TY - CONF A1 - Gleim, Tobias A1 - Soderer, Hannes A1 - Wille, Frank T1 - Accident-Induced Battery and Hydrogen Fires: Challenges for the Safe Transport of Packages with Dangerous Goods N2 - The transport of radioactive material is subject to stringent safety requirements defined in the IAEA regulations SSR-6 [1]. These requirements, particularly the thermal and mechanical accident conditions of transport (ACT), are rooted in studies established in the 1960s and have remained largely unchanged, especially regarding thermal boundary conditions. For many decades, the type of propulsion technology used for transporting dangerous goods has remained unchanged. In recent years, however, alternative drive technologies have made their breakthrough and are becoming increasingly established on the market. Since then, the rapid adoption of battery-electric and hydrogen-powered vehicles in heavy-duty freight and dangerous goods transport is altering the conditions under which accidents may occur. This raises a central question: Are current regulatory tests, such as the 800°C and 30-minute thermal test, still sufficiently conservative for ACT involving vehicles with alternative propulsion technologies? Battery fires pose specific challenges due to the properties of lithium-ion cells and emerging chemistries such as NMC, LFP, and NCA/LTO. Their highly flammable electrolytes, potential for thermal runaway, release of toxic gases, and long-duration or reigniting fires differ markedly from conventional fuel fires. Such behavior questions whether existing thermal test specifications adequately reflect realistic accident conditions involving electric vehicles. Hydrogen-powered vehicles introduce additional hazards. Accidental releases can form explosive mixtures, and ignitions may produce intense jet fires or explosions with high radiative heat fluxes. Near a package, these events can create thermal loads and transient pressures not fully captured by current regulatory test envelopes. Beyond peak temperatures and exposure time, parameters emphasized in IAEA SSG-26 [2], such as emissivity, absorptivity, heat flux, and fuel energy density, are critical for determining net heat input and require assessment with respect to realistic scenarios. Addressing these gaps requires a research program focused on vehicle fire scenarios and their implications for the safety assessment of packages for radioactive material. This includes developing conservative accident scenarios for various battery chemistries and performing large-scale experiments with calorimetric reference packages and instrumented setups. Notably, there are currently no experimental investigations of accidents involving transport vehicles with alternative propulsion in which the dangerous goods - the package and its loading - have been the central focus rather than the vehicle itself. A necessary research project must aim to assess the relevance of the IAEA's existing transport testing requirements regarding these new risks and, if necessary, propose changes or supplementary measures. Its methods and datasets should also support assessments for other dangerous goods, ensuring that regulatory measures continue to provide robust protection in an evolving transport landscape. T2 - International Conference on the Safe and Secure Transport of Nuclear and Radioactive Material CY - Wien, Austria DA - 23.03.2026 KW - IAEA Regulations KW - Fire Test Stand KW - Accident Scenario KW - Fire Qualification PY - 2026 SP - 1 EP - 5 PB - International Atomic Energy Agency CY - Wien AN - OPUS4-65806 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Gleim, Tobias A1 - Tazefidan, Kutlualp A1 - Wille, Frank T1 - Ai-Enhanced Documentation Analysis in Regulatory Safety Assessment of Transport Packages N2 - The transport of radioactive material requires regulatory approval based on the package type, as defined by the regulations of the International Atomic Energy Agency (IAEA). These approvals rely on comprehensive Package Design Safety Reports that evaluate mechanical, thermal, shielding, criticality and transport requirements, supported by specifications, inspections, certificates, drawings, and other technical documentations. Such safety reports contain numerous interconnected documents, and even minor changes, such as component modifications, updated material properties or revised regulations, may affect multiple sections. Although all reports follow the same regulatory framework, each package has unique design features, making every safety assessment distinct. Most documentation exists in digital form but remains largely non–machine-interpretable, limiting automated analysis of dependencies across documents. The extended synopsis argues that overcoming these limitations requires moving from simple digitization toward structured knowledge representation. A multi-stage approach begins with foundational AI technologies, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), which