Machine learning in aerodynamic shape optimization

Li, Jichao; Du, Xiaosong; Martins, Joaquim R. R. A.

doi:10.1016/j.paerosci.2022.100849

Cited by 113 publications

(55 citation statements)

References 320 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The developed ANN-based virtual clone model can ensure its viability in a variety of potential domains, including wind turbine technology, aircraft aerodynamic shape analysis, road vehicle aerodynamic performance optimization, and others [ 89 , 90 ]. Aerodynamic shape and performance optimization starts with a sampled parametric dataset, where the initial step is to design the 3D model of the intended system or device and then develop the critical simulation environment (CFD, FFT, etc.)…”

Section: Implications For Industry Of the Developed Virtual Clone Modelmentioning

confidence: 99%

Savonius wind turbine blade design and performance evaluation using ANN-based virtual clone: A new approach

Noman¹,

Tasneem²,

Abhi³

et al. 2023

Heliyon

View full text Add to dashboard Cite

Section: Implications For Industry Of the Developed Virtual Clone Modelmentioning

confidence: 99%

Savonius wind turbine blade design and performance evaluation using ANN-based virtual clone: A new approach

Noman¹,

Tasneem²,

Abhi³

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…The review by Li et al [23] provides an overview of the techniques for airfoil shape parametrization. Our aim in this work is to assess the suitability of variational autoencoders for shape parametrization when performing optimization.…”

Section: Airfoil Shape Parametrizationmentioning

confidence: 99%

“…Machine learning (ML) techniques are permeating in all fields of fluid dynamics [18,19,20,21,22] and aerodynamic optimization; see the recent review by Li et al [23]. In particular, unsupervised techniques for dimensionality reduction enable the treatment of problems with a large number of variables by coding the information into a reduced number of parameters (i.e., latent variables).…”

Section: Introductionmentioning

confidence: 99%

Aeroacoustic Airfoil Shape Optimization Enhanced by Autoencoders

et al. 2022

View full text Add to dashboard Cite

“…Moreover, accompanied by concrete optimization algorithms, ML models can serve as a powerful tool to solve many engineering problems. Li et al [30] reported an ML-assisted structural optimization scheme with a significantly higher computational efficiency for solving a topology optimization problem. Xing and Tong [31] demonstrated the unprecedented efficiency of ML-aided optimization for large-scale aerodynamic shapes.…”

Section: Introductionmentioning

confidence: 99%

Computational approach for plasma process optimization combined with deep learning model

Bae

Park³

et al. 2023

J. Phys. D: Appl. Phys.

View full text Add to dashboard Cite

As semiconductor device structures become more complex and sophisticated, the formation of finer and deeper patterns is required. To achieve a higher yield for mass production as the number of process steps increases and process variables become more diverse, process optimization requires extensive engineering effort to meet the target process requirements, such as uniformity. In this study, we propose an efficient process design framework that can efficiently search for optimal process conditions by combining deep learning (DL) with plasma simulations. To establish the DL model, a dataset was created using a two-dimensional (2D) hybrid plasma equipment model (HPEM) code for an argon inductively coupled plasma (ICP) system under a given process window. The DL model was implemented and trained using the dataset to learn the functional relationship between the process conditions and their consequential plasma states, which was characterized by 2D field data. The performance of the DL model was confirmed by comparison of the output with the ground truth, validating its high consistency. Moreover, the DL results provide a reasonable interpretation of the fundamental features of plasmas and show a good correlation with the experimental observations in terms of the measured etch rate characteristics. Using the designed DL, an extensive exploration of process variables was conducted to find the optimal processing condition using the multi-objective particle swarm optimization (MOPSO) algorithm for the given objective functions of high etch rate and its uniform distribution. The obtained optimal candidates were evaluated and compared to other process conditions experimentally, demonstrating a fairly enhanced etch rate and uniformity at the same time. The proposed computational framework substantially reduced trial-and-error repetitions in tailoring process conditions from a practical perspective. Moreover, it will serve as an effective tool to narrow the processing window, particularly in the early stages of development for advanced equipment and processes.

show abstract

Machine learning in aerodynamic shape optimization

Cited by 113 publications

References 320 publications

Savonius wind turbine blade design and performance evaluation using ANN-based virtual clone: A new approach

Savonius wind turbine blade design and performance evaluation using ANN-based virtual clone: A new approach

Aeroacoustic Airfoil Shape Optimization Enhanced by Autoencoders

Computational approach for plasma process optimization combined with deep learning model

Contact Info

Product

Resources

About