安全科学与韧性(英文)最新文献_第2页

Evolution of human factors research in aviation safety: A systematic review and bibliometric analysis of the intellectual structure 航空安全中人为因素研究的演变：知识结构的系统回顾与文献计量学分析

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-08-24 DOI: 10.1016/j.jnlssr.2025.100249

Elizabeth Amorkor Okine , Esmaeil Zarei , Brian J. Roggow , Naser Dehghan

Despite the multitude of research endeavors dedicated to Human Factors (HF) in aviation safety, a comprehensive review remains conspicuously scarce. Accordingly, this study presents the first in-depth systematic review and bibliometric analysis of the vital role played by HF in enhancing the safety and reliability of air transportation. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, we scrutinized the Scopus dataset spanning from 1937 to late 2023. A rigorous screening process was applied to identify relevant documents, ultimately subjecting critical analyses of 1663 documents to address four foundational research questions within HF associated with aviation safety. First, our analysis delves into the identification of key areas of emphasis that have characterized HF in the aviation industry since 1937. By tracing the trajectory of research over time, the study aims to discern the evolution of HF within the aviation context. Furthermore, an exploration of primary challenges and knowledge gaps crucial to research is highlighted, with proposed pathways for future investigations to maximize their impact on air transportation safety. Finally, the study extends its inquiry to compare the existing landscape of human reliability research within the aviation sector with that of Nuclear Power Plants (NPPs) and the Chemical Process Industry (CPI). This holistic approach to understanding HF not only contributes valuable insights into aviation safety but also contextualizes these findings within broader industrial frameworks, revealing the key gaps that exist in human reliability within the aviation industry. The outcomes of this study underscore the indispensable role of HF in establishing and advancing safer and more resilient air transportation systems.

尽管大量的研究努力致力于人为因素（HF）在航空安全，一个全面的审查仍然明显缺乏。因此，本研究首次对高频在提高航空运输的安全性和可靠性方面所起的重要作用进行了深入的系统回顾和文献计量分析。采用系统评价和荟萃分析的首选报告项目（PRISMA）指南，我们仔细检查了1937年至2023年底的Scopus数据集。采用严格的筛选过程来确定相关文件，最终对1663份文件进行批判性分析，以解决HF中与航空安全相关的四个基础研究问题。首先，我们的分析深入探讨了自1937年以来航空业HF特征的关键重点领域。通过追踪研究的轨迹，本研究旨在了解航空环境下高频的演变。此外，重点探讨了对研究至关重要的主要挑战和知识差距，并提出了未来调查的途径，以最大限度地提高其对航空运输安全的影响。最后，该研究扩展了其调查，以比较航空部门与核电站（NPPs）和化学过程工业（CPI）的现有人力可靠性研究格局。这种理解高频的整体方法不仅为航空安全提供了有价值的见解，而且还将这些发现置于更广泛的工业框架中，揭示了航空工业中人类可靠性存在的关键差距。这项研究的结果强调了HF在建立和推进更安全、更有弹性的航空运输系统方面不可或缺的作用。

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引用次数: 0

Assessing resilience potentials in management of occupational safety and health in hospitals: Development and validation of a tool 评估医院职业安全和健康管理的复原力潜力：开发和验证一种工具

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-08-08 DOI: 10.1016/j.jnlssr.2025.100247

J. Afonso-Fernandes , J. Barbosa , P. Arezes , C. Pardo-Ferreira , J.C. Rubio-Romero , M.A. Rodrigues

A resilient Occupational Safety and Health (OSH) management system is crucial for effectively addressing potential future public emergencies, ensuring the continuous protection of workers' safety and health. Therefore, it is essential for organizations, particularly hospitals, to assess their resilient performance and employ tools that are appropriate and tailored to their specific context. This study aims to enhance the understanding of resilience potentials in OSH management within hospital settings. To this end, an assessment tool was developed based on the Resilience Assessment Grid (RAG). A Delphi study involving subject matter experts was conducted to refine the tailored RAG tool. Following this, a pilot test was administered to 404 healthcare professionals across three public hospitals, with subsequent psychometric analysis. Exploratory Factor Analysis (EFA) identified a four-dimensional structure. Goodness-of-fit indices demonstrated acceptable values, confirming the adequacy of the measurement model. Reliability testing indicated that the 29 item assessment tool is both valid and reliable. The tailored RAG tool was successfully validated, enabling the identification of strengths and weaknesses in OSH management.