improve information retrieval but cannot capture the full complexity of safety report interrelationships. Building Knowledge Graphs (KGs) offers the necessary next step by transforming heterogeneous, unstructured, and semi-structured documents into a connected, queryable network. KGs enable precise tracing and visualization of dependencies across datasheets, simulations, experimental results, standards, and regulatory requirements. Such structured representations would allow automatic detection of changes, propagation of effects across related documents and validation of conditions using AI-supported tools, reducing manual workload, and improving safety and consistency. Human error remains a significant factor in drafting and reviewing safety reports. A digital quality infrastructure could reduce the number of iterations and further streamline the overall process. Integrating AI into this workflow has the potential not only to optimize assessments but also to improve their robustness by increasing the interpretability of documentation and thereby enhancing overall safety. This preliminary study examines the readiness and requirements for intelligent documentation analysis systems that support regulatory compliance for transport package safety. By analysing current documentation workflows, it demonstrates how LLM-based tools can interpret complex safety reports and identify critical interdependencies, and why KG-based architectures are essential for managing these dependencies reliably. T2 - International Conference on the Safe and Secure Transport of Nuclear and Radioactive Material CY - Wien, Austria DA - 23.03.2026 KW - Knowledge Graph KW - AI KW - RAG KW - LLM PY - 2026 SP - 1 EP - 5 PB - International Atomic Energy Agency CY - Wien AN - OPUS4-65808 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Gleim, Tobias T1 - Accident-Induced Battery and Hydrogen Fires: Challenges for the Safe Transport of Packages with Radioactive Material N2 - The transport of radioactive material is subject to stringent safety requirements defined in the IAEA regulations SSR-6 [1]. These requirements, particularly the thermal and mechanical accident conditions of transport (ACT), are rooted in studies established in the 1960s and have remained largely unchanged, especially regarding thermal boundary conditions. For many decades, the type of propulsion technology used for transporting dangerous goods has remained unchanged. In recent years, however, alternative drive technologies have made their breakthrough and are becoming increasingly established on the market. Since then, the rapid adoption of battery-electric and hydrogen-powered vehicles in heavy-duty freight and dangerous goods transport is altering the conditions under which accidents may occur. This raises a central question: Are current regulatory tests, such as the 800°C and 30-minute thermal test, still sufficiently conservative for ACT involving vehicles with alternative propulsion technologies? Battery fires pose specific challenges due to the properties of lithium-ion cells and emerging chemistries such as NMC, LFP, and NCA/LTO. Their highly flammable electrolytes, potential for thermal runaway, release of toxic gases, and long-duration or reigniting fires differ markedly from conventional fuel fires. Such behavior questions whether existing thermal test specifications adequately reflect realistic accident conditions involving electric vehicles. Hydrogen-powered vehicles introduce additional hazards. Accidental releases can form explosive mixtures, and ignitions may produce intense jet fires or explosions with high radiative heat fluxes. Near a package, these events can create thermal loads and transient pressures not fully captured by current regulatory test envelopes. Beyond peak temperatures and exposure time, parameters emphasized in IAEA SSG-26 [2], such as emissivity, absorptivity, heat flux, and fuel energy density, are critical for determining net heat input and require assessment with respect to realistic scenarios. Addressing these gaps requires a research program focused on vehicle fire scenarios and their implications for the safety assessment of packages for radioactive material. This includes developing conservative accident scenarios for various battery chemistries and performing large-scale experiments with calorimetric reference packages and instrumented setups. Notably, there are currently no experimental investigations of accidents involving transport vehicles with alternative propulsion in which the dangerous goods - the package and its loading - have been the central focus rather than the vehicle itself. A necessary research project must aim to assess the relevance of the IAEA's existing transport testing requirements regarding these new risks and, if necessary, propose changes or supplementary measures. Its methods and datasets should also support assessments for other dangerous goods, ensuring