upper (x) − y lower (x)) > 0 ; 0 < x < 1, ensures that the upper and lower surfaces do not cross each other (e.g., negative volume shapes).…”

Section: Problem Formulation: Optimization Objective and Constraintsmentioning

confidence: 99%

“…There, XFOIL is combined with Amiet's model and evolutionary optimization to design airfoils with less noise while maintaining the required lift. Ricks et al [16] proposed multiobjective aerodynamic-aeroacoustic shape optimization of airfoils based on a Reynolds-averaged Navier-Stokes solver with a state-of-the-art wall pressure spectrum model and Amiet's model for trailing edge noise. Bu et al [17] developed the framework for aerodynamic/aeroacoustic variable-fidelity optimization of helicopter rotor based on hierarchical Kriging model.…”

Section: Introductionmentioning

confidence: 99%

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Aeroacoustic Airfoil Shape Optimization Enhanced by Autoencoders

et al. 2022

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Section: Problem Formulation: Optimization Objective and Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aeroacoustic Airfoil Shape Optimization Enhanced by Autoencoders

et al. 2022

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Extension and Validation of the Turbomachinery Capabilities of SU2 Open Source Computational Fluid Dynamic Code

Yan,

Wang,

et al. 2024

Journal of Turbomachinery

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Computational Fluid Dynamic (CFD) tools have revolutionized the way to design engineering systems, but most established codes are proprietary and closed-source, making it difficult, if not impossible, to modify, debug or add new features to the code. To provide a freely available open-source CFD code for turbomachinery aerodynamics and aeroelasticity, this paper enhances the turbomachinery capabilities of the open-source SU2 code and demonstrates its capabilities of single-passage steady simulation, full-annulus unsteady simulation and aeroelasticity analysis in two high-speed compressors, namely NASA Stage 35 and TUDa-GLR-OpenStage, and a linear cascade SC1. For the steady simulation of NASA Stage 35, the SU2 results are validated against the measured data and verified against the commercial solver Ansys CFX, and the performance characteristics results are in reasonably good agreement with each other. For the steady simulation of TUDa-GLR-OpenStage, grid and turbulence model sensitivity studies are performed and results are validated against the measured data, and SU2 can predict both the performance characteristics and the radial profiles with sufficient accuracy. For the unsteady simulation of NASA Stage 35, it is demonstrated that SU2 can predict the propagation of inlet distortion equally well as Ansys CFX. For the linear cascade, SU2 can predict the unsteady pressure and aerodynamic damping coefficient accurately. The presented results demonstrate the turbomachinery aerodynamics and aeroelasticity capabilities of SU2. The major modifications of SU2 made in this work will be shared with the code maintainer and the community in the future.

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Modification of the Class-Shape-Transformation Parameterization Based on Radial Basis Functions

Du,

Lu,

Guo

et al. 2024

Journal of Aircraft

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To improve the capacity for utilizing a higher number of design variables and handling complex geometries in the class-shape-transformation parameterization method, this paper introduces an alternative for the shape function that employs radial basis functions to interpolate the deformations of the control points throughout the entire shape function. Two representative airfoils are parameterized first, and the results indicate that the presented method can avoid the ill-conditioning problem encountered when using high-order Bernstein polynomials in the traditional method. Moreover, it is more effective in parameterizing complex geometries. A comprehensive test on a database containing 1433 airfoils reveals that the presented method can cover a larger design space with an increase in the number of design variables. The presented method is further employed and validated in two typical aerodynamic shape optimization tasks: an airfoil inverse design and a benchmark problem for drag minimization in transonic viscous flow. The results suggest that the presented method has potential advantages in gaining better optimized results when more design variables are employed.

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A CFD-based methodology for aerodynamic-aeroacoustic shape optimization of airfoils

Cited by 4 publications

References 19 publications

Aeroacoustic Airfoil Shape Optimization Enhanced by Autoencoders

Aeroacoustic Airfoil Shape Optimization Enhanced by Autoencoders

Extension and Validation of the Turbomachinery Capabilities of SU2 Open Source Computational Fluid Dynamic Code

Modification of the Class-Shape-Transformation Parameterization Based on Radial Basis Functions

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