一个有弹性的职业安全与健康管理体系对于有效应对未来潜在的公共突发事件，确保持续保护工人的安全和健康至关重要。因此，各组织，特别是医院，必须评估其弹性绩效，并采用适合其具体情况的工具。本研究旨在增进对医院环境中职业安全卫生管理弹性潜力的了解。为此，开发了一种基于弹性评估网格（RAG）的评估工具。进行了涉及主题专家的德尔菲研究，以完善定制的RAG工具。在此之后，对三家公立医院的404名医疗保健专业人员进行了试点测试，随后进行了心理测量分析。探索性因子分析（EFA）确定了一个四维结构。拟合优度指数显示了可接受的值，证实了测量模型的充分性。信度检验表明，29项评价工具有效可靠。量身定制的RAG工具已成功验证，能够识别职业安全与健康管理中的优势和劣势。

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引用次数: 0

Optimizing urban infrastructure resilience: Analyzing cascading failures and critical node dependencies through multilayer network models 优化城市基础设施弹性：通过多层网络模型分析级联故障和关键节点依赖关系

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-08-07 DOI: 10.1016/j.jnlssr.2025.100245

Cong Lu , Jianjun She , Hezhi Pan , Zihao Guo , Xuanling Zhou , Zhijian Li

As urbanization and industrialization progress, urban infrastructure systems grow increasingly complex, heightening their vulnerability to cascading failures from natural disasters and human-induced disruptions. Strengthening the resilience of these systems is critical for sustainable urban development and sustaining residents’ quality of life. This study introduces a novel framework to analyze cascading failure propagation within infrastructure networks. Utilizing the implicit interdependency model, we construct a multilayer network that delineates interconnections and dependencies across infrastructure sectors. The PageRank algorithm is used to identify critical nodes by evaluating their network centrality, thereby highlighting key components within the system. Through simulations of random, PageRank-based, and betweenness-based attack scenarios, we explore failure dynamics and their propagation patterns. Additionally, we evaluate mitigation strategies, with the community periphery augmentation strategy proving most effective, enhancing resilience by linking peripheral nodes between communities. This research systematically connects the significance of key nodes to cascading effects, uncovering vulnerabilities and providing actionable insights for disaster response and recovery planning.

随着城市化和工业化进程的推进，城市基础设施系统变得越来越复杂，更容易受到自然灾害和人为破坏的连锁故障的影响。加强这些系统的复原力对于可持续城市发展和维持居民的生活质量至关重要。本研究引入了一个新的框架来分析基础设施网络中的级联故障传播。利用隐式相互依赖模型，我们构建了一个多层网络，描绘了基础设施部门之间的相互联系和依赖关系。PageRank算法通过评估其网络中心性来识别关键节点，从而突出显示系统中的关键组件。通过模拟随机的、基于pagerank的和基于之间的攻击场景，我们探索了故障动态及其传播模式。此外，我们还评估了缓解策略，其中社区外围增强策略被证明是最有效的，通过连接社区之间的外围节点来增强复原力。本研究系统地将关键节点的重要性与级联效应联系起来，揭示漏洞，并为灾难响应和恢复计划提供可操作的见解。

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引用次数: 0

Machine learning hybrid dynamic best model selection algorithm for real-time fire prediction using IoT-enabled multi-sensor data in buildings 机器学习混合动态最佳模型选择算法，用于建筑物中使用物联网多传感器数据的实时火灾预测

