Extreme Conditions Research Articles

Drone-based medical delivery in the extreme conditions of Himalayan region: a feasibility study

IntroductionUnmanned aerial vehicles, or drones, have emerged as versatile tools across various sectors, including defence, agriculture, surveillance, mining, infrastructure, emergency response, transportation, geospatial mapping and notably medical supply delivery to...

Machine learning-based tropospheric delay prediction for real-time precise point positioning under extreme weather conditions

Satellite signals from the Global Navigation Satellite System (GNSS) are refracted as they pass through the troposphere, owing to the variable density and composition of the atmosphere, causing tropospheric delay. Typically, tropospheric delay is treated as an unknown parameter in GNSS data processing. Given the growing need for real-time GNSS applications, accurate tropospheric delay predictions are crucial to improve Precise Point Positioning (PPP). In this paper, time-series of tomography data are used for wet refractivity prediction employing Machine Learning (ML) techniques in both Poland and California, under extreme weather conditions including sweeping rain bands and storms. The predicted wet refractivity is implemented for tropospheric delay determination through ray-tracing technique. PPP processing is conducted in both static and kinematic modes using different setups. These are: (1) common PPP, called Com-PPP, (2) Ray-PPP, which applies obtained tropospheric delay on GNSS observations and thus eliminates tropospheric parameters from unknowns, and (3) Dif-PPP, which applies the difference of estimated tropospheric delay from ray-tracing and GNSS measurements to compensate for the remaining tropospheric delay in the observations. The results show that Dif-PPP reduces the Mean Absolute Error (MAE) of the Three-Dimensional (3-D) component between 8 and 33% in static mode compared to the Com-PPP method. Additionally, it can improve the convergence time of the up component in the kinematic mode by between 6 and 17%.

Recent Trends in Extreme Temperature Events Across the Contiguous United States

ABSTRACTExtreme heat events (EHEs) are becoming prevalent across the globe and are a major factor in terms of temperature‐related mortality in the United States (US). In this study, we compare trends in extreme temperature events (ETEs) across the Contiguous US, from 3 reanalysis products, namely: European Centre for Medium‐Range Weather Forecasts Reanalysis Version 5 (ERA5), Modern‐Era Retrospective Analysis for Research and Applications Version 2 (MERRA2) and North American Regional Reanalysis (NARR). We focused on the trends (1980–2022) in absolute extreme heat and cold events (ECE) as well as seasonally relative extreme heat and cold events (REHE and RECE). ETEs are defined based on a duration‐intensity metric calculated from excess apparent temperature factors, based on the exceedance of apparent temperature beyond local percentile thresholds while incorporating an acclimatisation factor. Our results show that the reanalysis data sets generally produced consistent climatology of ETEs, though with some inconsistencies in their number and spatial distribution. ETE trends in the study region are spatially heterogeneous and were more consistent between MERRA2 and ERA5. Nonetheless, all data sets agree that the frequency of EHEs is significantly increasing in the western parts of the US, whereas REHEs are significantly increasing in the southern parts. The highest increase in the frequency of EHEs occurs in southern California and Nevada, while REHE trends are maximal in Florida. RECEs are significantly decreasing more in spatial scale and magnitude than ECEs, especially towards the coastal regions. The highest decrease in RECEs is in Florida peninsula, southern California and Nevada. The data sets show inconsistency in ECE trends. Trends in excess temperature factors further indicated that extreme cold conditions are decreasing faster compared to the increasing trends of extreme heat conditions. Our findings highlight the need for improving the monitoring of ETEs across the US and for policies that mitigate the impact of ETEs on biological systems.

Short-hair black holes and the strong cosmic censorship conjecture

The singularity problem has always been a key focus of physicists’ research. To address this issue, Penrose proposed the cosmic censorship conjecture, with the Strong Cosmic Censorship Conjecture (SCCC) being particularly crucial. However, verifying SCCC in different scenarios still faces many challenges. This paper, set against the background of short-hair black holes, explores whether they obey SCCC when subjected to scalar field perturbations. Using the WKB method, Prony method, Lyapunov exponent, and Weak Gravity Conjecture (WGC), the behavior of a short-hair black hole under different parameter conditions was systematically analyzed. Numerical results indicate that as the charge Q of the short-hair black hole approaches its extremal value, the SCCC is violated. However, as the order k of the metric equation and the angular quantum number ℓ increase, the violation of SCCC is delayed. These results suggest that the proximity of the black hole’s charge Q to its extremal value, along with the size of the angular quantum number ℓ and the order k, play key roles in exploring the physical properties of black holes and verifying SCCC. Finally, research using the WGC shows that when the charge-to-mass ratio in a scalar field meets certain conditions, short-hair black holes can still comply with the SCCC under extreme conditions. This study not only reveals the behavior of short-hair black holes in extreme situations but also offers a new perspective for further exploration of such black holes.

