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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Assessment of wind power scenario creation methods for stochastic power systems operations

Probabilistic scenarios of renewable energy production, such as wind, have been gaining popularity for use in stochastic variants of power systems operations scheduling problems, allowing for optimal decision-making under uncertainty. The quality of the scenarios has a direct impact on the value of the resulting decisions, but until now, methods for creating scenarios have not been compared under realistic operational conditions. Here, we compare the quality of scenario sets created using three different methods, based on a simulated re-enactment of stochastic day-ahead unit commitment and subsequent dispatch for a realistic test system. We create scenarios using a dataset of forecasted and actual wind power values, scaled to evaluate the effects of increasing wind penetration levels. We show that the choice of scenario set can significantly impact system operating cost, renewable energy use, and the ability of the system to meet demand. This result has implications for the ability of system operators to efficiently integrate renewable production into their day-ahead planning, highlighting the need for the use of performance-based assessments for scenario evaluation.

17 WIND ENERGY↗
DOE OSTI2025

Data Quality Assessment Process for Real-Time Data-Driven Traffic Microsimulation of Smart Corridor

Smart corridor digital twins are often created for the development and evaluation of emerging intelligent transportation systems and Connected and Autonomous Vehicle (CAV) technologies. However, limited guidance exists for data quality assessment for digital twin development. To address this, this paper discusses the data quality assessment utilized to develop data-driven real-time microscopic simulation models, i.e., digital twins, for two separate smart corridors: the North Avenue Smart Corridor in Atlanta, GA, and the Martin Luther King Smart Corridor in Chattanooga, Tennessee. This paper provides a summary of the author’s investigations of data requirements and data characteristics for the given smart corridor digital twin development efforts. With a focus on data, this summary includes a description of the data investigation process, key data issues observed, and strategies to address observed issues. Discussion is provided to help expand the lessons from these studies to other digital twin development efforts.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗
DOE OSTI2021

Sodium tracer measurements of an expanded dense aluminum plasma from e-beam isochoric heating

Spatially and temporally resolved visible absorption spectroscopy is performed on sodium D-lines present as surface contaminants on an expanded dense aluminum plasma plume. An 80-ns FWHM, intense, relativistic electron beam deposits 5.4 J into a 100- μ m-thick Al foil, which isochorically heats and subsequently hydrodynamically expands the material through the warm dense matter state and into a classical-like plasma state, with a coupling parameter of approximately 0.2 and a degeneracy parameter of approximately 270. The Na contamination, carried along with the expanding plume, shows saturated absorption features in the dense Al continuum for λ > 450 nm. X-ray photoelectron spectroscopy and laser-induced breakdown spectroscopy confirm Na is a surface contaminant with an atomic concentration of ~ 0.1 % when interrogating identical foil samples. Additioanlly, a spectroscopic-quality radiation transport model is used to post-process 2D hydrodynamic simulations to interpret the plasma conditions based on the measured Na 3p-3s doublet line profiles. A sodium number density of 3 × 10 15 cm -3 best matches the experimental spectra, which originate from a dense surface plasma with n e = 3.0 ± 0.8 × 10 18 cm -3 .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Novel dual-reflection design applied for ITER core x-ray spectrometer

Here. a novel dual-reflection configuration is introduced for the International Tokamak Experimental Reactor (ITER) core x-ray spectrometer to fit the allocated space where it will be placed accompanied by moving the detectors backward to reduce the incident radiation dose. The highly oriented pyrolytic graphite, which has a mosaic structure of microscopic crystallites, is chosen for the front reflector motivated by higher x-ray throughput and stronger misalignment tolerance compared to the perfect crystal reflector. In the ITER core x-ray spectrometer, a combination of several reflector-deflected Lines of Sight (LOSs) and a direct LOS is proposed for the first time named X-Ray Crystal Spectroscopy Core (XRCS-Core). The system is optimized to observe lines from externally seeded xenon and the intrinsic tungsten impurity, meeting both port integration needs and measurement requirements. Its spectral performance is simulated using an analytical-raytracing mixed code——XRSA, showing good imaging quality with a spectral resolution higher than 8000. The XRCS-Core system is thought to be applicable in various ITER scenarios through the assessment taking into account the spectrometers’ specifications and the chosen lines’ emissivity in different plasma parameters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