that regulatory measures continue to provide robust protection in an evolving transport landscape. T2 - International Conference on the Safe and Secure Transport of Nuclear and Radioactive Material CY - Vienna, Germany DA - 23.03.2026 KW - Fire Test Stand KW - Accident Scenario KW - IAEA Regu-lations KW - Fire Qualification PY - 2026 AN - OPUS4-65807 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Gleim, Tobias T1 - Accident-Induced Battery and Hydrogen Fires: Challenges for the Safe Transport of Packages with Dangerous Goods N2 - The transport of radioactive material is subject to stringent safety requirements defined in the IAEA regulations SSR-6 [1]. These requirements, particularly the thermal and mechanical accident conditions of transport (ACT), are rooted in studies established in the 1960s and have remained largely unchanged, especially regarding thermal boundary conditions. For many decades, the type of propulsion technology used for transporting dangerous goods has remained unchanged. In recent years, however, alternative drive technologies have made their breakthrough and are becoming increasingly established on the market. Since then, the rapid adoption of battery-electric and hydrogen-powered vehicles in heavy-duty freight and dangerous goods transport is altering the conditions under which accidents may occur. This raises a central question: Are current regulatory tests, such as the 800°C and 30-minute thermal test, still sufficiently conservative for ACT involving vehicles with alternative propulsion technologies? Battery fires pose specific challenges due to the properties of lithium-ion cells and emerging chemistries such as NMC, LFP, and NCA/LTO. Their highly flammable electrolytes, potential for thermal runaway, release of toxic gases, and long-duration or reigniting fires differ markedly from conventional fuel fires. Such behavior questions whether existing thermal test specifications adequately reflect realistic accident conditions involving electric vehicles. Hydrogen-powered vehicles introduce additional hazards. Accidental releases can form explosive mixtures, and ignitions may produce intense jet fires or explosions with high radiative heat fluxes. Near a package, these events can create thermal loads and transient pressures not fully captured by current regulatory test envelopes. Beyond peak temperatures and exposure time, parameters emphasized in IAEA SSG-26 [2], such as emissivity, absorptivity, heat flux, and fuel energy density, are critical for determining net heat input and require assessment with respect to realistic scenarios. Addressing these gaps requires a research program focused on vehicle fire scenarios and their implications for the safety assessment of packages for radioactive material. This includes developing conservative accident scenarios for various battery chemistries and performing large-scale experiments with calorimetric reference packages and instrumented setups. Notably, there are currently no experimental investigations of accidents involving transport vehicles with alternative propulsion in which the dangerous goods - the package and its loading - have been the central focus rather than the vehicle itself. A necessary research project must aim to assess the relevance of the IAEA's existing transport testing requirements regarding these new risks and, if necessary, propose changes or supplementary measures. Its methods and datasets should also support assessments for other dangerous goods, ensuring that regulatory measures continue to provide robust protection in an evolving transport landscape. T2 - ASNR-BAM Workshop CY - Paris, France DA - 31.03.2026 KW - Fire Test Stand KW - Accident Scenario KW - IAEA Regu-lations KW - Fire Qualification PY - 2026 AN - OPUS4-65804 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Gleim, Tobias T1 - Ai-Enhanced Documentation Analysis in Regulatory Safety Assessment of Transport Packages N2 - The transport of radioactive material requires regulatory approval based on the package type, as defined by the regulations of the International Atomic Energy Agency (IAEA). These approvals rely on comprehensive Package Design Safety Reports that evaluate mechanical, thermal, shielding, criticality and transport requirements, supported by specifications, inspections, certificates, drawings, and other technical documentations. Such safety reports contain numerous interconnected documents, and even minor changes, such as component modifications, updated material properties or revised regulations, may affect multiple sections. Although all reports follow the same regulatory framework, each package has unique design features, making every safety assessment distinct. Most documentation exists in digital form but remains largely non–machine-interpretable, limiting automated analysis of dependencies across documents. The extended synopsis argues that overcoming these limitations requires moving from simple