Reliability Engineering & System Safety最新文献_第7页

A reliable model based load shifting incorporating users' uncertain behavior to price fluctuations in demand response mechanisms 在需求响应机制中考虑用户不确定行为对价格波动的负荷转移可靠模型

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-13 DOI: 10.1016/j.ress.2026.112229

Jingzheng Li , Zhiwen Zhao , Mohamed A. Mohamed , Tao Jin

With the widespread implementation of demand response (DR) mechanisms, the dynamic response behaviors of users to price fluctuations have become increasingly important. To address the constraints of user response delays and behavioral uncertainties on the effectiveness of DR, this paper proposes a load shifting model that incorporates delay effects, aiming to accurately capture user response behaviors and support reliable grid scheduling. The model uses first- and second-order lag systems to represent user response delays under time-of-use pricing (TOU) and real-time pricing (RTP) mechanisms, combined with a variable-parameter logistic function to describe dynamic response behaviors. A corresponding parameterization methodology and reliability assessment framework is also developed. Case studies demonstrate that the proposed model effectively captures the response characteristics of different user types, with an average DR deviation rate reduced by approximately 30% compared to traditional models. The paper also quantifies the negative impact of delays on DR capabilities, showing that response delays significantly weaken the system's scheduling effectiveness. This model provides theoretical and practical support for precise grid scheduling in uncertain environments.

随着需求响应机制的广泛实施，用户对价格波动的动态响应行为变得越来越重要。为了解决用户响应延迟和行为不确定性对DR有效性的约束，本文提出了一种考虑延迟效应的负荷转移模型，旨在准确捕捉用户响应行为，支持可靠的电网调度。该模型采用一阶和二阶滞后系统来表示分时定价（TOU）和实时定价（RTP）机制下的用户响应延迟，并结合变参数逻辑函数来描述动态响应行为。提出了相应的参数化方法和可靠性评估框架。案例研究表明，该模型有效地捕获了不同用户类型的响应特征，与传统模型相比，平均DR偏差率降低了约30%。本文还量化了延迟对DR能力的负面影响，表明响应延迟显著削弱了系统的调度有效性。该模型为不确定环境下电网的精确调度提供了理论和实践支持。

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引用次数: 0

Response and reliability of MDOF Duhem hysteretic system excited by wideband random excitations 宽带随机激励下mof Duhem滞回系统的响应与可靠性

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-13 DOI: 10.1016/j.ress.2026.112226

Qiangfeng Lü , Maolin Deng , Danyu Li

Engineering structures subjected to random external loads often exhibit hysteretic nonlinear behavior. Analyzing the dynamic response of systems with hysteretic restoring forces is crucial for structural fatigue assessment and reliability evaluation. However, theoretical analysis of hysteretic systems remains challenging. Since hysteretic forces cannot be directly treated mathematically, equivalent linearization techniques are typically required. This becomes particularly difficult for multi-degree-of-freedom (MDOF) systems, as most existing studies focus on single-degree-of-freedom (SDOF) systems, and a systematic methodology for simplifying hysteretic forces in MDOF systems has yet to be established. Few studies have derived analytical equivalent expressions for MDOF hysteretic systems. To address this gap, this paper proposes a novel strategy for MDOF Duhem hysteretic systems. By combining the equivalent method with stochastic averaging method, the complex MDOF hysteretic system can be dimensionally reduced, enabling analytical solutions for both stationary response and system reliability. Two numerical examples are presented to demonstrate the proposed methodology. In both cases, analytical equivalent expressions for the Duhem hysteretic restoring force are derived, leading to analytical solutions for the stationary response. Furthermore, the second example additionally provides calculations of the conditional reliability function (CRF) and mean first-passage time (MFPT). Monte Carlo simulations conducted for both examples validate the theoretical predictions, confirming the effectiveness of the proposed method.