An Edge Alignment-Based Orientation Selection Method for Neutron Tomography

Neutron computed tomography (nCT) is a 3D char-acterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined set of orientations, and processing the resulting data using an analytic reconstruction algorithm. Typical nCT scans require hours to days to complete and are then processed using conventional filtered back-projection (FBP), which performs poorly with sparse views or noisy data. Hence, the main methods in order to reduce overall acquisition time are the use of an improved sampling strategy combined with the use of advanced reconstruction methods such as model-based iterative reconstruction (MBIR). In this paper, we propose an adaptive orientation selection method in which an MBIR reconstruction on previously-acquired measurements is used to define an objective function on orientations that balances a data-fitting term promoting edge alignment and a regularization term promoting orientation diversity. Using simulated and experimental data, we demonstrate that our method produces high-quality reconstructions using significantly fewer total measurements than the conventional approach.

Yang, Diyu↗
DOE OSTI2026

Magnetic Field Mapping of a 2.5 T Fixed-Field HTS Gantry Magnet for Proton Therapy

We present results from testing a high-temperature superconducting (HTS) magnet prototype for proton therapy. This magnet is specifically designed for a novel rotating gantry capable of delivering the entire proton beam energy range (70225 MeV) while maintaining a fixed magnetic field in the superconducting magnets. The gantry layout simplifies the magnet design, enabling the use of straight, flat racetrack Bi-2223 (DI-BSCCO) coil technology and operation at higher temperatures (1015 K). The magnet has a non-linear field distribution for bending and focusing the proton beams. To validate this feature, we developed a system for measuring the magnetic field distribution in the magnet aperture. We present the design of this hall probe array and experimental results from two different magnet tests at 4.2 K in a liquid helium bath. These results are compared with the simulated field distribution and discussed in the context of the required field quality for the application.

Mosat, M↗
DOE OSTI2022

ARETE: Accurate Error Assessment via Machine Learning-Guided Dynamic-Timing Analysis

Nanometer circuits are increasingly prone to timing errors, escalating the need for fault injection frameworks to accurately evaluate their impact on applications. Here in this paper, we propose ARETE, a novel cross-layer, fault-injection framework that combines dynamic-binary instrumentation with machine learning-guided dynamic-timing analysis. ARETE enables accurate fault-injection into any application by estimating the location of the injecting errors via dynamic-timing analysis. To accelerate fault-injection, we develop a novel, data-aware, machine learning-based mechanism that dynamically pre-selects the error-prone instructions and limits the application of the costly dynamic-timing analysis only to them. To evaluate ARETE's accuracy, our fully automated toolflow is configured to support fault-injection based on detailed post-layout gate-level simulations as well as via existing workload-agnostic error models. Our results for various workloads, including an autonomous-driving library, show that the location and time of injected errors performed by ARETE, is 89.9% consistent with fault-injection based on full gate-level simulation. On average, ARETE executes 84.6x faster than gate-level simulation and at a cost of 3.4% loss in the program output quality estimation. When compared to the existing statistical fault-injection tools that are based on workload-agnostic error models, ARETE improves the accuracy of fault-injection rate and output quality estimation by 143.9% and 40.4% on average, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

