A multi-objective airfoil shape optimization study using mesh morphing and response surface method

Kallath, Hariharan; Lee, Jung Hoon; Kholi, Foster Kwame; Ha, Man Yeong; Min, June Kee

doi:10.1007/s12206-021-0221-0

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…The results are presented in Table 2. Design-Expert software (Anderson and Whitcomb, 2017) was employed to determine the square regression coefficients (R 2 ) of the developed model, perform regression fitting, and conduct the response surface study (Kallath et al, 2021). The square regression coefficients (R 2 ) of the PF, MCF, CFE, and SEA were 0.9308, 0.9939, 0.7200, and 0.9975, respectively.…”

Section: Development and Verification Of Rsm Modelmentioning

confidence: 99%

Multiobjective optimization of dimension and position of elliptical crush initiator on crashworthiness performance of square tube using response surface methodology

Hafid,

Istiyanto,

Nasruddin

2023

Front. Mech. Eng.

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In this study, the crashworthiness performance of a thin-walled square steel-tube structure with an elliptical crush initiator under impact loading was investigated. The effect of the height, width, and distance of the crush initiator from the top of the tube on the crashworthiness performance was analyzed using several numerical simulations using ABAQUS Explicit. The response surface methodology was used to predict the crashworthiness performance indices, and optimization was performed to determine the optimal dimensions and position of the crush initiator. The optimization was aimed at minimizing the peak force (PF) while maximizing the mean crushing force (MCF), crush force efficiency (CFE), and specific energy absorption (SEA). The result was an elliptical crush initiator with a height of 15 mm, width of 24.784 mm, and distance of 15.08 mm. Validation was performed to verify these results. The optimal crush initiator effect resulted in a 10.12% decrease in the peak force, 13.67% increase in the crush force efficiency, and 2.23% increase in the mean crushing force. However, a slight decrease of 0.82% in specific energy absorption was observed.

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Section: Development and Verification Of Rsm Modelmentioning

confidence: 99%

Multiobjective optimization of dimension and position of elliptical crush initiator on crashworthiness performance of square tube using response surface methodology

Hafid,

Istiyanto,

Nasruddin

2023

Front. Mech. Eng.

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“…The airfoil structure significantly influences the energy efficiency of wind power generation Sharma, Gupta, Pandey, Sharma, & Mishra (2021) . Due to the high cost of CFD calculation Koziel & Leifsson (2013) , - The aerodynamic performance optimization Bedon et al (2016) ; Chen and Li (2019) ; Kallath et al (2021) ; Mukesh et al (2012) ; Saleem and Kim (2020) ; Tang et al (2017) and robustness optimization Reis et al (2019) of Airfoil Based on CFD are limited. Therefore, optimizing the efficiency of airfoil structures has been a focus of research community.…”

Section: Introductionmentioning

confidence: 99%

Multi-network collaborative lift-drag ratio prediction and airfoil optimization based on residual network and generative adversarial network

Zhao

Wu²,

Chen³

et al. 2022

Front. Bioeng. Biotechnol.

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As compared with the computational fluid dynamics(CFD), the airfoil optimization based on deep learning significantly reduces the computational cost. In the airfoil optimization based on deep learning, due to the uncertainty in the neural network, the optimization results deviate from the true value. In this work, a multi-network collaborative lift-to-drag ratio prediction model is constructed based on ResNet and penalty functions. Latin supersampling is used to select four angles of attack in the range of 2°–10° with significant uncertainty to limit the prediction error. Moreover, the random drift particle swarm optimization (RDPSO) algorithm is used to control the prediction error. The experimental results show that multi-network collaboration significantly reduces the error in the optimization results. As compared with the optimization based on a single network, the maximum error of multi-network coordination in single angle of attack optimization reduces by 16.0%. Consequently, this improves the reliability of airfoil optimization based on deep learning.

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“…Therefore, in this study, a dual-layer flapping airfoil structure was combined with a semiactive flapping airfoil power generator. Recently, a series of optimization methods were introduced to obtain optimization parameters combination of flapping airfoils for different engineering purposes, such as response surface method, 21 Taguchi experimental design, 22,23 genetic algorithm, 24 and so forth. In the present work, some geometric and dynamic parameters of the dual-layer flapping airfoil at certain ranges were considered to gain a better energy capture efficiency, a Taguchi experimental design and computational fluid dynamics (CFD) simulations were used to systematically analyze the influence of the dual-layer flapping airfoil structure on the semiactive flapping airfoil energy capture performance.…”

Section: Introductionmentioning

confidence: 99%

Study on the energy capture efficiency of flapping airfoil power generator using semiactive dual‐layer airfoils

Zhu

Xie

2022

Energy Science & Engineering

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To address the problem that the energy capture efficiencies of existing flapping airfoil power generators are lower than those of traditional turbine generators, a dual‐layer flapping airfoil structure was adopted to improve the energy capture performance of the semiactive flapping airfoil, inspired by the multiwing flying mode of dragonflies in nature. The influence of the dual‐layer flapping airfoil structure on the energy capture performance of a semiactive flapping airfoil was systematically analyzed by a Taguchi experimental design and computational fluid dynamics simulations. The results showed that the dual‐layer flapping airfoil structure mainly improved the energy capture efficiency of the flapping airfoil by increasing the lift force and reducing the maximum sweep distance. Under the optimized parameter combination, the energy capture efficiency of the dual‐layer flapping airfoil reached 28.80%, which was 17.74% higher than that of the single‐layer flapping airfoil. Furthermore, the influence of the dual‐layer flapping airfoil structure on the energy capture performance of a semiactive flapping airfoil was related to the mass ratio of the system, and a larger mass ratio (M* ≥ 40) caused the performance of the dual‐layer flapping airfoil to deteriorate. Analysis of the vortices around the flapping airfoil revealed that there were more attached vortices on the surface of the dual‐layer flapping airfoil, but the increase in the mass ratio led to an advance of the separation of the leading edge vortex, which was the reason that the dual‐layer flapping airfoil with a smaller mass ratio had a better energy capture performance.

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A multi-objective airfoil shape optimization study using mesh morphing and response surface method

Cited by 7 publications

References 24 publications

Multiobjective optimization of dimension and position of elliptical crush initiator on crashworthiness performance of square tube using response surface methodology

Multiobjective optimization of dimension and position of elliptical crush initiator on crashworthiness performance of square tube using response surface methodology

Multi-network collaborative lift-drag ratio prediction and airfoil optimization based on residual network and generative adversarial network

Study on the energy capture efficiency of flapping airfoil power generator using semiactive dual‐layer airfoils

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