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Aerospace
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Aerodynamic Shape Optimization
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Aerodynamic Shape Optimization

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 16520

Special Issue Editor

Dr. Simão Marques

E-Mail Website
Guest Editor
Academic and Research Departments, Department of Mechanical Engineering Sciences, University of Surrey, Guildford GU2 7XH, UK
Interests: numerical simulation; CFD simulation; computational fluid dynamics; fluid mechanics; aerodynamics; optimization

Special Issue Information

Dear Colleagues, 

It is with great pleasure that we invite contributions for a Special Issue on aerodynamic shape optimization. Aerodynamic shape optimization has made great strides over the last two decades, with sophisticated tools widely available to engineers across the world. Nevertheless, the relentless demands for more efficient designs require the community to develop new and ingenious ways to overcome present challenges, such as wider, more complex design spaces, shorter design cycles, integration with other disciplines, or robust optimums. As such, this Special Edition aims to report recent advances that facilitate the adoption and/or improve the efficiency of aerodynamic shape optimization, including:

  • Gradient methods;
  • Non-gradient methods;
  • Efficient parameterization strategies;
  • Multi-fidelity;
  • Multi-disciplinary;
  • Multi-objective methods;
  • Robust optimization algorithms. 

The above list above is not exhaustive, and any new methodologies that enhance the process of aerodynamic shape optimization will be equally considered.

Dr. Simão Marques
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (6 papers)

