For any large weldment with many welding seams, the welding sequence and direction have a strong influence on the assembly and service performance, especially for the side beam of the bogie frame of a high-speed rail passenger car (CHR3; CRRC, Changchun, China). Because different combinations of the welding sequence and direction greatly increase the computational time and research costs, a three-dimensional finite element approach was developed to investigate the optimal welding sequence and direction. Then, a surrogate model was established by design of experiment and used the concepts of a pointer and stack. Finally, the welding sequence and direction were optimized by the discrete particle swarm optimization algorithm. The max residual deformation and stress of the optimal result were 23.92 mm and 212.56 MPa, respectively, which is approximately 22% and 38% lower than the traditional enterprise plan, respectively. Furthermore, a weighted form of the residual deformation and stress was proposed to the end of optimum comprehensive effect, and the result also had 11% and 28% reduction, respectively. The simulation result of the optimal plans well reproduced the theoretical distribution results of the residual deformation and stress. It is proven that the optimal result can improve the welding quality and process of the side beam weldment in production.
Impingement cooling has been widely employed to cool gas turbine hot components such as combustor liners, combustor transition pieces, turbine vanes, and blades. A promising technology is proposed to enhance impingement cooling with water droplets injection. However, previous studies were conducted on blade shower head film cooling, and less attention was given to the transition piece cooling. As a continuous effort to develop a realistic mist impingement cooling scheme, this paper focuses on simulating mist impingement cooling under typical gas turbine operating conditions of high temperature and pressure in a double chamber model. Furthermore, the paper presents the effect of cooling effectiveness by changing the mass and size of the droplets. Based on the heat-mass transfer analogy, the results of these experiments prove that the mass of 3 − 3 kg/s droplets with diameters of 5-35 m could enhance 90% cooling effectiveness and reduce 122 K of wall temperature. The results of this paper can provide guidance for corresponding experiments and serve as the qualification reference for future more complicated studies with convex surface cooling.
To control the welding residual stress and deformation of metal inert gas (MIG) welding, the influence of welding process parameters and preheat parameters (welding speed, heat input, preheat temperature, and preheat area) is discussed, and a prediction model is established to select the optimal combination of process parameters. Thermomechanical numerical analysis was performed to obtain the residual welding deformation and stress according to a 100 × 150 × 50 × 4 mm aluminum alloy 6061-T6 T-joint. Owing to the complexity of the welding process, an optimal Latin hypercube sampling (OLHS) method was adopted for sampling with uniformity and stratification. Analysis of variance (ANOVA) was used to find the influence degree of welding speed (7.5–9 mm/s), heat input (1500–1700 W), preheat temperature (80–125 °C), and preheat area (12–36 mm). The range of research parameters are according to the material, welding method, thickness of the welding plate, and welding procedure specification. Artificial neural network (ANN) and multi-objective particle swarm optimization (MOPSO) was combined to find the effective parameters to minimize welding deformation and stress. The results showed that preheat temperature and welding speed had the greatest effect on the minimization of welding residual deformation and stress, followed by the preheat area, respectively. The Pareto front was obtained by using the MOPSO algorithm with ε-dominance. The welding residual deformation and stress are the minimum at the same time, when the welding parameters are selected as preheating temperature 85 °C and preheating area 12 mm, welding speed is 8.8 mm/s and heat input is 1535 W, respectively. The optimization results were validated by the finite element (FE) method. The error between the FE results and the Pareto optimal compromise solutions is less than 12.5%. The optimum solutions in the Pareto front can be chosen by designers according to actual demand.
The crash box of vehicle plays an important role in absorbing energy during collision. However, the crashworthiness optimization problem is nonlinear which means the relationship between the response and design variables is implicit. This paper constructed a response surface model instead of original model. Meanwhile, uniform design has been taken to select sampling points uniformly. Then PSO method was used to optimize the approximate model with high precision. Finally, the optimization results show that the crashworthiness of structure has enhanced and provide a guide for practical application of crashworthiness design.
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