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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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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Adrastea: An Efficient FPGA Design Environment for Heterogeneous Scientific Computing and Machine Learning

We present Adrastea, an efficient FPGA design environment for developing scientific machine learning applications. FPGA development is challenging, from deployment, proper toolchain setup, programming methods, interfacing FPGA kernels, and more importantly, the need to explore design space choices to get the best performance and area usage from the FPGA kernel design. Adrastea provides an automated and scalable design flow to parameterize, implement, and optimize complex FPGA kernels and associated interfaces. We show how virtualization of the development environment via virtual machines is leveraged to simplify the setup of the FPGA toolchain while deploying the FPGA boards and while scaling up the automated design space exploration to leverage multiple machines concurrently. Adrastea provides an automated build and test environment of FPGA kernels. By exposing design space hyper-parameters, Adrastea can automatically search the design space in parallel to optimize the FPGA design for a given metric, usually performance or area. Adrastea simplifies the task of interfacing with the FPGA kernels with a simplified interface API. To demonstrate the capabilities of Adrastea, we implement a complex random forest machine learning kernel with 10,000 input features while achieving extremely low computing latency without loss of prediction accuracy, which is required by a scientific edge application at SNS. We also demonstrate Adrastea using an FFT kernel and show that for both applications Adrastea is able to systematically and efficiently evaluate different design options, which reduced the time and effort required to develop the kernel from months of manual work to days of automatic builds.

Young, Aaron↗

DOE OSTI2024

Design and instrumentation for permanent magnet samples exposed to a radiation environment

This work is part of a larger program to study the effects of radiation on permanent magnets in an accelerator environment. In order to be sure that the permanent magnet samples are accurately placed, measured, and catalogued we have developed a system of sample racks, holders and measuring apparatuses. We have combined these holders and measurement racks with electronics to allow a single computer to catalogue the position and intensity of the magnet measurements. We outline the design of the apparatus, the collection software, and the methodology we will use to collect the data.

Accelerator Physics↗

DOE OSTI2025

Realizing the Materials-Designed-To-Environments Promise of Additive Manufacturing Through a Fundamentally Different Approach to Optimization of Nonlinear Solid Mechanics Structures

Additive Manufacturing (AM) is expected to play a large role in the labs-wide goals of accelerating innovation and leading in modern engineering. More specifically, AM is seen as a key enabling technology for increasing the agility of nuclear deterrence and other national security applications involving complex coupled environments. However, the impact of AM on these initiatives has not been as wide-ranging as hoped because – despite its unique qualities – the focus has mostly been on detailed qualification to force AM components into pre-existing performance envelopes. This paradigm fundamentally precludes the novel possibilities afforded by the geometric and material flexibility of AM. In particular, the engineering of small-scale features to undergo buckling and contact can cause large geometric and symmetry changes which provide responsiveness to different environments. Despite almost a decade of observing such behavior, there exists no way to systematically design for AM to exploit it. Our goal for this project was to connect material design to multi-environment component performance by reconceptualizing how to design for AM to exploit the buckling and contact of small-scale features.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Perturbation theory under the truncated Wigner approximation: How system-environment entanglement formation drives quantum decoherence

In discrete quantum systems, quantum decoherence is the disappearance of simple phase relations as a result of interactions with an environment. For many applications, the question is not necessarily how to avoid (inevitable) system-environment interactions, but rather how to design environments that optimally preserve a system's phase relations in spite of such interactions. The formation of system-environment entanglement is a major driving mechanism for decoherence, and a detailed understanding of this process could inform strategies for conserving coherence optimally. This requires scalable, flexible, and systematically improvable quantum dynamical methods that retain detailed information about the entanglement properties of the environment, yet very few current methods offer this combination of features. Here, in this work, we address this need by introducing a theoretical framework wherein we combine the truncated Wigner approximation with standard time-dependent perturbation theory, allowing for computing expectation values of operators in the combined system-environment Hilbert space. We demonstrate the utility of this framework by applying it to the spin-boson model, representative of qubits and simple donor-acceptor systems. For this model, our framework provides an analytical description of perturbative contributions to expectation values. We monitor how quantum decoherence at zero temperature is accompanied by entanglement formation with individual environmental degrees of freedom. Based on this entanglement behavior, we find that the selective suppression of low-frequency environmental modes is particularly effective for mitigating quantum decoherence.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2026