工程结构在随机外荷载作用下往往表现出滞回非线性行为。分析具有滞回恢复力的系统的动态响应是结构疲劳评估和可靠性评估的关键。然而，滞回系统的理论分析仍然具有挑战性。由于迟滞力不能直接用数学方法处理，因此通常需要等效的线性化技术。这对于多自由度（MDOF）系统来说尤其困难，因为大多数现有的研究都集中在单自由度（SDOF）系统上，并且尚未建立一个系统的方法来简化mof系统中的滞回力。很少有研究推导出多自由度滞回系统的解析等效表达式。为了解决这一问题，本文提出了一种新的mof - Duhem滞回系统策略。通过将等效方法与随机平均方法相结合，可以对复杂的多自由度滞回系统进行降维，从而实现平稳响应和系统可靠性的解析解。给出了两个数值例子来证明所提出的方法。在这两种情况下，导出了Duhem滞回恢复力的解析等效表达式，从而得到了平稳响应的解析解。此外，第二个例子还提供了条件可靠度函数（CRF）和平均首次通过时间（MFPT）的计算。对两个例子进行了蒙特卡罗模拟，验证了理论预测，证实了所提出方法的有效性。

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引用次数: 0

Health assessment for transmission wire ropes subject to dependent failure modes 受相关失效模式影响的传输钢丝绳的健康评估

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-13 DOI: 10.1016/j.ress.2026.112228

Rui Zheng , Zhenglong Liu , Yuan Xing , Tong Niu , Chengcheng Cai , Ercan Altinzoy , Xiangyun Ren

As a crucial component of a wire-driven structure, the transmission wire rope may experience plastic stretching and sliding friction during operation, resulting in lifetime reduction and potential medical accidents. Therefore, it is essential to assess the health status of transmission wire ropes. This paper investigates the health assessment of transition wires subject to dependent failure modes based on experimental data. A fatigue wear experiment is designed based on actual working conditions to test the tension loss of transmission wire ropes. Experimental results show that the transmission wire ropes are subject to two main failure modes: a soft failure indicating that the tension loss exceeds a predetermined level and a hard failure signifying the random fracture of some wire threads. The tension loss process is described by a Wiener process. The hard failure time, dependent on time and tension loss, is characterized as a proportional hazards model. A recursive approach is used to derive recursive formulas for various health indices such as conditional reliability, soft and hard failure probabilities, and remaining useful time. The results of health assessment can support the health management and maintenance decision-making of transmission wire ropes in surgical instruments. Comparison with existing methods demonstrates that the proposed method can produce accurate assessment results with higher efficiency and less memory.

传输钢丝绳作为钢丝绳驱动结构的关键部件，在运行过程中可能会发生塑性拉伸和滑动摩擦，从而导致使用寿命缩短和潜在的医疗事故。因此，对传输钢丝绳的健康状况进行评估是十分必要的。基于实验数据，研究了不同失效模式下过渡导线的健康评估。根据实际工况设计了疲劳磨损试验，对钢丝绳的张力损失进行了测试。试验结果表明，传输钢丝绳主要有两种失效模式：张力损失超过预定水平的软失效和部分钢丝绳随机断裂的硬失效。张力损失过程用维纳过程来描述。硬失效时间依赖于时间和张力损失，其特征为比例风险模型。采用递归方法推导了条件可靠性、软、硬故障概率、剩余使用时间等健康指标的递归公式。健康评估结果可为手术器械传动钢丝绳的健康管理和维修决策提供依据。与现有方法的比较表明，该方法能够以更高的效率和更小的内存产生准确的评估结果。

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引用次数: 0

NGBoost-Naïve Bayes collaborative deep learning for structural safety evaluation of bridges NGBoost-Naïve基于Bayes协同深度学习的桥梁结构安全评价

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-13 DOI: 10.1016/j.ress.2026.112230

