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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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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Integrated Issues and Risk Management: A Theoretical Framework Overview

The contractor requirements document for DOE O 226.1B, Implementation of Department of Energy Oversight Policy, requires DOE/NNSA contractors to establish an assurance system that includes, among other things, “Rigorous, risk-informed, and credible self-assessment and feedback and improvement activities. Assessment programs must be risk-informed, formally described and documented, and appropriately cover potentially high consequence activities” and “Contains an issues management process that is capable of categorizing the significance of findings based on risk and priority and other appropriate factors….” However, the term “risk-informed” is not defined in this or any other DOE order, and no formal guidance on how to integrate the two concepts currently exists. The Risk Management Guide for Defense Programs released by NA-18, Office of Systems Engineering and Integration (SE&I), states it is “a framework and general guidance to program office personnel on the effective management of program risks and issues”, however it then defines issues as “events with 100% likelihood of affecting program objectives” and states “unless specified otherwise, the term “risk” will also serve to represent issues for the remainder of this plan,” severally limiting its ability to provide adequate guidance on this topic. Outside of DOE scope, the U.S. Nuclear Regulatory Commission (U.S. NRC) imposes similar requirements. ASME NQA-1-2015 Requirement 16 states “Conditions adverse to quality shall be identified promptly and corrected as soon as practicable. In the case of a significant condition adverse to quality, the cause of the condition shall be determined, and corrective action taken to preclude recurrence. The identification, cause, and corrective action for significant conditions adverse to quality shall be documented and reported to appropriate levels of management. Completion of corrective actions shall be verified”. The purpose of this document is to provide a best-in-class framework for an integrated risk and issues management process. This process would provide a robust feedback loop between risk management and issues management to: Enhance risk identification and characterization, use risk handling principles to improve corrective action planning, and ensure regulatory compliance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Ablative Magnetohydrodynamic Rayleigh-Taylor Instability (Final Technical Report)

The Rayleigh-Taylor Instability (RTI) is a ubiquitous flow phenomenon occurring in a multitude of natural and engineered systems in which buoyancy forces exist. RTI is primarily a limiting process in many HED applications and there are significant efforts within the NNSA-ICF program to account for its effect in large scale simulations of ICF implosions both in two- and three-dimensions. The goal of the proposed research is to develop a fundamental understanding of the evolution and role of magnetic fields in the ablative Rayleigh–Taylor instability (aRTI) in the deeply nonlinear stage.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Los Alamos National Laboratory Laboratory Directed Research and Development Program: FY23 Annual Progress Report- Compendium of Project Summaries

The project summaries are organized by Capability Pillar – Complex Natural and Engineered Systems, Information Science and Technology, Materials for the Future, Nuclear and Particle Futures, Science of Signatures, and Weapons Systems. Project summaries for continuing projects appear first, followed by project summaries and technical outcomes for projects that ended in FY23.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

IEA Wind TCP Task 55: The IEA Wind 740-10-MW Reference Offshore Wind Plants

This report describes the first version of the regular and irregular IEA-Wind 740-10MW Reference Offshore Wind Plants (v0.1). The two plants have been developed within the second work package of IEA Wind Task 37 on Wind Energy Systems Engineering: Integrated RD&D. The plants aim at acting as reference for future research projects on wind energy, representing modern offshore wind plants. The designs are based on the Borssele III and IV offshore wind plant projects. The associated wind resource, allotted territory, and bathymetry measurements are used to define the site characteristics. 74 IEA 10-MW Reference Wind Turbines are arranged in two suggested layouts that are optimized for maximum annual energy production: one regular grid layout, one irregular layout. These reference wind plants have been described using the WindIO ontology and have been made available through an open-source repository on GitHub.

17 WIND ENERGY↗
DOE OSTI2025

WEIS and FAST.Farm Advancements Beyond Wind Turbine Aeroelasticity

Presentation at the 7th Wind Energy Systems Engineering workshop from NREL principal engineer Jason Jonkman, Ph.D., on NREL's numerical tools Wind Energy with Integrated Servo-control (WEIS), which focuses on the integrated design of floating wind turbines, and FAST.Farm, a simulation tool for multi-turbine wind farms based on popular aero-servo-hydro-elastic solver OpenFAST. The presentation discusses recent advancements beyond wind turbine aero-elasticity.

17 WIND ENERGY↗
DOE OSTI2026

NREL Overview of Grid-Tied Power Electronics

This presentation focuses on the power electronics related research efforts carried out by researchers at the Power Systems Engineering Center at NREL.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

SYSM 5630 Reflection Paper

This is the third Systems Engineering (SE) class I have taken through the partnership between Georgetown University and Lawrence Livermore National Laboratory (LLNL). In my previous jobs, I had minimal exposure to SE and was intrigued about the methodical approach to completing projects at LLNL.

