Aerodynamic inverse design using multifidelity models and manifold mapping

Ren, Jie; Leifsson, Leifur

doi:10.1016/j.ast.2018.12.008

Cited by 36 publications

(11 citation statements)

References 47 publications

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“…These polynomial approaches that are mathematically interpretable were widely used to parameterize airfoils. However, there have been some frontier studies which show that curves or surfaces of an airfoil exist in manifold space [14,15]. Hence, existing polynomial approaches can only capture features from Euclidean space, and some latent features (e.g., geometric-features from manifold space) are omitted.…”

Section: Related Workmentioning

confidence: 99%

An airfoil geometric-feature extraction and discrepant data fusion learning method

Xiang

Zhang

et al. 2022

Preprint

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The perception of geometric-features of airfoils is the basis in aerodynamic area for performance prediction, parameterization, aircraft inverse design, etc. There are three approaches to percept the geometric shape of airfoils, namely manual design of airfoil geometry parameters, polynomial definition and deep learning. The first two methods directly define geometric-features or polynomials of airfoil curves, but the number of extracted features is limited. Deep learning algorithms can extract a large number of potential features (called latent features). However, the features extracted by deep learning lack explicit geometrical meaning. Motivated by the advantages of polynomial definition and deep learning, we propose a geometric-feature extraction method (named Bézier-based feature extraction, BFE) for airfoils, which consists of two parts: manifold metric feature extraction and geometric-feature fusion encoder (GF encoder). Manifold metric feature extraction, with the help of the Bézier curve, captures manifold metrics (a sort of geometric-features) from tangent space of airfoil curves, and the GF-encoder combines airfoil coordinate data and manifold metrics together to form novel fused geometric-features. To validate the feasibility of the fused geometric-features, two experiments based on the public UIUC airfoil dataset are conducted. Experiment I is used to extract manifold metrics of airfoils and export the fused geometric-features. Experiment II, based on the Multi-task learning (MTL), is used to fuse the discrepant data (i.e., the fused geometric-features and the flight conditions) to predict the aerodynamic performance of airfoils. The results show that the BFE can generate more smooth and realistic airfoils than Auto-Encoder, and the fused geometric-features extracted by BFE can be used to reduce the prediction errors of C L and C D .

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Section: Related Workmentioning

confidence: 99%

An airfoil geometric-feature extraction and discrepant data fusion learning method

Xiang

Zhang

et al. 2022

Preprint

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“…The MM method is typically employed within a trust-region (Du et al, 2019) based optimization framework (Alexandrov et al, 1998), where in every iteration the objective function is optimized using the MM model in the current trust-region. As the optimization continues to the HF optimum, the MM model is corrected based on all the gathered data.…”

Section: Manifold Mappingmentioning

confidence: 99%

“…As the optimization continues to the HF optimum, the MM model is corrected based on all the gathered data. It should be noted that the MM modeling technique can be efficiently used without the availability of exact gradient information (Du et al, 2019), and still has shown to converge to a local optimum (Echeverr ıa, 2007; Echeverr ıa and Hemker, 2008;Siegler et al, 2016).…”

Section: Manifold Mappingmentioning

confidence: 99%

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Multifidelity modeling similarity conditions for airfoil dynamic stall prediction with manifold mapping

Raul

Leifsson

2021

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PurposeThe purpose of this work is to investigate the similarity requirements for the application of multifidelity modeling (MFM) for the prediction of airfoil dynamic stall using computational fluid dynamics (CFD) simulations.Design/methodology/approachDynamic stall is modeled using the unsteady Reynolds-averaged Navier–Stokes equations and Menter's shear stress transport turbulence model. Multifidelity models are created by varying the spatial and temporal discretizations. The effectiveness of the MFM method depends on the similarity between the high- (HF) and low-fidelity (LF) models. Their similarity is tested by computing the prediction error with respect to the HF model evaluations. The proposed approach is demonstrated on three airfoil shapes under deep dynamic stall at a Mach number 0.1 and Reynolds number 135,000.FindingsThe results show that varying the trust-region (TR) radius (λ) significantly affects the prediction accuracy of the MFM. The HF and LF simulation models hold similarity within small (λ ≤ 0.12) to medium (0.12 ≤ λ ≤ 0.23) TR radii producing a prediction error less than 5%, whereas for large TR radii (0.23 ≤ λ ≤ 0.41), the similarity is strongly affected by the time discretization and minimally by the spatial discretization.Originality/valueThe findings of this work present new knowledge for the construction of accurate MFMs for dynamic stall performance prediction using LF model spatial- and temporal discretization setup and the TR radius size. The approach used in this work is general and can be used for other unsteady applications involving CFD-based MFM and optimization.

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“…These polynomial approaches that are mathematically interpretable were widely used to parameterize airfoils. However, there have been some frontier studies which show that curves or surfaces of an airfoil exist in manifold space [13,14]. Hence, existing polynomial approaches can only capture features from Euclidean space, some latent features e.g.…”

Section: Related Workmentioning

confidence: 99%

A Geometry-Based Deep Learning Feature Extraction Scheme for Airfoils

Xiang¹,

Hu²,

Zhang³

et al. 2021

Preprint

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The perception of geometry-features of airfoils is the basis in aerodynamic area for performance prediction, parameterization, aircraft inverse design, etc. There are three approaches to percept the geometric shape of an airfoil, namely manual design of airfoil geometry parameter, polynomial definition and deep learning. The first two methods can directly extract geometry-features of airfoils or polynomial equations of airfoil curves, but the number of features extracted is limited. While deep learning algorithms can extract a large number of potential features (called latent features), however, the features extracted by deep learning are lacking of explicit geometrical meaning. Motivated by the advantages of polynomial definition and deep learning, we propose a geometry-based deep learning feature extraction scheme (named Bézier-based feature extraction, BFE) for airfoils, which consists of two parts: manifold metric feature extraction and geometry-feature fusion encoder (GF encoder). Manifold metric feature extraction, with the help of the Bézier curve, captures features from tangent space of airfoil curves, and GF encoder combines airfoil coordinate data and manifold metrics together to form a novel feature representation. A public UIUC airfoil dataset is used to verify the proposed BFE. Compared with classic Auto-Encoder, the mean square error (MSE) of BFE is reduced by 17.97% ~29.14%.

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Aerodynamic inverse design using multifidelity models and manifold mapping

Cited by 36 publications

References 47 publications

An airfoil geometric-feature extraction and discrepant data fusion learning method

An airfoil geometric-feature extraction and discrepant data fusion learning method

Multifidelity modeling similarity conditions for airfoil dynamic stall prediction with manifold mapping

A Geometry-Based Deep Learning Feature Extraction Scheme for Airfoils

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