Jin-Ling Zheng , Sheng-En Fang

In structural safety evaluation, machine learning (ML) based methods often exhibit strong data-fitting capabilities but struggle to effectively handle uncertainties in structural response data. Fortunately, Bayesian deep learning (BDL) algorithms can address this drawback by integrating the Bayesian theory with ML algorithms, thereby unifying perception and inference tasks within a single framework. For this purpose, a BDL framework has been proposed combining natural gradient boosting (NGBoost) and the Naïve Bayes theory. The NGBoost serves as the perception component, capturing correlations between deflections at various measurement locations of a healthy structure, while the Shapley Additive Explanation (SHAP) is employed to enhance interpretability. During the training process, the optimal hyperparameters of the NGBoost is objectively determined through Bayesian optimization (BO). The predicted probability distributions of these deflections are treated as hinge variables. By applying the triple standard deviation principle, a structural safety interval is defined to identify scenarios requiring further evaluation. The task-specific component, based on the Naïve Bayes theory, is then utilized to evaluate the structural condition. A bridge benchmark model was used to verify the safety assessment performance under the limited training samples. In addition, a continuous box-girder bridge was employed to further validate the effectiveness of the proposed structural condition indicator. As the structural degradation increased, the condition indicator accurately reflected the degradation variation.

在结构安全评估中，基于机器学习（ML）的方法通常表现出强大的数据拟合能力，但难以有效处理结构响应数据中的不确定性。幸运的是，贝叶斯深度学习（BDL）算法可以通过将贝叶斯理论与ML算法集成来解决这一缺陷，从而在单个框架内统一感知和推理任务。为此，提出了一个结合自然梯度增强（NGBoost）和Naïve贝叶斯理论的BDL框架。NGBoost作为感知组件，捕获健康结构不同测量位置的偏转之间的相关性，而Shapley加性解释（SHAP）用于增强可解释性。在训练过程中，通过贝叶斯优化（Bayesian optimization， BO）客观确定NGBoost的最优超参数。预测这些挠度的概率分布被视为铰链变量。通过应用三标准偏差原则，定义了结构安全区间，以确定需要进一步评估的情况。然后利用基于Naïve贝叶斯理论的任务特定组件来评估结构状况。利用桥梁基准模型验证了在有限训练样本下的安全评价效果。并以某连续箱梁桥为例，进一步验证了所提结构状态指标的有效性。随着结构退化程度的增加，工况指标准确反映了结构退化程度的变化。

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引用次数: 0

A customized approximate dynamic programming approach for the restoration optimization of disrupted infrastructures with drone inspection 基于无人机检测的基础设施修复优化自定义近似动态规划方法

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-12 DOI: 10.1016/j.ress.2026.112220

Fatao Zhang , Chi Zhang , Yanxia Chang

Post-disaster maintenance with drone inspection is crucial for enhancing the resilience of critical infrastructures. In this paper, we propose a novel stochastic dynamic programming model that integrates maintenance team scheduling with drone-based inspections by using repair vehicles as take-off and landing platforms (RVTLP) approach, so that drones can follow maintenance vehicles deep into disaster areas and dynamically update damage information. Our model explicitly considers travel time between infrastructure components and scenarios with multiple repair teams, aiming to maximize infrastructure resilience within a limited planning horizon. To deal with the computational complexity of our optimization model, we developed a customized approximate dynamic programming algorithm with unvisited-state approximation and limited-period storage and validated the algorithm's ability to solve large-scale problems. Finally, computational experiments under real-world scenarios reveal that drone inspection range, travel time, and the number of maintenance teams exert significant effects on the resilience of critical infrastructures, providing important insights into how the resilience evolves with these parameters.