Introduction to the Demonstration of Pyroshock Response Prediction Methods Using NASA ShockSat Data Virtual Collection

Mechanical shocks, particularly pyroshocks, are environments that all spacecraft and launch vehicles experience. Pyroshocks can be challenging design environments for space hardware because these events are often high frequency, high intensity transients. Furthermore, since design decisions must be made relatively early in the design phase, understanding, and accurately defining pyroshock excitation and the loads that it imparts on components is important to ensure the high reliability required of spacecraft and rockets. Here, general guidelines about shock transmission through spacecraft structures are provided in NASA and MIL handbooks and standards, but these are relatively broad guidelines, and recent studies have shown that more accurate and less-conservative predictions are needed. However, predicting shock response accurately is still a challenge because of the high frequencies in a pyroshock excitation.

Babuska, Vit [Sandia National Laboratories (SNL-NM↗

DOE OSTI2024

Living-off-the-land Techniques Unlikely to Supplant Energy Sector-Focused OT-Specific Malware

Despite increased reports of energy sector-focused threat actors using living-off-the-land (LOTL) techniques, it is unlikely LOTL techniques will wholly supplant malware in energy sector operational technology (OT)-focused cyber operations. Threat actors leverage LOTL techniques to access energy sector networks, abstracting process information and maintaining persistence. Although threat actors using LOTL techniques have successfully interrupted energy sector industrial control environments, designed features of OT-specific malware likely increase the cyber-physical impact of an attack and delay recovery of critical functions and services. Malicious actors will very likely continue to use LOTL techniques for stealth, while designing malware to bolster final impacts on cyber-physical systems in energy sector OT environments.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

VC3: Virtual Clusters for Community Computation (Final Technical Report)

A traditional HPC computing facility provides a large amount of computing power but has a fixed environment designed to satisfy local needs. This makes it very challenging for users to deploy complex applications that span multiple sites and require specific application software, scheduling middleware, or sharing policies. This project addressed many of these challenges by making it possible for researchers to easily aggregate and share resources, install custom software environments, and deploy clustering frameworks across multiple HPC facilities through the concept of “virtual clusters”. We designed and implemented a prototype virtual cluster facility that enabled unprivileged users to create dynamic aggregations of computing power across multiple sites, deployed with custom middleware and complex software dependencies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

VC3: Virtual Clusters for Community Computation

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Explainable physics-based constraints on reinforcement learning for accelerator optimization

We present a reinforcement learning (RL) framework for optimizing particle accelerator experiments that builds explainable physics-based constraints on agent behavior. The goal is to increase transparency and trust by letting users verify that the agent’s decision-making process incorporates suitable physics. Our algorithm uses a learnable surrogate function for physical observables, such as energy, and uses them to fine-tune how actions are chosen. This surrogate can be represented by a neural network or by an interpretable sparse dictionary model. We test our algorithm on a range of particle accelerator optimization environments designed to emulate the Continuous Electron Beam Accelerator Facility at Jefferson Lab. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment. In addition, we find that the introduction of a physics-based surrogate enables our RL algorithms to reliably converge for difficult high-dimensional accelerator optimization environments.

explainability↗

DOE OSTI2022

All-Electric Nonassociative Learning in Nickel Oxide

Habituation and sensitization represent nonassociative learning mechanisms in both non-neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular-like learning in oxygen-deficient NiO x devices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiO x under sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next-generation neuroinspired computing hardware.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Irradiation performance of a U-7Mo in Al-Si matrix dispersion full-size fuel plate assembly

