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This paper focuses on parameter optimization for the actually manufactured test vehicle. This method achieves high-precision, rapid computation of vehicle dynamic performance while fully preserving the strongly coupled nonlinear dynamic properties of the system. Firstly, by employing twin modeling technology, the model accurately reflects the physical dynamic characteristics of the actual vehicle, enabling us to determine how much improvement the optimized vehicle dynamic response will exhibit compared to the current state. Next, a mathematical model for multi-parameter, multi-objective collaborative optimization is constructed using big data search, and key parameters significantly influencing vehicle dynamics are identified through Sobol sensitivity analysis for dynamic optimization. Finally, an improved multi-start parallel simulated annealing algorithm is proposed to enhance the computational efficiency and reliability of the optimization results. The results demonstrate significant improvement in the dynamic performance of the experimental vehicle, validating the effectiveness of the proposed method. This approach overcomes the limitations of traditional linearization treatments, providing a new perspective for dynamic optimization of complex coupled systems and demonstrating significant engineering application value in the field of rail transportation.

The transmission efficiency and power of the existing Magnetically Coupled Resonant Wireless Power Transfer (MCR WPT) system are affected by distance, load, coil, and other factors and cannot reach the optimal state simultaneously. This paper proposes a multi-objective parameter optimization of the system to improve its performance. By analyzing the LCC-S topology, the main parameters affecting the charging efficiency and output power are studied from the perspective of an equivalent circuit. The Multi-Objective Multi-Verse Optimizer (MOMVO) algorithm is employed to alter the growth mode of wormhole refreshment probability from linear to logarithmic, thereby enhancing the algorithm’s capacity for effective search. Furthermore, the introduction of an adaptive compression factor and Cauchy’s variance serves to achieve a balance between global and local convergence, thus enhancing the overall efficacy of the algorithm and escape from the local extremes. This achieves multi-objective parameter optimization while analyzing the optimized model. A physical platform is built to conduct experiments based on the optimized parameters. The results demonstrate that the optimized MCR-WPT system enhances its long-distance transmission performance. The optimal transmission distance of the system is 0.25 m, and the maximum output power is 127 W. Finally, the enhanced model’s efficacy is substantiated through the construction of a prototype system.

Pump inducers are usually employed within a limited flow rate range since the performance is known to drop out significantly far from their design point. Therefore, finding an optimal geometry that ensures efficient operation for a relatively wide range of flow rates is challenging. The present study tackles this problem using multi-objective optimization to identify optimal inducer configurations, delivering high performance for a wide flow range. 3D RANS single-phase turbulent simulations were performed using the k - ω turbulence model. The optimization was done by employing the Non-dominated Sorting Genetic Algorithm (NSGA-II) coupled with computational fluid dynamics (CFD). An established in-house flow optimization library (OPAL++) was used to automatically control the numerical simulations. The objective is to optimize the inducer geometrical parameters to simultaneously maximize the efficiency and pressure head curves, considering different flow rates, i.e., 80% (part-load), 100% (nominal), and 150% (overload) of the optimal flow rate for the considered pump. The optimization involves 8 most relevant design parameters, i.e., the axial blade length, blade sweep angle, blade pitch, hub taper angle, tip clearance gap, blade thickness at the hub, blade thickness at the tip, and the number of blades. A total of 5178 simulations over 37 generations have been needed to get a Pareto front containing 5 optimal configurations. This article discusses quantitatively the influence of each geometrical parameter on flow behavior and inducer performance. The results reveal in general that blade length, blade sweep angle, tip clearance gap, and blade thickness should be kept low for the considered application; inducers with high hub taper angles and 3 blades lead to optimal performance.