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Research

23 pages, 7843 KiB  
Article
Aerodynamic Optimization Framework for a Three-Dimensional Nacelle Based on Deep Manifold Learning-Assisted Geometric Multiple Dimensionality Reduction
by Cong Wang, Liyue Wang, Chen Cao, Gang Sun, Yufeng Huang and Sili Zhou
Aerospace 2023, 10(7), 573; https://doi.org/10.3390/aerospace10070573 - 21 Jun 2023
Cited by 2 | Viewed by 2079
Abstract
As a core component of an aero-engine, the aerodynamic performance of the nacelle is essential for the overall performance of an aircraft. However, the direct design of a three-dimensional (3D) nacelle is limited by the complex design space consisting of different cross-section profiles [...] Read more.
As a core component of an aero-engine, the aerodynamic performance of the nacelle is essential for the overall performance of an aircraft. However, the direct design of a three-dimensional (3D) nacelle is limited by the complex design space consisting of different cross-section profiles and irregular circumferential curves. The deep manifold learning-assisted geometric multiple dimensionality reduction method combines autoencoders (AE) with strong capabilities for non-linear data dimensionality reduction and class function/shape function transformation (CST). A novel geometric dimensionality reduction method is developed to address the typical constraints of nacelle parameterization. Low-dimensional latent variables are extracted from the high-dimensional design space to achieve a parametric representation of 3D nacelle manifolds. Compared with traditional parametric methods, the proposed geometric dimensionality reduction method improves the accuracy and efficiency of geometric reconstruction and aerodynamic evaluation. A multi-objective optimization framework is proposed based on deep manifold learning to increase the efficiency of 3D nacelle design. The Pareto front curves under drag divergence constraints reveal the correlation between the geometry distribution and the surface isentropic Mach number distribution of 3D nacelles. This paper demonstrates the feasibility of the proposed geometric dimensionality reduction method for direct multi-objective optimization of 3D nacelles. Full article
(This article belongs to the Special Issue Aerodynamic Shape Optimization)
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21 pages, 7344 KiB  
Article
A New Approach for Deviation Modeling in Compressors: Sensitivity-Correlated Principal Component Analysis
by Mingzhi Li, Xianjun Yu, Dejun Meng, Guangfeng An and Baojie Liu
Aerospace 2023, 10(5), 491; https://doi.org/10.3390/aerospace10050491 - 22 May 2023
Cited by 4 | Viewed by 1873
Abstract
Studies on the geometry variation-related compressor uncertainty quantification (UQ) have often used dimension reduction methods, such as the principal component analysis (PCA), for the modeling of deviations. However, in the PCA method, the main eigenmodes were determined based only on the statistical behavior [...] Read more.
Studies on the geometry variation-related compressor uncertainty quantification (UQ) have often used dimension reduction methods, such as the principal component analysis (PCA), for the modeling of deviations. However, in the PCA method, the main eigenmodes were determined based only on the statistical behavior of geometry variations. While this process can cause some missing modes with a small eigenvalue, it is much more sensitive to blade aerodynamic performances, and thereby reducing the reliability of the UQ analysis. Hence, a novel geometry variation modeling method, named sensitivity-correlated principal component analysis (SCPCA), has been proposed. In addition, by means of the blade sensitivity analysis, the weighting factors for each eigenmode were determined and then used to modify the process of the PCA. As a result, by considering the covariance of geometry variations and the performance sensitivity, the main eigenmodes could be determined and used to reconstruct the blade samples in the UQ analysis. With 98 profile samples measured at the midspan of a high-pressure compressor rotor blade, both the PCA and SCPCA methods were employed for the UQ analysis. The results showed that, compared to the PCA method, the SCPCA method provided a more accurate reconstruction of sensitive deviations, leading to an 11.8% improvement in evaluating the scatter of the positive incidence range, while also maintaining the accuracy of the uncertainty assessment for other performances. Full article
(This article belongs to the Special Issue Aerodynamic Shape Optimization)
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17 pages, 1396 KiB  
Article
Two-Way Coupled Aero-Structural Optimization of Stable Flying Wings
by José D. Hoyos, Camilo Echavarría, Juan P. Alvarado, Gustavo Suárez, Juliana A. Niño and Jorge I. García
Aerospace 2023, 10(4), 346; https://doi.org/10.3390/aerospace10040346 - 2 Apr 2023
Cited by 1 | Viewed by 2784
Abstract
An aero-structural algorithm to optimize a flying wing in cruise conditions for preliminary design is developed using two-way interaction between the structure and aerodynamics. A particle swarm routine is employed to solve the multi-objective optimization, aiming to reduce the weight of the structure [...] Read more.
An aero-structural algorithm to optimize a flying wing in cruise conditions for preliminary design is developed using two-way interaction between the structure and aerodynamics. A particle swarm routine is employed to solve the multi-objective optimization, aiming to reduce the weight of the structure and the aerodynamic drag at the design point. Different shapes are evaluated during the optimization process until the algorithm reaches the optimal wing aspect ratio, taper ratio, angle of incidence, twist angle, swept angle, and airfoil shape, where a six-parameters method is employed to allow reflex airfoils. A main isotropic I-beam models the wing structure. An extended vortex lattice model is employed to model the aerodynamics, along with a high-order panel method with fully coupled viscous interaction. The finite element method is used to solve the flying-wing structure under static loads. An algorithm is developed to iterate between the deflection of the wing and its impact on the aerodynamics until convergence is reached. Different constraints are implemented into the objective function to fulfil the structural criteria and the longitudinal static stability. A comparison against a baseline optimization is carried out, achieving higher efficiency and promising results in elliptical lift distribution, and a high static margin, without the use of non-constant twist. The results suggest that combining both reflex airfoils and sweep with washout is the optimal solution to reduce the drag and weight, keeping the longitudinal static stability criteria for tailless aircraft in the lower end of the transonic regime. Full article
