Open Access

A reliable stress analysis of a cask for radioactive materials under dynamic load conditions requires a qualified numerical model. For this purpose, the cask is typically discretized using a mesh of finite elements. Certain parts of the mesh usually require a refinement to accurately determine the stresses and strains. Other parts of the mesh may not be of interest with respect to stresses and strains. In such parts, a coarse mesh is sufficient. The mesh density can vary considerably within a cask model. Transitions between regions with different mesh densities can be achieved either by gradually changing the element size or by using tie contact conditions. Such mesh transitions can sometimes lead to complications. In general, a finer mesh can transmit higher-frequency signals than a coarser mesh. The propagation of stress waves through the model may be influenced by the transition zone or by any artificially introduced interface. Stress waves arising within the fine mesh can be partially confined by the surrounding coarse mesh. Poor mesh transitions can therefore cause stress waves to be partially reflected or to change their shape. A thin rod is examined to demonstrate the effects. It is modeled with a varying number of elements or varying size of elements respectively. A stress pulse is applied to one end of the rod, while the opposite end remains free. The generated stress wave is observed at various locations along the rod, and its shape and amplitude are analyzed in relation to the mesh density. Inappropriate meshing can lead to incorrect simulation results without the finite element code issuing warnings or error messages. Such problems are often not obvious. As a result, when using finite element meshing, the maximum size of the finite elements required to model the expected stress wave propagation should not be exceeded. In other words, the correct modeling of stress wave propagation determines the minimum number of finite elements required to mesh a cask component. This study illustrates the ASME Guidance Document “Use of Explicit Finite Element Analysis for the Evaluation of Radioactive Material Transport Packages and Storage Casks in Energy-Limited Impact Events”.

The safety assessment of packages for the transport of radioactive material is a highly regulated and knowledge-intensive process. Regulatory authorities must evaluate complex safety reports that integrate mechanical, thermal, shielding, criticality, and operational analyses, supported by extensive heterogeneous documentation such as drawings, certificates, test results, and inspection records. Although these documents are increasingly available in digital form, they remain largely unstructured and weakly interconnected, requiring manual cross-checking of dependencies and assumptions. This paper explores the potential of AI-driven documentation analysis to support regulatory safety assessments in the context of IAEA-regulated transport of radioactive materials. It examines the limitations of conventional digital approaches and introduces a multi-layered architecture based on Retrieval-Augmented Generation (RAG), multimodal document processing, and structured knowledge representations. In particular, the paper argues that standard RAG systems are insufficient to capture the deep interdependencies across safety documentation and proposes the integration of Knowledge Graphs and Graph-RAG techniques to enable traceable, multi-step reasoning. Beyond technical feasibility, the paper emphasizes the importance of human-centered design, explainability, and trustworthiness in safety-critical and regulated domains. Concepts such as Explainable AI and Human-in-the-Loop operation are discussed as essential prerequisites for regulatory acceptance and long-term resilience. Finally, implementation challenges and future developments are outlined, including machine-readable standards and continuous compliance validation. The study demonstrates that AI-supported documentation analysis can significantly enhance efficiency, transparency, and robustness of safety assessments, provided that technical innovation is carefully aligned with regulatory, organizational, and human factors.

The global transition toward alternative propulsion technologies is leading to a rapid increase in battery-electric transport vehicles, including their use in the carriage of dangerous goods. This development introduces new accident scenarios whose thermal characteristics may challenge existing safety assumptions embedded in international transport regulations for radioactive materials. In particular, the International Atomic Energy Agency SSR-6 regulations define standardized mechanical and thermal accident tests that were developed decades ago, based largely on fires involving fossil-fuel-powered vehicles. Recent research demonstrates that fires involving high-voltage lithium-ion batteries and other types can exhibit high heat release rates, extended fire durations, toxic emissions, and re-ignition phenomena, all of which differ substantially from conventional vehicle fires. However, the interaction between battery-electric transport vehicle fires and packages for dangerous goods has not yet been systematically investigated, representing a significant research gap. This paper reviews the historical development of the IAEA thermal test requirements, summarizes the current state of knowledge on battery fire behavior, and presents the experimental concept of large-scale fire tests conducted at the German Federal Institute for Materials Research and Testing (BAM). These experiments aim to generate realistic and conservative thermal boundary conditions to support future regulatory assessments of transport with dangerous goods under emerging vehicle technologies.

