Investigation on the modeling and dynamic characteristics of a fast-response and large-flow water hydraulic proportional cartridge valve

2020 ◽  
Vol 234 (22) ◽  
pp. 4415-4432
Author(s):  
Mingxing Han ◽  
Yinshui Liu ◽  
Kan Zheng ◽  
Youchun Ding ◽  
Defa Wu
Keyword(s):  
Genetic Algorithm ◽  
Flow Rate ◽  
Dynamic Characteristics ◽  
Fast Response ◽  
Dynamics Simulation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Flow Capacity ◽  
Water Hydraulic ◽  
Large Flow

In large-power and high-pressure hydraulic systems, the maximum instantaneous flow rate is often several thousand liters per minute. Normal proportional valves are often difficult to meet their requirements for large flow rate and fast response at the same time. And the leakage of hydraulic oil will seriously pollute the environment. Therefore, a novel water hydraulic proportional valve with fast response and high flow capacity is presented for the large transient power hydraulic system in this paper. The valve utilizes a two-stage structure with two 2/2-way water hydraulic proportional valves as the pilot stage and a cartridge poppet valve as the main stage to achieve fast-response and large-flow capacity simultaneously. A detailed and precise nonlinear mathematical model of the valve considering both structural parameters and flow force is developed. A comprehensive performance optimization has been carried out, which can be mainly divided into computational fluid dynamics simulation optimization based on reducing flow force and multi-objective optimization based on genetic algorithm. The effects of double U-grooves' parameters on the flow force (flow-induced loads) have been studied in detail by numerical simulation. Through the grooves geometry optimization, the maximum flow force can be reduced by 10%. Then, the influences of structure parameters on the performance of step response have been studied, and the optimal parameters of the valve have been obtained by multi-objective optimization based on genetic algorithm. The maximum overshoot has been reduced from 15% to 6% (about 60%) and the adjusting time has been reduced from 58 ms to 48 ms. The dynamic characteristics of the valve have been improved effectively. Finally, a test apparatus which has the ability to provide transient large flow is built. The accuracy of simulation model and optimization design method is verified by test results.

Mathematical Modelling and Multi-Objective Optimization Design of a Large Flow Water Hydraulic Proportional Cartridge Valve

2017 ◽  
Author(s):  
Mingxing Han ◽  
Yinshui Liu ◽  
Huaijiang Tan ◽  
Defa Wu
Keyword(s):  
Dynamic Characteristics ◽  
High Performance ◽  
Step Response ◽  
Design Parameters ◽  
Main Stage ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Water Hydraulic ◽  
Simulation Results ◽  
Large Flow

The large flow water hydraulic proportional cartridge valve is one of the most important components and also the technical difficulties in the high performance large-tonnage engineering machinery, such as the die casting machine. The structure and principle of the large flow water hydraulic proportional cartridge valve are presented in this paper. The valve utilizes a two-stage structure with the high performance proportional valves as the pilot stage and the cartridge poppet valve as the main stage to overcome the fundamental tradeoff between the flow capacity and dynamic characteristics. A detailed and precise nonlinear mathematical model of the cartridge valve considering both structural parameters and nonlinear factors (compressibility of water, leakage, friction, flow force, etc.) is established. And then MATLAB/Simulink software is employed to build the simulation model and the dynamic simulation is carried out. Compared with the simulation results of the valve with different design parameters, the static and dynamic characteristics of the proportional cartridge throttle valve have been analyzed. The impacts of the parameters on the performance of step-response have been studied. Finally, based on multi-objective optimization method, the optimal parameters of the cartridge valve have been obtained. The simulation results show that the performances have been significantly improved.

Multi-objective optimization of hydro-viscous flexible drive for dynamic characteristics using genetic algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jianzhong Cui ◽  
Hu Li ◽  
Dong Zhang ◽  
Yawen Xu ◽  
Fangwei Xie
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Characteristics ◽  
Transmission Efficiency ◽  
Outer Radius ◽  
Transmission Model ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Dynamic Transmission Model

Purpose The purpose of this study is to investigate the flexible dynamic characteristics about hydro-viscous drive providing meaningful insights into the credible speed-regulating behavior during the soft-start. Design/methodology/approach A comprehensive dynamic transmission model is proposed to investigate the effects of key parameters on the dynamic characteristics. To achieve a trade-off between the transmission efficiency and time proportion of hydrodynamic and mixed lubrication, a multi-objective optimization of friction pair system by genetic algorithm is presented to obtain the optimal combination of design parameters. Findings Decreasing the engagement pressure or the ratio of inner and outer radius, increasing the lubricating oil viscosity or the outer radius will result in the increase of time proportion of hydrodynamic and mixed lubrication, as well as the transmission efficiency and its maximum value. After optimization, main dynamic parameters including the oil film thickness, angular velocity of the driven disk, viscous torque and total torque show remarkable flexible transmission characteristics. Originality/value Both the dynamic transmission model and multi-objective optimization model are established to analyze the effects of main design parameters on the dynamic characteristics of hydro-viscous flexible drive.