(This article belongs to the Special Issue Aerodynamic Shape Optimization)
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15 pages, 5642 KiB  
Article
Shape Parameterization Optimization of Thermocouples Used in Aeroengines
by Yufang Wang, Jian Zhao and Ruijia Zhao
Aerospace 2023, 10(2), 202; https://doi.org/10.3390/aerospace10020202 - 20 Feb 2023
Cited by 1 | Viewed by 2133
Abstract
Aiming at the problem that thermocouples used in different parts of aeroengines need a lot of repeated design work in application and the high precision requirements as special test sensors, a parameterized optimization method for a thermocouple shape combined with a numerical simulation [...] Read more.
Aiming at the problem that thermocouples used in different parts of aeroengines need a lot of repeated design work in application and the high precision requirements as special test sensors, a parameterized optimization method for a thermocouple shape combined with a numerical simulation method is proposed. The performance of a dual-screen thermocouple (DST), single-screen thermocouple (SST), and no-screen thermocouple (NST) is tested by a numerical simulation method, and is represented by the velocity error σV and the restitution coefficient r. The dual-screen thermocouple (DST) is the best one, and it is selected as the object to parametric optimization, and the parametric optimization methods based on it, geometrically modeled parametrically, adaptive mesh generation and parametric numerical simulation, are proposed. For a dual-screen thermocouple (DST), eight design structural parameters and four environment parameters are suggested for geometrically modeled parametrically and parametric numerical simulation, respectively. The dichotomy method is used to find the optimal length of the screen L, which is considered the most relevant parameter for thermocouple performance. It can be found that the length of the screen L corresponding to the optimal restitution coefficient r ranges from 56.25 to 62.5 mm. Full article
(This article belongs to the Special Issue Aerodynamic Shape Optimization)
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17 pages, 3465 KiB  
Article
Aerodynamic Shape Optimisation Using Parametric CAD and Discrete Adjoint
by Dheeraj Agarwal, Simão Marques and Trevor T. Robinson
Aerospace 2022, 9(12), 743; https://doi.org/10.3390/aerospace9120743 - 23 Nov 2022
Cited by 8 | Viewed by 3154
Abstract
This paper presents an optimisation framework based on an open-source CAD system and CFD solver. In this work, the high-fidelity flow solutions and surface sensitivities are obtained using the primal and discrete adjoint formulations of SU2. This paper shows the [...] Read more.
This paper presents an optimisation framework based on an open-source CAD system and CFD solver. In this work, the high-fidelity flow solutions and surface sensitivities are obtained using the primal and discrete adjoint formulations of SU2. This paper shows the direct use of CAD models for optimisation by developing a CAD system application programming interface and creating a link between the CAD-MESH-CFD analysis. A methodology to obtain geometric sensitivities is introduced, enabling the calculation of accurate gradients with respect to CAD variables and the deformation of the analysis mesh during the optimisation process. This methodology guarantees that the new surface mesh lies exactly on the CAD geometry. The optimisation framework is applied to a rectangular wing and a three section high-lift aerofoil configuration derived from the NASA CRM-HL configuration. Both geometries are created using FreeCAD. The performance objectives are to decrease the drag while constraining the lift to be above a desired value. The twist distribution of the wing was parameterised within the CAD system, allowing the minimisation of the induced drag by obtaining a nearly elliptical lift distribution. For the high-lift configuration, the position and rotation of the flap and slat were parameterised with respect to the original section; the final optimised positions yield a drag reduction of approximately 16.5%. These results show that the CAD parameterisation can be reliably used to obtain efficient optimums while operating directly on the CAD geometries. Full article
(This article belongs to the Special Issue Aerodynamic Shape Optimization)
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26 pages, 14108 KiB  
Article
Aerodynamic Data-Driven Surrogate-Assisted Teaching-Learning-Based Optimization (TLBO) Framework for Constrained Transonic Airfoil and Wing Shape Designs
by Xiaojing Wu, Zijun Zuo and Long Ma
Aerospace 2022, 9(10), 610; https://doi.org/10.3390/aerospace9100610 - 17 Oct 2022
Cited by 7 | Viewed by 2632
Abstract
The surrogate-assisted optimization (SAO) process can utilize the knowledge contained in the surrogate model to accelerate the aerodynamic optimization process. The use of this knowledge can be regarded as the primary form of intelligent optimization design. However, there are still some difficulties in [...] Read more.
The surrogate-assisted optimization (SAO) process can utilize the knowledge contained in the surrogate model to accelerate the aerodynamic optimization process. The use of this knowledge can be regarded as the primary form of intelligent optimization design. However, there are still some difficulties in improving intelligent design levels, such as the insufficient utilization of optimization process data and optimization parameters’ adjustment that depends on the designer’s intervention and experience. To solve the above problems, a novel aerodynamic data-driven surrogate-assisted teaching-learning-based optimization (TLBO) framework is proposed for constrained aerodynamic shape optimization (ASO). The main contribution of the study is that ASO is promoted using historically aerodynamic process data generated during the gradient free optimization process. Meanwhile, nonparametric adjustment of the TLBO algorithm can help relieve manual design experience for actual engineering applications. Based on the structure of the TLBO algorithm, a model optimal prediction method is proposed as the new surrogate-assisted support strategy to accelerate the ASO process. The proposed method is applied to airfoil and wing shape designs to verify the optimization effect and efficiency. A benchmark aerodynamic design optimization is employed for the drag minimization of the RAE2822 airfoil. The optimized results indicate that the proposed method has advantages of high efficiency, strong optimization ability, and nonparametric characteristics for ASO. Moreover, the results of the wing shape optimization verify the advantages of the proposed methods over the surrogate-based optimization and direct optimization frameworks. Full article
(This article belongs to the Special Issue Aerodynamic Shape Optimization)
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