Demos for SPP EMT Training
This contains demos for EMT training at SPP.
Joseph, Francis [ORNL] (ORCID:0000000191226256)↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation
Interfaces play a pivotal role in dictating the performance and reliability of all-solid-state batteries (ASSBs), where complex electro-chemo-mechanical phenomena at grain boundaries (GBs) and interfaces can lead to degradation and failure. Traditional atomistic simulation methods, such as first-principles calculations and classical molecular dynamics, face limitations in modeling these interfaces due to either high computational cost or insufficient transferability to the diverse atomic environments evolving at interfaces. Machine-learning interatomic potentials (MLIPs) have emerged as a transformative approach, enabling large-scale, high-accuracy simulations of disordered and chemically complex systems by leveraging the predictability of machine learning models trained on first-principles data. Recent applications of MLIPs have demonstrated their ability to capture intricate behaviors at ASSB interfaces, including ion transport, interfacial evolution, and degradation mechanisms, with accuracy and efficiency unattainable by conventional methods. This prospective paper presents comprehensive analysis and practical guidance for MLIP development for GBs and interfaces in ASSBs, with a focus on three key pillars: data generation, model selection, and validation. Here, we review the current state of MLIP applications for GBs and interfaces in both general and ASSB-specific materials, highlighting best practices and challenges in constructing diverse and representative datasets, choosing appropriate machine learning architectures, and rigorously validating model performance. We also discuss emerging strategies and opportunities for improved reliability and efficiency of MLIPs to simulate realistic interfaces in ASSBs.
Energy - Storage↗
45Q Carbon Oxide Utilization LCA Training Workshop: Toolkit Overview
This training workshop was offered to inform stakeholders and LCA professionals of the tools and resources available from the Department of Energy’s National Energy Technology Laboratory (NETL) to support the development of an LCA that is compliant with the 45Q requirements. This presentation provides a descriptive overview which preceded a demonstration of the new and existing tools, available to view on the USEA website. A description and recording on USEA website here: https://usea.org/event/doenetl-45q-carbon-oxide-conversion-lca-training-workshop-hybrid.
Krynock, Michelle↗
PARETO Training Workshop: Optimized Decision-Making for Produced Water Networks
The PARETO training workshop is a two-hour, interactive experience intended to teach participants how to: install PARETO software, input data into PARETO; run PARETO optimization; model a variety of complex network scenarios; and analyze, interpret, and compare optimization results.
Arnold, Travis↗
Evaluation of the Radioactive Material Release in the Harborview Research and Training Building and Implications for Emergency Response
On May 2, 2019, during the 137 Cs source recovery operation, a source capsule in a research irradiator containing approximately 77.1 TBq was breached. Based on a geometric reconstruction analysis of the damage to the capsule, approximately 46.3 GBq (0.04%) was impacted by the chop saw (grinder) inside a Mobile Hot Cell (MHC) on the loading dock at the University of Washington Harborview Research and Training (HRT) Building. A very small fraction of the material impacted, less than 1%) was released from the Mobile Hot Cell and then to the rest of the HRT Building. The objectives of this project were to assess the accidental release of 137 CsCl and its implications related to emergency response methods and the ramifications of 137 CsCl transport. The phenomenology of this event was also compared with past alkali halide dispersal events. The vast number of measurements and samples collected by the remediation contractors, the Department of Energy's Nuclear Emergency Support Team, and the small number of retrospective samples collected by the authors informed the analysis. The techniques included (1) autoradiography and electron microscopy of samples collected from the HRT Building and the irradiator, (2) 3D visualization of deposition on surfaces and within the ventilation system, and (3) a study of the damage to the source capsule to evaluate the Cs particle size and particle composition due to the grinding accident. Subsequently, the cesium contaminant transport through the numerous pathways in the building was reconstructed to assess the deposition on surfaces as a function of particle size. The implications for emergency response are relevant to data quality and management. A Data Quality Objective (DQO) guides data collection methods so that they have appropriate accuracy and precision for the intended application. Recommendations were made with respect to the sample collection protocols and sample archival.
61 RADIATION PROTECTION AND DOSIMETRY↗
Decode the Workload: Training Deep Learning Models for Efficient Compute Cluster Representation
Monitoring the status of a high throughput computing cluster running computationally intensive production jobs is a crucial yet challenging system administration task due to the complexity of such systems. To this end, we train autoencoders using the Linux kernel CPU metrics of the cluster. Additionally, we explore assisting these models with graph neural networks to share information across threads within a compute node. The models are compared in terms of their ability to: 1) Produce a compressed latent representation that captures the salient features of the input, 2) Detect anomalous activity, and 3) Make distinction between different kinds of jobs run at Jefferson Lab. The goal is to have a robust encoder whose compressed embeddings are used for several downstream tasks. We extend this study further by deploying these models in a human-in-the-loop production-based setting for the anomaly detection task and discuss the associated implementation aspects such as continual learning and the criterion to generate alarms. This study represents a first step in the endeavor towards building self-supervised large-scale foundation models for computing centers.
