In this paper, a precise and efficient method to optimize corrugated tube heat exchangers is proposed by combining computational fluid dynamics simulation with optimization. The optimization of tubular heat exchangers involves contradictory Colburn coefficient j, and the friction coefficient f, so it is a multi-objective optimization problem. The approximate model is obtained by an extreme learning machine, and the structure parameter of the heat exchanger is optimized by the nondominated sorting genetic algorithm-Ⅱ. Compared to the results between the original and optimized tube, the optimized structure Colburn coefficient increased by 5.1 % and the friction coefficient decreased by 9.3 %. Finally, the internal flow field is compared qualitatively from temperature, pressure, and velocity. The optimization effect is further emphasized by using the field synergy theory.

This paper compares the performances of three Computational Fluid Dynamics (CFD) turbulence models, Reynolds Average Navier-Stokes (RANS), Detached Eddy Simulation (DES), and Large Eddy Simulation (LES), for simulating the flow field of a wheel loader engine compartment. The distributions of pressure fields, velocity fields, and vortex structures in a hybrid-grided engine compartment model are analyzed. The result reveals that the LES and DES can capture the detachment and breakage of the trailing edge more abundantly and meticulously than RANS. Additionally, by comparing the relevant calculation time, the feasibility of the DES model is proved to simulate the three-dimensional unsteady flow of engine compartment efficiently and accurately. This paper aims to provide a guiding idea for simulating the transient flow field in the engine compartment, which could serve as a theoretical basis for optimizing and improving the layout of the components of the engine compartment.

This article presents an accurate and efficient optimization method for heat exchanger. The structure of the original heat exchanger was optimized by combining LHS sampling, CFD simulation, radical basis function, and multi‐objective optimization. Since the Colburn factor j and the friction factor f are a pair of conflicting goals, so the multi‐objective optimization is adopted. The optimization results showed that the Colburn factor j increased by 5.43% and the friction factor f decreased by 23.31%, indicating that the optimized structure had higher heat transfer efficiency and lower resistance performance. The heat transfer mechanism and optimization effect of heat exchanger are explained by using the field synergy principle, which provides a theoretical basis for the structural design optimization of heat exchanger.

This paper presents a method for optimizing wavy plate-fin heat exchangers accurately and efficiently. It combines CFD simulation, Radical Basis Functions (RBF) with multi-objective optimization to improve the performance. The optimization of the Colburn factor j and the friction coefficient f is regarded as a multi-objective optimization problem, due to the existence of two contradictory goals. The approximation model was obtained by Radical Basis Functions, and the shape of the heat exchanger was optimized by multi-objective genetic algorithm (MOGA). The optimization results showed that j increased by 17.62% and f decreased by 20.76%, indicating that the heat exchange efficiency was significantly enhanced and the fluid structure resistance reduced. Then, from the aspects of field synergy and tubulence energy, the performance advantage of the optimized structure was further confirmed.

An efficient and accurate method for optimizing capsule-type plate heat exchangers is proposed in this paper. This method combines computational fluid dynamics simulation, a backpropagation algorithm and multi-objective optimization to obtain better heat transfer performance of heat exchanger structures. For plate heat exchangers, the heat transfer coefficient j and friction coefficient f are a pair of contradictory objectives. The optimization of capsule-type plate heat exchangers is a multi-objective optimization problem. In this paper, a backpropagation neural network was used to construct an approximate model. The plate shape was optimized by a multi-objective genetic algorithm. The optimized capsule-type plate heat exchanger has lower flow resistance and higher heat exchange efficiency. After optimization, the heat transfer coefficient is increased by 8.3% and the friction coefficient is decreased by 14.3%, and the heat transfer effect is obviously improved. Further, analysis of flow field characteristics through field co-ordination theory provides guidance for the further optimization of plates.

This study establishes a thermal management model to improve the heat exchange performance and uniformity of the flow-field distribution in the engine compartment of a wheel loader. Flow-field analyses are performed for an XG956 wheel loader in a virtual wind tunnel using the combined engine compartment thermal management model and computational fluid dynamics. The Fluent calculations revealed various problems. For example, the inlet flow rate at both sides of the engine compartment is small, which accounts for about 8.5% of the total flow, and the flow uniformity of radiator becomes worse with the increase in the air flow. The original cooling system is improved based on the simulation results and then verified by field testing. A comparison of the test data with the simulations indicates that the values obtained using the thermal management model of the engine compartment are largely in agreement with the experimental values, with a maximum deviation of the heat transfer rate at the rated speed of 5.1%. The research method presented in this article could further help to increase the productivity of the non-road mobile machinery cooling system and lower design costs. The temperature of pressurized air, hydraulic oil, transmission oil, and engine cooling fluid decreased by 22.5%, 8.7%, 2.2%, and 8.4% in the improved loader, respectively.