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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The XtalOpt Evolutionary Algorithm for Crystal Structure Prediction

Significant progress has been made in the field of a priori crystal structure prediction, with a number of recent remarkable success stories. Herein, we briefly outline the methods that have been developed for finding the global minimum structure and interesting local minima without the need for experimental information. Focus is placed on describing the XTALOPT evolutionary algorithm (EA) developed in our group towards this end. XTALOPT is published under well-known open-source licenses, and the EA searches can be analyzed via the AVOGADRO chemical editor and visualizer. We describe new algorithmic developments that have made it possible to predict the structures of ever-more complex crystalline lattices. Benchmark tests, which clearly illustrate how the new developments improve the success rate and accelerate the discovery of the global minimum structure, are carried out. Lastly, we describe how XTALOPT has been employed to predict novel ternary hydrides that have the propensity for high-temperature superconductivity under pressure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Novel indium phosphide charged particle detector characterization with a 120 GeV proton beam

Thin film detectors which incorporate semiconductor materials other than silicon have the potential to build upon their unique material properties and offer advantages such as faster response times, operation at room temperature, and radiation hardness. To explore the possibility, promising candidate materials were selected, and particle tracking detectors were fabricated. An indium phosphide detector with a metal-intrinsic-metal structure has been fabricated for particle tracking. The detector was tested using radioactive sources and a high energy proton beam at Fermi National Accelerator Laboratory. In addition to its simplistic design and fabrication process, the indium phosphide particle detector showed a very fast response time of hundreds of picoseconds for the 120 GeV protons, which are comparable to the ultra-fast silicon detectors. This fast-timing response is attributed to the high electron mobility of indium phosphide. Such material properties can be leveraged to build novel detectors with superlative performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Measurement of Møller scattering at 2.5 MeV

Møller scattering is one of the most fundamental processes in QED, and a variety of modern experiments require its knowledge to high precision. A recent calculation considered the radiative process at low energy, where the electron mass cannot be neglected. To test the calculation, an experiment was carried out using the Van de Graaff accelerator at the MIT High Voltage Research Laboratory. Momentum spectra at three scattering angles are reported here and compared to simulation, based on our previous calculation. In this work, good agreement between the measurements and our calculation is observed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

We suggest a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118-and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Extreme Fast Charging: Effect of Positive Electrode Material on Crosstalk

Extreme fast charging (XFC) is a key requirement for the adoption of battery-based electric vehicles by the transportation sector. However, XFC has been shown to accelerate degradation, causing the capacity, life, and safety of batteries to deteriorate. We tested cells containing five positive electrode chemistries, LFP (olivine structure), LMO (spinel), LCO (layered), NMC811 (layered) and NCA (layered), using fast-charging protocols. After testing, the negative electrodes from cells containing positive electrodes crystallizing with a layered structure were found to have more lithium deposited on their surfaces. Further, those crystallizing with a layered structure also tended to have a larger increase in impedance than those crystallizing with a spinel or olivine structure. Characterization of the negative electrodes by X-ray photoelectron spectroscopy showed that using the concentrations of LiF and Li x PO y F z as metrics, the concentration of LiF in the SEI from the cell with different positive electrodes is LFP > LMO > LCO ~ NMC811 > NCA; and for Li x PO y F z , the order is LMO > LFP > NCA > NMC811 > LCO. Clearly, the positive-electrode material was influencing the amounts of these materials formed.

25 ENERGY STORAGE↗
DOE OSTI2020

Radiation Testing for Nuclear Inspection Systems (Final CRADA Report)

Higher-radiation hardened video cameras are needed in the operation and remote handling of equipment in nuclear reactor inspection and refueling applications. Vega Wave Systems has developed a radiation-hardened vision system for Nuclear Energy applications. The high-radiation hardness testing of this vision system was conducted using the Argonne Low-Energy Accelerator Facility (LEAF).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Calibrating LArPix for TinyTPC

LArTPCs provide sensitivity to GeV signals, such as accelerator neutrinos and part of the supernova neutrino spectrum. TinyTPC is a LArTPC test stand for R&D of LAr doping to expand the reach of LArTPCs down to the 1-10 MeV range, which would substantially enhance the flagship analyses of experiments like DUNE, while enabling low energy analyses. We aim to dope LAr with Xe and pho- tosensitive dopants to expand the LArTPC range by converting hard-to-detect scintillation light to efficiently detected ionization charge. A critical element of the data analysis in TinyTPC is calibrating the readout. This poster will cover the calibration of TinyTPC, a pixelated liquid argon detector, where we find the distance a muon travels through each pixel. We then calculate the energy loss of muons traveling through the detector from cosmic data. We can reconstruct the path of the particles through the TPC using a density-based clustering algorithm designed to sort straight cosmic muon tracks from low energy radioactive decay curled paths and electronic noise

