Multi-objective optimization of suspension parameters for rail vehicles based on a virtual prototype surrogate model

ABSTRACTThis work deals with multi-objective optimization for the suspension parameters of railcars and proposes an optimization strategy combining the collaborative optimization method and the metamodel method to optimize the suspension parameters of railcars. In this strategy, the system-level optimization and subsystem-level optimization problems are efficiently calculated by the collaborative optimization method, and the metamodel replaces the actual model in the subsystem-level to improve the convergence and robustness of the optimization algorithm. Finally, an optimal combination of suspension parameters is determined by the effective implementation of the proposed optimization method, and compared with the original design in terms of performance and robustness. Here, the lateral running stability and vertical running smoothness of railcars were increased by 10.96% and 10.1%, respectively, and the derailment coefficient and the reduction ratio of wheel weight of railcars were increased by 6.14% and 6.36%, respectively. The optimization results show that the dynamic performance of railcars is improved remarkably with the robust collaborative optimization of the suspension parameters, and the reliability and effectiveness of the proposed robust collaborative optimization method for the suspension parameters' optimization of the railcars have also been verified.Abbreviation: SBD: simulation-based design; DOF: degree of freedom; RCO: robust collaborative optimization

The hub motor significantly increases the unsprung mass of electric in-wheel vehicles, which deteriorates the ride comfort and safety of vehicles and which can be effectively improved by optimizing the main suspension parameters of vehicles reasonably, so a multi-objective optimization method of main suspension parameters based on adaptive particle swarm algorithm is proposed and the dynamic model of a half in-wheel electric vehicle is established. Taking the stiffness coefficient of the suspension damping spring and damping coefficient of the damper as independent variables, the vertical acceleration of the body, the pitch acceleration and the vertical impact force of the hub motor as optimization variables, and the dynamic deflection of the suspension and the dynamic load of the wheel as constraint variables, the multi-objective optimization function is constructed, and the parameters are simulated and optimized under the compound pavement. The simulation results show that the vertical acceleration and pitch acceleration are reduced by 20.2% and 18.4% respectively, the vertical impact force of the front hub motor is reduced by 3.7%, and the ride comfort and safety are significantly improved.

There is an increasing trend in the use of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems of the allocation of water resources. However, typically the outcome is a set of Pareto optimal solutions which make up a trade-off surface between the objective functions. For decision makers to choose a satisfactory alternative from a set of Pareto-optimal solutions, this paper suggests a new method based on least squares support vector machine (LSSVM) and k-means clustering for ranking the optimal solutions for the multi-objective allocation of water resources. First, the k-means clustering method was adopted to reduce the large set of solutions to a few representative solutions. Then, to capture and represent the decision maker's preferences as well as to select the most desirable alternative, the LSSVM method was applied to obtain the utility value for each representative solution. According to the magnitude of the utility values, the final priority orders of the representative solutions were determined. Finally, this methodology was applied to rank the Pareto optimal solution set obtained from the multi-objective optimization problems of water resources allocation for the water-receiving areas of the South-to-North Water Transfer Project in Hebei Province, China. Moreover, the comparisons of the proposed method with the information entropy method and the artificial neural network (ANN) model were given. The results of the comparison indicate that the proposed method has the ability to rank the non-dominated solutions of the multi-objective operation optimization model and that it can be employed for decision-making on water allocation and management in a river basin.

Multi-objective shape optimization of autonomous underwater vehicle by coupling CFD simulation with genetic algorithm

Coordinated optimization method for suppressing transient overvoltage caused by HVDC commutation failure considering large wind power integration

This paper aims to promote rural revitalization and support the long-term development of national agriculture by optimizing planting strategies, developing organic farming according to local conditions, and rationalizing the use of cultivated land. Based on the data related to cultivated land area in North China from 2017 to 2022, a multi-objective function optimization model is constructed, which comprehensively considers constraints such as land type, crop planting area limitations, crop continuous cropping effects, and legume crop rotation requirements. On this basis, the target-MOTAD model and importance sampling-Monte Carlo simulation are introduced to establish a comprehensive objective function for maximizing revenue and minimizing risk, which is solved by a hybrid genetic algorithm. Furthermore, complementarity coefficients and substitution coefficients are introduced to establish a market response model based on principal-agent game, which is dynamicized and solved by the NSGA-II algorithm, aiming to provide scientific decision support for agricultural producers and improve their revenue.

