Natural Laminar-Flow Airfoil Optimization Design Using a Discrete Adjoint Approach

Shi, Yayun; Mader, Charles A.; He, Sicheng; Halila, Gustavo Luiz Olichevis; Martins, Joaquim R. R. A.

doi:10.2514/1.j058944

Cited by 60 publications

(10 citation statements)

References 58 publications

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“…For example, laminar-turbulent transition dominates the aerodynamic performance in low-Reynolds-number aerodynamic shape optimization. To capture the flow transition, the simulation model includes functions that do not have continuous derivatives, which introduces difficulties to the adjoint implementation [113]. This issue in evaluating aerodynamic derivatives causes optimization difficulties.…”

Section: Existing Challengesmentioning

confidence: 99%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

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Large volumes of experimental and simulation aerodynamic data have been rapidly advancing aerodynamic shape optimization (ASO) via machine learning (ML), whose effectiveness has been growing thanks to continued developments in deep learning. In this review, we first introduce the state of the art and the unsolved challenges in ASO. Next, we present a description of ML fundamentals and detail the ML algorithms that have succeeded in ASO. Then we review ML applications contributing to ASO from three fundamental perspectives: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands like interactive design optimization. However, practical large-scale design optimizations remain a challenge due to the costly ML training expense. A deep coupling of ML model construction with ASO prior experience and knowledge, such as taking physics into account, is recommended to train ML models effectively.

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Section: Existing Challengesmentioning

confidence: 99%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

Self Cite

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“…It is evident that aerofoil geometry model selection is fundamental for the whole process of optimisation [19]. One way to characterize the aerofoil geometry is to use control points.…”

Section: Aerofoil Geometry Model Selectionmentioning

confidence: 99%

Improving Initial Aerofoil Geometry Using Aerofoil Particle Swarm Optimisation

Müller

2022

mendel

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Advanced optimisation of the aerofoil wing of a general aircraft is the main subject of this paper. Meta-heuristic optimisation techniques, especially swarm algorithms, were used. Subsequently, a new variant denoted as aerofoil particle swarm optimisation (aPSO) was developed from the original particle swarm optimisation (PSO). A parametric model based on B-spline was used to optimise the initial aerofoil. The simulation software Xfoil was calculating basic aerodynamic features (lift, drag, moment).

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“…Natural laminar-flow airfoils and supercritical airfoils show promising potential for reducing fuel consumption and emissions in commercial aviation. 2,3 The aerodynamic characteristics of airfoils, including pressure distribution, play a pivotal role in determining shock wave locations, lift, and other factors. 4,5 Particularly, the pressure distribution on the airfoil’s upper surface significantly influences drag creep and drag divergence.…”

Section: Introductionmentioning

confidence: 99%

A novel deep-learning-based pressure distribution prediction approach of airfoils

Zhang

2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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Pressure distribution is a crucial flow characteristic and a key consideration in supercritical airfoil design. Traditionally, obtaining the pressure distribution involves time-consuming and computationally expensive wind tunnel experiments and computational fluid dynamics calculations. This study proposes a deep-learning-based approach to directly map input geometric information to the pressure distribution output, thereby avoiding costly wind tunnel experiments and iterative computational fluid dynamics simulations based on Navier–Stokes equations to address these challenges. Conventional surrogate models typically focus on predicting simple force factors, such as lift and drag coefficients, or require the conversion of airfoil data into images for model training. The novel approach utilizes a Variational Autoencoder for pressure distribution characteristic extraction and reconstruction from feature variables. Unlike conventional models, this approach avoids image conversion and employs a radial basis function neural network for effective mapping. The model exhibits good fitting and generalization capabilities on both training and test datasets, offering a promising solution for rapid pressure distribution prediction in airfoil design. This novel deep-learning-based approach advances airfoil design methodologies, offering significant advantages in computational efficiency and performance prediction. By directly mapping geometric information to pressure distribution, it provides an innovative and promising tool for airfoil design optimization.

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Natural Laminar-Flow Airfoil Optimization Design Using a Discrete Adjoint Approach

Cited by 60 publications

References 58 publications

Machine Learning in Aerodynamic Shape Optimization

Machine Learning in Aerodynamic Shape Optimization

Improving Initial Aerofoil Geometry Using Aerofoil Particle Swarm Optimisation

A novel deep-learning-based pressure distribution prediction approach of airfoils

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