Pareto based multi-objective optimization of a cyclone vortex finder using CFD, GMDH type neural networks and genetic algorithms

Khalkhali, Abolfazl; Safikhani, Hamed

doi:10.1080/0305215x.2011.564619

Cited by 27 publications

(4 citation statements)

References 14 publications

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“…Vehicles 2019, 1, FOR PEER REVIEW 8 These two main concepts include a hybrid GA and SVD are involved to optimally design such a polynomial neural network [34]. The method that was used in that study was successfully used in this paper to obtain the polynomial models of the energy absorption, maximum force, and critical buckling force.…”

Section: Gmdh Modeling Of Crashworthiness Parametersmentioning

confidence: 99%

“…Also, many research studies have been conducted to implement evolutionary methods in identifying the system parameters [31,32]. Among them, the group method of data handling (GMDH) algorithm is a self-organizing approach that characterizes the complicated models based on their performance as a set of multiple inputs and single output pairs [33,34]. This method was proposed by Ivakhnenko [35] and can model complex systems without having prior knowledge about that system.…”

Section: Introductionmentioning

confidence: 99%

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A Study of Multi-Objective Crashworthiness Optimization of the Thin-Walled Composite Tube under Axial Load

Seyedi

Khalkhali

2020

Vehicles

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In recent decades, thin-walled composite components have been widely used in the automotive industry due to their high specific energy absorption. A large number of experimental and numerical studies have been conducted to characterize the energy absorption mechanism and failure criteria for different composite tubes. Their results indicate that the energy absorption characteristics depend highly on the failure modes that occur during the impact. And failure mechanism is dependent on fiber material, matrix material, fiber angle, the layout of the fibers, as well as the geometry of structure and load condition. In this paper, first, the finite element (FE) model of the CFRP tube was developed using the Tsai-Wu failure criterion to model the crush characteristics. The FE results were validated using the published experimental. Then, a series of FE simulations were conducted considering different fiber directions and the number of layers to generate enough data for constructing the GMDH-type neural network. The polynomial expression of the three outputs (energy absorption, maximum force, and critical buckling force) was extracted using the GMDH algorithm and was used to perform the Pareto-based multi-objective optimizations. Finally, the failure mechanism of the optimum design point was simulated in LS-DYNA. The main contribution of this study was to successfully model the CFRP tube and damage mechanism using appropriate material constitutive model’s parameters and present the multi-objective method to find the optimum crashworthy design of the CFRP tube.

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Section: Gmdh Modeling Of Crashworthiness Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Study of Multi-Objective Crashworthiness Optimization of the Thin-Walled Composite Tube under Axial Load

Seyedi

Khalkhali

2020

Vehicles

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“…Therefore, a reasonable comparison of optimization results cannot be taken ahead. Khalkhali and Safikhani (2012) developed the models of p and η according to the four cyclone geometric parameters based on CFD and GMDH neural network. The developed cyclone models were optimized simultaneously using NSGA-II.…”

Section: Research Backgroundmentioning

confidence: 99%

“…3Multi-objective optimization of the cyclone models that have the best predictive performance using NSGA-II: The optimized results compare not only the improvement of the cyclone performance models ( p, η) with the reference model, but also analyze from the viewpoint of physical justification of the optimization result. The limitations of previous optimization researches lack an analysis of why the optimization was made (Elsayed & Lacor, 2013;Khalkhali & Safikhani, 2012).…”

Section: Study Objectivesmentioning

confidence: 99%

Multi-objective optimization and comparison of surrogate models for separation performances of cyclone separator based on CFD, RSM, GMDH-neural network, back propagation-ANN and genetic algorithm

Park

Cha

Kim

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Pressure drop (p) and collection efficiency (η) are used to evaluate the separation performance of the cyclone separator. In this study, we conducted comparative study of cyclone models using response surface methodology (RSM), back propagation neural network (BPNN), and group method of data handling (GMDH) networks to develop optimal predictive cyclone models. Also, we conducted multi-objective optimization for maximizing model and minimizing model using genetic algorithm (GA). CFD was performed instead of experimental method to get the estimated values for modeling of p and η. The validation results of CFD showed 0.5% and 2% errors for p and η, respectively, compared with the experimental data. Second, design of experiment (DOE) analysis for 10 cyclone geometrical parameters was executed to obtain the significant geometrical parameters. Vortex finder diameter D x , inlet width a, inlet height b and cone height H co have a significant effect on η and p. However, interaction effects between the geometrical parameters have small effects. The cyclone models by RSM, BPNN and GMDH based on 25 CFD training set were developed. The predictive performance results by the cyclone models were compared by 25 CFD test set. The GMDH method achieved the best prediction for p (R 2 = 99.7, RMSE = 0.102) R 2 adjusted = 98.99, RMSE = 0.0119) than the RSM, BPNN cyclone models. Additionally, uncertainty analysis was performed to estimate the quantitative performance of cyclone models. The results show that the uncertainty width of GMDH models achieved the best prediction (η: ±0.0065, p: ±0.0188). Finally, GA was applied to optimize the GMDH models simultaneously. GA generated 70 non-dominant solutions. Reproducibility of five optimal points was validated by using CFD. The trade-off optimal point showed improvement by 24.31%, 18% and 8.79% for p d 50 and η, respectively.

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Multi-objective crashworthiness optimization of perforated square tubes using modified NSGAII and MOPSO

Khalkhali

Mostafapour

Tabatabaie

et al. 2016

Struct Multidisc Optim

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Pareto based multi-objective optimization of a cyclone vortex finder using CFD, GMDH type neural networks and genetic algorithms

Cited by 27 publications

References 14 publications

A Study of Multi-Objective Crashworthiness Optimization of the Thin-Walled Composite Tube under Axial Load

A Study of Multi-Objective Crashworthiness Optimization of the Thin-Walled Composite Tube under Axial Load

Multi-objective optimization and comparison of surrogate models for separation performances of cyclone separator based on CFD, RSM, GMDH-neural network, back propagation-ANN and genetic algorithm

Multi-objective crashworthiness optimization of perforated square tubes using modified NSGAII and MOPSO

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