Multi-objective optimization of the cascade parameters of a torque converter based on CFD and a genetic algorithm

2021 ◽  
pp. 095440702098055
Author(s):  
Zilin Ran ◽  
Wenxing Ma ◽  
Chunbao Liu ◽  
Jing Li
Keyword(s):  
Genetic Algorithm ◽  
Flow Field ◽  
Surrogate Model ◽  
High Efficiency ◽  
Cfd Simulation ◽  
Pareto Frontier ◽  
Torque Converter ◽  
Solution Set ◽  
Multi Objective Optimization ◽  
Multi Objective

It is hard to simultaneously improve the peak efficiency (η *) and the width of the high-efficiency region ( Gη) for a hydrodynamic torque converter. A combination of comprehensive CFD simulation and multi-objective optimization was pretested. The elaborate CFD simulation calculation included a reasonable mesh layout, a robust algorithm and a correct turbulence model, whose results were also experimentally verified. In our study, the Kriging surrogate model was first used to construct a nonlinear relationship between the inlet and outlet angle and the economic performance index of the hydrodynamic torque converter. To ensure that the accuracy of the surrogate model meet the requirements, we also used 10 sets of sample points to verify the accuracy of our surrogate model. The accuracy is found to meet the requirements, which shows that the accuracy of the constructed surrogate model is relatively high. We choose to apply the second-generation non-dominant sorting genetic algorithm (NSGA-II) to solve our problem. After solving the Pareto frontier solution set, we obtain a set of global optimal solutions on the Pareto frontier solution set. The optimization results show that the η * is increased by 2.49% and that the Gη is increased by 14.23%. We extracted the flow field structure near the turbine region, characterized the difference between original and optimal model from the flow field perspective, and demonstrated the accuracy of our optimization results. Finally, we used CFD to verify our optimization results, further illustrating the accuracy of the optimization results prediction. Literature research indicates that a large amount of experiments to optimize the η * and the Gη of the hydrodynamic torque converter will bring huge trial cost and time cost. We conclude from our research that the proposed calculation method can solve such problems well.

Steam Turbine Exhaust Optimization Based on Gaussian Covariance Networks Using Transient CFD Simulations

2018 ◽  
Author(s):  
Kevin Cremanns ◽  
Dirk Roos ◽  
Andreas Penkner ◽  
Simon Hecker ◽  
Christian Musch
Keyword(s):  
Steam Turbine ◽  
Surrogate Model ◽  
Power Plants ◽  
Thermal Loading ◽  
High Efficiency ◽  
Cfd Simulation ◽  
Surrogate Models ◽  
Training Data ◽  
Multi Objective Optimization ◽  
Multi Objective

Renewable energies are increasingly contributing to the overall volume of the electricity grid and demand besides high efficiency, greater flexibility of the conventional fossil power plants. To optimize these objectives, extensive CFD calculations are required in most cases. For example, transient CFD calculations are only rarely combined with an optimizer because of their high demand on computational resources and time. Surrogate models, which are mathematical methods to learn and approximate the relationship between input and output parameters, are a common way to solve these problems. Once they are trained, they can perform the evaluations within seconds and replace the expensive simulation. Of course, real calculations are still needed to generate the training data. Therefore, it is useful to apply efficient and sequentially extensible design plans. This paper presents a new surrogate model method, based on a deep neural network learning the non-stationary hyperparameters of combined Gaussian process covariance matrices. It is used to approximate the complex and time consuming transient CFD simulation of a combined high-intermediate pressure steam turbine double shell outer casing. To minimize the exergy loss, the exhaust geometry is optimized in a single and multi-objective optimization on the surrogate models. The multi-objective optimization also includes the uniform velocity distribution of the steam in different areas of the casing, to predict the thermal loading of the steam turbine inner casing and to avoid an imbalanced thermal loading. A sequential sampling approach combined with a sensitivity analysis is used to find the minimum number of samples needed to train the surrogate models in order to gain sufficient prediction quality. Additionally, the paper describes the initial geometry, its numerical setup and the required control mechanisms to avoid noisy designs, which might complicate the surrogate model training. There is also a comparison of the initial and chosen optimal designs.

Multi-objective optimization design for the blade angles of hydraulic torque converter with adjustable pump

2020 ◽  
Vol 234 (13) ◽  
pp. 2523-2536
Author(s):  
Zhi-Ying Zheng ◽  
Quan-Zhong Liu ◽  
Yong-Kang Deng ◽  
Biao Li
Keyword(s):  
Performance Optimization ◽  
Optimization Design ◽  
High Efficiency ◽  
Influencing Factor ◽  
Torque Converter ◽  
Multi Objective Optimization ◽  
Trailing Edge ◽  
Multi Objective ◽  
Second Stage ◽  
Hydraulic Torque Converter

To improve the efficiency of a hydraulic torque converter with adjustable pump at low load and thus increase the operation scope of high efficiency, multi-objective optimization design is carried out for the blade angles by incorporating three-dimensional steady computational fluid dynamics numerical simulation, design of experiments, Kriging surrogate model and multi-objective genetic algorithm. The results show that the angle of blade trailing edge in first-stage stator is the main influencing factor of the efficiency of hydraulic torque converter with adjustable pump. All the peak efficiencies of hydraulic torque converter with adjustable pump at three openings of the pump are improved after optimization, and the increased extent increases with decreasing opening of the pump. The operation scope of high efficiency consequently increases from 2.46 to 2.67. Besides, the improvement for the efficiency of hydraulic torque converter with adjustable pump is achieved by increasing the efficiency of the pump. The increase of angle of blade trailing edge in first-stage stator and the decrease of angle of blade leading edge in second-stage turbine after optimization induce the positive angle of attack at the inlet of second-stage turbine, thus realizing the performance optimization of hydraulic torque converter with adjustable pump. This also explains the increased proportion of the torque of second-stage turbine at larger speed ratios after optimization and the fact that the angle of blade trailing edge in first-stage stator is the main influencing factor of the efficiency of hydraulic torque converter with adjustable pump. The established multi-objective optimization method provides a reference solution for the optimization design of blade angles and for the improvement of integrated efficiency of hydraulic torque converter.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Marcelo Ramos Martins ◽  
Diego F. Sarzosa Burgos
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Ship Design ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Total Cost ◽  
Multi Objective ◽  
The Cost

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies.