Double ceramic layer thermal barrier coatings (DLC-TBCs) are favored for combining the benefits of top and bottom ceramic materials. The thickness ratio of the top and bottom ceramic layers significantly impacts the performance of the DLC-TBCs. In the design process, it is generally desired to balance its thermal insulation properties with a long service life. Therefore, this study establishes a multi-objective parameter optimization design method based on NSGA-II to optimize the thickness of the CeYSZ/Al 2 O 3 DCL-TBCs. Experimental verification of the coating performance was conducted based on the optimization results. Firstly, based on theoretical and numerical models, a quantitative analysis was conducted on the effects of the thickness of each material in the CeYSZ/Al 2 O 3 DCL-TBCs system on thermal insulation and thermal stress. Space parameters were obtained using optimal Latin hypercube sampling, and a radial basis function (RBF) neural network surrogate model was constructed based on the numerical calculation results. Sensitivity analysis was employed to evaluate the impact of the total thickness of the TBCs and the thickness of the Al 2 O 3 ceramic layer on the objective function. Finally, NSGA-II was utilized for optimization. The obtained Pareto optimal solution set was validated, showing that the performance of the CeYSZ 190 μm/Al 2 O 3 120 μm DLC-TBCs satisfied the requirements. Therefore, TBCs of different thicknesses were sprayed and subjected to thermal insulation and thermal shock experiments. The results demonstrated that the optimized TBCs significantly improved service life without compromising thermal insulation, providing a new approach for the subsequent design of DLC-TBCs structures.

There are a large number of holes to be machined on aeroengine components such as blisks, casings, etc. In order to ensure position accuracy, these holes usually need to be drilled continuously in one process. To ensure the machining quality of holes, either replacing the cutting tools in advance leads to an increase in manufacturing costs, or adjusting process parameters leads to a decrease in production efficiency, which is difficult to meet the requirements of efficient and low-cost manufacturing. In response to this issue, this paper proposes a multi-objective optimization strategy for the process parameters of porous continuous drilling of superalloys alloys. A unified mathematical model for multi-objective optimization of drilling parameters has been established, and a tool life prediction model based on machining parameters and a machining process energy consumption model have been established as objective functions. The proposed optimization strategy can select different optimization strategies for different optimization objectives, including: maximum tool life, minimum machining energy consumption, and multi-objective drilling parameter optimization. Finally, experimental verification was conducted on the proposed strategy, and the results showed that the proposed optimization strategy can significantly reduce drilling processing energy consumption and increase the service life of drilling tools.

A twin-screw multiphase pump is essential equipment for the transfer of gas-liquid multiphase mixtures in oil and gas operations. This work addresses rotor deformation in real applications by correcting the rotor profile using the arc transition approach, eliminating teeth tips, mitigating local stress concentration, and reducing the danger of rotor deformation. Simultaneously, in conjunction with the oil and gas mixed transportation requirements of the Changqing Oilfield, the MPC208-67 twin-screw mixed transportation pump was engineered, and the essential structural specifications were established. This paper employs the Mixture multiphase flow model and the SST k-ω turbulence model to simulate the internal flow field of the pump in Changqing Oilfield, aiming to examine the impact of high-gas-content conditions on the pump’s performance and ensure it aligns with design specifications. The modeling findings indicate that the pressure in the pump progressively rises along the axial direction and remains constant within the chamber. As the void fraction of the medium increases, the pressure differential between the inlet and exit of the rotor fluid domain progressively diminishes, resulting in high-velocity fluid emerging in the interstice between driving and driven rotors. The simultaneous increase in rotational speed elevates the overall fluid velocity while diminishing the pressure value. Under rated conditions, the output pressure and flow rate of the planned multiphase pump achieve 1.8 MPa and 300 m3/h, respectively, thereby fully satisfying the design specifications. This work employs the response surface approach to optimize multi-objective performance parameters, including leakage and pressurization capacity, to enhance the pump’s operational performance under high gas content situations. The optimization results indicate a 17.87% reduction in pump leakage, an 8.86% rise in pressurization capacity, and a substantial enhancement in pump performance.

