KeWei Song

Enhancing thermal transport while reducing flow resistance remains a fundamental challenge in bluff body flows. Downstream splitter plates can alleviate leeward form drag but fail to reduce the windward form drag. To simultaneously minimize both the windward and leeward form drag while enhancing heat transfer, a novel heat transfer channel design with upstream splitter plates is proposed. The impact of the plate length ratios L/D of the upstream splitter plates on the flow and thermal fields is numerically studied. The results show that the upstream splitter plates notably reduce the adverse pressure gradient on the leeward side of the tube, which delays the boundary layer separation, thereby shrinking the wake size. Meanwhile, the upstream splitter plates reduce the favorable pressure gradient on the windward side of the tube, which reduces the windward form drag. These two mechanisms effectively reduce the total form drag of the cross-flow tube bundle. Furthermore, the combined effects of the delay of the separation point and the splitter plates lead to a significant improvement in heat transfer. Compared with the channel without splitter plates, f decreases by up to 23.49%, while Nu increases by up to 64.20%. Under identical flow conditions, the optimal overall performance η is 1.42 at L/D = 1.0, whereas under identical pump power, it is 1.31 at L/D = 1.5. These findings indicate that installing upstream splitter plates is an effective passive strategy for simultaneously reducing flow resistance and enhancing heat transfer in cross-flow tube bundle channels.

Grounded in the core principle of green transportation, this paper proposes a metro train service planning approach aimed at enhancing efficiency and reducing carbon emissions. The approach integrates environmental, passenger, and operator benefits, employing multiple train formations under full-length and short-turning route operations. Considering the high dimensionality of model variables and the complexity of the solution process, improvements are made to the neighborhood search strategy in the Adaptive Large-scale Neighborhood Search (ALNS) algorithm, and the improved algorithm is applied to the model solving process. Comprehensive data experiments are conducted to calibrate the algorithm parameters. Using Jinan Metro as a case study, the approach is empirically validated. The results demonstrate that, compared to the single-route and single-formation train service plans, the multi-route and multi-formation plan delivers superior performance in terms of carbon emissions, enterprise operating costs, and passenger travel time costs. Additionally, the Improved Adaptive Large-scale Neighborhood Search (IALNS) algorithm significantly outperforms the ALNS algorithm in both computational efficiency and solution quality. The main contribution of this paper is to balance the interests of both enterprises and passengers while effectively reducing carbon emissions. It also contributes to providing decision support for the green operation and sustainable development of metro systems.