42 ENGINEERING↗
DOE OSTI2026

Superstructure Optimization for Brine Valorization from Brackish Water Desalination

This poster presents preliminary results from a superstructure optimization framework developed to identify cost-optimal brine valorization configurations for brackish water desalination plants across diverse U.S. regional feed chemistries. The study uses brackish groundwater compositions from Arizona, California, Florida, New Mexico, and Texas. Using Pyomo Generalized Disjunctive Programming (GDP) within the WaterTAP modeling environment, the optimization framework simultaneously evaluates thousands of candidate treatment configurations, spanning nanofiltration, reverse osmosis, and chemical precipitation, to minimize the levelized cost of water (LCOW) while meeting water recovery targets and product recovery constraints. Results across eight representative feed clusters demonstrate water recovery rates of 57–87% and net LCOW values ranging from -$0.032/m³ (net revenue-positive) to $0.80/m. Notably, no single process configuration was optimal across all feed types, underscoring the necessity of feed-specific optimization. Products targeted include calcium carbonate (CaCO₃) at $0.01/kg and sodium chloride (NaCl) at $0.10/kg, both at 95% purity, with product revenues offsetting treatment costs in several scenarios. The work advances NAWI's process systems engineering capabilities for multi-configuration screening.

58 GEOSCIENCES↗
DOE OSTI2026

Ultra-High Operation Temperature SiC-matrix Solar Thermal Air Receiver (HOTSSTAR) enabled by additive manufacturing: Test Facility & Performance Evaluations

Solar Heat for Industrial Processes (SHIP) cavity receivers are capable of generating electricity or industrial process heat by absorbing thermal energy from solar radiation, focused on a small area. The concentration of solar radiation on the small area of the receiver enables the achievement of high temperatures (ranging from 400°C to 1,100°C) of a working fluid, thus making the SHIP technology thermodynamically comparable with conventional power plants. A volumetric receiver consists of a porous structure-generally made of silicon carbide or metal, which absorbs solar radiation and converts it into heat energy. Heat energy from the porous materials is then transferred to the fluid following through them. A volumetric receiver acts as a convective heat exchanger, transferring heat to the fluid through convection. Open-loop volumetric receivers work with air at atmospheric pressure and are suitable for single-cycle or multi-cycle energy plants. A Model Based Systems Engineering (MBSE) approach was used to develop a test bed at Sandia national Laboratories (SNL) capable of demonstrating an open-loop volumetric air receiver developed by General Electric Aerospace (GE Aerospace). This paper presents the development of the various MBSE methods, test bed, and testing operations for the GE air receiver, which was experimentally demonstrated to achieve 1,350°C for over 3 hours of operation and an approximate 70% receiver efficiency. By being able to achieve such high temperatures >1,000°C, this work provides the potential to support many SHIP industrial use cases.

14 SOLAR ENERGY↗
DOE OSTI2021

The moderately defficient enzyme: Catalysis-related damage in vivo and its repair

Enzymes have in vivo lifespans. Analysis of lifespans – lifetime totals of catalytic turnovers – suggests that non-survivable collateral chemical damage from the very reactions that enzymes catalyze is a common but underdiagnosed cause of enzyme death. Analysis also implies that many enzymes are moderately deficient in that their active-site regions are not naturally as hardened against such collateral damage as they could be, leaving room for improvement by rational design or directed evolution. Enzyme lifespan might also be improved by engineering systems that repair otherwise fatal active-site damage, of which a handful are known and more are inferred to exist. Unfortunately, the data needed to design and execute such improvements is lacking: there are too few measurements of in vivo lifespan, and existing information on the extent, nature, and mechanisms of active-site damage and repair during normal enzyme operation is too scarce, anecdotal, and speculative to act on. Fortunately, advances in proteomics, metabolomics, cheminformatics, comparative genomics, and structural biochemistry now empower a systematic, data-driven approach to identify, predict, and validate instances of active-site damage and its repair. These capabilities would be practically useful in enzyme redesign and improvement of in-use stability, and could change thinking about which enzymes die young in vivo, and why.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The moderately defficient enzyme: Catalysis-related damage in vivo and its repair

