The resurgence of passenger flows after the pandemic poses a significant challenge to the safe operation of rail transit. Therefore, adopting the waiting hall of an ultralarge railway station hub as an example, thermal radiation and evacuation simulations were conducted by the Fire Dynamics Simulator and Pathfinder, respectively. Island-style shops, known for their high crowd density and fire load, were defined as fire sources, and the effectiveness of a 6 m wide fire isolation zone was validated via the adoption of the dual-validation model. By comparing the relationships between the total evacuation population after passenger flow recovery and various evacuation parameters, it was shown that passengers were not evenly distributed among the exits in the waiting hall during an emergency, leading to uneven utilization. Furthermore, to gain a comprehensive understanding of the evacuation process under simulated fire conditions, an evacuation simulation involving 10,000 evacuees over a duration of 324.8 s was conducted. This study provides a theoretical basis for optimizing fire emergency evacuation plans for ultralarge railway station hubs.

Secondary flows had been found in radial turbine volutes in the past, but the flow mechanisms governing their existence are far from clear. This paper describes an analytic and numerical work on the mechanism of the secondary flows, and the effects of volute shape, A/R distribution, and turbine speed on the development and associated losses of the secondary flows in the turbine volutes. The results show that the centrifugal and viscous forces acting on the wall boundary-layer, as well as the diffusion of radial flow in volute’s cross-sections, are the main generating mechanism of secondary flows, but they are worked against by volute discharging. Compared with the rectangular and trapezoidal shape volutes, the circular volute generates stronger secondary flows, resulting in no hydrodynamic benefit over the other two volutes. By using the dissipation function to separate the secondary flow loss from the total loss in the three volutes, it shows for the first time that although the loss from secondary flows alone is small, the flows can push the main flows toward volute side walls thereby increase wall friction loss. Three twin-entry volutes with different aspect ratios of their cross-sections are also studied. The offset of the cross-section center to volute exit affects volute discharging and the aspect ratio impacts on the radial flow in sidewall boundary layers, they therefore influence the secondary flows in the volutes. The slope of A/R curves is found to correlate well to the strength of the secondary flows in the volutes, and the reason behind is explained. Turbine speed’s influence on the secondary flows is largely through flow compressibility, and if the speed change reduces turbine inlet Mach number, it will increase discharge flow angle at the volute exit, and so too the strength of the secondary flows, but the changes will be small.

In recent years, new fire loads dominated by power banks have caused multiple fire incidents in transportation hubs, posing severe challenges to fire safety. This study uses experimental testing and machine learning regression analysis to explore the heat release rate (HRR) characteristics and influencing factors of box-type power banks under fire conditions. A 1 MW calorimeter was used to conduct four sets of experiments involving a total of 15 box-type power banks, measuring the HRR and analyzing its correlation with oxygen consumption, carbon dioxide generation, smoke temperature, and the fire growth rate. Based on the experimental data, HRR prediction models were constructed using decision tree regression (DT), K-nearest neighbor regression (KNN), and linear regression (LR). The results indicate that the DT model performs best in HRR prediction (MAE = 0.4889, MSE = 0.7414, RMSE = 0.8571, R² = 0.9991), effectively capturing the nonlinear variation patterns in the HRR. The correlation analysis and regression analysis conducted in this study provide crucial data support for fire combustion characteristics research, fire risk assessment, and fire safety optimization. Furthermore, the findings provide crucial data support for research on fire combustion characteristics and data-driven fire risk assessment, which may serve as a foundation for future AI-based real-time fire detection applications.