Configuration Optimization and Performance Comparison of STHX-DDB and STHX-SB by A Multi-Objective Genetic Algorithm

Abstract

Based on the thermohydraulic calculation model verified in this study and Non-dominated Sorted Genetic Algorithm-II (NSGA-II), a multi-objective configuration optimization method is proposed, and the performances of shell-and-tube heat exchanger with disc-and-doughnut baffles (STHX-DDB) and shell-and-tube heat exchanger with segmental baffles (STHX-SB) are compared after optimization. The results show that, except in the high range of heat transfer capacity of 16.5–17 kW, the thermohydraulic performance of STHX-DDB is better. Tube bundle diameter, inside tube bundle diameter, number of baffles of STHX-DDB and tube bundle diameter, baffle cut, number of baffles of STHX-SB are chosen as design parameters, and heat transfer capacity maximization and shell-side pressure drop minimization are considered as common optimization objectives. Three optimal configurations are obtained for STHX-DDB and another three are obtained for STHX-SB. The optimal results show that all the six selected optimal configurations are better than the original configurations. For STHX-DDB and STHX-SB, compared with the original configurations, the heat transfer capacity of optimal configurations increases by 6.26% on average and 5.16%, respectively, while the shell-side pressure drop decreases by 44.33% and 19.16% on average, respectively. It indicates that the optimization method is valid and feasible and can provide a significant reference for shell-and-tube heat exchanger design.

Performance calculation and configuration optimization of annular radiator by heat transfer unit simulation and a multi-objective genetic algorithm

“…a. Obtain functional relationships between T l , T a , and T l 0 , T a 0 , as shown by equations ( 6) to (7), by data fitting based on the CFD simulation data acquired in Step 2. In equation ( 8), Q HX , C p , and q m represent AR heat transfer capacity, liquid specific heat capacity, and liquid mass flow rate, respectively.…”

“…In some other researches, an optimization algorithm is combined with theoretical arithmetic, rather than CFD simulation. Xu et al 7 applied e-NTU method to heat transfer capacity and shell-side pressure drop calculation of the shell-and-tube heat exchangers separately with disc-and-doughnut baffles and segmental baffles. Non-dominated Sorted Genetic Algorithm-II (NSGA-II) was adopted to optimize configuration parameters of these two kinds of heat exchangers, and the optimal Pareto points of them were compared.…”

“…According to T 0 and AR air inlet temperature T HXAI , liquid outlet temperature T 1l $ T nl and air outlet temperature T 1a $ T na of HTUs numbers 1 $ n can be calculated by equations (6) to(7). d. According to T 1a $ T na and AR liquid inlet temperature T HXLI , liquid outlet temperature T (nþ1)l $ T 2nl of HTUs numbers (n þ 1) $ 2n can be calculated by equation (6) in sequence.…”

One Convenient Method to Calculate Performance and Optimize Configuration for Annular Radiator Using Heat Transfer Unit Simulation

“…M. Chahartaghi et al [20] combined NSGA-II with entransy dissipation theory to minimize the entransy dissipation numbers separately caused by thermal conduction and fluid friction for STHX. Z. Xu et al [21] calculated the performances of two kinds of STHXs by theoretical formulas and optimized their structural parameters using NSGA-II, respectively. In order to optimize the configuration of a PFHX with offset strip fins, NSGA-II combined with the ε-NTU method was adopted in the research of R. Song et al [22].…”