Enzymes have in vivo lifespans. Analysis of lifespans – lifetime totals of catalytic turnovers – suggests that non-survivable collateral chemical damage from the very reactions that enzymes catalyze is a common but underdiagnosed cause of enzyme death. Analysis also implies that many enzymes are moderately deficient in that their active-site regions are not naturally as hardened against such collateral damage as they could be, leaving room for improvement by rational design or directed evolution. Enzyme lifespan might also be improved by engineering systems that repair otherwise fatal active-site damage, of which a handful are known and more are inferred to exist. Unfortunately, the data needed to design and execute such improvements is lacking: there are too few measurements of in vivo lifespan, and existing information on the extent, nature, and mechanisms of active-site damage and repair during normal enzyme operation is too scarce, anecdotal, and speculative to act on. Fortunately, advances in proteomics, metabolomics, cheminformatics, comparative genomics, and structural biochemistry now empower a systematic, data-driven approach to identify, predict, and validate instances of active-site damage and its repair. These capabilities would be practically useful in enzyme redesign and improvement of in-use stability, and could change thinking about which enzymes die young in vivo, and why.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Adapting the Technology Performance Level Integrated Assessment Framework to Low-TRL Technologies Within the Carbon Capture, Utilization, and Storage Industry, Part I

With the urgent need to mitigate climate change and rising global temperatures, technological solutions that reduce atmospheric CO 2 are an increasingly important part of the global solution. As a result, the nascent carbon capture, utilization, and storage (CCUS) industry is rapidly growing with a plethora of new technologies in many different sectors. There is a need to holistically evaluate these new technologies in a standardized and consistent manner to determine which technologies will be the most successful and competitive in the global marketplace to achieve decarbonization targets. Life cycle assessment (LCA) and techno-economic assessment (TEA) have been employed as rigorous methodologies for quantitatively measuring a technology's environmental impacts and techno-economic performance, respectively. However, these metrics evaluate a technology's performance in only three dimensions and do not directly incorporate stakeholder needs and values. In addition, technology developers frequently encounter trade-offs during design that increase one metric at the expense of the other. The technology performance level (TPL) combined indicator provides a comprehensive and holistic assessment of an emerging technology's potential, which is described by its techno-economic performance, environmental impacts, social impacts, safety considerations, market/deployability opportunities, use integration impacts, and general risks. TPL incorporates TEA and LCA outputs and quantifies the trade-offs between them directly using stakeholder feedback and requirements. In this article, the TPL methodology is being adapted from the marine energy domain to the CCUS domain. Adapted metrics and definitions, a stakeholder analysis, and a detailed foundation-based application of the systems engineering approach to CCUS are presented. The TPL assessment framework is couched within the internationally standardized LCA framework to improve technical rigor and acceptance. It is demonstrated how stakeholder needs and values can be directly incorporated, how LCA and TEA metrics can be balanced, and how other dimensions (listed earlier) can be integrated into a single metric that measures a technology's potential.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Towards Quantitative Inference of Nanoscale Defects in Irradiated Metals and Alloys

Quantifying the population of nanoscale defects that are formed in metals and alloys exposed to extreme radiation environments remains a pressing challenge in materials science. These defects both fundamentally alter material properties and seed long-timescale performance degradation, which often limits the lifespan of engineering systems. Unlike ceramic and semiconducting materials, these defects in metals and alloys are not spectroscopically active, forcing characterization to rely on indirect measurements from which the distribution of nanoscale defects may be inferred. In this mini-review, different experimental methodologies which have been employed for defect inference are highlighted to capture the current state of the art. Future directions in this area are proposed, which, by combining data streams from multiple and complementary characterization methods in concert with multi-scale modeling and simulation, will enable the ultimate goal of quantifying the full spectrum of defects in irradiated metals and alloys.

Hirst, Charles A.↗
DOE OSTI2023

Early Fault Detection in Particle Accelerator Power Electronics Using Ensemble Learning

Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, the authors successfully conducted 21 early fault detection experiments, where fault precursors are introduced in the system to a degree enough to cause degradation in the waveform signals, but not enough to reach a real fault. Nine different machine learning techniques based on ensemble trees, convolutional neural networks, support vector machines, and hierarchical voting ensembles are proposed to detect the fault precursors. Although all 9 models have shown a perfect and identical performance during the training and testing phase, the performance of most models has decreased in the next test phase once they got exposed to realworld data from the 21 experiments. The hierarchical voting ensemble, which features multiple layers of diverse models, maintains a distinguished performance in early detection of the fault precursors with 95% success rate (20/21 tests), followed by adaboost and extremely randomized trees with 52% and 48% success rates, respectively. The support vector machine models were the worst with only 24% success rate (5/21 tests). The study concluded that a successful implementation of machine learning in the SNS or particle accelerator power systems would require a major upgrade in the controller and the data acquisition system to facilitate streaming and handling big data for the machine learning models. In addition, this study shows that the best performing models were diverse and based on the ensemble concept to reduce the bias and hyperparameter sensitivity of individual models.

43 PARTICLE ACCELERATORS↗