The global transition toward alternative propulsion technologies is leading to a rapid increase in battery-electric transport vehicles, including their use in the carriage of dangerous goods. This development introduces new accident scenarios whose thermal characteristics may challenge existing safety assumptions embedded in international transport regulations for radioactive materials. In particular, the International Atomic Energy Agency SSR-6 regulations define standardized mechanical and thermal accident tests that were developed decades ago, based largely on fires involving fossil-fuel-powered vehicles. Recent research demonstrates that fires involving high-voltage lithium-ion batteries and other types can exhibit high heat release rates, extended fire durations, toxic emissions, and re-ignition phenomena, all of which differ substantially from conventional vehicle fires. However, the interaction between battery-electric transport vehicle fires and packages for dangerous goods has not yet been systematically investigated, representing a significant research gap. This paper reviews the historical development of the IAEA thermal test requirements, summarizes the current state of knowledge on battery fire behavior, and presents the experimental concept of large-scale fire tests conducted at the German Federal Institute for Materials Research and Testing (BAM). These experiments aim to generate realistic and conservative thermal boundary conditions to support future regulatory assessments of transport with dangerous goods under emerging vehicle technologies.

The transport of dangerous goods, like radioactive material requires, a safety evaluation to fulfill the regulatory requirements. The package design safety assessment shall be conducted in compliance with the international regulations and documented in a comprehensive package design safety report. This safety report evaluates a broad range of requirements from the regulations, including mechanical, thermal, shielding, criticality and transport requirements and controls, and testing assessments. Additionally, it encompasses supporting documents such as specifications, inspections, certifications, drawings, and guidelines in a variety of complex documents. Safety and manufacturing reports contain multiple interconnected sub-reports covering various topics. Changes, such as component modifications, material property updates, or regulatory revisions, often impact multiple sections of the safety analysis reports, making even minor adjustments complex and time-consuming. Each transport package has unique requirements to be fulfilled, making every safety report distinct, despite following the same regulatory framework. Most documentation exists in standard digital formats but is often not machine interpretable, preventing automated analysis of the critical dependencies between them. This paper argues that moving beyond simple digitization towards structured knowledge representation is essential for addressing these challenges. We propose a multi-stage approach, beginning with foundational AI technologies such as Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), and progressing toward the construction of Knowledge Graphs (KGs). KGs convert unstructured and semi-structured information into a connected, queryable network, enabling precise tracing and visualization of complex interdependencies within the documentation landscape. By linking interpretable content directly to datasheets, tables, simulations, experimental results, standards, and regulations, such a system would automatically identify changes and interdependencies. Related conditions could be validated using AI-based tools, reducing the need for manual intervention, improving both efficiency and safety. Human error plays a significant role in drafting, reviewing, and revising safety reports, often requiring iterative review cycles and multiple reviewers before approval. A digital quality infrastructure could reduce iterations and further improve efficiency. Integrating AI into this process could optimize safety assessments and enhance their robustness by leveraging interpretability to enhance safety. This preliminary study explores the readiness and requirements for using intelligent documentation analysis system in the context of regulatory compliance for package safety for the transport of radioactive material. By analyzing current documentation workflows, we identify how LLM-based tools can interpret complex safety reports and highlight critical interdependencies and then demonstrate why a KG-based architecture is necessary to robustly manage and query critical interdependencies. This lays the groundwork for future agentic AI systems capable of proactively supporting the safety assessment lifecycle, while stressing the importance of robust data governance and AI reliability in this highly regulated context.