fpikachu96/KRED-data

Exploiting nature’s catalysts for non-natural transformations that are inaccessible to chemocatalysis is highly desirable but challenging. On the one hand, the widespread nicotinamide-dependent oxidoreductases have not been utilized for single-electron-transfer-induced bimolecular cross-couplings; on the other, the addition of catalytic asymmetric radical conjugate to terminal alkenes remains a challenge owing to strong racemic background reaction and unselective termination of prochiral radical species. Here we report a chemomimetic biocatalysitic approach for construction of alpha-carbonyl stereocentres via an unnatural intermolecular conjugate addition of N-(acyloxy)phthalimides-derived radicals with acceptor-substituted terminal alkenes, by combination of visible-light excitation and nicotinamide-dependent ketoreductases (KREDs). Based on protein crystal structure, we engineered KREDs via a semi-rational mutagenesis strategy to improve reaction outcomes with a small and high-quality variants library. Mechanistic investigations combining wet experiments, crystallographic studies and computational simulations demonstrate that the repurposed biocatalyst can suppress racemic background reaction and unselected side reactions, yielding enantioselectivity that is challenging to achieve by chemocatalysis.

Huang, Xiaoqiang↗
DOE OSTI2022

The role of toroidal rotation in the very high energy confinement quality observed in super H-mode experiments on DIII-D

In this paper, we report the key role that toroidal rotation and the related ExB shear physics played in the very high energy confinement quality (H98y2>1.5) of super H-mode experiments on DIII-D. Experiments show that the energy confinement quality decreases when toroidal rotation decreases due to the decreased externally controlled torque per particle. Meanwhile, the total pedestal pressure in the experiments remains very high during the rotation and confinement quality change. TGYRO transport modeling suggests the contribution from rotation in the ExB shear is responsible for the confinement quality in excess of standard H-mode (H98y2~1). CGYRO gyrokinetic simulations reveal the governing physics in the core plasma of super H-modes: significant up-shift of nonlinear the ITG critical gradient is observed when applying ExB shear physics in the modeling based on experimental data. The effects of other physical parameters and contribution from pedestal height, which may play minor roles in this study, are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

High Energy Density Physics of Inertial Confinement Fusion Ablator Materials

The historic December 5, 2022 experiment at Lawrence Livermore National Lab’s (LLNL) National Ignition Facility (NIF) reached fusion energy ignition for the first time. This is the most important scientific breakthrough of the 21st century paves the way to future clean inertial fusion energy (IFE). The diamond (high density carbon (HDC)) ablator material used in this experiment displays detrimental effects due to the development of hydrodynamic instabilities at the diamond/fuel interface under shock compression. New alternatives to diamond ablators are required to step up the energy yield in ICF experiments. The unique combination of mechanical strength (approaching that of diamond), the ability to accommodate high-Z dopants (in contrast to diamond), and the tunability of the properties (through synthesis material with varying sp 3 content) make amorphous carbon (a-C) a promising material for next-generation IFE ablative capsules. However, despite its critical importance to the IFE program, the behavior of a-C carbon at extreme temperatures and pressures remains largely unexplored. The primary goals of this project were to perform groundbreaking dynamic compression experiments and predictive simulations to uncover the fundamental high-energy-density physics of amorphous carbon. Our goals were (1) to uncover the metastability range of amorphous carbon and probe phase transitions to diamond or metastable supercooled liquid carbon; (2) to acquire high-quality equation of state (EOS) data and develop an experimentally validated EOS from machine-learning MD simulations of the complex states of carbon; and (3) to uncover the complex behavior of carbon liquid in both thermodynamically stable and metastable supercooled states by accessing large areas of carbon phase diagram with amorphous samples with variable sp 3 content. Our proposed experimental program included measurements of equation of state and diffraction measurements using the Omega EP laser at the Laboratory of Laser Energetics at the University of Rochester. The theoretical/simulation program involved the development of machine-learning models of the complex response of amorphous carbon under dynamic compression by performing molecular dynamics simulations at experimental time and length scales using leadership class DOE supercomputers. Simulations guided experiments to observe predicted phenomena and acquire critical experimental data in specific pressure-temperature domains to validate theoretical models. This research delivered fundamental properties of novel amorphous carbon IFE ablator material, including phase diagram and EOS. These results will aid in IFE target design and implosion experiments. A unique combination of predictive simulations and dynamic and static experiments provided a highly inspirational intellectual environment for graduate students and postdocs involved in this project.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Optimized Quantum Phase Estimation for Simulating Electronic States in Various Energy Regimes