The Korea Atomic Energy Research Institute (KAERI) is leading the Ki-Jang Research Reactor (KJRR) project with the intent to develop a new reactor for medical isotope production and other nuclear research purposes. The KJRR core is designed to use high density fuel system where uranium alloyed with 7 wt% molybdenum (U-7Mo) particles are dispersed in a matrix of aluminum alloyed with 5 wt% silicon (Al-5Si) and clad in aluminum alloy 6061 (Al-6061) to form fuel plates. KAERI developed a fabrication facility to construct KJRR fuel assemblies and partnered with the Idaho National Laboratory (INL) to irradiate a full-size fuel assembly, with 21 total fuel plates, in the Advanced Test Reactor (ATR). Irradiation testing and subsequent Post Irradiation Exam (PIE) campaigns were performed successfully over a multi-year project. Monte Carlo neutronic calculations, coupling with a depletion code, were performed based on ATR’s as-run power history which showed that the highest power plate (plate 20) reached 83.1 % end-of-life (EOL) local burnup based on initial 235 U content. Additionally, finite element thermal modeling was performed based as-run power history which showed a beginning-of-life (BOL) peak local heat flux of 184 W/cm 2 . No anomalous fuel performance was observed during the irradiation and target test conditions were achieved. PIE showed favorable performance of the fuel assembly regarding all important phenomena. This paper describes the KJRR fuel assembly irradiation conditions and PIE data to support the conclusion that it performed well, without evidence of unexpected or problematic fuel performance, within an irradiation test designed to bound the KJRR design environment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Mjölnir: a miniature triaxial rock deformation apparatus for 4D synchrotron X-ray microtomography

An X-ray transparent experimental triaxial rock deformation apparatus, here named 'Mjölnir', enables investigations of brittle-style rock deformation and failure, as well as coupled thermal, chemical and mechanical processes relevant to a range of Earth subsurface environments. Designed to operate with cylindrical samples up to 3.2 mm outside-diameter and up to 10 mm length, Mjölnir can attain up to 50 MPa confining pressure and in excess of 600 MPa axial load. The addition of heaters extends the experimental range to temperatures up to 140°C. Deployment of Mjolnir on synchrotron beamlines indicates that full 3D datasets may be acquired in a few seconds to a few minutes, meaning full 4D investigations of deformation processes can be undertaken. Mjölnir is constructed from readily available materials and components and complete technical drawings are included in the supporting information.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Proton pulse charge calculation algorithm in Beam Power Limiting System at Spallation Neutron Source

A proton pulse charge calculation algorithm in the Beam Power Limiting System (BPLS) at the Spallation Neutron Source (SNS) was developed and implemented in an FPGA. The algorithm calculates one-minute running average of the pulse charges and issues a fault to the Personal Protection System (PPS) and the Machine Protection System (MPS) when a limit is reached. A bit-accurate model of the algorithm was first developed and tested in Matlab® and then implemented and simulated in VHDL using Vivado® design environment. Finally, the algorithm was verified on a µTCA-based hardware platform.

Bobrek, Miljko [ORNL] (ORCID:0000000332763451)↗

DOE OSTI2022

Exotanium DOE SBIR Phase I Results Summary

Exotanium demonstrated this technology with the Idaho National Laboratory’s MASTODON application, a Multiphysics environment designed to run typical high-performance computing (HPC) simulations for structural dynamics, seismic analysis, and risk assessment. The MASTODON application was packaged into a container using Docker, Deployed on Amazon ECS, and managed through a custom Scale-Out Compute on AWS (SOCA) implementation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

