A design indicators prediction model using the Bell-Delaware method for a shell-and-tube heat exchanger with segmental baffles (STHX-SB) is constructed and validated by experiment. The average errors of heat transfer capacity and tube-side pressure drop are 8.52% and 7.92%, respectively, and the predicted weight is the same as the weight obtained by Solidworks commercial software, which indicates the model’s reliability. Parametric influences of the outside diameter of the heat dissipation tube, clearance between heat dissipation tubes, heat dissipation tube length, and tube bundle bypass flow clearance on heat transfer capacity per tube-side pressure drop and heat transfer capacity per weight are studied, and it indicates that whether the interaction between factors is considered or not, both heat transfer capacity per tube-side pressure drop and heat transfer capacity per weight are the most sensitive to outside diameter of heat dissipation tube and the least sensitive to heat dissipation tube length based on the Sobol’ method. To avoid falling into local optima due to algorithm convergence being too fast and to improve the reliability of solving complex optimization problems, Non-Dominated Sorted Genetic Algorithm II (NSGAII) and Multi-Objective Particle Swarm Optimization (MOPSO) embedded grouping cooperative coevolution (NSGAII-MOPSO-GCC) is proposed to optimize the studied four configuration parameters to maximize heat transfer capacity per tube-side pressure drop and heat transfer capacity per weight for STHX-SB, simultaneously. Compared with the original structure, heat transfer capacity per tube-side pressure drop and heat transfer capacity per weight of the chosen solutions separately increased by 57.66% and 4.63%, averagely, and in the optimization comparison of NSGAII, MOPSO, and NSGAII-MOPSO-GCC, NSGAII-MOPSO-GCC has the best performance, which shows that the proposed method is effective and feasible and can supply beneficial solutions and valuable guidance for heat exchanger design and improvement.

The method of effectiveness-number of heat transfer units (ε-NTU) is adopted to establish a design indicator prediction model for plate fin precooler (PFP), and experimental verification is conducted. The average error between the experimental heat transfer capacity and the calculated heat transfer capacity is 4.65%, and the predicted mass matches the mass computed via the commercial software SolidWorks 2020. This outcome confirms the model’s reliability. An investigation is conducted into the influences of parametric factors, including hot stream flow length, cold stream flow length, hot side number of layers, and hot side fin pitch on the heat transfer capacity and mass of the PFP. To realize the maximization of heat transfer capacity and the minimization of mass, optimization is performed on the four sensitive configuration parameters by leveraging the multi-objective grey wolf optimizer (MOGWO). This optimization can significantly reduce the mass while ensuring the stability of the heat transfer capacity. Three classes of optimal configurations were derived from Pareto optimal points. Compared to the original structure, the selected schemes exhibit an average 2.95% rise in heat transfer capacity and a 10.7% reduction in mass. These findings show that the optimization method proposed in this study is effective and provides valuable guidance for precooler design.