In order to optimize the local search efficiency of multi-objective parameters of flux switching permanent motor based on traditional NSGA-II algorithm, an improved NSGA-II (iNSGA-II) algorithm is proposed, with an anti-redundant mutation operator and forward comparison operation designed for quick identification of non-dominated individuals. In the initial stage of the iNSGA-II algorithm, half of the individual populations were randomly generated, while the other half was generated according to feature distribution information. Taking the flux switching permanent motor stator/rotor gap, permanent magnets width, stator tooth width, rotor tooth width and other parameters as optimization variables, the flux switching permanent motor maximum output shaft torque and minimum torque ripple are taken as optimization objectives, thus a multi-objective optimization model is established. Real number coding was adopted for obtaining the Pareto optimal solution of flux switching permanent motor structure parameters. The results showed that the iNSGA-II algorithm is better than the traditional NSGA-II on convergence. A 1.8L TOYOTA PRIUS model was selected as the prototype vehicle. By using the optimized parameters, a joint optimization simulation model was established by calling ADVISOR’s back-office function. The simulation results showed that the entire vehicle’s 100-km acceleration time is under 8 s and the battery’s SOC value maintains at 0.5–0.7 in the entire cycle, implying that the iNSGA-II algorithm optimizes the flux switching permanent motor design and is suitable for the initial design and optimizing calculation of the flux switching permanent motor.

Selection of electricity fee payment site (EFPS) is a complex multi-objective optimization problem. Proper site selection of EFPS can not only facilitate the users to pay the electricity payments, but also save costs in running processes of the power enterprises (PEs). Firstly, two-dimensional genetic code is improved and the multi-object optimization model of EFPS is established. Secondly, considering a model city as an example, the NSGA-II algorithm is applied to solve the optimization model. Finally, the Pareto optimal solution set and several locating schemes based on different preferences are obtained by simulation. In the follow-up of the site selection work, experts can choose the method of adaptive weights and other methods to determine which scheme to apply. Example analysis shows that the results of the study has strong applicability, and it has practical significance to the site selection of the EFPS or other business halls.

There are many current active orthosis designs to assist with disabilities related to foot-drop, with most of them intervening during the whole gait cycle. We propose that, for the treatment of foot-drop, it is possible to design an ankle–foot device that will assist a walking user only during the dorsiflexion stages of the gait, avoiding interference with other stages, by using a single actuator with a simple transmission and a suspension block. This design can be improved by the use of multi-objective optimization to obtain a static set of parameters that are applicable to varying initial conditions. We present a computer simulation study of an active ankle–foot orthosis design, based on the interaction of a cam and lever with a suspension block, with the objective of assisting only with dorsiflexion during the gait cycle, leaving the rest of the movements unimpeded while reducing the complexity and weight of the device. This design is validated using a full simulation environment that includes the movements of the lower leg and foot, as they interact with our device and a ground element. As part of the design and validation, we found sets of mechanical and control parameters that provoke adequate output behavior of the orthosis to help the wearer perform a moderate-speed, normal gait. To optimize the design, we proposed three objectives to warrant ankle angle accuracy, minimal oscillations, and low energy consumption. A set of solutions was obtained with multi-objective optimization algorithms NSGA-II and RVEA to tune the parameters of the active orthosis. The solutions set from RVEA resulted in lower mean and standard deviation values for the oscillations and energy objectives in comparison to the solutions from NSGA-II, while for the MSE objective, NSGA-II obtained lower mean and standard deviation; for the energy consumption objective, the mean score using RVEA is 17% less than with NSGA-II. The orthosis is shown to be robust to differences in initial ankle angles. We observed that it is possible to obtain a broad set of solutions with a good performance during the gait cycle in controlled spaces and that in this application, the RVEA algorithm results in a better option for optimization to balance the objectives.

Multi-objective optimization method for reactor shielding design based on SMS-EMOA

Train-tram railway vehicles implement the connection between urban tramlines and the surrounding railway network. Train-tram railway vehicles, which use existing infrastructure, can help to avoid large investments in new railways or tramlines and make interchanges between city center and surrounding cities unnecessary. However, present train-tram rail vehicle cannot carry out the integration of operating by means of high speed in intercity railways with operating on small radius of curvature in inner city tramlines. This paper aims to develop a new model for solid wheelsets train-tram railway vehicles, which will not only pass the curve of 25mR radius of curvature traveling on inner city tramlines with the speed of 18 km/h, but also can travel on straight railway with 200 km/h high speed between intercity. In this paper, a new train-tram model, including five car-body and five motor bogies with ten traction motors, is addressed. Expect as a real rail vehicle testing, this study prefer virtual simulation, which is an effective way to show the rail vehicle performance, such as ride stability, ride comfort and ride safety, by means of evaluating the dynamic characteristics of rail vehicle. Moreover, Design of Experiment (DOE) method is used to optimize solid wheelsets bogie system on improving passenger comfort, safety and stability of train-tram. Parameters of components of bogie system are tuned to minimize the derailment coefficient and the ride comfort index. The results shows that the best comfort index for passenger and minimum derailment coefficient are found. The results also show that this optimized new train-tram model is reliable and practical enough to be applied on real rail vehicle design.

