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Passenger thermal comfort in high-speed train (HST) carriages presents unique challenges due to factors such as extensive operational areas, longer travel durations, larger spaces, and higher passenger capacities. This study aims to propose a new prediction model to better understand and address thermal comfort in HST carriages. The proposed prediction model incorporates skin wettedness, vertical skin temperature difference (ΔTd), and skin temperature as parameters to predict the thermal sensation vote (TSV) of HST passengers. The experiments were conducted with 65 subjects, evenly distributed throughout the HST compartment. Thermal environmental conditions and physiological signals were measured to capture the subjects' thermal responses. The study also investigated regional and overall thermal sensations experienced by the subjects. Results revealed significant regional differences in skin temperature between upper and lower body parts. By analyzing data from 45 subjects, We analyzed the effect of 25 variables on TSV by partial least squares (PLS), from which we singled out 3 key factors. And the optimal multiple regression equation was derived to predict the TSV of HST occupants. Validation with an additional 20 subjects demonstrated a strong linear correlation (0.965) between the actual TSV and the predicted values, confirming the feasibility and accuracy of the developed prediction model. By integrating skin wettedness and ΔTd with skin temperature, the model provides a comprehensive approach to predicting thermal comfort in HST environments. This research contributes to advancing thermal comfort analysis in HST and offers valuable insights for optimizing HST system design and operation to meet passengers' comfort requirements. [ABSTRACT FROM AUTHOR]

[Display omitted] • A machine-learning model is constructed based on experimental data to predicate the gas-sensing performance of oxides. • Six important features are proposed as the descriptors for gas sensing of oxides. • The active sites on oxides for gas sensing are described as ionic oxygen loosely bonded to metal atoms. Machine learning, a new research paradigm, is promising for device design, material discovery, and theoretical exploration, overcoming the drawbacks of experimental approaches. In this study, we used machine learning to explore the gas-sensing reactions of metal oxides. The training data were experimental results. Three types of features — metal oxide and gas molecule properties and operating conditions — were extracted as inputs for the machine learning algorithms. The genetic algorithm-adjusted artificial neural network achieved the best performance, and the neural network test was performed using two new experimental datasets. Further, Shapley Additive exPlanations were employed to understand the findings and explore key descriptors for gas sensing. The feature importance was ranked, and the first six features were selected as key descriptors for gas sensing using metal oxides. Furthermore, theoretical investigations were conducted, and the correlations between features and outcomes were analyzed. We found that the active site for gas sensing on pristine metal oxides comprises ionic oxygen loosely bonded to the metal sites. This study provides an example of the use of machine learning for material discovery. We expect that this study will encourage more studies on machine learning for gas sensing. [ABSTRACT FROM AUTHOR]

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback. • This paper presents thermal comfort model for high-speed train passengers based on skin temperature, galvanic skin response and electrocardiogram. • Differences in physiological signals and thermal sensation votes among different populations (Gender, BMI and position) were analyzed. • The features of the four combinations were used as input factors to obtain accuracy using different machine learning models. • Sensitivity analysis of the best prediction model (Random Forest) was performed using SHAP and DSA methods. [ABSTRACT FROM AUTHOR]

In high-speed train operations, especially during the passage of two trains or when passing through tunnels, the surface of the train experiences intense transient alternating pressure. The resulting alternating aerodynamic loads can lead to fatigue damage in the train's structure. To address this issue, an experimental device is developed in this study to accurately replicate the alternating aerodynamic loads experienced by trains. In this research, a novel method for simulating variable loads using triangular pressure wave patterns is proposed. Validation tests are conducted to assess the effectiveness of the device in replicating the desired load profiles. Furthermore, a noise suppression mechanism is incorporated into the device design. By incorporating noise source analysis and optimization techniques, the device design effectively controls the noise level within 85 dBA during the loading process. The experimental results demonstrate that the proposed device can achieve a triangular wave loading frequency of 2.5 cycles per minute, with a pressure range of ±3000 Pa for a 250 m3 high-speed train cabin. The pressure load output stability has high consistency in the pressure‒time curves at various measurement points inside the tested object, with a maximum difference of 0.99%. The experimental device designed in this study meets the requirements for simulating the aerodynamic loads on the surface of high-speed trains travelling at speeds of up to 350 km/h. • Novel device accurately simulates aerodynamic alternating loads on high-speed trains. • Integrated mechanism maintains noise levels below 85 dBA during loading. • Achieves stable triangular wave loading, crucial for replicating aerodynamic stresses on train cabin.. [ABSTRACT FROM AUTHOR]