Mohammed, Ahmed↗
Assessment and Planning of Decarbonization Research and Training at UTRGV
During the one-year project, we identified research areas that align with Fossil Energy and Carbon Management (FECM) mission goals and assessed our university's current research capability and resources. In addition, we determined the resources needed to support FECM-related research and development (R&D) at our minority-serving institution, University of Texas Rio Grande Valley (UTRGV) in to increase our competitiveness for future funding opportunities in this area. We also gathered information on the current academic courses, programs and curriculum at UTRGV that aligns with FECM goals and described additional needs pertaining to student training.
54 ENVIRONMENTAL SCIENCES↗
Enhancing Training Through Extended Reality and Data Fusion
Enhancing Training Through Extended Reality (XR) and Data Fusion will go over XR headsets and software built here at Sandia.
Bramley, Shane Michael [Sandia National Laboratori↗
Scalable Solar Fuels Production in A Reactor Train System by Thermochemical Redox Cycling of Novel Nonstoichiometric Perovskites
Hydrogen production via two-step thermochemical water splitting redox cycles using nonstoichiometric redox-active metal oxides has the potential to dramatically increase fuel production rates. At moderate-to-low water splitting temperatures, surface reaction kinetics co-limit the process. In such cases, stable and high surface area microstructures that allow exploitation of the full thermodynamic potential of the materials are essential as is tight thermal integration of the reactor module. This project’s goals were the development of novel nonstoichiometric perovskite oxides with high stability and favorable thermodynamic and kinetic properties, to optimize their microstructure for maximizing the fuel productivity, and to build a prototype reactor train system (RTS) comprising at least one reactor to meet specific performance targets: (1) capable of an in-house solar thermochemical hydrogen (STCH) productivity ≥ 12 mL g -1 for stable continuous operation ≥ 20 cycles; and (2) demonstration of scalable solar fuels production at practical solar reactor level in an industrial-scale concentrated solar tower (CST) using developed perovskites to achieve a hydrogen production rate ≥ 1 g h -1 .
08 HYDROGEN↗
Compelling ReLU Networks to Exhibit Exponentially Many Linear Regions at Initialization and During Training
Deep ReLU networks have exponential expressive potential but behave identically to their shallow counterparts under random initialization. This paper introduces a novel training paradigm that constrains weights into mathematically optimized patterns. This enables the resulting network to make exponential use of its depth.
97 MATHEMATICS AND COMPUTING↗
Synthetic Digital Environments for Training Robots on Earth & Beyond [Poster]
Creating physical replicas of real-world environments to train robots for challenging outdoor tasks, whether constructing energy infrastructure like solar farms on Earth or on the Moon and Mars, is prohibitively expensive. This project will prototype a high-fidelity digital twin framework using NVIDIA IsaacSim to create realistic digital representations of robotic systems and their operating conditions, including varied terrains and environmental factors, allowing robots to learn and adapt in a faster, safer, and more affordable way to tackle unpredictable challenges in terrestrial and extraterrestrial applications. The Robotic Space Exploration (RoSE) Lab at Colorado School of Mines focused on the development and testing of synthetic digital twins to explore multi-physics interactions between robots and unstructured environments, with emphasis on lunar conditions such as deformable regolith, reduced gravity, and terrain-robot contact dynamics. The Industrialized Construction Innovation (ICI) team at National laboratory of the Rockies (NLR), simulated robotic apparatus and construction workflows using synthetic digital twins to inform real-world deployment, targeting application-driven use cases such as robotic construction of a scaled prototype of a photovoltaic energy infrastructure. Joint efforts between RoSE and ICI are continuing to explore how environment-scale multi-physics modeling and application-level robotic system simulation could be integrated to support robotic construction of energy infrastructure in highly unstructured environments, including scenarios relevant to the Lunar South Pole.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Demonstration of Control of Laser-Plasma Instabilities in the Laboratory Using STUD Pulses (Spike Trains of Uneven Duration and Delay)
The success of laser Driven Inertial Fusion Technology (LaDrIFT) hinges on controlling laser-plasma instabilities (LPI) for effective and non-deleterious energy coupling, together with the control of implosion hydrodynamic instabilities (IHI) for target integrity. Conventional approaches ignore LPI and focus on IHI. LPI control suggests the use of low intensities, short wavelengths, and thus the slow implosions of thinner shells, while IHI control calls for thicker shells, fast implosions and thus at higher laser intensities and ablation pressures. These contradicting requirements severely restrict LaDrIFT design space, flexibility and scalability. This program demonstrates, with theoretical designs and their preliminary experimental realizations, that STUD pulses (Spike Trains of Uneven Duration and Delay) can control LPI in high-energy-density (HED) laser-created plasmas and explore this physics for the first time with high repetition (rep) rate lasers.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Autonomous Radiation Cartographer (ARC) System Training Manual
This training manual is designed to provide end users with a comprehensive knowledge base for the safe and effective use of the Autonomous Radiation Cartographer (ARC) System. The ARC is a fully autonomous radiation detection robot based on the Spot Robot platform manufactured by Boston Dynamics.