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-08-06 DOI: 10.1016/j.jnlssr.2025.100236

Mujeeb Ali Khan , Weiguo Song , Abbas Khan , Mazhar Ali , Rehmat Karim , Jun Zhang

Fire disasters in urban areas, including homes, offices, and industrial facilities, have increased significantly over the past decade, causing extensive damage and loss of life. Integrating intelligent fire detection systems with machine learning (ML) is crucial for providing early warnings and facilitating effective response coordination. In this research, a novel hybrid dynamic best model selection (HDBMS) ML-based algorithm is proposed for internet of things (IoT)-enabled fire detection in buildings, which outperforms traditional static models by providing higher accuracy and adaptability across diverse fire scenarios. The proposed algorithm employs feature selection pre-processing techniques, followed by the synergistic integration of five classifiers: support vector classifier (SVC), logistic regression, random forest, Gaussian Naïve Bayes (Gaussian NB), and decision tree, aiming to enhance prediction accuracy and robustness across various fire scenarios. This system dynamically selects the optimal classifier based on real-time performance metrics such as precision, accuracy, F1-score, and recall. This approach was rigorously validated using our developed dataset and real-time sensor data to monitor smoke, temperature, and humidity under various fire scenarios. Following algorithm validation, a laboratory-constructed multi-sensor fire detection node prototype wirelessly feeds sensor data to the ThingSpeak cloud platform for real-time data analysis and communication with ML algorithms in the back-end. The system's improved precision, accuracy, and root mean squared error (RMSE) confirm its effectiveness. The results demonstrate that the proposed approach achieves superior classification accuracy compared with existing methods in the literature. Furthermore, this study's novelty lies in the dynamic selection of the most effective model in real-time, a characteristic that is currently lacking in fire detection systems, thereby enhancing the system's flexibility and efficiency in various fire scenarios.

在过去的十年里，城市地区的火灾，包括家庭、办公室和工业设施，显著增加，造成了广泛的破坏和生命损失。将智能火灾探测系统与机器学习（ML）相结合对于提供早期预警和促进有效的响应协调至关重要。在这项研究中，提出了一种新的基于混合动态最佳模型选择（HDBMS） ml的算法，用于支持物联网（IoT）的建筑物火灾探测，该算法通过在不同火灾场景中提供更高的准确性和适应性，优于传统的静态模型。该算法采用特征选择预处理技术，然后将支持向量分类器（SVC）、逻辑回归、随机森林、高斯Naïve贝叶斯（高斯NB）和决策树五种分类器协同集成，旨在提高不同火灾场景下的预测精度和鲁棒性。该系统根据精密度、准确度、f1分数和召回率等实时性能指标动态选择最优分类器。该方法通过我们开发的数据集和实时传感器数据进行了严格验证，以监测各种火灾场景下的烟雾、温度和湿度。在算法验证之后，实验室构建的多传感器火灾探测节点原型将传感器数据无线馈送到ThingSpeak云平台，进行实时数据分析，并与后端ML算法进行通信。系统的精度、准确度和均方根误差（RMSE）的提高证实了它的有效性。结果表明，与文献中已有的分类方法相比，该方法具有更高的分类精度。此外，本研究的新颖之处在于实时动态选择最有效的模型，这是目前火灾探测系统所缺乏的特性，从而提高了系统在各种火灾场景下的灵活性和效率。

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引用次数: 0

Human detection with smoke occlusion based on AI-generated images 基于人工智能生成图像的烟雾遮挡人体检测

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-08-05 DOI: 10.1016/j.jnlssr.2025.100235

