Hydrogen energy storage can be used to achieve goals of national energy security, renewable energy integration, and grid resilience. Adapting underground natural gas storage facility (UNGSF) infrastructure for underground hydrogen storage (UHS) is one method of storing large quantities of hydrogen that has already largely been proven to work for natural gas. There are currently some underground salt caverns in the United States that are being used for hydrogen storage by commercial entities, but it is still a fairly new concept in that it has not been widely deployed nor has it been done with other geologic formations like depleted hydrocarbon reservoirs. Assessments of UHS systems can help identify and evaluate risks to people both working within the facility and residing nearby. This report provides example risk assessment methodologies and analyses for generic wellhead and processing facility configurations, specifically in the context of the risks of unintentional hydrogen releases into the air. Assessment of the hydrogen containment in the subsurface is also critically important for a safety assessment for a UHS facility, but those geomechanical assessments are not included in this report.
Bayesian inference with a simple Gaussian error model is used to efficiently compute prediction variances for energies, forces, and stresses in the linear SNAP interatomic potential. The prediction variance is shown to have a strong correlation with the absolute error over approximately 24 orders of magnitude. Using this prediction variance, an active learning algorithm is constructed to iteratively train a potential by selecting the structures with the most uncertain properties from a pool of candidate structures. The relative importance of the energy, force, and stress errors in the objective function is shown to have a strong impact upon the trajectory of their respective net error metrics when running the active learning algorithm. Batched training of different batch sizes is also tested against singular structure updates, and it is found that batches can be used to significantly reduce the number of retraining steps required with only minor impact on the active learning trajectory.
Project Overview and Goal: Create a Python Toolkit for UQ (PyTUQ) to facilitate the interoperability of FASTMath UQ tools (Dakota, UQTk, KLPC), as well as other ASCR funded capabilities for UQ workflows.
This report investigates topics of interest with regard to Nuclear Power Plants (NPPs) utilizing flexible plant operations and generation (FPOG). Previous reports have identified the risk associated with co-located hydrogen generation facilities. This report evaluates special topics with regard to co-location of both hydrogen and syngas production facilities. A literature review was conducted to evaluate overpressure mitigation techniques that may be available to the NPP to reduce the consequence of an overpressure event. Also, the overpressure consequence of a catastrophic hydrogen storage tank failure event was analyzed. A comparison of the similarities and differences between the methodology utilized in HyRAM+ and Regulatory Guide 1.91 (R.G. 1.91) was performed for overpressure analysis. Also, the trinitrotoluene (TNT) equivalency methodology was utilized to evaluate an overpressure event at a Syngas production facility.
This is a presentation for a short course on accelerators at the 2024 International Power Modulator and High Voltage Conference (Indianapolis, IN, May 28-Jun 1, 2024), and largely consists of already-published material.
Sustainable aviation fuels (SAFs) offer an effective pathway to decarbonize the aviation sector, which accounts for about 5% of the global net effective radiative forcing, and is expected to double in the next two decades. A primary objective of the SAF GC Roadmap is to develop sustainable fuels that avoid “sooting, aerosols, and other contributors to vapor trail emissions." Another parts of this project is motivated by ongoing efforts to develop aromatic-free SAFs by using cycloalkanes to match seal swell characteristics of current fossil-based jet fuel (e.g. Jet A).
A thermally driven, micrometer-scale switch technology has been created that utilizes the ErH3/Er2O3 materials system. The technology is comprised of novel thin film switches, interconnects, on-board micro-scale heaters for passive thermal environment sensing, and on-board micro-scale heaters for individualized switch actuation. Switches undergo a thermodynamically stable reduction/oxidation reaction leading to a multi-decade (>11 orders) change in resistance. The resistance contrast remains after cooling to room temperature, making them suitable as thermal fuses. An activation energy of 290 kJ/mol was calculated for the switch reaction, and a thermos-kinetic model was employed to determine switch times of 120 ms at 560 °C with the potential to scale to 1 ms at 680 °C.
Artificial intelligence (AI) and machine learning (ML) are near-ubiquitous in day-to-day life; from cars with automated driver-assistance, recommender systems, generative content platforms, and large language chatbots. Implementing AI as a tool for international safeguards could significantly decrease the burden on safeguards inspectors and nuclear facility operators. The use of AI would allow inspectors to complete their in-field activities quicker, while identifying patterns and anomalies and freeing inspectors to focus on the uniquely human component of inspections. Sandia National Laboratories has spent the past two and a half years developing on-device machine learning to develop both a digital and robotic assistant. This combined platform, which we term INSPECTA, has numerous on-device machine learning capabilities that have been demonstrated at the laboratory scale. This work describes early successes implementing AI/ML capabilities to reduce the burden of tedious inspector tasks such as seal examination, information recall, note taking, and more.
