A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design

Owoyele, Opeoluwa; Pal, Pinaki

doi:10.1016/j.apenergy.2021.116455

Cited by 33 publications

(18 citation statements)

References 29 publications

(31 reference statements)

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“…Also, the reasonable domains can be identified by using approximate models trained by aerodynamic data. For example, Owoyele and Pal [467] proposed a machine-learning-driven active optimizer (ActivO) method using two different machine-learning-based surrogate models called "weak" and "strong" learners, where the weak learner identifies promising regions in the design space to explore, while the strong learner determines the exact location of the optimum in identified promising regions. Preliminary proof-of-concept studies in an engine design with nine variables demonstrated superior convergence rates (speedups of 4 to 5×) compared to state-of-the-art optimization methods such as PSO.…”

Section: Surrogate-based Optimizationmentioning

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: Surrogate-based Optimizationmentioning

confidence: 99%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

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“…A machine learning based algorithm is applied to the airfoil optimization problem. The methodology used for the machine learning based algorithm is brought up by Opeoluwa Owoyele [10], who uses the machine learning models to make decisions for the selection of the computational points towards the most promising region. Different from any other optimization approach, it is a deterministic approach that at each step of sampling.…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…Therefore, a better optimization algorithm is in need, which can not only reach closer to the global optimum point, but also converge faster to the global optimum. Recently, Owoyele et al [10] developed a machine learning based optimization method for the internal combustion engine design. Different from any other machine learning involved methods, it no longer relies on the initial DoE for training the machine learning surrogate models, which helps to greatly reduce the useless computation of sampling far away from the global optimum.…”

Section: Introductionmentioning

confidence: 99%

Airfoil optimization using a machine learning-based optimization algorithm

Song

Wang

Luo

2022

J. Phys.: Conf. Ser.

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For the design of wind turbines, airfoil optimization is widely required as the operation efficiency of wind turbines is closely dependent on the airfoil aerodynamic performance, where the accuracy of the optimization method is of great importance. In this paper, a machine learning-based optimization algorithm is proposed to improve the airfoil performance. A low-speed airfoil of NACA0012 is selected as the original airfoil for the optimization. The class-shape-transformation (CST) method is used to construct the geometry for the airfoil, and the aerodynamic performance is calculated using the panel code XFOIL. The validation of the simulation is carried out by comparing the predicted aerodynamic performance with the experimental data. In order to improve aerodynamic performance of the airfoil, maximizing the lift-to-drag ratio is selected as the main objective of the optimization problem while maintaining the lift coefficient not smaller than the original values. The results show that the present machine learning-based algorithm has fairly good convergence, and can obtain much higher lift-to-drag ratio for the optimized airfoil compared with the original one. Compared with the traditional genetic algorithm method, the machine learning based method can achieve much better aerodynamic performance and much shorter simulation time for the same airfoil optimization problem. In the future, the machine learning-based method is very promising for fluid machinery design optimization.

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“…Some algorithms are inspired by machine learning, reinforcement learning, and learning classifier systems [60][61][62]. For example, ActivO is an ensemble machine learning-based optimization algorithm [63]. ActivO combines strong and weak learner strategies to perform a search for optimal solutions.…”

Section: Evolutionary Swarm Intelligencementioning

confidence: 99%

Stock exchange trading optimization algorithm: a human-inspired method for global optimization

Emami

2021

J Supercomput

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In this paper, a human-inspired optimization algorithm called stock exchange trading optimization (SETO) for solving numerical and engineering problems is introduced. The inspiration source of this optimizer is the behavior of traders and stock price changes in the stock market. Traders use various fundamental and technical analysis methods to gain maximum profit. SETO mathematically models the technical trading strategy of traders to perform optimization. It contains three main actuators including rising, falling, and exchange. These operators navigate the search agents toward the global optimum. The proposed algorithm is compared with seven popular meta-heuristic optimizers on forty single-objective unconstraint numerical functions and four engineering design problems. The statistical results obtained on test problems show that SETO is capable of providing competitive and promising performances compared with counterpart algorithms in solving optimization problems of different dimensions, especially 1000-dimension problems. Out of 40 numerical functions, the SETO algorithm has achieved the global optimum on 36 functions, and out of 4 engineering problems, it has obtained the best results on 3 problems.

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A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design

Cited by 33 publications

References 29 publications

Machine Learning in Aerodynamic Shape Optimization

Machine Learning in Aerodynamic Shape Optimization

Airfoil optimization using a machine learning-based optimization algorithm

Stock exchange trading optimization algorithm: a human-inspired method for global optimization

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