While quantum algorithms for simulation exhibit better asymptotic scaling than their classical counterparts, they currently cannot be implemented on real-world devices. Instead, chemists and computer scientists rely on costly classical simulations of these quantum algorithms. In particular, the quantum phase estimation (QPE) algorithm is among several approaches that have attracted much attention in recent years for its genuine quantum character. However, it is memory-intensive to simulate and intractable for moderate system sizes. Herein, this paper discusses the performance and applicability of QPESIM, a new simulation of the QPE algorithm designed to take advantage of modest computational resources. In particular, we demonstrate the versatility of QPESIM in simulating various electronic states by examining the ground and core-level states of H 2 O. For these states, we also discuss the effect of the active-space size on the quality of the calculated energies. For the high-energy core-level states, we demonstrate that new QPE simulations for active spaces defined by 15 active orbitals significantly reduce the errors in core-level excitation energies compared to earlier QPE simulations using smaller active spaces.

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

FENIX: Towards a Fully Integrated Multiphysics Framework for Plasma Facing Component Modeling

Computational tools have a crucial role to play in accelerating the deployment of fusion as a clean, reliable, abundant, and sustainable energy source. Multiphysics, high-fidelity simulation capabilities can help model, study, and predict intricate interactions between materials performance, plasma exposure, neutron irradiation, and engineering processes. As such, they can assist in the resolution of scientific and engineering challenges underpinning design, construction, and commission of fusion power plants. To address these needs, ongoing efforts are leveraging the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework and delivering new computational tools for the fusion community. These tools inherit crucial attributes from MOOSE. They are open-source, modular, integrated with nuclear industry-standard software quality assurance processes, and enable multiphysics, multi-fidelity, fully integrated, zero- to three-dimensional, and massively parallel simulations. After a short overview of these capabilities, we will present the development of Fusion ENergy Integrated multiphys-X (FENIX), a MOOSE-based application designed to enable plasma facing component design and performance evaluation. Throughout their lifetime, plasma facing components are exposed to extreme thermal loads, repeated thermal shocks, and irradiation by plasma ions, neutral particles, and high-energy neutrons. Consequently, designing a plasma facing component with acceptable lifetime degradation is extremely challenging. FENIX aims to model the multiphysics environment in which plasma facing components evolve to accelerate their design studies. To that end, FENIX couples existing MOOSE capabilities such as heat transfer, thermomechanics, and thermal hydraulics, with tritium transport via the MOOSE-based Tritium Migration Analysis Program, Version 8 (TMAP8), with neutronics via the MOOSE-based high-fidelity neutron-photon transport and fluid dynamics code Cardinal, and finally with Particle-in-Cell plasma simulation capabilities being developed in this project. In this study, we present the current FENIX capabilities and preliminary results of its application to model the Tritium Plasma Experiment set up at Idaho National Laboratory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Semi-Automatic Geographic Information System Framework for Creating Photo-Realistic Digital Twin Cities to Support Autonomous Driving Research

Digital twin cities are frequently used in vehicle and traffic simulations to render realistic on-road driving scenarios under various traffic and environmental conditions. These digital twins provide a high-fidelity replica of the physical world (e.g., buildings, roads, infrastructures, traffic) to create three-dimensional (3D) virtual-physical environments to support various emerging vehicle and transportation technologies such as connected and automated vehicles. These virtual environments provide a cost-effective digital proving ground to evaluate, validate, and test emerging technologies that include control algorithms, localization, perception, and sensors. Replicating a real-world traffic scenario in a digital twin using a traditional 3D modeling approach is a time-consuming and labor-intensive effort. Here this paper presents a semi-automated spatial framework to construct realistic 3D digital twin cities to support autonomous driving research using readily available geographic information system (GIS) data and 3D prefabricated (prefab) models. We start with a comprehensive review of geospatial data sources of essential digital entities required in a 3D digital twin city and present an integrated GIS-3D modeling pipeline using customized QGIS/GDAL and Blender scripting in Python. The pipeline outputs are realistic 3D digital twin cities compatible with common vehicle simulation software, such as CARLA and IPG CarMaker. The paper closes with a showcase to demonstrate the quality and usability of a digital twin city created to replicate the Shallowford Road corridor in Chattanooga in both Unity and Unreal engine-based virtual environment. The generated digital twin city can be applied to a hardware-in-the-loop simulation environment with an actual testing vehicle to facilitate autonomous driving research.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Fast Hyperspectral Neutron Tomography