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

DARHT Axis 1 Bremsstrahlung Dose Measurements

he Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility provides flash radiography ca pabilities using two electron Linear Induction Accelerators (LIA’s). Axis-1 of DARHT produces, nominally, a 20-MeV, 1.5-kA, 80-ns-Full Width at Half Max (FWHM) electron beam. The elec tron beam is focused on to a tantalum target to produce a Bremsstrahlung x-ray dose for flash radiography of dynamic systems. This paper will describe and compare a variety of techniques for determining the x-ray dose for two different beam currents produced by a 55mm and 70mm diameter cathode. The data was taken in November of 2022 on DARHT Axis 1. The experimen tal measurement techniques include a platinum calorimeter, a diamond radiation detector (DRD), and a Compton diode (CD). Gafchromic EBT3 self-developing dosimetry film was also used, but no useful results were obtained. Beam charge and energy were recorded and dose was calculated with this information using xtr with Dosecalcx, and simple dose charge-energy scaling. In this pa per, we will discuss the target geometry and experimental layout, the methods of measuring dose, dose diagnostics and measurement of beam charge and energy, and the comparisons of data and calculations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118- and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

alternating direction method of multipliers↗
DOE OSTI2024

Atom probe datasets from neutron irradiated Fe-Cr alloys

A series of model Fe–Cr alloys containing 3–18 at.% Cr was neutron irradiated at a nominal temperature of 563 K to 1.82 dpa. Solute distributions were analyzed by atom probe tomography, which revealed α′ precipitation for alloys containing more than 9 at.% Cr. Both the Cr concentration dependence of α′ precipitation and the measured matrix compositions are in agreement with the recently published Fe–Cr phase diagrams. An irradiation-accelerated precipitation process is strongly suggested. Irradiation was carried out in the Advance Test Reactor (ATR) at Idaho National Laboratory. A series of six Fe–Cr alloys of nominal compositions 3, 6, 9, 12, 15 and 18 at.% Cr was irradiated at a neutron fluence (E > 1 MeV) of 1.1 × 1021 n cm−2 at 563 ± 15 K and to a damage level of 1.82 displacements per atom (dpa). Nominal neutron flux and dpa rate are 2.3 × 1014 n cm−2 s−1 and 3.4 × 10−7 dpa s−1, respectively. The microstructures of the Fe–Cr alloys were studied by atom probe tomography (APT) using a Cameca 4000X HR instrument. APT specimens were prepared by a standard lift-out process using a Quanta 3D 200i dual beam scanning electron microscope ensuring that the analyses were performed away from grain boundaries.

Bachhav, Mukesh↗
DOE OSTI2025

6cr

A series of model Fe–Cr alloys containing 3–18 at.% Cr was neutron irradiated at a nominal temperature of 563 K to 1.82 dpa. Solute distributions were analyzed by atom probe tomography, which revealed α′ precipitation for alloys containing more than 9 at.% Cr. Both the Cr concentration dependence of α′ precipitation and the measured matrix compositions are in agreement with the recently published Fe–Cr phase diagrams. An irradiation-accelerated precipitation process is strongly suggested. Irradiation was carried out in the Advance Test Reactor (ATR) at Idaho National Laboratory. A series of six Fe–Cr alloys of nominal compositions 3, 6, 9, 12, 15 and 18 at.% Cr was irradiated at a neutron fluence (E > 1 MeV) of 1.1 × 1021 n cm−2 at 563 ± 15 K and to a damage level of 1.82 displacements per atom (dpa). Nominal neutron flux and dpa rate are 2.3 × 1014 n cm−2 s−1 and 3.4 × 10−7 dpa s−1, respectively. The microstructures of the Fe–Cr alloys were studied by atom probe tomography (APT) using a Cameca 4000X HR instrument. APT specimens were prepared by a standard lift-out process using a Quanta 3D 200i dual beam scanning electron microscope ensuring that the analyses were performed away from grain boundaries.

