Principal optimization of semi-submersible support platform based on multi-island genetic algorithm

Mixed flow pumps driven by hydraulic motors have been widely used in drainage in recent years, especially in emergency pump trucks. Limited by the power of the truck engine, its operating efficiency is one of the key factors affecting the rescue task. In this study, an automated optimization platform was developed to improve the operating efficiency of the mixed flow pump. A three-dimensional hydraulic design, meshing, and computational fluid dynamics (CFD) were executed repeatedly by the main program. The objective function is to maximize hydraulic efficiency under design conditions. Both meridional shape and blade profiles of the impeller and diffuser were optimized at the same time. Based on the CFD results obtained by Optimal Latin Hypercube (OLH) sampling, surrogate models of the head and hydraulic efficiency were built using the Radial Basis Function (RBF) neural network. Finally, the optimal solution was obtained by the Multi- Island Genetic Algorithm (MIGA). The local energy loss was further compared with the baseline scheme using the entropy generation method. Through the regression analysis, it was found that the blade angles have the most significant influence on pump efficiency. The CFD results show that the hydraulic efficiency under design conditions increased by 5.1%. After optimization, the incidence loss and flow separation inside the pump are obviously improved. Additionally, the overall turbulent eddy dissipation and entropy generation were significantly reduced. The experimental results validate that the maximum pump efficiency increased by 4.3%. The optimization platform proposed in this study will facilitate the development of intelligent optimization of pumps.

Based on the system reserve energy index as objective optimization function of the structure vibration system, sets up the multi-objective optimal disposition mathematical model which refer to the system control gain and the sensors and actuators placements, quantities, lengths of the active vibration control system, adopts the multi-island genetic algorithm (MIGA) as optimization strategy. Experiments with intelligent cantilever beam as an example, the vibration control effect is very distinct. Hence, approves the method correctness and feasibility.

Elastic wheel is an important component of the low floor tram using in urban rail transit, so it is of significance to optimize its structure and predict its service lifetime. Firstly, the fatigue characteristics of rubber material are studied based on the crack energy theory, and a finite element model of an elastic wheel for fatigue lifetime prediction is constructed. The numerical simulation results show that the fatigue lifetime of such a elastic wheel does not match with the expected one. Secondly, based on the orthogonal numerical test and the Sobol model, an improved sensitivity analysis method is proposed. Taking the minimum strain as an optimization objective, and selecting the structural parameters determined by the sensitivity analysis method as the design variables, the second-order response surface model is established, and the multi-island genetic algorithm is used to carry out the single objective and multi-factor optimization design of the rubber bodies of elastic wheel under the condition of stiffness constraint. The simulation results of the elastic wheel show that the fatigue lifetime of the optimized structure is 30% higher than that of the original one. Finally, the optimized elastic wheel is tested, and the results show that the stiffness coefficient, and the fatigue lifetime as well, are all satisfy with the design requirements.

The fatigue life of a spherical hinge suspender connecting the locomotive structural frame and axle box is the key factor affecting the reliability of locomotive body. Firstly, the ABAQUS software is used to establish the finite element model of a spherical hinge suspender, and the fatigue life of the spherical hinge suspender is predicted. The result shows that the fatigue life of the spherical hinge suspender does not satisfy with the expected life. Then, the SolidWorks is used to establish a 3D model of spherical hinge suspender, and the HyperMesh is adopted to mesh the rubber parts. The variables of support reaction force and displacement are derived by using ABAQUS to calculate the radial stiffness of spherical hinge suspender. The geometric parameters of free surface of spherical hinge are selected as the design variables, and the structural optimization with the fatigue life as an objective function is carried out under the condition of radial stiffness restraint. Under the Isight environment, the multi-island genetic algorithm is used to obtain the reasonable free surface geometric parameters of spherical hinge. The result shows that the fatigue life of the optimized spherical hinge suspender satisfies the design requirements. Finally, the stiffness tests for the original and optimized structures are individually carried out in a radial loading mode, the results show that the calculated stiffness coefficient of the optimized structure is consistent with the test one, and the simulation life is well fit with that obtained from the fatigue life curve.