无人机检查的灾后维护对于提高关键基础设施的恢复能力至关重要。本文提出了一种新的随机动态规划模型，将维修车辆作为起降平台（RVTLP）方法，将维修团队调度与无人机巡检相结合，使无人机能够跟随维修车辆深入灾区并动态更新损坏信息。我们的模型明确地考虑了基础设施组件和多个维修团队之间的旅行时间，旨在在有限的规划范围内最大化基础设施的弹性。为了解决优化模型的计算复杂性，我们开发了一种具有未访问状态近似和限期存储的自定义近似动态规划算法，并验证了该算法解决大规模问题的能力。最后，在真实场景下的计算实验表明，无人机检查范围、飞行时间和维护团队数量对关键基础设施的弹性有显著影响，为弹性如何随这些参数演变提供了重要见解。

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引用次数: 0

An interpretable multivariate remaining useful life prediction method of mechanical equipment based on adaptive threshold aggregation causal discovery 基于自适应阈值聚合因果发现的可解释多变量机械设备剩余使用寿命预测方法

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-12 DOI: 10.1016/j.ress.2026.112214

Juan Xu , Zhengyu Deng , Mingguang Dai , Xinhang Yu , Xu Ding , Ruqiang Yan

Accurately predicting the remaining useful life (RUL) of mechanical equipment is crucial for ensuring the safety and reliability of mechanical systems. However, existing deep learning-based RUL prediction methods often face challenges related to interpretability and robustness when dealing with complex multivariate time series data. To address this, this paper proposes a multivariate RUL prediction method based on adaptive threshold aggregation causal discovery. Specifically, a Bayesian Information Criterion-based causal discovery method is employed to explore the relationships between variables (i.e., sensor signals) across multiple samples, yielding a corresponding causal graph. An adaptive threshold mechanism is then designed to aggregate these sample-level graphs into a global structure that highlights key dependencies. Based on this, causal effect estimation is performed by combining front-door and back-door adjustment methods to generate a causal effect matrix. This matrix, together with the multivariate time series data, is input into a Temporal Graph Convolutional Network to capture dynamic dependencies and causal associations for RUL prediction. Experimental results on the C-MAPSS dataset show that the proposed method achieves an average RMSE of 13.3, outperforming state-of-the-art benchmarks and providing more reliable and interpretable insights into RUL prediction.

准确预测机械设备的剩余使用寿命（RUL）是保证机械系统安全可靠运行的关键。然而，现有的基于深度学习的RUL预测方法在处理复杂的多变量时间序列数据时，往往面临着可解释性和鲁棒性方面的挑战。针对这一问题，本文提出了一种基于自适应阈值聚合因果发现的多元规则推理预测方法。具体而言，采用基于贝叶斯信息准则的因果发现方法来探索多个样本中变量（即传感器信号）之间的关系，从而生成相应的因果图。然后设计一个自适应阈值机制，将这些样本级图聚合到一个突出显示关键依赖关系的全局结构中。在此基础上，结合前门和后门调整方法进行因果效应估计，生成因果效应矩阵。该矩阵与多变量时间序列数据一起输入到时序图卷积网络中，以捕获RUL预测的动态依赖关系和因果关系。在C-MAPSS数据集上的实验结果表明，该方法的平均RMSE为13.3，优于最先进的基准，并为RUL预测提供了更可靠和可解释的见解。

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引用次数: 0

Reliability estimation for the multicomponent stress-strength model based on objective Bayesian method 基于客观贝叶斯方法的多分量应力强度模型可靠性估计

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-11 DOI: 10.1016/j.ress.2026.112209

Tiefeng Zhu

The multicomponent stress-strength model has many important applications in reliability analysis. Most existing studies assume that stress and strength follow the same distribution and employ maximum likelihood (ML) estimation for reliability inference. However, this assumption restricts the model’s applicability, as there is often no physical rationale for requiring identical distributions for stress and strength. Hence, this paper discusses the reliability inference of a multicomponent stress-strength model under the assumption that the strength and stress variables belong to different distributions. An objective Bayesian method (OBM) framework is applied to infer the model parameters and system reliability based on derived Jeffreys and two reference priors. To ensure the validity of reliability inference, the proper properties of posterior distributions of the model parameters are proved and a Gibbs sampling algorithm is developed. Simulations are implemented to compare the OBM with the considered methods and the results show the superiority of the proposed OBM for the small sample size case. Finally, one real example is analyzed for illustrative purposes.

