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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Full Technical Report: High-Rate Carbide Growth by Evaporation

Boron carbide (B 4 C) is an attractive inertial confined fusion (ICF) ablator and a unique ultra-hard material with numerous current and potential applications. What makes B 4 C attractive as an ablator is its relatively high density and the ability to form a stable glassy phase combined with excellent chemical and mechanical stability. Despite these advantages, the fabrication of B 4 C ablators has remained a challenge. Magnetron sputtering, our current frontrunner approach for B 4 C deposition, exhibits low deposition rates (< 2 μm/h) and undesirable nodular defects within the film structure that are believed to generate from the dusty plasma around the sputtering source. The goals of this project were to (1) establish alternative plasma-free, electron beam evaporation (EBE) capabilities for the deposition of thin films that are needed in mission-critical programs and; (2) demonstrate the feasibility to deposit B 4 C films at high rates by EBE in a bottom-up geometry and characterize their stoichiometry. In this feasibility study, we initially developed the capability to convert a general-use vacuum chamber into an electron beam evaporator as needed, or on demand. Then, we successfully used this setup to deposit films made of boron carbide and various other materials. By adjusting the deposition parameters and the evaporation source material, we were able to demonstrate the feasibility of reaching deposition rates as high as 8 μm/h, which is about 4 times the maximum rates obtained through sputtering methods. We predict that these values can still be further improved by implementing enhanced thermal control measures for the target source and optimizing electron beam parameters (ie. current, sweeping and frequency) since ceramic materials with similar densities to B 4 C can exhibit deposition rates of about twice this value. We anticipate that the skills and capabilities developed throughout this project will prove relevant not only to the HED and ICF campaigns but also to numerous existing and prospective applications focused on the development of ultra-hard carbide coatings. The fresh set of tools here developed will also facilitate the preparation and research on future new target materials of interest beyond B 4 C.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Efficient green and yellow light-emitting diodes (LEDs) for solid-state lighting applications

In this project, Lumileds, the University of Michigan, the University of New Mexico, Sandia National Laboratories, and Ohio State University conducted a collaborative and comprehensive research effort to investigate the most promising approaches to improve green and yellow InGaN LEDs. Advanced LED characterization methods and predictive model calculations were applied to design optimization experiments more effectively and accelerate the pace of improvements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Non-conformity Scores for High-Quality Uncertainty Quantification from Conformal Prediction

High-quality uncertainty quantification (UQ) is a critical component of enabling trust in deep learning (DL) models and is especially important if DL models are to be deployed in high-consequence applications. Conformal prediction (CP) methods represent an emerging nonparametric approach for producing UQ that is easily interpretable and, under weak assumptions, provides a guarantee regarding UQ quality. This report describes the research outputs of an Exploratory Express Laboratory Directed Research and Development (LDRD) project at Sandia National Laboratories. This project focused on how best to implement CP methods for DL models. This report introduces new methodology for obtaining high-quality UQ from DL models using CP methods, describes a novel system of assessing UQ quality, and provides experimental results that demonstrate the quality of the new methodology and utility of the UQ quality assessment system. Avenues for future research and discussion of potential impacts at Sandia and in the wider research community are also given.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Data Visualization and Analytics for Optimal Process Parameter Selection in Turning

The objective of this project is to research physics-guided machine learning methods to recommend optimal tools and machining process parameters for turning applications using the MSC test database. For a given turning application, the MSC metalworking specialist needs to make decisions on tools and the associated process parameters for the MSC customer. For a given material, there are many alternatives for tools and a wide range of process parameters to consider. MSC has built a database of tools and parameters for different applications from the historical turning tests completed at various customer sites. The research project aims to use machine learning methods to predict optimal tools and process parameters for the MSC metalworking specialists using the MSC test database. This enables continuous learning of optimal tool and process parameters for different applications as new information is collected from testing. Through MSC, this information can be shared with machining shops across the US leading to improved productivity and efficiency.

42 ENGINEERING↗
DOE OSTI2024

Advancing the Understanding of Manufacturing Tools for Hardware Security

This project’s goal was to explore new methods and tools to evaluate the focused ion beam (FIB) effect on active electrical devices, which is becoming increasingly challenged by the continual decrease in transistor geometry. Novel hole transfer methods leveraging FIB patterning were demonstrated utilizing selective area atomic layer deposition (ALD) and metal assisted chemical etching. A FIB damage electrical tester device was fabricated, and the effects of FIB beams were characterized by examining change in performance of damaged transistors. Detailed characterization of end-of-range damage for common FIB ions were correlated to modeling methods. Finally, undamaged and damaged devices were simulated by Charon to begin understanding the FIB effects on active devices. This test platform along with modeling methods give a powerful way to assess FIB damage in materials and devices, and with more development can help establish methods to predict FIB damage effects on electrical devices.