An Information-Theoretic Entropy Metric for Assessing Multi-Objective Optimization Solution Set Quality

2003 ◽  
Vol 125 (4) ◽  
pp. 655-663 ◽  
Author(s):  
Ali Farhang-Mehr ◽  
Shapour Azarm
Keyword(s):  
Evolutionary Algorithms ◽  
Pareto Frontier ◽  
Optimization Techniques ◽  
Solution Set ◽  
Multi Objective Optimization ◽  
Information Theoretic ◽  
Multi Objective ◽  
Speed Reducer ◽  
Quantitative Basis

An entropy-based metric is presented that can be used for assessing the quality of a solution set as obtained from multi-objective optimization techniques. This metric quantifies the “goodness” of a set of solutions in terms of distribution quality over the Pareto frontier. The metric can be used to compare the performance of different multi-objective optimization techniques. In particular, the metric can be used in analysis of multi-objective evolutionary algorithms, wherein the capabilities of such techniques to produce and maintain diversity among different solution points are desired to be compared on a quantitative basis. An engineering test example, the multi-objective design optimization of a speed-reducer, is provided to demonstrate an application of the proposed entropy metric.

Multi-objective optimization design for a hydrodynamic retarder based on CFD simulation considering cavitation effect

2021 ◽  
pp. 095440622110145
Author(s):  
Konghua Yang ◽  
Chunbao Liu ◽  
Qingtao Wu ◽  
Xuesong Li
Keyword(s):  
Flow Field ◽  
Optimization Design ◽  
Cfd Simulation ◽  
Internal Flow ◽  
Structure Design ◽  
Multi Objective Optimization ◽  
Hexahedral Mesh ◽  
Nsga Ii ◽  
Multi Objective ◽  
Cavitation Effect

It is important to suppress cavitation phenomenon for lower vibration and noise, which can be realized by structure optimization to reduce cavitation bubbles of flow field. Nonetheless, performance factors in hydrodynamic retarder are usually conflicted when conducting a structure design, it is hard to simultaneously restrain cavitation and improve the retarding performance. In our study, a combination of comprehensive CFD simulation and multi-objective optimization is developed to improve the retarding torque ([Formula: see text]), lessen the volume of Retarder ([Formula: see text]) and reduce the volume of bubbles ([Formula: see text]) in the internal flow field. First, the elaborate CFD simulation calculation, included a refined hexahedral mesh and the stress-blended eddy simulation (SBES), is proposed to investigate the unsteady flow field considering the cavitation, and its accuracy is validated by experimental data. Then, the RSM (Respond Surface Method) approximation model is constructed by combination of DOE (Design of Method) and CFD methods. The NSGA-II (Non-Dominated Sorting Genetic Algorithm) is selected as multi-objective optimization algorithm, and the weight and scale factor of each sub objective are specified. The optimization results, verified by theoretical calculation, show that [Formula: see text] is increased by 22%–24%, [Formula: see text] is reduced by 32%–45% and [Formula: see text] is reduced by 1%. Furthermore, the comparison of the vortex distributions before and after optimization demonstrates that the optimization improves the flow field impact and pressure loss in the retarder and reduces the number of bubbles resulting in the increasing vortex. Additionally, parameters’ effect on the cavitation and the braking performance are analyzed to efficiently achieve the best comprehensive performance of the retarder design. The newly-developed optimization method, which can understand the optimization principle and guide a balance between the cavitation and the retarding performance improvement, will reduce huge trial cost and time cost in the manufacture.

Design Optimization of a Centrifugal Compressor Impeller by Multi-Objective Genetic Algorithm

2009 ◽  
Author(s):  
Jin-Hyuk Kim ◽  
Jae-Ho Choi ◽  
Afzal Husain ◽  
Kwang-Yong Kim
Keyword(s):  
Genetic Algorithm ◽  
Centrifugal Compressor ◽  
Surrogate Model ◽  
Total Pressure ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Compressor Impeller

This paper presents design optimization of a centrifugal compressor impeller with hybrid multi-objectives evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Latin hypercube sampling of design of experiments is used to generate design points within the selected design space. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε-constraint strategy for local search coupled with surrogate model is used for multi-objective optimization. The surrogate model, Radial Basis Neural Network is trained on the numerical solutions by carrying out leave-one-out cross-validation for the data set. The trade-off between the two objectives has been found out and discussed in light of the Pareto-optimal solutions. The optimization results show that isentropic efficiencies and total pressure ratios of the cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

MULTI-OBJECTIVE OPTIMIZATION OF OFFSHORE TSUNAMI OBSERVATION POINTS BY GENETIC ALGORITHM

2020 ◽  
Vol 76 (2) ◽  
pp. I_325-I_330
Author(s):  
Kazutoshi KURAMOTO ◽  
Fumiyasu MAKINOSHIMA ◽  
Anawat SUPPASRI ◽  
Fumihiko IMAMURA
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective
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