The Hypatia Experiment: Yttrium Hydride and Highly Enriched Uranium Critical Experiment
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This presentation discusses the objective of the research which is to update n+ 63,65 Cu cross section evaluations with recently measured data to resolve discrepancies in benchmark performance. It also discusses the models used which are the R -matrix analysis and the analysis of angular distribution coefficients. Additionally, the validation methods that were used are discussed, including the Rez shielding benchmark and the ICSBEP criticality benchmarks. In conclusion, he n+ 63,65 Cu cross sections have been updated via R-matrix analysis up to 100 keV. An increased average capture cross section and the adoption of experimentally based Legendre coefficients lead to improved performance in reactivity benchmarks. In the fast region, the adoption of the JENDL-4.0 cross sections above 4.0 MeV improves the performance in shielding benchmarks. Ultimately, the n 63,65 Cu ENDF files will be submitted to ENDF/B-VIII.1.
The National Criticality Experiments Research Center (NCERC) located at the Nevada National Security Site (NNSS) in the Device Assembly Facility (DAF) and operated by Los Alamos National Laboratory (LANL) is the only general purpose critical experiments facility in the United States. Experiments from subcritical to critical and above prompt critical are carried out at NCERC on a regular basis. In recent years, NCERC has become more involved in experiments related to nuclear energy, including the Kilopower/KRUSTY demonstration and the recent Hypatia experiment. Multiple nuclear energy related projects are currently ongoing at NCERC. This paper discusses NCERC’s role in advanced reactor design and how that role may change in the future.
Dust ejecta can affect visibility during a lunar landing, erode nearby coated surfaces and get into mechanical assemblies of in-place infrastructure. Regolith erosion was observed at many of the Apollo landing sites. This problem needs to be addressed at the beginning of the lunar base missions, as the amount of infrastructure susceptible to problems will increase with each landing. Protecting infrastructure from dust and debris is a crucial step in its long term functionality. A proposed way to mitigate these hazards is to build a lunar launch pad. Other areas of a lunar habitat will also need surface stabilization methods to help mitigate dust hazards. Roads would prevent dust from being lifted during movement and dust free zones might be required for certain areas critical to crew safety or to critical science missions. Work at NASA Kennedy Space Center (KSC) is investigating methods of stabilizing the lunar regolith including: sintering the regolith into a solid and using heat or UV cured polymers to stabilize the surface. Sintering, a method in which powders are heated until fusing into solids, has been proposed as one way of building a Lunar launch/landing pad. A solar concentrator has been built and used in the field to sinter JSC-1 Lunar stimulant. Polymer palliatives are used by the military to build helicopter landing pads and roads in dusty and sandy areas. Those polymers are dispersed in a solvent (water), making them unsuitable for lunar use. Commercially available, solvent free, polymer powders are being investigated to determine their viability to work in the same way as the solvent borne terrestrial analog. This presentation will describe the ongoing work at KSC in this field. Results from field testing will be presented. Physical testing results, including compression and abrasion, of field and laboratory prepared samples will be presented.
The identification, modelling, and analysis of root causes of accidents and incidents dominate conventional safety management approaches. However, the effect of humans’ safety-producing behavior on the overall resilience of the system is often neglected. Additionally, emerging aviation markets are giving rise to concepts of operation, such as urban air mobility and optionally piloted air cargo operations, that are leading to a shift in locus of control between humans and automation. Without an understanding of the human contribution to safety, it is difficult to assess the effects of these novel role allocations on overall system safety. In this work, safety-producing behaviors are identified and abstracted into resilient performance strategies. Production rules that encapsulate these strategies are then generated and classified in the Soar cognitive architecture. The strategies are then applied to a remotely-operated air cargo example to demonstrate how safe learning is facilitated. The learned rules and strategies are then formally verified.
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United States and IAEA regulations for the transportation of radioactive materials require that fissile materials must be subcritical under expected ambient temperatures ranging from -40°C to 38°C. While historical experiments have been conducted at room and elevated temperatures, no criticality integral benchmarks exist for temperatures below room temperature. The Low-Temperature Thermal Epithermal eXperiments (LT-TEX) aim to provide benchmark evaluations down to -40°C. The LT-TEX design incorporates stacked HEU plates interspersed with varying thicknesses of polyethylene disks to provide varying levels of moderation, with five configurations at 20°C having already been benchmarked. A novel vacuum/cryogenic chamber was fabricated to contain the LT-TEX configurations in a thermally isolated environment and cool it to -40°C. Prior to testing with fissile materials, testing with surrogate fuel plates was performed to characterize the thermal properties of the system, ensure no condensation forms on the stack, and to test that the uranium plates would not warp, or fracture during the thermal cycling. Surrogate testing has characterized the cryogenic chamber and verified its viability to perform the benchmark experiments, allowing for the next steps in the LT-TEX experimental campaign. The results of the surrogate testing are discussed and compared to heat transfer calculations performed as part of the experiment design process, to establish confidence in the thermal behavior of the system.
This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.
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The presentation discusses the primary goal of the NCSP Task IP&D-5 which is to use available data from HPRR operation to create a benchmark report for inclusion in the ICSBEP, as a Criticality Accident Alarm System (CAAS) shielding benchmark. The presentation elaborates on the HPRR operation and how it relates to the KENO VI model, sulfur pellets measurements, and calculation methodology between KENO and MAVRIC FW-CADIS. The presentation concludes by stating that some work is still needed to evaluate uncertainties and to explain the discrepancy computation/experiment.