Cartesian meshes based on the immersed boundary method are widely adopted for their convenience in implementing automated mesh generation. The performance of Cartesian mesh generation, particularly for complex geometries, is a crucial factor that directly impacts the efficiency of computational fluid dynamics solutions. In this study, we present an innovative strategy for efficient Cartesian mesh generation. Our methodology centers around the inner product test (IPT), enabling precise and efficient interior/exterior determination for intersected Cartesian cells. For this purpose, we enhance the k-d tree based intersection determination process to provide essential information of surface mesh for IPT, while concurrently improving the efficiency of the determination process. Our new methods offer automatic generation capabilities for arbitrary complex geometries. Through a series of tests, the new methods illustrate the notable performance improvements with the generation time of less than 4 min for the Cartesian mesh with 108 cells. Overall, our work optimizes Cartesian mesh generation for complex geometries, presenting significant potential for addressing the challenges of moving boundary problems. [ABSTRACT FROM AUTHOR]

Dendrite formation and water-triggered side reactions on the surface of Zn metal anodes severely restrict the commercial viability of aqueous zinc-ion batteries (AZIBs). In this work, we introduce erythritol (Et) as an electrolyte additive to enhance the reversibility of zinc anodes, given its cost-effectiveness, mature technology, and extensive utilization in various domains such as food, medicine, and other industries. By combining multiscale theoretical simulation and experimental characterization, it was demonstrated that Et molecules can partially replace the coordination H2O molecules to reshape the Zn2+ solvation sheath and destroy the hydrogen bond network of the aqueous electrolyte. More importantly, Et molecules tend to adsorb on the zinc anode surface, simultaneously inhibit water-triggered side reactions by isolating water and promote uniform and dense deposition by accelerating the Zn2+ diffusion and regulating the nucleation size of the Zn grain. Thanks to this synergistic mechanism, the Zn anode can achieve a cycle life of more than 3900 h at 1 mA cm−2 and an average Coulombic efficiency of 99.77%. Coupling with δ-MnO2 cathodes, the full battery delivers a high specific capacity of 228.1 mAh g−1 with a capacity retention of 76% over 1000 cycles at 1 A g−1. [ABSTRACT FROM AUTHOR]

In this study, the sliding grid method is used to simulate a 350 km/h high-speed train passing through a 350 m standard single-line tunnel. The cross-sectional pressure distribution ahead of the train is investigated, the three-dimensional characteristics and propagation mechanism of pressure waves are described based on iso-pressure surfaces, and a newly accurate pressure sensor layout is proposed. The results show that the farther the cross-section is from the train, the smaller the cross-sectional pressure difference is. Influenced by the shape of the train nose and tunnel cross-section, the pressure waves are not simple planar waves, but spherical waves that vary in complexity over the propagation process. As the pressure difference of the 16.6 m cross-section before the train is within 1 Pa, the pressure waves behind this cross-section are defined as planar waves. Besides, the pressure peak-to-peak value difference between the wall and construction clearance reaches 13% at 70–90 m cross-sections, so the pressure sensors should be placed on the surface of the appurtenances rather than on the tunnel wall. In summary, this study is significant for understanding the formation and transmission mechanisms of pressure waves and guiding the scientific layout of pressure sensors in tunnels. [ABSTRACT FROM AUTHOR]

With the increase in the speed of metro trains, the aerodynamic effects caused by the train passing through the tunnel, especially the pressure change, will be significantly enhanced. This pressure will have a significant effect on the auxiliary facilities in the tunnel and the passengers in the carriage. Based on the sliding grid technique, Fluent software was used to simulate the aerodynamic performance of six trains passing through different shield transfer structures, and the influences of the shield cross-section area, length, and form on train aerodynamic performance were studied. The results show that when the cross-sectional area of the shield transfer structure increases from 42 m2 to 62 m2, the positive pressure peak P max on the train surface increases significantly, and the peak-to-peak value and positive pressure gradient peak in the large-section tunnel are increased by 20.50 % and 106.83 %, respectively; when the length of the shield transfer structure is increased from 80 m to 120 m, the positive pressure peak and pressure gradient peak in the large-section tunnel are increased by 22.53 % and 21.70 %, respectively; for constant cross-sectional area and ellipsoidal structural forms, the slanting structural form has a smaller train surface pressure amplitude as well as a smaller peak pressure and peak pressure gradient in a large-section tunnel. These findings will contribute to the design of shield transfer structure in subway and tunnel systems. [ABSTRACT FROM AUTHOR]