42 ENGINEERING↗
DOE OSTI2025

Modal Field Reconstruction in Resonant Cavities in the Fundamental and Undermoded Frequency Regimes

Theory, simulations, and experiments are presented that demonstrate reconstruction of electromagnetic fields in a cavity from sparse probe measurements. Such techniques are often referred to as virtual sensing, allowing fields at unobserved locations to be predicted. These methods are appropriate for the fundamental and undermoded regimes, providing the ability to estimate fields (and shielding effectiveness) throughout an arbitrarily shaped cavity from a few judiciously spaced probes. A modal simulation method is implemented that allows the response of arbitrarily shaped cavities to be rapidly computed with respect to varying probe locations and slot parameters, enabling statistical analysis of probe placement on reconstruction performance. A cylindrical vessel with numerous probe holes is developed for experiments, referred to as Perforated Vessel 2 (PV2). Experiments are performed on the vessel with and without a steel box inside, where transmit power is delivered into the vessel either through probes (probe injection) or through slots using an external antenna (slot excitation). Simulations and experiments illustrate that when the number of probes is minimal (equal to the number of mode coefficients to be estimated at each frequency), probe placement is critical to avoid missed peaks and to have acceptable reconstruction error. Probe placement becomes less important as the number of probes is increased, but care is still required to avoid probe locations giving poor performance.

42 ENGINEERING↗
DOE OSTI2023

Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts

Despite a recent global decrease in suicide rates, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts to combat this growing epidemic. In this study, we aim to identify potential risk factors of suicide attempt using geospatial features in an Artificial intelligence framework. We use iterative Random Forest, an explainable artificial intelligence method, to predict suicide attempts using data from the Million Veteran Program. This cohort incorporated 405,540 patients with 391,409 controls and 14,131 attempts. Our predictive model incorporates multiple climatic features at ZIP-code-level geospatial resolution. We additionally consider demographic features from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. In total 1,784 features were included in the predictive model. Our results show that geographic areas with higher concentrations of married males living with spouses are predictive of lower rates of suicide attempts, whereas geographic areas where males are more likely to live alone and to rent housing are predictive of higher rates of suicide attempts. We also identified climatic features that were associated with suicide attempt risk by age group. Additionally, we observed that firearms and alcohol vendors were associated with increased risk for suicide attempts irrespective of the age group examined, but that their effects were small in comparison to the top features. Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Multiscale Models for Fibril Formation: Rare Events Methods, Microkinetic Models, and Population Balances

Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for “bottom-up” multiscale fibril growth rate predictions. These were compared to “top-down” results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description.

Shayesteh Zadeh, Armin (ORCID:0000000299896710)↗
DOE OSTI2023

Observations of extreme wave runup events on the US Pacific Northwest coast

Extreme, tsunami-like wave runup events in the absence of earthquakes or landslides have been attributed to trapped waves over shallow bathymetry, long waves created by atmospheric disturbances, and long waves generated by abrupt breaking. These runup events are associated with inland excursions of hundreds of meters and periods of minutes. While the theory of radiation stress implies that nearshore energy transfer from the carrier waves to the infragravity waves can also lead to very large runup, there have not been observations of runup events induced by this process with magnitudes and periods comparable to the other three mechanisms. This work presents observations of several runup events in the US Pacific Northwest that are comparable to extreme runup events related to the other three mechanisms. It also discusses possible generation mechanisms and shows that energy transfer from carrier waves to bound infragravity waves is a plausible generation mechanism. In addition, a method to predict and forecast extreme runup events with similar characteristics is presented.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Apparatus and method for determining state of change (SOC) and state of health (SOH) of electrical cells

