An Optimum Neural Network for Evolutionary Aerodynamic Shape Design

Timnak, N.; Jahangirian, A.; Seyyedsalehi, Seyyed Ali

doi:10.24200/sci.2017.4308

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…If the cross-sectional area of the pipe is constant and the velocity fluctuation regularity in the pipe is stable, Ct(u,A) can be converted to Ct(u). For a stable pumping process, the velocity of the fluid in the tube is in an evolving process, if the average velocity of any section in the pump is regarded as R times of the inlet velocity, then for any section A i , the velocity relationship is shown in equation (22), Ct(u,A) can be converted to Ct(R).…”

Section: Jet Pump Headmentioning

confidence: 99%

“…Tong 21 optimized the design parameters of centrifugal pumps based on three alternative models: the quadratic response surface, radial Kigassian response surface, and Kriging models. Timnak 22 proposed new data preprocessing methods utilizing genetic algorithms to enhance the capabilities of neural networks in order to apply them to the estimation of the fitness function of aerodynamic shape optimization. The above research results all prove that machine learning technology can be used to determine the optimal working parameters of fluid machinery.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploration of the mechanism and prediction of jet pump feature structure optimization for the enhanced pumping performance

Sun

Fan

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part A:

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It is widely concerned that the non-ideal design of characteristic parameters of throat pipe of jet pump leads to the low pumping efficiency. The paper studies the pumping efficiency of jet pump under different characteristic parameters through numerical calculation to determine the influences of single parameter on pumping efficiency. And the coupling influences of parameters are predicted through XGBoost technology. The research shows that the coupling relation between the pumping efficiency of jet pump and multiple characteristic parameters can be predicted with the accuracy of 98.83%. The reasonable choice of characteristic parameters can improve pumping efficiency by 23.6%. The research will help designers to look for the best coupling of jet pump parameters accurately through numerical calculation and machine learning technologies.

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Section: Jet Pump Headmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploration of the mechanism and prediction of jet pump feature structure optimization for the enhanced pumping performance

Sun

Fan

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part A:

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show abstract

“…Weight functions are determined by using the backpropagation algorithm. The number of hidden neurons is regulated by the growing and pruning method proposed by Timnak et al (2017). In the growing process, a neuron with a maximum error is split into two neurons.…”

Section: Prediction Of Tire/road Noisementioning

confidence: 99%

A comprehensive study on statistical prediction and reduction of tire/road noise

Mohammadi

Ohadi

Parizi

2021

Journal of Vibration and Control

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Promoting safe tires with low external rolling noise increases the environmental efficiency of road transport. Although tire builders have been striving to reduce emitted noise, the issue’s sophisticated nature has made it difficult. This article aims to make the problem straightforward, relying on recent significant improvements in statistical science. In this regard, the prediction ability of new methods in this field, including support vector machine, relevance vector machine, and convolutional neural network, along with the new architecture of the neural network is compared. Tire noise is measured under the coast-by condition. Two training strategies are proposed: extracting features from a tread pattern image and directly importing an image to the model. The relevance vector method, which is trained using the first strategy, has provided the most accurate results with an error of 0.62 dB(A) in predicting the total noise level. This precise model is used instead of experimentation to analyze the sensitivity of tire noise to its parameters using a small central composite design. The parametric study reveals striking tips for reducing noise, especially in terms of interactions between parameters that have not previously been shown. Finally, a novel two-stage approach for reducing noise by tread pattern optimization is proposed, inspired by two regression models derived from statistical investigation and variance analysis. Changes in tread pattern specifications of two case studies and their randomization have resulted in a reduction of 3.2 dB(A) for a high-noise tire and 0.4 dB(A) decrement for a quieter tire.

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“…As a matter of fact, the data in the design space can be classified by the information conveyed by data itself. 41 Different levels of information hidden in physics can be extracted via different procedures. For example, multiple design alternatives have to be quickly iterated in preliminary design to make initial decisions without high-fidelity simulations.…”

Section: Direct Applications Of Ann Surrogate Modeling In Aerodynamicmentioning

confidence: 99%

A review of the artificial neural network surrogate modeling in aerodynamic design

Sun

Wang²

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

114

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Artificial neural network surrogate modeling with its economic computational consumption and accurate generalization capabilities offers a feasible approach to aerodynamic design in the field of rapid investigation of design space and optimal solution searching. This paper reviews the basic principle of artificial neural network surrogate modeling in terms of data treatment and configuration setup. A discussion of artificial neural network surrogate modeling is held on different objectives in aerodynamic design applications, various patterns of realization via cutting-edge data technique in numerous optimizations, selection of network topology and types, and other measures for improving modeling. Then, new frontiers of modern artificial neural network surrogate modeling are reviewed with regard to exploiting the hidden information for bringing new perspectives to optimization by exploring new data form and patterns, e.g. quick provision of candidates of better aerodynamic performance via accumulated database instead of random seeding, and envisions of more physical understanding being injected to the data manipulation.

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An Optimum Neural Network for Evolutionary Aerodynamic Shape Design

Cited by 3 publications

References 15 publications

Exploration of the mechanism and prediction of jet pump feature structure optimization for the enhanced pumping performance

Exploration of the mechanism and prediction of jet pump feature structure optimization for the enhanced pumping performance

A comprehensive study on statistical prediction and reduction of tire/road noise

A review of the artificial neural network surrogate modeling in aerodynamic design

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