Zhen Xu, Xiyan Tang, Wenting Li, Yujie Zhao, Donglian Gu, Yuan Tian

Detecting occupants trapped in buildings is challenging during fire accidents because of the dense, rapidly spreading smoke. To this end, this paper proposes a human detection method that accounts for smoke occlusion, based on artificial intelligence (AI)-generated images and an improved real-time detection transformer (RT-DETR) model. First, a procedure is designed to construct a dataset of human images with smoke occlusion using generative AI, addressing the current dataset shortage. Second, an RT-DETR model is optimized using a transformer-based global-local interaction network to increase the sensitivity to human characteristics with smoke occlusion. Third, corresponding validations were conducted. The results indicated that the proposed method could achieve remarkable average precision (up to 93.8 %) for smoke occlusion scenarios. Finally, the proposed method was employed in real-world fire cases. The proposed method can accurately detect humans with smoke occlusion in building fires, effectively aiding fire emergency evacuation and rescue efforts.

在火灾事故中，由于浓烟密集，迅速蔓延，探测被困在建筑物中的人员是一项挑战。为此，本文基于人工智能（AI）生成的图像和改进的实时检测变压器（RT-DETR）模型，提出了一种考虑烟雾遮挡的人工检测方法。首先，设计了一个程序，使用生成式人工智能构建具有烟雾遮挡的人体图像数据集，解决了当前数据集的不足。其次，利用基于变压器的全局-局部交互网络对RT-DETR模型进行优化，以提高对烟雾遮挡下人体特征的敏感性。第三，进行了相应的验证。结果表明，该方法在烟雾遮挡场景下的平均精度可达93.8%。最后，将该方法应用于实际火灾案例。该方法能够准确地探测到建筑火灾中被烟雾遮挡的人员，有效地辅助火灾应急疏散和救援工作。

引用次数: 0

Exploring cascading failures in supply chain risk management: A systematic review, 2013-2024 供应链风险管理中的级联失效：系统综述，2013-2024

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-08-05 DOI: 10.1016/j.jnlssr.2025.100234

Xiaoxin Zhu , Jiahui Zhu , David Regan , Zhimin Wen

Supply chain risk management has become increasingly crucial due to the complexity and interconnectivity of modern supply chains. Among various risk factors, cascading failures have emerged as a significant concern for risk management. This study systematically reviews the literature published between 2013 and 2024, focusing on cascading failures in supply chain networks. A total of 92 articles were selected from the Web of Science (WoS) database for in-depth analysis. The findings indicate that future research needs to achieve breakthroughs in three dimensions: data acquisition, theoretical innovation, and practical application. Specifically, integrating multi-source data is crucial for enhancing the accuracy, comprehensiveness, and timeliness of data in supply chain networks. Higher-order network modeling and digital twin technology will aid in more accurately simulating and predicting cascading failures. From a theoretical standpoint, exploring hybrid failure mechanisms and multi-level propagation patterns will deepen the understanding of failure propagation in complex networks. From a practical perspective, developing resilience design standards, intelligent early warning systems, and differentiated policy tools will strengthen the risk resistance capabilities of supply chains. Overcoming these challenges will require a holistic approach that combines data-driven insights, theoretical advancements, and practical solutions to build a safer and more resilient global supply chain system.

由于现代供应链的复杂性和互联性，供应链风险管理变得越来越重要。在各种风险因素中，级联失效已成为风险管理的重要问题。本研究系统地回顾了2013年至2024年间发表的文献，重点关注供应链网络中的级联故障。从Web of Science （WoS）数据库中选取92篇文章进行深入分析。研究结果表明，未来的研究需要在数据采集、理论创新和实际应用三个维度上取得突破。具体来说，集成多源数据对于提高供应链网络中数据的准确性、全面性和及时性至关重要。高阶网络建模和数字孪生技术将有助于更准确地模拟和预测级联故障。从理论角度来看，探索混合故障机制和多层次传播模式将加深对复杂网络中故障传播的理解。从实践角度看，制定弹性设计标准、智能预警系统和差异化政策工具将增强供应链的抗风险能力。克服这些挑战需要一种综合的方法，将数据驱动的见解、理论进步和实际解决方案相结合，以建立一个更安全、更有弹性的全球供应链系统。