scholarly journals Multi-objective optimization of microfiltration of baker’s yeast using genetic algorithm

2017 ◽  
pp. 211-220 ◽  
Author(s):  
Natasa Lukic ◽  
Marija Bozin-Dakic ◽  
Jovana Grahovac ◽  
Jelena Dodic ◽  
Aleksandar Jokic
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Energy Consumption ◽  
Flow Rate ◽  
Transmembrane Pressure ◽  
Feed Flow Rate ◽  
Multi Objective Optimization ◽  
Permeate Flux ◽  
Multi Objective ◽  
Reduction Of Energy Consumption

This paper presents a multi-objective optimization model by applying genetic algorithm in order to search for optimal operating parameters of microfiltration of baker?s yeast in the presence of static mixer as a turbulence promoter. The operating variables were the suspension concentration, transmembrane pressure, and feed flow rate. Two conflicting objective functions, maximizing the permeate flux and maximizing the reduction of energy consumption, were considered. This multi-objective optimization problem was solved by using the elitist non-dominated sorting genetic algorithm in the Matlab R2015b software. The Pareto fronts along with the process decision variables correspondding to the optimal solutions were obtained. It was found that lower suspension concentrations (2-4.5 g/L), feed flow rate in the range 109-127 L/h, and transmembrane pressure of 1 bar were the optimal process parameters which yielded maximum permeate flux (177-191 L/(m2h)) and maximum reduction of energy consumption (44-50%). Finally, the results were compared with the previously published results obtained by applying desirability function approach. Given that genetic algorithms have generated multiple solutions in a single optimization run, the study proved that genetic algorithms are preferable to classical optimization methods.

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

Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods

2020 ◽  
Vol 40 (4) ◽  
pp. 360-371
Author(s):  
Yanli Cao ◽  
Xiying Fan ◽  
Yonghuan Guo ◽  
Sai Li ◽  
Haiyue Huang
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Regression Model ◽  
Process Parameters ◽  
Multi Objective Optimization ◽  
Entropy Weight ◽  
Multi Objective ◽  
Injection Molded ◽  
Plastic Parts

AbstractThe qualities of injection-molded parts are affected by process parameters. Warpage and volume shrinkage are two typical defects. Moreover, insufficient or excessively large clamping force also affects the quality of parts and the cost of the process. An experiment based on the orthogonal design was conducted to minimize the above defects. Moldflow software was used to simulate the injection process of each experiment. The entropy weight was used to determine the weight of each index, the comprehensive evaluation value was calculated, and multi-objective optimization was transformed into single-objective optimization. A regression model was established by the random forest (RF) algorithm. To further illustrate the reliability and accuracy of the model, back-propagation neural network and kriging models were taken as comparative algorithms. The results showed that the error of RF was the smallest and its performance was the best. Finally, genetic algorithm was used to search for the minimum of the regression model established by RF. The optimal parameters were found to improve the quality of plastic parts and reduce the energy consumption. The plastic parts manufactured by the optimal process parameters showed good quality and met the requirements of production.

Multi-objective optimization of squirrel cage fan for range hood based on Kriging model

2021 ◽  
pp. 095440622199586
Author(s):  
Qianhao Xiao ◽  
Jun Wang ◽  
Boyan Jiang ◽  
Weigang Yang ◽  
Xiaopei Yang
Keyword(s):  
Flow Rate ◽  
Optimization Design ◽  
Volume Flow Rate ◽  
Kriging Model ◽  
Volume Flow ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Squirrel Cage

In view of the multi-objective optimization design of the squirrel cage fan for the range hood, a blade parameterization method based on the quadratic non-uniform B-spline (NUBS) determined by four control points was proposed to control the outlet angle, chord length and maximum camber of the blade. Morris-Mitchell criteria were used to obtain the optimal Latin hypercube sample based on the evolutionary operation, and different subsets of sample numbers were created to study the influence of sample numbers on the multi-objective optimization results. The Kriging model, which can accurately reflect the response relationship between design variables and optimization objectives, was established. The second-generation Non-dominated Sorting Genetic algorithm (NSGA-II) was used to optimize the volume flow rate at the best efficiency point (BEP) and the maximum volume flow rate point (MVP). The results show that the design parameters corresponding to the optimization results under different sample numbers are not the same, and the fluctuation range of the optimal design parameters is related to the influence of the design parameters on the optimization objectives. Compared with the prototype, the optimized impeller increases the radial velocity of the impeller outlet, reduces the flow loss in the volute, and increases the diffusion capacity, which improves the volume flow rate, and efficiency of the range hood system under multiple working conditions.

Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1041-1051 ◽  
Author(s):  
H Sayyaadi ◽  
H R Aminian
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Heat Exchangers ◽  
Mixed Integer ◽  
Design Stage ◽  
Tube Length ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

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