Hyperspectral neutron computed tomography is a tomographic imaging technique in which thousands of wavelength-specific neutron radiographs are measured for each tomographic view. In conventional hyperspectral reconstruction, data from each neutron wavelength bin are reconstructed separately, which is extremely time-consuming. These reconstructions often suffer from poor quality due to low signal-to-noise ratios. Consequently, material decomposition based on these reconstructions tends to produce inaccurate estimates of the material spectra and erroneous volumetric material separation. In this paper, we present two novel algorithms for processing hyperspectral neutron data: fast hyperspectral reconstruction and fast material decomposition. Both algorithms rely on a subspace decomposition procedure that transforms hyperspectral views into low-dimensional projection views within an intermediate subspace, where tomographic reconstruction is performed. The use of subspace decomposition dramatically reduces reconstruction time while reducing both noise and reconstruction artifacts. We apply our algorithms to both simulated and measured neutron data and demonstrate that they reduce computation and improve the quality of the results relative to conventional methods.

Chowdhury, Mohammad Samin Nur [Purdue University]↗
DOE OSTI2024

Mechanism of Antiferroelectricity in Polycrystalline ZrO 2

The size and electric field dependent induction of polarization in antiferroelectric ZrO 2 is the key to several technological applications that are unimaginable a decade ago. However, the lack of a deeper understanding of the mechanism hinders progress. Molecular dynamics simulations of polycrystalline ZrO 2 , based on machine-learned interatomic forces with near ab initio quality, shed light on the fundamental mechanism of the size effect on the transition fields. Stress in the oxygen sublattice is the most important factor. The so constructed interatomic forces allow the calculation of the transition fields as a function of the ZrO 2 film thickness and predict the ferroelectricity at large thickness. The simulation results are validated with electrical and piezo response force microscopy measurements. The results allow a clear interpretation of the properties of the double-hysteresis loops as well as the construction of the free energy landscape of ZrO 2 grains.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Potential Chemical Impacts of Subsurface CO 2 : An Integrated Experimental and Numerical Assessment for a Case Study of the Ogallala Aquifer

Leakage from geologic CO 2 sequestration (GCS) reservoirs to overlying underground sources of drinking water (USDW) is a tangible risk. This study is an integrated assessment that combines column experiments and reactive transport simulations of sediments sampled from the Ogallala aquifer above an active commercial-scale GCS site (the Farnsworth Unit in northern Texas). Experimental and simulation results suggest that carbonate mineral (calcite and dolomite) dissolution is the most significant reaction following CO 2 intrusion, and is also the dominant source of trace metal release. Cation exchange is another key mechanism controlling trace metal release by cation interference. Most of the trace metals, including Ba, Sr, As, Pb, and Zn, show a short-term release and quickly drop to the baseline values, suggesting low risk to the overlying USDW quality. Other trace metals, such as Mn and U, exhibit a tangible increase of their concentrations in the beginning, and drop to a higher level compared to the baseline, which may become a potential concern of long-term USDW quality change with CO2introduction. This study provides a comprehensive example of combining laboratory experiments and simulations for assessment of CO 2 -sediment interactions with combined release mechanisms in shallow groundwater aquifers. Data presented here provides useful insights for quantitative risk assessment and effective public education regarding CO 2 geological sequestration, and trace metal reactive transport studies in shallow groundwater aquifers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Toward Robust Autotuning of Noisy Quantum dot Devices

The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a “gatekeeper” system, ensuring that only reliable data are processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9)% when tested on experimental data. We then validate the functionality of the data quality control module by showing that the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