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引用次数: 0

Studying the dynamics of crowd panic propagation during emergency evacuation 研究紧急疏散过程中人群恐慌传播的动态

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-07-31 DOI: 10.1016/j.jnlssr.2025.03.001

Yushan Li , Changchun Liu , Yi Yang

Casualties during emergency evacuations are often attributed to people’s panic-driven extreme behaviors rather than the accidents themselves. The propagation of panic is influenced by various factors. Based on the susceptible–infectious–recovered–susceptible (SIRS) model, a system dynamics (SD) model was developed using AnyLogic software to investigate the spread of panic emotions within a population. A case study focused on hospital emergency evacuations was conducted, wherein factors influencing panic propagation were divided into individual and group levels. The population was classified into three categories—staff, caregivers, and patients—and the effect of the ratio of these categories on evacuation efficiency was examined. Based on these classifications, an evacuation simulation experiment was conducted to examine the effects of panic emotions on evacuation efficiency. Results indicate that optimal hospital evacuation efficiency is achieved with a staff:caregiver:patient ratio of 2:2:1. The overall evacuation process is significantly impacted by panic, resulting in a 64 % increase in evacuation times when panic propagation is considered compared to scenarios where it is not. Furthermore, the initial 10 s following a disaster were identified as crucial for managing severe panic. Valuable insights for improving emergency evacuation management are provided by this study.

紧急疏散中的人员伤亡往往是由于人们的恐慌导致的极端行为，而不是事故本身。恐慌的传播受到多种因素的影响。在易感-感染-恢复-易感（SIRS）模型的基础上，利用AnyLogic软件建立了系统动力学（SD）模型，以调查人群中恐慌情绪的传播。以医院紧急疏散为研究对象，将影响恐慌传播的因素分为个体和群体两个层面。将人群分为三类——工作人员、护理人员和患者——并检查这些类别的比例对疏散效率的影响。在此基础上，进行了疏散模拟实验，考察了恐慌情绪对疏散效率的影响。结果表明，工作人员：护理人员：患者的比例为2:2:1时，医院后送效率达到最佳。整个疏散过程受到恐慌的严重影响，与不考虑恐慌传播的情况相比，考虑恐慌传播的疏散时间增加了64%。此外，灾难发生后最初的10秒被认为是管理严重恐慌的关键。本研究为改进应急疏散管理提供了有价值的见解。

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引用次数: 0

Influencing factors and mechanisms of super high-rise buildings safety risks: A Fuzzy-DEMATEL-AISM analysis 超高层建筑安全风险的影响因素及机理：模糊- dematel - aism分析

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-07-25 DOI: 10.1016/j.jnlssr.2025.100232

Ziyu Jia , Jiakai Xiu , Ke Liu , Lihua Zhao , Wei Zhang , Ziqi Song , Na Xiao , Tiantian Du

As global urbanization accelerates, super high-rise buildings increasingly face critical safety challenges due to mid-life vulnerabilities that can cause casualties and economic losses. This study examines two typical risk systems in super high-rise buildings—primary structure damage and building envelope detachment—to address urban risks and emerging safety management needs.

Through a mixed-methods approach, we developed a theoretical framework based on "environmental pressure - building physical condition - management response" relationships. Using literature analysis and the Delphi method, we established two comprehensive risk indicator systems and analyzed their interaction mechanisms using fuzzy DEMATEL-AISM. Our findings reveal that root-level environmental pressures (e.g., seismic activity, wind/snow loads) trigger structural disasters, while intermediate-level building physical conditions mediate these pressures and management responses, forming core disaster pathways. Management factors influence safety outcomes throughout a building’s lifecycle by operating across both root and result levels, with significant feedback loops in both risk systems.