A Minimal-Data Approach for Film Thickness Prediction in Tribological Contacts Using Venner’s Equation

The accurate design of tribological contacts, such as those in bearings and gearboxes, makes them highly efficient and helps reduce emission in all driven systems. Traditionally, this process requires more lubricant data than data sheets typically provide, mainly kinematic viscosity at 40 °C and 100 °C and density, which limits the design process. This study introduces a simplified methodology for determining lubricant film thickness, one of the main design critical parameters, using minimal viscosity measurements obtained with a high-pressure viscometer. The researchers demonstrate that essential lubricant parameters can be derived effectively from a few measurements. By combining state-of-the-art models for film thickness with practical measurements from an EHL tribometer, this study confirms that reliable film thickness predictions can be made from basic viscosity data. This approach streamlines the design process, making tribological simulations more accessible and cost-effective, and enhances the design of tribological contacts under extreme conditions.

Research on a Passenger Flow Prediction Model Based on BWO-TCLS-Self-Attention

In recent years, with the rapid development of the global demand and scale for deep underground space utilization, deep space has gradually transitioned from single-purpose uses such as underground transportation, civil defense, and commerce to a comprehensive, livable, and disaster-resistant underground ecosystem. This shift has brought increasing attention to the safety of personnel flow in deep spaces. In addressing challenges in deep space passenger flow prediction, such as irregular flow patterns, surges in extreme conditions, large data dimensions, and redundant features complicating the model, this paper proposes a deep space passenger flow prediction model that integrates a Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) network. The model first employs a dual-layer LSTM network structure with a Dropout layer to capture complex temporal dynamics while preventing overfitting. Then, a Self-Attention mechanism and TCN network are introduced to reduce redundant feature data and enhance the model’s performance and speed. Finally, the Beluga Whale Optimization (BWO) algorithm is used to optimize hyperparameters, further improving the prediction accuracy of the network. Experimental results demonstrate that the BWO-TCLS-Self-Attention model proposed in this paper achieves an R2 value of 96.94%, with MAE and RMSE values of 118.464 and 218.118, respectively. Compared with some mainstream prediction models, the R2 value has increased, while both MAE and RMSE values have decreased, indicating its ability to accurately predict passenger flow in deep underground spaces.

Research on Climate Drivers of Ecosystem Services’ Value Loss Offset in the Qinghai–Tibet Plateau Based on Explainable Deep Learning

As one of the highest and most ecologically vulnerable regions in the world, the Qinghai–Tibet Plateau (QTP) presents significant challenges for the application of existing ecosystem service value (ESV) assessment models due to its extreme climate changes and unique plateau environment. Current models often fail to adequately account for the complex climate variability and topographical features of the QTP, making accurate assessments of ESV loss deviations difficult. To address these challenges, this study focuses on the QTP and employs a modified ESV loss deviation model, integrated with explainable deep learning techniques (LSTM-SHAP), to quantify and analyze ESV loss deviations and their climate drivers from 1990 to 2030. The results show that (1) between 1990 and 2020, the offset index in the eastern QTP consistently remained low, indicating significant deviations. Since 2010, low-value clusters in the western region have significantly increased, reflecting a widening range of ecological damage caused by ESV losses, with no marked improvement from 2020 to 2030. (2) SHAP value analysis identified key climate drivers, including temperature seasonality, diurnal temperature variation, and precipitation patterns, which exhibit nonlinear impacts and threshold effects on ESV loss deviation. (3) In the analysis of nonlinear relationships among key climate drivers, the interaction between diurnal temperature range and precipitation in wet seasons demonstrated significant effects, indicating that the synergistic action of temperature variation and precipitation patterns is critical to ecosystem stability. Furthermore, the complex nonlinear interactions between climate factors exacerbated the volatility of ESV loss deviations, particularly under extreme climate conditions. The 2030 forecast highlights that wet season precipitation and annual rainfall will become key factors driving changes in ESV loss deviation. By combining explainable deep learning methods, this study advances the understanding of the relationship between climate drivers and ecosystem service losses, providing scientific insights for ecosystem protection and sustainable management in the Qinghai–Tibet Plateau.