This presentation discusses the primary goal of software testing which is to test the code for correctness. It also discusses the results for individual suites and the role of validation and verification also referred to in the presentation as V&V. In summation, the V&V framework enables easy comparison between calculations performed with different code versions and/or nuclear data libraries. This entire framework will be distributed with the upcoming MCNP6.3 release. V&V test suites shown and several that were not (Criticality, LAQGSM, Lockwood) will be distributed in the new framework.
BACKGROUND Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future exploration EVA on the Moon and Mars are expected to be higher compared to EVA currently conducted from the International Space Station (ISS). Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient characterization of the cognitive capabilities required prior to, during, and following EVA. Furthermore, it is unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis of exploration EVA to characterize the cognitive capabilities, critical safety decisions, and contributing factors (e.g., lunar communications delay) important to monitor for optimal performance in future exploration EVA. This cognitive task analysis was conducted through interviews with astronauts and subject matter experts in EVA research and operations. Interviews focused on exploration EVA and elicited feedback on the cognitive capabilities required for specific EVA tasks and subtasks. The information from this cognitive task analysis will help close the gap in our understanding of the key cognitive capabilities required for safe decision-making during exploration mission class EVA on the Moon and Mars. METHOD We used an applied cognitive task analysis method1 over the course of interviews with a total of 15 NASA astronauts and subject matter experts in EVA. Each interview was led by a scientist with expertise in cognitive neuroscience from the Behavioral Health & Performance (BHP) Laboratory at NASA Johnson Space Center. Notes were taken by a research coordinator in the BHP Laboratory and interviews were recorded on Microsoft Teams to ensure the accuracy of notetaking. In the first interview protocol, participants were asked about the specific tasks and cognitive demands associated with EVA. This provided a high-level overview of the steps involved in the major tasks conducted during exploration EVA, as well as which of the steps require the most cognitive skill. Next, participants completed a knowledge audit, which employs a set of probes designed to describe types of domain knowledge of skill and elicit appropriate examples. In the second interview protocol, completed with a separate set of subject matter experts, interviewees were asked to complete a simulated incapacitated crew rescue (ICR) scenario2, which provided specific context that allowed probing around relevant issues such as situational awareness and potential errors. Experts were then asked to identify the knowledge, skills, and abilities (KSAs) underlying each EVA task and to provide ratings on the importance and cognitive demand of each KSA. Finally, participants also described the most likely and consequential critical safety incidents related to decrements in cognitive performance during exploration EVA and assessed the impact of lunar communication delay on cognitive performance. RESULTS & DISCUSSION Interviews for this cognitive task analysis are nearly complete and results will be presented in full at IWS 2025. Results will include a summary of all expert ratings of EVA tasks and subtasks, qualitative summaries of content from each interview part, and a discussion of future directions for products addressing cognitive performance monitoring and cognitive domain mapping in exploration EVA.
The X-33 program requires the use of multiple telemetry ground stations to cover the launch, ascent, transition, descent, and approach phases for the flights from Edwards AFB to landings at Dugway Proving Grounds, UT and Malmstrom AFB, MT. This paper will discuss the X-33 telemetry requirements and design, including information on fixed and mobile telemetry systems, best source selection, and support for Range Safety Officers. A best source selection system will be utilized to automatically determine the best source based on the frame synchronization status of the incoming telemetry streams. These systems will be used to select the best source at the landing sites and at NASA Dryden Flight Research Center to determine the overall best source between the launch site, intermediate sites, and landing site sources. The best source at the landing sites will be decommutated to display critical flight safety parameters for the Range Safety Officers. The overall best source will be sent to the Lockheed Martin's Operational Control Center at Edwards AFB for performance monitoring by X-33 program personnel and for monitoring of critical flight safety parameters by the primary Range Safety Officer. The real-time telemetry data (received signal strength, etc.) from each of the primary ground stations will also be compared during each nu'ssion with simulation data generated using the Dynamic Ground Station Analysis software program. An overall assessment of the accuracy of the model will occur after each mission. Acknowledgment: The work described in this paper was NASA supported through cooperative agreement NCC8-115 with Lockheed Martin Skunk Works.
The envisioned transformation of the National Airspace System to integrate an In-time Aviation Safety Management System(IASMS)to assure safety in Advanced Air Mobility(AAM)brings unprecedented challenges to the design of human interfaces and management of safety information. Safety in design and operational safety assurance are critical factors for how humans will interact with increasingly autonomous systems. The IASMS Concept of Operations builds from traditional commercial operator safety management and scales in complexity to AAM. The transformative changes in future aviation systems pose potential new critical safety risks with novel types of aircraft and other vehicles having different performance capabilities, flying in increasingly complex airspace, and using adaptive contingencies to manage normal and non-normal operations. These changes compel development of new and emerging capabilities that enable innovative ways for humans to interact with data and manage information. In-creasing complexity of AAM corresponds with use of predictive modeling, data analytics, machine learning, and artificial intelligence to effectively address known hazards and emergent risks. The roles of humans will dynamically evolve in increments with this technological and operational evolution. The interfaces for how humans will interact with increasingly complex and assured systems designed to operate autonomously and how information will need to be presented are important challenges to be resolved.
Banner Highlight for Structure-Dependent Doppler Broadening Using a Generalized Thermal Scattering Law [DOI 10.3390/jne2020013].