42 ENGINEERING↗
Thermal Radiation Transport with Tensor Trains
We present a novel tensor network algorithm to solve the time-dependent, gray thermal radiation transport equation. The method invokes a tensor train (TT) decomposition for the specific intensity. The efficiency of this approach is dictated by the rank of the decomposition. When the solution is “low rank,” the memory footprint of the specific intensity solution vector may be significantly compressed. The algorithm, following a step-then-truncate approach of a traditional discrete ordinates method, operates directly on the compressed state vector, thereby enabling large speedups for low-rank solutions. To achieve these speedups, we rely on a recently developed rounding approach based on the Gram-SVD. We detail how familiar S N algorithms for (gray) thermal transport can be mapped to this TT framework and present several numerical examples testing both the optically thick and thin regimes. The TT framework finds low-rank structure and supplies up to ≃60× speedups and ≃1000× compressions for problems demanding large angle counts, thereby enabling previously intractable SN calculations and supplying a promising avenue to mitigate ray effects.
79 ASTRONOMY AND ASTROPHYSICS↗
Multigroup Thermal Radiation Transport with Tensor Trains
We investigate the application of tensor-train (TT) algorithms to multigroup thermal radiation transport (i.e., photon radiation transport). The TT framework enables simulations at discretizations that might otherwise be computationally infeasible on conventional hardware. We show that solutions to certain multigroup problems possess an intrinsic low-rank structure, which the TT representation leverages effectively. This enables us to solve problems where the discretized solution size exceeds a trillion parameters on a single node. The solver is evaluated on a range of test problems with varying levels of complexity, consistently achieving compression factors greater than 100× and speedups exceeding 2×. We also investigate alternative TT topologies by analyzing the low-rank structure of the merged spatio-spectral core to assess the potential for greater compression. This analysis suggests that compression gains could increase by factors as large as 7. Our results indicate that the low-rank structure of the merged spatio-spectral core captures the spatio-spectral complexity of the solution, largely driven by the opacity structure of the medium. Beyond identifying opportunities for improved compression, this analysis highlights the types of errors that may arise in angle-integrated quantities when exploiting this low-rank structure.
79 ASTRONOMY AND ASTROPHYSICS↗
Energy Finance Training [Slides]
The Energy 101: Energy Financing Training presentation, developed for the Energy Technology Innovation Partnership Project (ETIPP), provides an overview of energy project financing. It covers fundamental concepts, technologies, considerations, case studies, and additional resources.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Decoherence Noise on the Superconducting Qubits Training Program
Quantum computing is a growing field with promising applications in a variety of fields such as healthcare, energy consumption, and cryptography. Quantum computing leverages the principles of quantum mechanics - superposition and entanglement. Yet, in the Noisy Intermediate Scale Quantum (NISQ) Era - quantum systems face the major challenge of decoherence due to noise. This era is characterized by low amounts of qubits and high gate error. Decoherence leads to the loss of the quantum information stored in the qubit. Noise occurs with any quantum system that is exposed to the environment. It should also be noted that quantum information can be stored in the cavity - Fermilab specializes in coupling transmons to ultrahigh-Q SRF cavities. The Superconducting Qubits Training Program (SQTP) provides a visualization for beginners in quantum computing. The open quantum system simulated is a superconducting qubit (two-level atom) coupled to a microwave cavity whose excitations are photons. The Rotating Wave Approximation of the Jaynes-Cumming Hamiltonian is used. SQTP utilizes open-source Python-based libraries scQubits, NumPy, and QuTiP alongside the Master Lindblad equation. In this project, we study the different decay behaviors of qubits and cavities with collapse operators.
Lopez, Sara↗
Generation IV International Forum – Knowledge Management and Knowledge Preservation: An initiative from the Education and Training Working Group
Dr. Patricia Paviet is the Chair of the Generation IV International Forum – Education and Training Working Group. As such, she is overseeing and directing several initiative: The GIF webinar series and the Pitch your Gen IV Research competition. This group has launched a new initiative on knowledge management and knowledge preservation of advanced reactor systems and how lessons learned are passed on in an international context. The paper is a summary of the various GIF member countries’ strategies to preserve and transfer the knowledge of advanced reactors.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