Collaborative optimization with relaxation factor is proposed for the lines design of an underwater vehicle. The hydrodynamic performances and energy consumption are considered in optimization framework. Hydrodynamic performances include the resistance, sway force and yaw moment. The efficient power of the propeller is selected to reflect the energy consumption. Analytic hierarchy process (AHP) combined with Delphi method is used to allocate the weights of disciplines in the objective function at the top level. A gradient-based algorithm, sequential quadratic programming (SQP) in combination with an intelligent-based algorithm, the multi-island genetic algorithm (MIGA) is taken into account as the optimization algorithm. To increase the efficiency of optimization, an approximate model based on optimal Latin hypercube and radial basis function (RBF) is introduced to replace the time-consuming discipline analysis model. Full-appendage SUBOFF model is used to test the proposed optimization scheme. The optimization results show that the drag of the underwater vehicle is reduced by 2.05 %, the lateral force by 6.38 %, the yaw moment by 5.90 %, and the energy consumption by 2.15 %. Compared with a single algorithm (e.g., PSO), the proposed hybrid algorithm (MIGA-SQP) reduces the value of the comprehensive objective function by 2.5-4.8 %.The innovations of this paper are as follows: 1. The Delphi-AHP method is combined with the cooperative optimization of relaxation factors to improve the objectivity of weights; 2. An OLH-RBF surrogate model is constructed, which increases the CFD calculation efficiency by 4 times.

Thermoelectric generators (TEGs) have become a topic of interest for vehicle exhaust energy recovery. Electrical power generation is deeply influenced by temperature differences, temperature uniformity and topological structures of TEGs. When the dimpled surfaces are adopted in heat exchangers, the heat transfer rates can be augmented with a minimal pressure drop. However, the temperature distribution shows a large gradient along the flow direction which has adverse effects on the power generation. In the current study, the heat exchanger performance was studied in a computational fluid dynamics (CFD) model. The dimple depth, dimple print diameter, and channel height were chosen as design variables. The objective function was defined as a combination of average temperature, temperature uniformity and pressure loss. The optimal Latin hypercube method was used to determine the experiment points as a method of design of the experiment in order to analyze the sensitivity of the design variables. A Kriging surrogate model was built and verified according to the database resulting from the CFD simulation. A multi-island genetic algorithm was used to optimize the structure in the heat exchanger based on the surrogate model. The results showed that the average temperature of the heat exchanger was most sensitive to the dimple depth. The pressure loss and temperature uniformity were most sensitive to the parameter of channel rear height, h 2. With an optimal design of channel structure, the temperature uniformity can be greatly improved compared with the initial exchanger, and the additional pressure loss also increased.

In order to explore the optimization and design method of the rough surface on a vehicle body, a pitted rough surface, a convex rough surface and a grooved rough surface are respectively arranged at the rear of the MIRA model. Computational fluid dynamics and wind tunnel tests are adopted to analyse the relationships between the aerodynamic drag and the factors of the rough surface. The distance L and the height H of the surface roughness are considered as the design variables, and the aerodynamic drag coefficient CD is the objective evaluation function. Sample points are obtained by the Latin hypercube sampling method, and the kriging approximate model is set up according to the sample points and the response values. Then the multi-island genetic algorithm is used as the optimization algorithm to find the optimum solution. The aerodynamic effects of the rough surface model, with optimization and without optimization, are compared and analysed. Experimental and simulation results show that the aerodynamic drag coefficient further decreases after parameter optimization and that the optimization method is valid and general. The results obtained may provide a helpful reference and guidance for future rough surface applications on a vehicle body.

In order to solve the problems arising from the manual calibration method in the developing process of vehicle automatic transmission control unit (TCU), known as time-consuming, heavy workload, high cost and over-dependence on subjective experience, this article researches on a virtual calibration method based on an approximate model to obtain optimal parameters for TCU. The neural network approximate model is established from the test data chosen with the method of DoE (Design of Experiment). The virtual calibration method is then conducted through Optimal Latin Hypercube Design (OLHD) and multi-island genetic algorithm (MIGA) to search the optimal parameters. By comparing the new calibration method with original manual one on the condition of gear 1 up to gear 2, the result shows that the new method can increase the efficiency significantly.