The Impact of Floods on the Mobility of Automobile Commuters in Shanghai Under Climate Change

AbstractAs sea level rises, low-lying coastal cites face increasing threat of flood disruption, particularly in terms of human mobility. Commuters are vulnerable to bad weather, as it is difficult to cancel trips even in extreme weather conditions. Using Shanghai’s automobile commuting population as an example, we categorized commuters by travel distance and income level to assess disruptions and delays due to floods, considering future sea level rise. The results show that local flooding disrupts commuting patterns by affecting roadways, with disruption decreasing with distance from the flooded area. This offers a mobility perspective on the indirect impacts of floods. During baseline flood events, long-distance commuters and the low-income group are most affected, while short-distance commuters and the high-income group are less impacted. As sea level rises, floods will threaten all commuting groups, especially the high-income group. Using inaccessibility-commuting delay bivariate maps, this study revealed how socioeconomic differences impact mobility recovery after floods under climate change. The research highlights the differential impacts of floods on various socioeconomic groups in the context of climate change, offering insights for future urban planning and disaster mitigation strategies.

Enhancing Mobile Eye-Tracking in Extreme Urban Lighting Conditions

One of the methods for understanding residents' needs and socially improving urban spaces in terms of transportation, safety, landscape protection, and managing tourist traffic load is eye-tracking (ET). Researchers using mobile ET for outdoor studies face significant challenges, particularly due to sunlight affecting data quality. Existing solutions often overlook participant comfort. This article introduces a novel accessory designed for extreme lighting conditions, such as bright days, sunsets, and snowy or water-filled environments. The goal is to eliminate disruptions caused by uncontrolled sunlight on participants' eyes and enables studies in urban environments. A custom sun shield, designed for ETs based on spectacle frames, prioritizes both physical and psychological comfort. The lightweight shield is easy to install, minimally restricts the field of view, and does not interfere with eye-tracking components. It is cost-effective and suitable for DIY 3D printing. Control studies and field research confirmed its effectiveness, with feedback from over 100 users improving the final design. The shield enhances eye-tracking research credibility in sunny conditions, supports efficient calibration, and improves participant recruitment and well-being. Jakość danych znacząco wzrosła co obrazuje porównanie danych dotyczących sposobu detekcji źrenicy. Thanks to this solution, it will be possible to conduct research aimed at better understanding the behavior of city users, while ensuring their comfort and safety. It will also be possible to conduct research within the framework of so-called living labs. Importantly, studies show that the approach to designing subsequent mobile ETs based on IR should undergo significant modification.

Anatomical adaptations of mangroves to the intertidal environment and their dynamic responses to various stresses.

Mangroves are intertidal plants that survive extreme environmental conditions through unique adaptations. Various reviews on diverse physiological and biochemical stress responses of mangroves have been published recently. However, a review of how mangroves respond anatomically to stresses is lacking. This review presents major mangrove anatomical adaptations and their modifications in response to dynamic environmental stresses such as high salinity, flooding, extreme temperatures, varying light intensities, and pollution. The available research shows that plasticity of Casparian strips and suberin lamellae, variations in vessel architecture, formation of aerenchyma, thickening of the cuticle, and changes in the size and structure of salt glands occur in response to various stresses. Mangrove species show different responses correlated with the diversity and intensity of the stresses they face. The flexibility of these anatomical adaptations represents a key feature that determines the survival and fitness of mangroves. However, studies demonstrating these mechanisms in detail are relatively scarce, highlighting the need for further research. An in-depth understanding of the structural adaptations of individual mangrove species could contribute to appropriate species selection in mangrove conservation and restoration activities.

Analyzing sorbitol biosynthesis using a metabolic network flux model of a lichenized strain of the green microalga Diplosphaera chodatii.