多分量应力强度模型在可靠性分析中有许多重要的应用。现有的研究大多假设应力和强度遵循相同的分布，并采用最大似然估计进行可靠性推断。然而，这种假设限制了模型的适用性，因为通常没有物理上的理由要求应力和强度的相同分布。因此，本文讨论了假设强度和应力变量属于不同分布的多分量应力-强度模型的可靠性推断。采用客观贝叶斯方法（OBM）框架，基于导出的Jeffreys和两个参考先验来推断模型参数和系统可靠性。为了保证可靠性推断的有效性，证明了模型参数后验分布的适当性质，并提出了Gibbs抽样算法。仿真结果表明，该方法在小样本情况下具有较好的优越性。最后，为了说明问题，分析了一个真实的例子。

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引用次数: 0

TARD: Test-time domain adaptation for robust fault detection under evolving operating conditions 基于测试时域自适应的运行条件下鲁棒故障检测

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-11 DOI: 10.1016/j.ress.2025.112135

Han Sun, Olga Fink

Fault detection is essential in complex industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. With the growing availability of condition monitoring data, data-driven approaches have increasingly applied in detecting system faults. However, these methods typically require large, diverse, and representative training datasets that capture the full range of operating scenarios, an assumption rarely met in practice, particularly in the early stages of deployment.

Industrial systems often operate under highly variable and evolving conditions, making it difficult to collect comprehensive training data. This variability results in a distribution shift between training and testing data, as future operating conditions may diverge from those previously observed ones. Such domain shifts hinder the generalization of traditional models, limiting their ability to transfer knowledge across time and system instances, ultimately leading to performance degradation in practical deployments.

To address these challenges, we propose a novel method for continuous test-time domain adaptation, designed to support robust early-stage fault detection in the presence of domain shifts and limited representativeness of training data. Our proposed framework –Test-time domain Adaptation for Robust fault Detection (TARD) – explicitly separates input features into system parameters and sensor measurements. It employs a dedicated domain adaptation module to adapt to each input type using different strategies, enabling more targeted and effective adaptation to evolving operating conditions. We validate our approach on two real-world case studies from multi-phase flow facilities, delivering substantial improvements in both fault detection accuracy and model robustness over existing domain adaptation methods under real-world variability.

故障检测在复杂的工业系统中是必不可少的，通过区分异常和正常的操作条件来防止故障和优化性能。随着状态监测数据的日益可用性，数据驱动方法越来越多地应用于系统故障检测。然而，这些方法通常需要大量的、多样化的、具有代表性的训练数据集，以捕获所有操作场景，这一假设在实践中很少得到满足，特别是在部署的早期阶段。工业系统经常在高度可变和不断变化的条件下运行，因此很难收集全面的培训数据。这种可变性导致训练和测试数据之间的分布变化，因为未来的操作条件可能与以前观察到的情况不同。这样的领域转移阻碍了传统模型的泛化，限制了它们跨时间和系统实例传递知识的能力，最终导致实际部署中的性能下降。为了解决这些挑战，我们提出了一种新的连续测试时域自适应方法，旨在支持在域移位和训练数据代表性有限的情况下进行鲁棒的早期故障检测。我们提出的框架-测试-时域自适应鲁棒故障检测(TARD) -明确地将输入特征分离为系统参数和传感器测量。它采用专用的领域适应模块，采用不同的策略适应每种输入类型，从而更有针对性和有效地适应不断变化的操作条件。我们在多相流设施的两个实际案例研究中验证了我们的方法，在实际变化情况下，与现有的领域自适应方法相比，在故障检测精度和模型鲁棒性方面都有了实质性的改进。

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引用次数: 0

Linear two-dimensional consecutive k-type systems in multi-state case 多态情况下线性二维连续k型系统

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-10 DOI: 10.1016/j.ress.2026.112215