digitization toward structured knowledge representation. A multi-stage approach begins with foundational AI technologies, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), which improve information retrieval but cannot capture the full complexity of safety report interrelationships. Building Knowledge Graphs (KGs) offers the necessary next step by transforming heterogeneous, unstructured, and semi-structured documents into a connected, queryable network. KGs enable precise tracing and visualization of dependencies across datasheets, simulations, experimental results, standards, and regulatory requirements. Such structured representations would allow automatic detection of changes, propagation of effects across related documents and validation of conditions using AI-supported tools, reducing manual workload, and improving safety and consistency. Human error remains a significant factor in drafting and reviewing safety reports. A digital quality infrastructure could reduce the number of iterations and further streamline the overall process. Integrating AI into this workflow has the potential not only to optimize assessments but also to improve their robustness by increasing the interpretability of documentation and thereby enhancing overall safety. This preliminary study examines the readiness and requirements for intelligent documentation analysis systems that support regulatory compliance for transport package safety. By analysing current documentation workflows, it demonstrates how LLM-based tools can interpret complex safety reports and identify critical interdependencies, and why KG-based architectures are essential for managing these dependencies reliably. T2 - International Conference on the Safe and Secure Transport of Nuclear and Radioactive Material CY - Vienna, Austria DA - 23.03.2026 KW - AI KW - RAG KW - LLM KW - Knowledge Graph PY - 2026 AN - OPUS4-65809 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Gleim, Tobias T1 - AI-Enhanced Documentation Analysis in Regulatory Safety Assessment of Dangerous Goods Packages N2 - The transport of radioactive material requires regulatory approval based on the package type, as defined by the regulations of the International Atomic Energy Agency (IAEA). These approvals rely on comprehensive Package Design Safety Reports that evaluate mechanical, thermal, shielding, criticality and transport requirements, supported by specifications, inspections, certificates, drawings, and other technical documentations. Such safety reports contain numerous interconnected documents, and even minor changes, such as component modifications, updated material properties or revised regulations, may affect multiple sections. Although all reports follow the same regulatory framework, each package has unique design features, making every safety assessment distinct. Most documentation exists in digital form but remains largely non–machine-interpretable, limiting automated analysis of dependencies across documents. The extended synopsis argues that overcoming these limitations requires moving from simple digitization toward structured knowledge representation. A multi-stage approach begins with foundational AI technologies, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), which improve information retrieval but cannot capture the full complexity of safety report interrelationships. Building Knowledge Graphs (KGs) offers the necessary next step by transforming heterogeneous, unstructured, and semi-structured documents into a connected, queryable network. KGs enable precise tracing and visualization of dependencies across datasheets, simulations, experimental results, standards, and regulatory requirements. Such structured representations would allow automatic detection of changes, propagation of effects across related documents and validation of conditions using AI-supported tools, reducing manual workload, and improving safety and consistency. Human error remains a significant factor in drafting and reviewing safety reports. A digital quality infrastructure could reduce the number of iterations and further streamline the overall process. Integrating AI into this workflow has the potential not only to optimize assessments but also to improve their robustness by increasing the interpretability of documentation and thereby enhancing overall safety. This preliminary study examines the readiness and requirements for intelligent documentation analysis systems that support regulatory compliance for transport package safety. By analysing current documentation workflows, it demonstrates how LLM-based tools can interpret complex safety reports and identify critical interdependencies, and why KG-based architectures are essential for managing these dependencies reliably. T2 - ASNR-BAM Workshop CY - Paris, France DA - 31.03.2026 KW - AI KW - RAG KW - LLM KW - Knowledge Graph PY - 2026 AN - OPUS4-65802 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -