Multi-objective approach to optimize cure process for thick composite based on multi-field coupled model with RBF surrogate model

2021 ◽  
Vol 24 ◽  
pp. 100671
Author(s):  
Zhenyi Yuan ◽  
Lingfei Kong ◽  
Dajing Gao ◽  
Xinxing Tong ◽  
Yu Feng ◽  
...  
Keyword(s):  
Surrogate Model ◽  
Coupled Model ◽  
Cure Process ◽  
Multi Objective

Multi-objective optimization of high-speed on-off valve based on surrogate model for water hydraulic manipulators

2021 ◽  
pp. 112949
Author(s):  
Qingtong Qingtong ◽  
Fanglong Fanglong ◽  
Songlin Songlin ◽  
Ruidong Ruidong ◽  
Hui Hui
Keyword(s):  
Surrogate Model ◽  
High Speed ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Hydraulic Manipulators ◽  
Water Hydraulic

scholarly journals Multi-Objective Optimization of a High Specific Speed Centrifugal Volute Pump Using 3D Inverse Design Coupled With CFD Simulations

2019 ◽  
Author(s):  
Luying Zhang ◽  
Gabriel Davila ◽  
Mehrdad Zangeneh
Keyword(s):  
Surrogate Model ◽  
Design Method ◽  
Inverse Design ◽  
Multi Objective Optimization ◽  
Blade Angle ◽  
Multi Objective ◽  
Specific Speed ◽  
Meridional Shape ◽  
Inverse Design Method ◽  
Conventional Optimization

Abstract This paper presents three different multi-objective optimization strategies for a high specific speed centrifugal volute pump design. The objectives of the optimization consist of maximizing the efficiency and minimizing the cavitation while maintaining the Euler head. The first two optimization strategies use a 3D inverse design method to parametrize the blade geometry. Both meridional shape and 3D blade geometry is changed during the optimization. In the first approach Design of Experiment method is used and the efficiency computed from CFD computations, while cavitation is evaluated by using minimum pressure on blade surface predicted by 3D inverse design method. The design matrix is then used to create a surrogate model where optimization is run to find the best tradeoff between cavitation and efficiency. This optimized geometry is manufactured and tested and is found to be 3.9% more efficient than the baseline with little cavitation at high flow. In the second approach the 3D inverse design method output is used to compute the efficiency and cavitation parameters and this leads to considerable reduction to the computational time. The resulting optimized geometry is found to be similar to the more computationally expensive solution based on 3D CFD results. In order to compare the inverse design based optimization to the conventional optimization an equivalent optimization is carried out by parametrizing the blade angle and meridional shape. Two different approaches are used for conventional optimization one in which the blade angle at TE is not constrained and one in which blade angles are constrained. In both cases larger variation in head is obtained when compared with the inverse design approach. This makes it impossible to create an accurate surrogate model. Furthermore, the efficiency levels in the conventional optimization is generally lower than the inverse design based optimization.

Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model

2021 ◽  
pp. 283-299
Author(s):  
Wenning Zhang ◽  
Qinglei Zhou ◽  
Chongyang Jiao ◽  
Ting Xu
Keyword(s):  
Test Data ◽  
Surrogate Model ◽  
Firefly Algorithm ◽  
Test Data Generation ◽  
Data Generation ◽  
Multi Objective

Multi-objective optimisation of cost–benefit of urban flood management using a 1D2D coupled model

2011 ◽  
Vol 63 (5) ◽  
pp. 1053-1059 ◽  
Author(s):  
S. W. Delelegn ◽  
A. Pathirana ◽  
B. Gersonius ◽  
A. G. Adeogun ◽  
K. Vairavamoorthy
Keyword(s):  
Flow Model ◽  
Coupled Model ◽  
Cost Benefit ◽  
Flood Management ◽  
Flood Damage ◽  
Surface Flow ◽  
Nsga Ii ◽  
Urban Flood ◽  
Multi Objective ◽  
Evolutionary Optimisation

This paper presents a multi-objective optimisation (MOO) tool for urban drainage management that is based on a 1D2D coupled model of SWMM5 (1D sub-surface flow model) and BreZo (2D surface flow model). This coupled model is linked with NSGA-II, which is an Evolutionary Algorithm-based optimiser. Previously the combination of a surface/sub-surface flow model and evolutionary optimisation has been considered to be infeasible due to the computational demands. The 1D2D coupled model used here shows a computational efficiency that is acceptable for optimisation. This technological advance is the result of the application of a triangular irregular discretisation process and an explicit finite volume solver in the 2D surface flow model. Besides that, OpenMP based parallelisation was employed at optimiser level to further improve the computational speed of the MOO tool. The MOO tool has been applied to an existing sewer network in West Garforth, UK. This application demonstrates the advantages of using multi-objective optimisation by providing an easy-to-comprehend Pareto-optimal front (relating investment cost to expected flood damage) that could be used for decision making processes, without repeatedly going through the modelling–optimisation stage.

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