He Yi , Narayanaswamy Balakrishnan , Xiang Li

In the context of consecutive k-type systems, multi-state system models are only considered in the one-dimensional case and not in the two-dimensional case due to the complexity involved. In this paper, we consider several linear two-dimensional consecutive k-type systems in the multi-state case for the first time, as generalization of consecutive k-out-of-n systems and l-consecutive-k-out-of-n systems without/with overlapping. These systems include multi-state linear connected-(k, r)-out-of-(m, n): G systems, multi-state linear connected-(k, r)-or-(r, k)-out-of-(m, n): G systems, multi-state linear l-connected-(k, r)-out-of-(m, n): G systems without/with overlapping, and multi-state linear l-connected-(k, r)-or-(r, k)-out-of-(m, n): G systems without/with overlapping. We then derive their reliability functions by using the finite Markov chain imbedding approach (FMCIA) in a new way. We also present several examples to illustrate all the results developed here.

在连续k型系统中，由于其复杂性，只考虑一维情况下的多状态系统模型，而不考虑二维情况下的多状态系统模型。本文首次考虑了多态情况下的若干线性二维连续k型系统，作为连续k-out- n系统和无/有重叠的l-连续k-out- n系统的推广。这些系统包括多状态线性连接-(k, r)-out of-(m, n): G系统，多状态线性连接-(k, r)-or-(r, k)-out of-(m, n): G系统，多状态线性l-连接-(k, r)-out of-(m, n): G系统，无/有重叠，多状态线性l-连接-(k, r)-or-(r, k)-out of-(m, n): G系统，无/有重叠。然后用有限马尔可夫链嵌入法（FMCIA）导出了它们的可靠性函数。我们还提供了几个示例来说明这里开发的所有结果。

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引用次数: 0

Advanced quantitative risk analysis through the integration of computational fluid dynamics for individual and societal risk 先进的定量风险分析，通过计算流体动力学的个人和社会风险的整合

IF 11 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2026-01-10 DOI: 10.1016/j.ress.2026.112223

Mohamed Seddik Hellas , Rachid Chaib

Quantitative Risk Analysis (QRA) is extensively applied in the process industry to assess and manage major hazards. Nevertheless, its outcomes are often challenged by significant uncertainties, largely due to the use of simplified models for dispersion, fire, and explosion scenarios. These uncertainties mainly result from assumptions regarding meteorological conditions, local topography, and input parameters, which may bias the estimation of both individual and societal risks. To address these limitations, integrating Computational Fluid Dynamics (CFD) into QRA has emerged as a promising development. CFD provides a more detailed representation of atmospheric dispersion by incorporating local effects and obstacles, enabling more accurate simulations of fire dynamics and thermal radiation, and strengthening the reliability of individual and societal risk curves through a dynamic and realistic approach. In this study, the integrated methodology is applied to a critical scenario: the catastrophic rupture of an atmospheric hydrocarbon storage tank in an industrial zone in Bechar, Algeria. The findings reveal that the QRA–CFD coupling delivers a more realistic quantification of dynamic individual risk along evacuation paths, enhances the assessment of dynamic societal risk, and serves as a valuable decision-support tool for emergency planning and for improving industrial resilience.

定量风险分析（Quantitative Risk Analysis， QRA）广泛应用于过程工业中对重大危害进行评估和管理。然而，其结果经常受到重大不确定性的挑战，这主要是由于使用了分散、火灾和爆炸情景的简化模型。这些不确定性主要来自对气象条件、当地地形和输入参数的假设，这些假设可能会对个人和社会风险的估计产生偏差。为了解决这些限制，将计算流体动力学（CFD）集成到QRA中已经成为一个有前途的发展。CFD通过结合局部影响和障碍物，提供了更详细的大气扩散表示，实现了更准确的火灾动力学和热辐射模拟，并通过动态和现实的方法增强了个人和社会风险曲线的可靠性。在这项研究中，综合方法应用于一个关键场景：阿尔及利亚Bechar工业区大气碳氢化合物储罐的灾难性破裂。研究结果表明，QRA-CFD耦合可以更真实地量化疏散路径上的动态个体风险，增强动态社会风险的评估，并为应急规划和提高工业弹性提供有价值的决策支持工具。

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引用次数: 0