Diplosphaera chodatii, a unicellular terrestrial microalga found either free-living or in association with lichenized fungi, protects itself from desiccation by synthesizing and accumulating low-molecular-weight carbohydrates such as sorbitol. The metabolism of this algal species and the interplay of sorbitol biosynthesis with its growth, light absorption, and carbon dioxide fixation are poorly understood. Here, we used a recently available genome assembly for D. chodatii to develop a metabolic flux model and analyze the alga's metabolic capabilities, particularly, for sorbitol biosynthesis. The model contains 151 genes, 155 metabolites, and 194 unique metabolic reactions participating in 12 core metabolic pathways and five compartments. Both photoautotrophic and mixotrophic growths of D. chodatii were supported by the metabolic model. In the presence of glucose, mixotrophy led to higher biomass and sorbitol yields. Additionally, the model predicted increased starch biosynthesis at high light intensities during photoautotrophic growth, an indication that the "overflow hypothesis-stress-driven metabolic flux redistribution" could be applied to D. chodatii. Furthermore, the newly developed metabolic model of D. chodatii, iDco_core, captures both linear and cyclic electron flow schemes characterized in photosynthetic microorganisms and suggests a possible adaptation to fluctuating water availability during periods of desiccation. This work provides important new insights into the predicted metabolic capabilities of D. chodatii, including a potential biotechnological opportunity for industrial sorbitol biosynthesis.IMPORTANCELichenized green microalgae are vital components for the survival and growth of lichens in extreme environmental conditions. However, little is known about the metabolism and growth characteristics of these algae as individual microbes. This study aims to provide insights into some of the metabolic capabilities of Diplosphaera chodatii, a lichenized green microalgae, using a recently assembled and annotated genome of the alga. For that, a metabolic flux model was developed simulating the metabolism of this algal species and allowing for studying the algal growth, light absorption, and carbon dioxide fixation during both photoautotrophic and mixotrophic growth, in silico. An important capability of the new metabolic model of D. chodatii is capturing both linear and cyclic electron flow mechanisms characterized in several other microalgae. Moreover, the model predicts limits of the metabolic interplay between sorbitol biosynthesis and algal growth, which has potential applications in assisting the design of bio-based sorbitol production processes.

Impact of Moisture Absorption on Optical Fiber Sensors: New Bragg Law Formulation for Monitoring Composite Structures

In recent decades, the aviation industry has increasingly adopted composite materials for various aircraft components, due to their high strength-to-weight ratio and durability. To ensure the safety and reliability of these structures, Health and Usage Monitoring Systems (HUMSs) based on fiber optics (FO), particularly Fiber Bragg Grating (FBG) sensors, have been developed. However, both composite materials and optical fibers are susceptible to environmental factors such as moisture, in addition to the well-known effects of mechanical stress and thermal loads. Moisture absorption can lead to the degradation of mechanical properties, posing a risk to the structural integrity of aircraft components. This research aims to quantify and monitor the impact of moisture on composite materials. A new formulation of the Bragg equation is introduced, incorporating mechanical strain, thermal expansion, and hygroscopic swelling to accurately measure Bragg wavelength variations. Experimental validation was performed using both uncoated and polyimide-coated optical fibers subjected to controlled hygrothermal conditions in a climate chamber. The results demonstrate that uncoated fibers are insensitive to humidity, whereas coated fibers exhibit measurable wavelength shifts due to moisture absorption. The proposed model effectively predicts these shifts, with errors consistently below 2.6%. This approach is crucial for improving the performance and reliability of HUMSs in monitoring composite structures, ensuring long-term safety in extreme environmental conditions.

Protonated molecular clusters: promoting molecular complexity

Abstract Molecules identified in Space display a diversity greater than ever, yet the mechanisms responsible for this complexity remain largely unknown. The extreme conditions faced by these molecules under astrophysical conditions raise several questions about their persistence and evolution. In this work, we explore the role of gas-phase protonated molecular dimers, which act both as protectors of existing molecules and as promotors in the emergence of new species. Experiments were conducted using the DIAM irradiation device on four protonated dimers of astrophysical interest, namely the pyridine, methanol, glycine pure dimers and the diglycine-glycine mixed dimer. Analysis of the observed relaxation channels following an energy deposition mimicking cosmic radiation reveals three main mechanisms of evaporation, covalent bond breaking, and unimolecular reaction. Focusing on the latter, our experiments combined with quantum chemical calculations of the relevant pathways shed new light onto the role of the protonation site on the reactants.