Based on these findings, we propose four integrated management strategies: root-level environmental pressure monitoring for early prevention; efficient identification of physical defects from surface damage; full-lifecycle risk management with specialized operations and maintenance; and collaborative diagnostics driven by loop factor assessment. This research bridges engineering perspectives and safety management, offering stakeholders—including building owners of super high-rise buildings, government authorities, and design professionals—practical approaches to enhance safety for millions of inhabitants while contributing a comprehensive theoretical framework for risk analysis.

随着全球城市化进程的加速，超高层建筑日益面临着严重的安全挑战，因为它们的中期脆弱性可能会造成人员伤亡和经济损失。本研究考察了超高层建筑中两种典型的风险系统——初级结构破坏和建筑围护结构脱离——以解决城市风险和新兴的安全管理需求。通过混合方法，我们开发了一个基于“环境压力-建筑物理条件-管理响应”关系的理论框架。采用文献分析法和德尔菲法，建立了两个综合风险指标体系，并运用模糊DEMATEL-AISM分析了它们的相互作用机制。研究结果表明，深层环境压力（如地震活动、风/雪荷载）触发结构性灾害，而中层建筑物理条件调节这些压力和管理反应，形成核心灾害路径。在整个建筑生命周期中，管理因素通过在根源和结果层面上的操作来影响安全结果，在两个风险系统中都有重要的反馈循环。基于这些研究结果，我们提出了四种综合管理策略：监测根部环境压力以早期预防；从表面损伤中有效识别物理缺陷；全生命周期风险管理，专业化运维；以及由循环因子评估驱动的协同诊断。本研究将工程观点与安全管理结合起来，为利益相关者（包括超高层建筑的业主、政府当局和设计专业人员）提供实用的方法，以提高数百万居民的安全，同时为风险分析提供全面的理论框架。

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引用次数: 0

Skeleton-based detection of anomalous personal protective equipment doffing behaviors among healthcare workers 基于骨骼的卫生保健工作者异常个人防护装备脱落行为检测

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-07-23 DOI: 10.1016/j.jnlssr.2025.100229

Qiang Zhang, Lixin Yang, Ying Qi, Teng Wan, Qiushi Li, Renwen Miao

Identification of doffing behaviors of personal protective equipment (PPE) plays a crucial role in ensuring the safety of healthcare workers. With the continuous emergence of new infectious diseases, accurate detection of anomalous behaviors during PPE doffing procedures has become increasingly critical. In complex medical environments, conventional visual methods have demonstrated limited capability in accurately capturing the subtle movements involved in the multistep PPE doffing process. To address the challenges of low motion heterogeneity and minimal amplitude variations in PPE doffing procedures, this study presents a skeleton keypoint-based anomaly detection model. The proposed model innovatively integrates spatiotemporal embedding modules and adaptive attention mechanisms, allowing the precise detection of subtle changes in localized hand movements. In contrast to the limitations of conventional methods in characterizing fine-grained feature differences, this model demonstrates significantly enhanced capability in identifying anomalous PPE doffing behaviors. Extensive experimental results indicate that the model outperforms existing methods in key metrics, including precision and recall, providing novel technical support for the management of standardized PPE in medical settings.

识别个人防护装备的脱落行为对确保医护人员的安全起着至关重要的作用。随着新发传染病的不断出现，准确检测个人防护装备脱手过程中的异常行为变得越来越重要。在复杂的医疗环境中，传统的视觉方法在准确捕捉多步骤PPE脱落过程中涉及的细微运动方面的能力有限。为了解决PPE脱模过程中低运动异质性和最小幅度变化的挑战，本研究提出了一种基于骨架关键点的异常检测模型。该模型创新性地集成了时空嵌入模块和自适应注意机制，能够精确检测手部局部运动的细微变化。与传统方法在描述细粒度特征差异方面的局限性相比，该模型在识别异常PPE脱落行为方面表现出显著增强的能力。大量实验结果表明，该模型在准确率和召回率等关键指标上优于现有方法，为医疗环境中标准化PPE管理提供了新的技术支持。