Bachhav, Mukesh↗
DOE OSTI2022

Spectral Rear Irradiance Testing and Modeling for Degradation and Performance of Solar Fields: Preprint

This work investigates how the spectrum of irradiance incident on the rear of solar modules impacts their degradation, and in case of bifacial PV their performance. We calculate the spectral irradiance incident on the rear of modules through raytrace simulations and validate with measured field data collected from a 75kW single axis-tracked bifacial test site. An equation to calculate relative degradation is shown, showing that current acceleration factors for UV damage in chambers can be off by +3% for the rear surfaces. Finally, a method for improving non-spectral simulations is proposed.

bifacial performance↗
DOE OSTI2024

National Reactor Innovation Center Annual Report

The National Reactor Innovation Center (NRIC), established in August 2019, is a national United States (U.S.) Department of Energy (DOE) program. NRIC’s mission is to partner with industry and national laboratories to bridge the gap between the concept, demonstration, and commercialization of advanced nuclear technology. NRIC accomplishes this through building or enhancing existing DOE infrastructure to support the testing of components and systems that are key to successfully deploying advanced nuclear technology. NRIC works to inspire stakeholders and the public, empower innovators, and deliver successful outcomes through efficient collaboration and coordination with partners. NRIC’s vision is that by 2028, NRIC will be partnered with industry and accelerating the demonstration and deployment of advanced nuclear technology using DOE national laboratory infrastructure and expertise. NRIC will establish four new experimental facilities and two large reactor test beds for integrated technology demonstrations and experimentation by 2028 and complete two advanced nuclear technology tests by 2030. Achieving this vision will enable urgently needed abundant and affordable clean energy both domestically and internationally. NRIC’s success will inspire our nation and the global community to embrace the promising contribution of innovative nuclear reactor technologies to the clean energy economy and re-establish the U.S. as the global leader in advanced nuclear energy. NRIC is tasked with expediting the development of advanced nuclear energy technologies by bringing together private-sector technology developers and the world-class capabilities of the DOE national laboratory system. Through this program, the U.S. private sector is given access to the physical infrastructure available at DOE national laboratories to test and demonstrate their reactor concepts. NRIC works closely with the Gateway for Accelerated Innovation in Nuclear (GAIN),; which is the DOE-Nuclear Energy (NE) program that grantings access to technical, regulatory, and financial support for commercializing nuclear energy. As observed in Figure 1, NRIC builds upon these new reactor concepts and technology successes to effectively strengthen U.S. nuclear leadership.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

A New UV Spot Test for Quality Control

Here, we present a fast, low-cost ultraviolet (UV) spot test to screen photovoltaic modules for UV-induced degradation (UVID). The measurement is based on a fiber-coupled arc lamp that delivers a highly accelerated local UV dose-approximately five years of equivalent field exposure in less than a day. Spot-exposed areas are evaluated using electroluminescence imaging. We test a “known bad” product, which was observed to experience UVID in the field, and a “known good” product which did not exhibit UVID in the field. Results indicate that the method may be useful as a quick and relatively inexpensive quality control screening tool for customers and manufacturers.

14 SOLAR ENERGY↗
DOE OSTI2020

Concrete Structure Health Monitoring Using Vibro-acoustic Testing and Machine Learning

Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on margins of safety in existing codes. Research is required to deploy online monitoring capability to assess the health of concrete structures in nuclear power plants and perform predictive analytics based on heterogeneous measurements, in order to reduce the operation and maintenance costs. The research effort described in this report focuses on the health monitoring of concrete slabs containing reactive aggregates using the vibro-acoustic modulation (VAM) technique and predictive modeling. The research is developing an approach to enable detection, localization, and estimation of the extent of degradation in concrete slabs due to alkali-silica reaction (ASR). The project outcomes are generic and extendable to other degradation modes in concrete. A concrete slab without reinforcements was cast at Vanderbilt University and cured in aggressive conditions to accelerate ASR degradation. In this report, we develop a methodology for the automation of VAM testing, and the construction of machine learning models to facilitate damage localization in concrete structures. We investigate how machine learning models based on physics simulation data can be effectively utilized for damage localization (damage location and depth) and estimation in a concrete slab specimen. Our study indicates that these models could potentially support damage localization and estimation for NPP structures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

PIP-II LLRF Master Oscillator and Precision Reference Line - Station Level Design and Testing

The PIP-II superconducting linac at Fermilab requires a highly stable RF Reference Line to maintain phase syn- chronization throughout the accelerator. Temperature- induced changes in the electrical length of long coaxial cables can introduce phase drift and measurement errors. The reference-line architecture mitigates these effects by phase averaging the forward and reflected RF signals, while a phase-locked loop anchors the system to the mas- ter oscillator. This work focuses on the characterization, validation, and mechanical integration of station-level RF assemblies using CAD modeling, vector network analyzer measurements, and spectrum analyzer testing. PID-controlled thermal plates will stabilize critical RF components and further reduce temperature-dependent phase and amplitude variations. These methods sup- port repeatable, standardized designs that can be reliably integrated across the different reference-line stations.

Mosher, Alexander [U. Illinois, Chicago]↗
DOE OSTI2024

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

47 OTHER INSTRUMENTATION↗