High Temperature Optocoupler for 3D High Density Power Modules

The goal of this proposed research is to develop a reliable high-temperature optocouplers, which can operate at 250°C with at least ten-year lifetime, and replace isolation transforms as the galvanic isolation solution for the 3D integration of high density power modules. The electrification of future transportations (i.e., electric vehicles) will continuously drive the demand for high density power modules. Optocouplers (i.e., packaged light emitter and detector) as a promising candidate to replace bulky isolation transformers are highly desirable to facilitate the continuous scale-down of gate driver circuitry that will lead to 3D high density power modules and achieve disruptive performance in terms of thermal management, power density, power efficiency, reliability and operating environments. However, regular semiconductor optoelectronic materials and devices have significant difficulty functioning in the harsh environments designated for high density power module usage (such as operation at high temperatures). Ultimately, it is not the intrinsic properties of power devices that prevent their use at higher temperatures, but rather the low voltage electronics needed to drive them and the packaging that surrounds them. The typical operating temperature for optocouplers is only up to 100°C, due to the limitations of light emitting diode (LED) devices inside and packaging materials. A systematic characterization methodology will be developed to analyze the performance, lifetime and reliability of LED devices and distinguish multiple failure mechanisms at high temperatures. An original methodology of “design for reliability” will be developed to design the optoelectronic devices with high reliability and long lifetime at high temperatures. A new architecture of high temperature high reliable optocouplers will be developed, fabricated and demonstrated with continuous operating at 250°C. The development of efficient, reliable high density 3D power modules is the foundation for energy efficiency and energy reliability. Enabled with advanced 3D integration and packaging technologies, high density power module solutions can achieve much more superior performance over the conventional discrete solutions in terms of efficiency, thermal management and power density. The proposed concept of high temperature optocouplers as the galvanic isolation solution for high density power modules will bring together interdisciplinary research involving the wide bandgap materials, optoelectronics, high reliable device design, electronics packaging and power modules. A streamline of skilled personnel would be trained including graduate and undergraduate students, local engineers and scientists which are in great demand to both academia and optoelectronics industry. The proposed research topics, such as, solid state lighting and high temperature device reliability, are currently of major interest at the Department of Energy, in particular, Sandia National Laboratories. This project can enhance collaborations between the University of Arkansas (UA) and Sandia National Laboratories. The findings of the proposed research are expected to be integrated into high density 3-D power modules at the Engineering Research Center for Power Optimization for Electro-Thermal Systems (POETS).

42 ENGINEERING↗

DOE OSTI2023

Causal CCF Parameter Estimations 2020

This report documents the quantitative results of the causal common-cause failure (CCF) parameter estimations for the failure cause groups “component,” “design,” “environment,” “human,” and “other,” based on CCF data through 2020 in the U.S. Nuclear Regulatory Commission (NRC) CCF database: https://rads.inl.gov/Pages/CCF.aspx. This report utilizes the same data period (2006–2020) and CCF templates as INL/EXT-21-62940, Revision 1, CCF Parameter Estimations, 2020 Update. The 2015 causal CCF prior distributions for the specific failure cause groups (instead of the 2015 generic CCF prior distributions) were used in this report to estimate the associated causal CCF parameters. All the 2015 causal CCF prior distributions and generic CCF prior distributions were developed in INL/EXT-21-43723, Developing Generic Prior Distributions for Common Cause Failure Alpha Factors and Causal Alpha Factors, using CCF data from 1997 to 2015. These quantitative results were developed to support the causal alpha factor model and should be used as appropriate in probabilistic risk assessment (PRA) studies such as the NRC Significance Determination Process for commercial nuclear power plants in the United States.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Simulating Quantum Systems with NWQ-Sim on HPC

NWQ-Sim is a cutting-edge quantum system simulation environment designed to run on classical multi-node, multi-CPU/GPU heterogeneous HPC systems. In this work, we provide a brief overview of NWQ-Sim and its implementation in simulating quantum circuit applications, such as the transverse field Ising model. We also demonstrate how NWQ-Sim can be used to examine the effects of errors that occur on real quantum devices, using a combined device noise model. Moreover, NWQ-Sim is particularly well-suited for implementing variational quantum algorithms where circuits are dynamically generated. Therefore, we also illustrate this with the variational quantum eigensolver (VQE) for the Ising model. In both cases, NWQ-Sim's performance is comparable to or better than alternative simulators. We conclude that NWQ-Sim is a useful and flexible tool for simulating quantum circuits and algorithms, with performance advantages and noise-aware simulation capabilities.

Suh, In-Saeng↗