Kriging metamodels-based multi-objective shape optimization applied to a multi-scale heat exchanger

Mastrippolito, Franck; Aubert, Stéphane; Ducros, F.

doi:10.1016/j.compfluid.2021.104899

Cited by 10 publications

(4 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The kriging model uses a stationary random process with an average value of 0 to describe the real response relationship of the model [42], and the model is as follows equation ( 14):…”

Section: Theory Of Kriging Response Surface Modelmentioning

confidence: 99%

Multi-condition stability analysis and lightweight research of mining wide-body dump truck compartment

Liu,

Tian

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

This paper proposes a multi-condition optimization method based on single condition optimization and weight. This method analyzes and discusses various working states of the wide-body dump truck and optimize its structure. Firstly, the modal shape and natural frequency of the carriage under modal conditions are analyzed using the finite element method. Combined with the mechanic’s theory, the carriage's force, deformation, and stress are studied under the turning and partial load conditions. This part provides a basis for subsequent stability analysis and lightweight research. According to the filtering white noise method and differential equation method, the f-class road model and the vehicle system dynamics model are established in MATLAB to study the influence of road vibration on the stability of the dump truck during operation. The carriage vibration data prove that the road vibration causes the low order resonance of the carriage and affects the stability of the dump truck. This part provides a basis for the subsequent stability optimization. Based on the above analysis, a multi-objective genetic algorithm (MOGA) based on the response surface is used to optimize the structural parameters of the carriage under partial load conditions, turning conditions and modal conditions, to realize the optimization of stability, mass, stress and deformation of the carriage. Finally, the entropy weight method is used to fuse the optimization results of the three working conditions. The correctness of the comprehensive optimization results is verified, and the wide-body dump truck carriage optimization is realized, proving the feasibility of the proposed method.

show abstract

“…The kriging model uses a stationary random process with an average value of 0 to describe the real response relationship of the model [42], and the model is as follows equation ( 14):…”

Section: Theory Of Kriging Response Surface Modelmentioning

confidence: 99%

Multi-condition stability analysis and lightweight research of mining wide-body dump truck compartment

Liu,

Tian

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Mastrippolito et al 28 Used a genetic algorithm coupled with Kriging-based metamodeling for evaluating heat exchanger performances.…”

Section: Introductionmentioning

confidence: 99%

Decision support system based on a hybrid genetic algorithm–Kohonen map for combined mode conduction–radiation heat transfer in a porous medium: A comparative assessment of three variations of the Kohonen map

Ansari,

Mishra,

Sahu

et al. 2024

Heat Trans

View full text Add to dashboard Cite

A hybrid genetic algorithm (GA)–Kohonen map, with its three variants, is explored for the first time for the decision‐making system in a porous ceramic matrix (PCM)‐based burner through determination of the regime of operation. Four different attributes of PCMs such as convective coupling (P2), extinction coefficient (β), downstream porosity (ϕ2), and scattering albedo (ω) are selected for determining the regime of operation of a PCM‐based burner. Changes in any of these attributes of a PCM lead to significant changes in the temperature profiles of the gas and solid phases. Temperature profiles of the gas and solid phases are computed by developing a numerical model. Various samples corresponding to different regimes are generated and used in a hybrid GA–Kohonen map. The best architectural details such as the neuron number and training epochs are obtained from GA as output. The best Kohonen map is trained with the input data, and regimes of operation for new temperature profiles are predicted. A supervised Kohonen map is able to provide the highest average class prediction of more than 40%. All the variants are assessed under two different types of neuron grids: hexagonal and rectangular. Comparative assessments of the three different variants of Kohonen maps, in terms of CPU time and average class prediction, are carried out.

show abstract

“…A constitutive model is an approximate model that can replace or represent the actual model of a specific phenomenon. It can be used as a means of optimization or to represent the aspects of resource-consuming tests and FE simulations in a simple equation [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Approximation Method for Stress–Strain Using Metamodel Parameter Updating

2022

View full text Add to dashboard Cite

The properties of the material applied to the finite element (FE) simulation can be expressed by constitutive models, and simple constitutive and complex constitutive models can be used to show the actual phenomenon. The technology to improve the accuracy of the constitutive model applied to FE simulation is the inverse method. The inverse method is a method to curve fit the FE simulation result to the test data by utilizing finite element model updating (FEMU). Inverse methods are general approaches to update material properties. The inverse method can iteratively run many FE simulations for constitutive model optimization and consider metamodel-based simulation optimization (MBSO) to reduce this resource waste. With MBSO, one can obtain significant results with fewer resources. However, the MBSO algorithm has the problem in that the optimization performance deteriorates as the number of parameters increases. The typical process of the inverse method is to adjust these factor values individually. If there are many factors in the constitutive model, the optimization result may deteriorate owing to the performance limit of the MBSO when the structural method is used. This paper proposes a method of fitting a stress–strain constitutive model with a scaling factor to improve the efficiency of the inversion method using MBSO. For this purpose, a process was performed to determine the curve characteristics during the pretreatment stage. The results show that the proposed method significantly improved the prediction efficiency of the combination function. Thus, we conclude that initializing the combination function and setting the parameters of the inverse method by applying the proposed approach improves the efficiency of large deformation analyses.

show abstract

Kriging metamodels-based multi-objective shape optimization applied to a multi-scale heat exchanger

Cited by 10 publications

References 61 publications

Multi-condition stability analysis and lightweight research of mining wide-body dump truck compartment

Multi-condition stability analysis and lightweight research of mining wide-body dump truck compartment

Decision support system based on a hybrid genetic algorithm–Kohonen map for combined mode conduction–radiation heat transfer in a porous medium: A comparative assessment of three variations of the Kohonen map

Approximation Method for Stress–Strain Using Metamodel Parameter Updating

Contact Info

Product

Resources

About