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引用次数: 0

Coupled effects of ammonia addition proportions and pipeline diameter on self-ignition in high-pressure hydrogen leakage 氨添加比例和管道直径对高压氢气泄漏自燃的耦合影响

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-07-21 DOI: 10.1016/j.jnlssr.2025.100233

Qingzhao Li , Jialin Wu , Bo Chen , Jinghong Wang , Hongcheng Lu , Juncheng Jiang , Zhe Yang , Qiangling Duan

Hydrogen's intrinsic flammability and explosiveness pose serious safety hazards. This study systematically investigates the coupled effects of pipe diameter and ammonia addition proportions on self-ignition during high-pressure hydrogen leaks. Utilizing computational fluid dynamics (CFD) techniques integrated with the Large Eddy Simulation (LES) and the Eddy Dissipation Concept (EDC) combustion model, the effects of ammonia addition proportions (0 %∼15 %) on shockwave propagation characteristics and critical self-ignition conditions are analyzed for a range of pipeline diameters (5 mm, 10 mm, and 15 mm). A constant volume zero-dimensional homogeneous adiabatic reactor model in Chemkin-Pro was utilized to analyze the generation and consumption rates of H₂ and O₂, together with the corresponding elementary reaction kinetics. Principal findings indicate that increasing the ammonia mixing ratio significantly reduces the generation and consumption rates of H₂ and O₂, suppresses elementary reaction intensities, and elevates the threshold for self-ignition. Notably, mild ammonia blending produces suppression effects comparable to pipeline diameter expansion. A 5 % NH₃ mixture decreases the critical release pressure for a 5 mm pipeline from 1.61 MPa to 1.42 MPa, equivalent to the suppression efficacy of increasing the pipeline to 15 mm in pure air conditions. Furthermore, the combined chemical-physical synergistic mechanism between ammonia addition and diameter scaling amplifies ignition inhibition. In a 15 mm pipeline with 15 % NH₃ blending, the intensity of the leading shockwave after hydrogen leakage decreases by 23.18 % compared to a 5 mm pure-air pipeline, while the critical release pressure for ignition increases by a factor of 2.57. This study clarifies the chemical-physical coupling mechanisms of ammonia and pipeline diameter in mitigating self-ignition caused by high-pressure hydrogen leakage, providing theoretical foundations for the safety design and risk assessment of hydrogen energy transport systems.

氢的内在可燃性和爆炸性构成了严重的安全隐患。本文系统地研究了管道直径和氨添加比例对高压氢气泄漏自燃的耦合影响。利用计算流体动力学（CFD）技术，结合大涡流模拟（LES）和涡流耗散概念（EDC）燃烧模型，分析了氨添加比例（0% ~ 15%）对管道直径（5mm、10mm和15mm）范围内冲击波传播特性和临界自燃条件的影响。利用Chemkin-Pro中的等体积零维均匀绝热反应器模型，分析了H₂和O₂的生成速率和消耗率，以及相应的基本反应动力学。主要研究结果表明，增加氨混合比可显著降低H₂和O₂的生成和消耗率，抑制初等反应强度，提高自燃阈值。值得注意的是，温和的氨共混产生的抑制效果与管道直径膨胀相当。5%的nh3混合物将5mm管道的临界释放压力从1.61 MPa降低到1.42 MPa，相当于在纯空气条件下将管道增加到15mm的抑制效果。此外，氨的加入和直径结垢之间的化学-物理协同作用机制增强了着火抑制作用。在混合15% NH₃的15 mm管道中，氢气泄漏后的先导冲击波强度比5 mm纯空气管道降低了23.18%，而点火临界释放压力增加了2.57倍。本研究阐明了氨气与管道直径在缓解高压氢气泄漏自燃中的化学物理耦合机制，为氢能输送系统的安全设计和风险评估提供了理论依据。

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引用次数: 0