Calibration methodology for energy management system of a plug-in hybrid electric vehicle

Optimal design of an autonomous underwater helicopter's shape based on combinatorial optimization strategy

In this study, we improved the energy acquisition efficiency of tidal turbines with bionic airfoils by optimizing the seagull, long-eared owl, sparrowhawk, and two-dimensional airfoils, thereby obtaining a better lift–drag ratio. We used Isight software to integrate the Integrated Computer Engineering and Manufacturing (ICEM) software and used Fluent simulation software, batch operation file, and multi-island genetic algorithm to maximize the lift–drag ratio as the objective function for optimizing the original airfoil. The optimized upper airfoil profile was distributed upward from the starter wing, and the thickness of the upper wing was the greatest. Meanwhile, the lower airfoil profile was thinner and more curved. The thickness of the three airfoils was distributed backward from the front of the wing, with the maximum thickness at the front, and the maximum camber was distributed backward from the front. The three optimized wings exhibited a maximum lift–drag ratio at an angle of attack of approximately 5°, with the sparrowhawk wing having a maximum lift–drag ratio of 80.87 at an angle of attack of 6°, the seagull wing having a maximum lift–drag ratio of 76.82 at an angle of attack of 4°, and the long-eared owl wing having a maximum lift–drag ratio of 68.43 at an angle of attack of 5°.

Aero-thermal optimization on multi-rows film cooling of a realistic marine high pressure turbine vane

In allusion to the transmission of a new type of truck with three shafts four levels, it's Collaborative Optimization framework including mathematical model is set up. Through taking multidisciplinary optimization software iSIGHT as optimization design platform to integrate disciplinary analysis tool ANSYS and Response Surface Method, Collaborative Optimization of transmission is completed and satisfying optimization scheme is obtained, where Multi-Island Genetic Algorithm is adopted for system-level optimization and Sequential Quadratic Programming is adopted for subsystem-level optimization. The optimization method used in the paper provides important references for optimization design of other complex systems.

This paper systematically investigates the infrared staking (IS) process via process modeling, numerical simulations, and experimental validation. The objective of this work is to optimize process parameters for improving the joint strength of polypropylene car door trim. A holistic approach based upon numerical simulation was proposed considering the manufacturing history of sequential processing steps, including heating, forming, and cooling. The process parameters evaluated were heating time, cooling time, and airflow rate, while the structural testing force was considered as the objective. Firstly, numerical simulations were applied in conjunction with the Box-Behnken design (BBD) experimental method and a response surface methodology (RSM) to create the quadratic mathematical model of the testing force. An analysis of variance (ANOVA) was then conducted to investigate the adequacy of the model and to identify significant factors. Finally, a multi-island genetic algorithm (MIGA) was applied to determine optimal values of process parameters and the resulting response. The testing force was maximized at the optimal parameters of 14 s, 14 s, and 60 ft3/h for heating time, cooling time, and airflow rate, respectively. Correlation between simulated and experimental results was conducted to illustrate the effectiveness of the proposed approach. This work is expected to contribute toward improving the manufacturing efficiency of the infrared staking process.

Nowadays the biggest challenge to GIS is the model design of theme analyzing. Many GIS applications need more functions in analysis and decision-making than in data organization or visualization. While confronted with an application like planning a new metro line in a city, the typical GIS cannot accomplish it by itself, unless some human experts or artificial intelligence technology are involved. Numbers of GIS applications essentially are models of theme analyzing, and they are also examples of problem solving in AI. Therefore many theories and technologies from AI can be embedded in GIS to strengthen its ability of automatic analysis. Fortunately mathematical programming is a kind of powerful tool to settle problems with a demand of optimization under some constraint conditions. Many GIS applications happen to be this type, and some mathematical models can usually approximate them accurately. Two new questions aroused. The first is what objective function and constraint conditions that constitute a mathematical programming should be, and the second is how to acquire those mathematical expressions. Due to the establishment of a mathematical programming is highly knowledge-based, so an AI subsystem is designed to autonomously create a mathematical programming. This problem has a typical characteristic of multiple domains crossing from perspective of Knowledge Engineering, so some novel knowledge-processing methods, like knowledge dependence, knowledge transition and knowledge reference, are studied. And a knowledge system represented by frames and a reference-based knowledge-using method are presented here.