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

25 ENERGY STORAGE↗
DOE OSTI2022

Next Generation System Analysis Model: Recently Added Features and Future Plans

To better enable informed decision-making regarding the back-end of the nuclear fuel cycle, the Integrated Waste Management System (IWMS) program within the U.S. Department of Energy, Office of Nuclear Energy (DOE-NE) has been sponsoring the development and application of system analysis tools capable of analyzing various system options for the management of spent nuclear fuel (SNF) and high-level radioactive waste (HLW).With these tools, IWMS architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). The Next Generation System Analysis Model (NGSAM) is an agent-based simulation software tool expressly designed to be capable of modeling features within various IWMS architectures. NGSAM imports data from Oak Ridge National Laboratory (ORNL)’s unified database (e.g., historic assembly information, thermal profiles for assembly heat, and at-reactor dry storage loadings) to ensure that each simulation initializes with a realistic representation of the state of commercial SNF in the U.S. Recent major enhancements implemented into NGSAM in the period since NGSAM was last presented at the WM2019 conference include: • Tracking of railroad escort car acquisition and buffer car acquisition • The addition of heavy haul truck (HHT) and barge routes for some sites, as well as support for user-defined inter-modal routes • Updates to the logic that checks the transportation cask thermal limit maps prior to package transport • An allocation method that predicts when reactor sites would pack assemblies from their spent fuel pools for dry storage, and prioritizes shipments directly from the pools of those reactor sites in the preceding periods (before the predicted loadings to dry storage), thus reducing the number of casks loaded into dry storage at reactor sites • The addition of “reactor site family” operational limits to restrict the number of loads of SNF taken from the pool and from dry storage at a given reactor site each year • Added support for multiple canister loading map options and packages with multiple compatible transportation overpacks • Updates to the handling of non-commercial SNF, including a new database that contains data to support the updates • Updates to allow analysis of hypothetical scenarios which include repackaging at reactor sites, e.g., for possible comparative analysis with other scenarios • Implementation of additional output reports, or modification of existing ones • The ability to generate and implement user edits via the NGSAM website • The ability to model loading SNF from pool storage at an interim storage facility (ISF) into dry storage at the ISF • The ability to model consolidating SNF from different existing storage containers at a DOE site into the same DOE standard canister • The ability to model transferring SNF casks from one transportation mode to another, e.g., from HHT to rail, referred to as transloading. These new features have improved NGSAM capabilities and users’ experience with the model. Preliminary NGSAM requirements for modeling advanced reactor fuels, reprocessing, treatment, and conditioning were considered, and this paper describes them at a high level. Other nuclear fuel cycle system analysis tools developed under sponsorship of DOE-NE, like the VISION code developed at Idaho National Laboratory, might be better suited for initial high-level analysis of those technologies and advanced fuel cycles. As technologies are developed and system concepts evolve, NGSAM could provide value by providing more detailed modeling of transport, storage, and disposal of spent fuel and wastes from advanced reactors and advanced fuel cycles at the fuel element and waste container level.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Next Generation System Analysis Model Recently Added Features and Future Plans - Abstract

The Nuclear Waste Policy Act of 1982, as amended (NWPA 1982), established the federal government’s responsibility to accept spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from waste owners and generators for ultimate disposition. SNF generated by the current fleet of commercial nuclear reactors is being stored at the reactor sites in spent fuel pools (SFPs) and in dry independent spent fuel storage installations (ISFSIs). The US Department of Energy Office of Nuclear Energy (DOE-NE) is developing an Integrated Waste Management Program (IWMP) comprising a suite of options and supporting analyses to enable future informed choices. The IWMP is applying integrated waste management system architecture analysis, system engineering, and decision analysis principles to inform potential future decisions regarding potential nuclear waste management system architectures. Architecture analyses of the IWM system are being conducted to support the future deployment of a comprehensive system for managing nuclear waste that considers all major aspects of the back end of the nuclear fuel cycle (i.e., transportation, storage, and disposal). The Next Generation System Analysis Model (NGSAM) is an agent-based simulation software tool designed for the express purpose of modeling the IWM system. NGSAM imports data from the Oak Ridge National Laboratory (ORNL) Unified Database (e.g., historic assembly information, thermal profiles for assembly heat, at-reactor dry storage loadings) to ensure that the simulation initializes with a realistic representation of the state of commercial SNF in the United States. Recent major enhancements that have been implemented into NGSAM since NGSAM was last presented at the WM2019 conference include: • Tracking of railroad escort and buffer car acquisition. • Addition of heavy haul and barge routes for some sites, as well as support for user-defined inter-modal routes. • Updates to the logic that checks the thermal maps prior to package transport. • Addition of an allocation method that predicts when reactor sites will pack assemblies from their pools for dry storage and allocates packages to those reactor sites in the preceding periods, favoring direct transport packages and reducing the number of packages that reactor sites pack for dry storage at their ISFSIs. • Addition of reactor site family operational limits, which are used to limit the number of loads from the pool and from dry storage at a given reactor site per year. • Support has been added for multiple canister loading maps and packages having multiple compatible transportation overpacks. • Updates in the handling of non-commercial fuel, including a new database containing data to support the updates. • Support for repackaging at reactor sites. • Implementing additional output reports or modifying existing reports. • User edits can now be created and edited via the NGSAM website. • Ability to load packages for dry storage at ISF pools. • Same-type package blending at DOE sites. • Support for multi-mode transloading at reactor sites. These new features have improved NGSAM capabilities and/or improve the user experience with the model and will be discussed in more detail. The initial NGSAM requirements for advanced reactor fuels, reprocessing, treatment, and conditioning are preliminary and are described at a high level in this paper: analysts will provide more specific requirements to the NGSAM team in the future. Additionally, there are many data needs associated with modeling advanced reactors in NGSAM, but many of the data or plans are still in progress and/or yet to be fully defined. However, this document describes an initial exploration of the data relevant to this program. Advanced reactor data will likely require revision as concepts evolve and new considerations are made. This is a technical paper that does not take into account contractual limitations or obligations under the Standard Contract for Disposal of Spent Nuclear Fuel and/or High-Level Radioactive Waste (Standard Contract) (10 CFR Part 961). For example, under the provisions of the Standard Contract, spent nuclear fuel in multi-assembly canisters is not an acceptable waste form, absent a mutually agreed to contract amendment. To the extent discussions or recommendations in this paper conflict with the provisions of the Standard Contract, the Standard Contract governs the obligations of the parties, and this paper in no manner supersedes, overrides, or amends the Standard Contract. This paper reflects technical work which could support future decision making by DOE. No inferences should be drawn from this paper regarding future actions by DOE, which are limited both by the terms of the Standard Contract and Congressional appropriations for the Department to fulfill its obligations under the Nuclear Waste Policy Act including licensing and construction of a spent nuclear fuel repository.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Apparatus and method for determining state of change (SOC) and state of health (SOH) of electrical cells