To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge, this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis. This bridge employs an unprecedented tower-girder constraint method, with all vertical supports placed at the transition piers at both ends. This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure, relying on finite element (FE) analysis. Initially, based on the Weigh In Motion (WIM) data, a random vehicle load model is generated and applied to the finite element model. Several longitudinal constraint systems are proposed, and their effects on the structural response of the bridge are compared. The most reasonable system, balancing girder-end displacement and transitional pier stress, is selected. Subsequently, the study examines the impact of different viscous damper parameters on key structural response indicators, including cumulative longitudinal displacement at the girder ends, maximum longitudinal displacement at the girder ends, cumulative longitudinal displacement at the pier tops, maximum longitudinal displacement at the pier tops, longitudinal acceleration at the pier tops, and maximum bending moment at the pier bottoms. Finally, the coefficient of variation (CV)-TOPSIS method is used to optimize the viscous damper parameters for multiple objectives. The results show that adding viscous dampers at the side towers, in addition to the existing longitudinal limit bearings at the central tower, can most effectively reduce the response of structural indicators. The changes in these indicators are not entirely consistent with variations in damping coefficient and velocity exponent. The damper parameters significantly influence cumulative longitudinal displacement at the girder ends, cumulative longitudinal displacement at the pier tops, and maximum bending moments at the pier bottoms. The optimal damper parameters are found to be a damping coefficient of 5000 kN/(m/s)0.2 and a velocity exponent of 0.2.

A multi-objective full-parameter optimization particle swarm optimization (MOFOPSO) algorithm is proposed in this paper to overcome the drawbacks of poor accuracy, low efficiency, and instability of the existing algorithms in the inverse kinematics(IK) solution of the manipulator. In designing the multi-objective function, the proposed algorithm considers the factors such as position, posture, and joint. To improve PSO, the proposed algorithm comprehensively analyzes all factors affecting the global and local searching abilities. Based on this, the initial population is designed following the localized uniform distribution method. Meanwhile, the inertia weight, asynchronous learning factor, and time factor are respectively designed by introducing the iteration factor. Finally, this paper tests the performance of MOFOPSO with three typical functions to obtain a better inverse kinematics solution of the 6-DOF manipulator. Also, six other algorithms are taken for performance comparison. The experimental results indicate that the proposed method not only ensures the stability of the manipulator but also achieves high accuracy and efficiency in solving the inverse kinematics of the 6-DOF manipulator.

With the rapid development of cities, the automated and intelligent garbage transportation has become an important direction for technological innovation of sanitation vehicles. In this paper, a vehicle-mounted trash can-handling robot is proposed. In order to reduce the cost of the robot and increase the loading capacity of the intelligent sanitation vehicles, a lightweight design method is proposed for the truss structure of the robot. Firstly, the parameters of the robot that are related to the load are optimized by multi-objective parameter optimization based on particle swarm optimization. Then, the material distribution of the truss structure is optimized by topology optimization under multiple load cases. Finally, the thickness of the truss structure parts is optimized by discrete optimization under multiple load cases. The optimization results show that the mass of the truss structure is reduced by 8.72%, the inherent frequency is increased by 61.08%, and the maximum stress is reduced by 10.98%. The optimization results achieve the goal of performance optimization of the intelligent sanitation vehicle, and prove the feasibility of the proposed lightweight design method.

Purpose: The purpose of this research is to develop an optimization model of CNC turning process. The objective function of the model is to minimize processing time and carbon emission. We implemented the results of optimization with real machining application using a certain workpiece. Design/methodology/approach: The model in this research used multi objective optimization involving two objective functions, namely processing time which includes cutting time and auxiliary time and carbon emissions resulted from the electricity energy consumptions, cutting tool, cutting fluid or coolant, raw materials production, and chip removal. Findings: The results of multi objective optimization indicate that the model can be used to minimize the processing time and carbon emissions with the optimal cutting speed and feed rate are 193.7 m/minute and 0.405 mm/rev. The results of sensitivity analysis showed that the higher weights of processing time will decrease the cutting speed, while the higher carbon emissions weight will result in faster cutting speed. The weight has no effects on feed rate. Originality/value: This paper gives a real machining application to show the applicability of the optimization model.