In this paper, the combination of Pro\/E and ADAMS to build a virtual prototype model of gear transmission, the dynamic simulation of the gear meshing process, study on dynamic speed, mesh force bearing load, where, for the gear box of the engineering analysis and optimization design. A 3D modeling and designing software, established a parametric model of a gear box of the rack and pinion drive mechanism, which is used in large gantry milling machine XK2425-600. The data interface module MECHANISM/Pro is used to convert the Pro/E model data to ADAMS, and then the virtual prototype is built in it. The rotational speeds, meshing forces, and reactive loads at bearings are obtained by dynamic simulation of the virtual prototype, which can be used in finite element analysis as boundary condition, helped to calculate the strength and stiffness of the box.

In this study, Response Surface Methodology (RSM) and multi-objective genetic algorithm were used to obtain optimum parameters of the channels with frustum of a cone with better flow and heat transfer performance. Central composite face-centered design (CCF) was applied to the experimental design of the channel parameters, and on this basis, the response surface models were constructed. The sensitivity of the channel parameters was analyzed by Sobol’s method. The multi-objective optimization of the channel parameters was carried out with the goal of achieving maximum Nusselt number ratio (Nu/Nu0) and minimum friction coefficient ratio (f/f0). The results show that the root mean square errors (RSME) of the fitted response surface models are less than 0.25 and the determination coefficients (R2) are greater than 0.93; the models have high accuracy. Sobol’s method can quantitatively analyze the influence of the channel parameters on flow and heat transfer performance of the channels. When the response is Nu/Nu0, from high to low, the total sensitivity indexes of the channel parameters are frustum of a cone angle (α), Reynolds number (Re), spanwise spacing ratio (Z2/D), and streamwise spacing ratio (Z1/D). When the response is f/f0, the total sensitivity indexes of the channel parameters from high to low are Re, Z1/D, α and Z2/D. Four optimization channels are selected from the Pareto solution set obtained by multi-objective optimization. Compared with the reference channel, the Nu/Nu0 of the optimized channels is increased by 21.36% on average, and the f/f0 is reduced by 9.16% on average.

Injection molding (IM) is an ideal technique for the low-cost mass production of moderately thick plane lenses (MTPLs). However, the optical performance of injection molded MTPL is seriously degraded by the warpage and sink marks induced during the molding process with complex historical thermal field changes. Thus, it is essential that the processing parameters utilized in the molding process are properly assigned. And the challenges are further compounded when considering the MTPL molding energy consumption. This paper presents a set of procedures for the optimization of injection molding process parameters, with warpage, sink marks reflecting the optical performance, and clamping force reflecting the molding energy consumption as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the S/N response shows that these three objectives cannot reach the optimal values simultaneously. Second, considering the experimental data scale, the back propagation neural network updated by the particle swarm optimization method (PSO-BPNN) was applied to establish the complex nonlinear mapping relationship between the process parameters and these three trade-off objectives respectively. Then, the Pareto optimal frontier of the multi-objective optimization problem was attained by multi-objective particle swarm optimization using a mutation operator and dominance coefficient algorithm (OMOPSO). And the competitive relationship between these objectives was further confirmed by the corresponding pairwise Pareto frontiers. Additionally, the TOPSIS method with equal weights was employed to achieve the best optimal solution from the Pareto optimal frontier. The simulation results yielded that the maximum values of warpage, sink marks, and clamping force could be reduced by 7.44%, 40.56%, and 5.56%, respectively, after optimization. Finally, MTPL products were successfully fabricated.

In optimizing the propagation paths of automotive product design changes, we applied the complex network theory to automotive product structure modeling and established a directed weighted auto part network model. In this model, we defined the parts as the nodes, the physical connections between the parts as the edges, and the impacts of change propagation as the weights of the edges. According to the characteristics of change propagation, we constructed a multiobjective optimization model that simultaneously considered the development time, development cost, and change risk. Using a multiobjective particle swarm optimization (MOPSO) algorithm, we solved the model and obtained the Pareto set of optimal solutions, which provided a basis for designers to choose the optimal design change plan. We conducted the multiobjective optimization of the air-conditioning system design change propagation path in a car model to verify the feasibility and effectiveness of the method.