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

Steingart, Daniel Artemis↗
DOE OSTI2023

Methods and systems for coordinating predictive cruise control, engine-off coasting, and hybrid power split

The enclosed disclosure relates to hybrid vehicles and systems with an engine, a drivetrain with a clutch and a transmission, an electric machine, and a controller. The controller receives lookahead information within a lookahead window and present state information of the hybrid vehicle. The controller determines a predicted coasting opportunity exceeding a predetermined threshold within the lookahead window and determines a cruise control reference speed, a power split between the engine and the electric machine, and a timing of enabling engine-off coasting during the coasting opportunity. The controller deactivates the engine and disengages the clutch at a start of the coasting opportunity when the engine-off coasting is enabled.

Gupta, Rohinish↗
DOE OSTI2026

Broad frequency tuning of a Nb$_{3}$Sn superconducting microwave cavity for dark matter searches

We demonstrate a novel broad-frequency tuning mechanism for superconducting microwave cavities designed for dark matter searches. Using a Nb$_3$Sn-coated cigar-shaped cavity operating at approximately 9 GHz, we achieve continuous frequency tuning exceeding 1 GHz by mechanically separating the two cavity halves: a "tuning-by-opening" technique. Finite-element method simulations predict that radiative losses do not degrade the quality factor even for large openings, as a closed cavity with an intrinsic quality factor of $10^7$ maintains this value for apertures up to 9 mm, corresponding to a tuning range from 9.0 to 7.5 GHz. Experimental validation using both copper ring spacers and a continuous sliding mechanism confirms $Q_0$ values exceeding the dark matter quality factor across the entire explored frequency range, despite mechanical imperfections and film non-uniformities. This tuning approach avoids inserting elements into the resonant volume, making it particularly suitable for high-Q superconducting cavities in axion haloscope experiments and readily applicable to REBCO-based implementations capable of operating in multi-tesla magnetic fields.

Maiello, D. [Padua U.; INFN, Padua] (ORCID:0009000↗
DOE OSTI2022

Predicting Oxygen Off-Stoichiometry and Hydrogen Incorporation in Complex Perovskite Oxides

Chemically and structurally complex solid compounds, including those with significant off-stoichiometry, are rapidly extending new material functionality across a variety of applications. Accelerated development of these compounds requires accurate predictions of material defect properties including effective defect formation energies and equilibrium defect concentrations. Traditional first-principles approaches typically examine dilute defect concentrations and relatively ordered atomic structures to identify the lowest energy defect sites. These approaches are rarely suitable for describing the disorder present in these systems and its influence on defect formation, which can lead to unphysically large predictions for defect concentrations. Here, we demonstrate a new method to accurately predict the temperature and pressure dependence of oxygen vacancy concentrations and proton interstitial concentrations in complex oxides. This method extends standard dilute defect calculations to incorporate atomic and magnetic disorder, employs the ensemble descriptions of defect sites resulting in improved predictions of defect formation energies, and accounts for effects beyond the dilute defect limit. To demonstrate our method, we show that the predicted defect concentrations in perovskites used as ceramic fuel cell cathodes, including Ba0.5Sr0.5Fe0.8Zn0.2O3-d, Ba0.5Sr0.5Co0.8Fe0.2O3-d, and BaCo1-x-y-zFexZryYzO3-d, are in good agreement with experimental values, thereby opening the door for predictive design of complex oxides by these applications.

DFT↗
DOE OSTI2021

Structural- and Functional-Informed Machine Learning for Protein Function Prediction

In this project we aimed to extend methods for protein function prediction to include structural prediction data, and benchmark methods against existing tools. We proposed to apply the method to large metagenome datasets, and develop approaches to examine activity-based protein profiling results for protein function-structure patterns. Nitrogen cycle protein families were previously identified and are used here to provide a proof-of-principle for use of structure prediction in protein function classification.

59 BASIC BIOLOGICAL SCIENCES↗