Geodesics of Finsler Hayward black hole surrounded by quintessence

This research paper delves into examining the Hayward black hole structure surrounded by quintessence within the framework of Finsler geometry. Our focus centers on the Finsler metric tensor development for black holes. This newly derived metric introduces significant deviations from regular black hole metrics found in general relativity due to the Finslerian term γ presence, thus shedding fresh insights into the geometry and nature of black holes. Our findings reveal that the metric structure aligns closely with known Riemannian limits, affirming the congeniality of our model with existing theories. Furthermore, we extended our analysis to derive critical mass values and determine the normalization factor for the Hayward black hole within the Finlserian framework. The study encompasses a detailed description of the horizons and extremal conditions of the Hayward black hole surrounded by quintessence and the impact of the Finsler parameter γ on them. Specifically, we explore the case where the quintessence state parameter is set to ω=-2/3. Our analysis delves into the effective potential, providing insights into null geodesics for various energy levels and examining the behavior of horizons by utilizing the definition of the effective potential. We also discuss the impact of γ for the same. We compute and analyze the radius of circular orbits, the period, the instability characteristics of circular orbits, and the force acting on photons with the newly introduced parameter γ within the quintessence field. We have thoroughly looked over the obtained results and discussed them. Additionally, we explore the shadow of the black hole in this context. Thereby, the validity and consistency of our Finslerian model are strengthened. In addition to increasing our understanding of black hole physics, this study paves the way for further research in the Finsler geometry domain and its applications in astrophysics.

The Influence of Changing Belt Loading Conditions on the Operational Condition of the Belt Transmission

Given the fact that belt drives are used to transmit power to a fairly large extent, it is natural to devote scientific attention to their transmission with an effort to contribute to the constant technical and technological progress in the field of belt production and use. For testing and monitoring belt drives, a measuring system was designed and manufactured, which allowed the installation of various types of belt drives and, under a controlled load, to monitor selected parameters and the behavior of individual transmission elements. The presented contribution presents both the measuring system itself and experimental measurements on three V-belts of the same size manufactured by three different manufacturers. During the experimental measurements, parameters such as belt tension were changed by changing the axial distances of the pulley axes; by connecting electric motors through frequency converters, it was possible to control the change in the input speed of the transmission and, at the same time, the load on the output pulley. On the proposed specific design solution for testing belt drives, the actual speed of the input and output pulleys was measured by sensors to determine the belt slip, and the belt’s floating in one plane was monitored using high-precision distance measurement sensors. The analysis of the belt drives also included an assessment of their impact on other parts of the machine or equipment (for example, when transmitting large forces, this can have a negative impact on bearings and gearbox components) on which they are installed; therefore, vibration measurements were also performed. The results of the experimental measurements can contribute to designers choosing a belt drive, for example, even under boundary load parameters and extreme conditions.

High-Performance Advanced Composites in Multifunctional Material Design: State of the Art, Challenges, and Future Directions

This review examines high-performance advanced composites (HPACs) for lightweight, high-strength, and multi-functional applications. Fiber-reinforced composites, particularly those utilizing carbon, glass, aramid, and nanofibers, are highlighted for their exceptional mechanical, thermal, and environmental properties. These materials enable diverse applications, including in the aerospace, automotive, energy, and defense sectors. In extreme conditions, matrix materials—polymers, metals, and ceramics—and advanced reinforcement materials must be carefully chosen to optimize performance and durability. Significant advancements in manufacturing techniques, such as automated and additive methods, have improved precision, reduced waste, and created highly customized and complex structures. Multifunctional composites integrating structural properties with energy storage and sensing capabilities are emerging as a breakthrough aligned with the trend toward smart material systems. Despite these advances, challenges such as recyclability, scalability, cost, and robust quality assurance remain. Addressing these issues will require the development of sustainable and bio-based composites, alongside efficient recycling solutions, to minimize their environmental impact and ensure long-term technological viability. The development of hybrid composites and nanocomposites to achieve multifunctionality while maintaining structural integrity will also be described.

Cryo-Rolled AA5052 Alloy: Insights into Mechanical Properties, Formability, and Microstructure

Industries operating in extreme conditions demand materials with exceptional strength, fatigue resistance, corrosion resistance, and formability. While AA5052 alloy is widely used in such industries due to its high fatigue strength and corrosion resistance, its strength frequently falls short of stringent standards. For AA5052 alloy, this study explores the combined use of solutionizing and cryo-rolling, followed by annealing, to improve strength. Although several alloys have been reported to undergo solution treatment before cryo-rolling, this study focuses on how post-processing via annealing can lessen the formability constraints usually connected to conventional cryo-rolling. The study sheds light on the ways that solutionizing, cryo-rolling, and annealing interact to affect the alloy’s mechanical characteristics. Microstructure analysis shows that solutionizing improves the grain structure by reducing dynamic recovery, promoting dislocation density, and facilitating precipitate formation. Sheets subjected to solutionizing + cryo-rolling and partially annealed at 250 °C produce optimal results. Interestingly, formability is decreased when cryo-rolling alone is used instead of cold rolling, whereas formability is successfully increased when solutionizing is used. Comparing solutionized + cryo-rolled sheets that are partially annealed at 250 °C to cold-rolled sheets that are annealed at the same temperature, the former show notable quantitative improvements: a notable 17% increase in ultimate strength, a 10% boost in yield strength, and a noteworthy 13% enhancement in microhardness. Formability has improved with the solutionized + cryo-rolled specimens by annealing. This proposed approach led to noticeable gains in formability, hardness, and strength, which would significantly improve material performance for industrial applications.

Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism

The segmentation and recognition of power lines are crucial for the UAV-based inspection of overhead power lines. To address the issues of class imbalance, low sample quantity, and long-range dependency in images, a specialized semantic segmentation network for power line segmentation called Axial-UNet++ is proposed. Firstly, to tackle the issue of long-range dependencies in images and low sample quantity, a gated axial attention mechanism is introduced to expand the receptive field and improve the capture of relative positional biases in small datasets, thereby proposing a novel feature extraction module termed axial-channel local normalization module. Secondly, to address the imbalance in training samples, a new loss function is developed by combining traditional binary cross-entropy loss with focal loss, enhancing the precision of image semantic segmentation. Lastly, ablation and comparative experiments on the PLDU and Mendeley datasets demonstrate that the proposed model achieves 54.7% IoU and 80.1% recall on the PLDU dataset, and 79.3% IoU and 93.1% recall on the Mendeley dataset, outperforming other listed models. Additionally, robustness experiments show the adaptability of the Axial-UNet++ model under extreme conditions and the augmented image dataset used in this study has been open sourced.

Triple Regulation of Water Molecules Behavior to Realize High Stability and Broad Temperature Tolerance in Aqueous Zinc Metal Batteries via a Novel Cost‐Effective Eutectic Electrolyte

AbstractThe high activity of water in aqueous electrolyte causes drastic side reactions on the Zn anodes, severely limiting the electrochemical performance of aqueous zinc metal batteries (AZMBs) under extreme conditions. Herein, levulinic acid is developed as the hydrated deep eutectic solvent (DES), to build a novel non‐flammable and cost‐effective ZnSO4‐based eutectic electrolyte with triple regulation of water molecules behavior, enabling highly stable AZMBs over a wide temperature. In situ experiments, molecular dynamics simulations, and spectroscopy analysis jointly reveal that the DES is capable of comprehensively lowering the water activity by simultaneously controlling the behavior of the free, solvated, and interfacial water molecules within the eutectic electrolyte system. Consequently, the Zn anodes exhibit ultralong cycling stability (4500 h at 1 mA cm−2/1 mA h cm−2), decent Coulombic efficiency of 99.39%, and excellent temperature tolerance (−20–50 °C). Notably, the designed 2.0 Ah Zn//VOX pouch cell exhibits a recorded actual energy density of 37.46 Wh Kg−1 and 95.38 Wh L−1 at the whole cell level, with a remarkable capacity retention of 81.01% after 150 cycles, demonstrating the potential for scale‐up into real AZMBs. This work provides an in‐depth understanding of the correlation between the water molecule behavior and electrochemical properties of AZMBs.

Simulations on Evacuation Strategy and Evacuation Process of the Subway Train Under the Fire

This study focuses on the safe evacuation strategy and evacuation process in the subway train under the fires. The subway station evacuation mode should be adopted if the power system of a subway train is normal on fire. While, the tunnel evacuation mode should be adopted if the power system of the train fails because of the effects of fire. Under the tunnel evacuation mode, the direction of tunnel smoke should be opposite to that of most passengers, and passengers should be evacuated toward the fresh wind. By using the numerical simulation software Pathfinder and PyroSim, the passenger evacuation time under different conditions is calculated, and the safety of the evacuation process is evaluated. The results show that the evacuation time of the station evacuation mode is obviously shorter than that of the tunnel evacuation mode. With the same conditions, the evacuation time of the tunnel evacuation mode is 2193 s, which is about four times as much as the evacuation time of the station evacuation mode (526 s). The total evacuation time increases with the total number of passengers and the proportion of older people and children. Under an oil pool fire, which is an extreme fire condition, the fire environment inside the train may reach a level threatening the passengers’ safety before the evacuation is complete, even before the door opens; therefore, special attention should be paid to the safety issues in stage from the fire begins to the evacuation complete.