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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 OSTI2020

Savannah River Site Composite Analysis (FY2019 Annual Review)

The Savannah River Site (SRS) is a Department of Energy (DOE) site encompassing approximately 310 square miles in South Carolina. It is bounded on the southwest by the Savannah River (SR) and is situated approximately 12 miles south of Aiken, South Carolina, and 15 miles southeast of Augusta, Georgia. Construction of and subsequent operations at the SRS began in 1951 under the direction of the Atomic Energy Commission. The primary mission of the SRS was to produce tritium and plutonium for the national nuclear weapons complex. Between 1953 and 1955, SRS brought five reactors and various support facilities into operation in support of its primary mission. Support facilities included two chemical separations plants, a heavy water extraction plant, a nuclear fuel and target fabrication facility, a tritium extraction facility and waste management facilities. With the declining need for a large nuclear weapons stockpile since the end of the Cold War, many SRS facilities no longer produce or process nuclear materials. All reactors were shut down by 1993. However, the SRS Tritium Extraction Facility continues to supply DOE with tritium. Additionally, operations at the K-Area Complex currently provide interim safe storage for much of DOE’s excess plutonium (Pu) and high enriched uranium, in a building which formerly housed K Reactor. As the SRS mission has changed, many surplus facilities are being dispositioned safely and economically. SRS has completed extensive decommissioning activities in D-Area, M-Area, P-Area, R-Area, and T-Area, with groundwater (GW) remediation ongoing. High-level waste tanks continue to be emptied and closed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

ExaWind: Open‐source CFD for hybrid‐RANS/LES geometry‐resolved wind turbine simulations in atmospheric flows

Abstract Predictive high‐fidelity modeling of wind turbines with computational fluid dynamics, wherein turbine geometry is resolved in an atmospheric boundary layer, is important to understanding complex flow accounting for design strategies and operational phenomena such as blade erosion, pitch‐control, stall/vortex‐induced vibrations, and aftermarket add‐ons. The biggest challenge with high‐fidelity modeling is the realization of numerical algorithms that can capture the relevant physics in detail through effective use of high‐performance computing. For modern supercomputers, that means relying on GPUs for acceleration. In this paper, we present ExaWind, a GPU‐enabled open‐source incompressible‐flow hybrid‐computational fluid dynamics framework, comprising the near‐body unstructured grid solver Nalu‐Wind, and the off‐body block‐structured‐grid solver AMR‐Wind, which are coupled using the Topology Independent Overset Grid Assembler. Turbine simulations employ either a pure Reynolds‐averaged Navier–Stokes turbulence model or hybrid turbulence modeling wherein Reynolds‐averaged Navier–Stokes is used for near‐body flow and large eddy simulation is used for off‐body flow. Being two‐way coupled through overset grids, the two solvers enable simulation of flows across a huge range of length scales, for example, 10 orders of magnitude going from O(μm) boundary layers along the blades to O(10 km) across a wind farm. In this paper, we describe the numerical algorithms for geometry‐resolved turbine simulations in atmospheric boundary layers using ExaWind. We present verification studies using canonical flow problems. Validation studies are presented using megawatt‐scale turbines established in literature. Additionally presented are demonstration simulations of a small wind farm under atmospheric inflow with different stability states.

17 WIND ENERGY↗
DOE OSTI2023

Complementarity and the unitarity of the black hole S -matrix

Recently, Akers et al. proposed a non-isometric holographic map from the interior of a black hole to its exterior. Within this model, we study properties of the black hole S-matrix, which are in principle accessible to observers who stay outside the black hole. Specifically, we investigate a scenario in which an infalling agent interacts with radiation both outside and inside the black hole. Because the holographic map involves postselection, the unitarity of the S-matrix is not guaranteed in this scenario, but we find that unitarity is satisfied to very high precision if suitable conditions are met. If the internal black hole dynamics is described by a pseudorandom unitary transformation, and if the operations performed by the infaller have computational complexity scaling polynomially with the black hole entropy, then the S-matrix is unitary up to corrections that are superpolynomially small in the black hole entropy. Furthermore, while in principle quantum computation assisted by postselection can be very powerful, we find under similar assumptions that the S-matrix of an evaporating black hole has polynomial computational complexity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Reliable extrapolation of deep neural operators informed by physics or sparse observations

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity, via the 2-Wasserstein distance between two function spaces and propose a new strategy of bias–variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information—the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Furthermore, our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed.

42 ENGINEERING↗
DOE OSTI2024

Design and analysis of actively-cooled, edge-transport diagnostic for long-pulsed operation in WEST

Next step fusion devices that will operate in steady-state will require complex plasma-facing components (PFCs) that can survive the harsh environment over long timescales not common in current devices. This will require robust plasma facing surfaces that are integrated with active cooling systems. In a collaboration between CEA and ORNL, a plasma-interacting diagnostic is being designed for the W Environment in Steady-state Tokamak (WEST) in Cadarache, France which requires plasma-facing protection like those needed for steady-state PFCs. This integrated diagnostic studies edge transport and impurity migration within WEST, and is planned to include imbedded temperature Langmuir probe sensors, as well as removeable sample slots for ex-situ surface analysis of plasma-material interactions. The entire assembly is expected to move into the plasma edge for periods up to 1000 s having an energy removeable capability of ∼6 kW and seeing a peak heat flux of more than 7 MW/m2. The assembly compliments WEST high fluence campaigns that plan for multiple 1000 s pulses. Within these specifications, the assembly will require a refractory metal plasma facing surface and integral cooling in order to function within the limited space allotted for such diagnostics. Because of the limited space and linear actuator needs, additive manufacturing of the high heat flux working end of the assembly is being considered which could allow for precision cooling-channels and lighter weight designs. Conceptual design along with simulation and analysis results will be presented for this complex diagnostic with novel PFCs.

Lumsdaine, Arnold↗
DOE OSTI2022

Continuous electrocoagulation-membrane distillation unit for treating hydraulic fracturing produced water

Hydraulic fracturing oil and gas produced water is frequently highly impaired. While it is often deep well injected, there is great interest in treating this water for beneficial uses. Given the complexity of these produced waters, multiple unit operations are necessary. Electrocoagulation has been considered as a promising pretreatment technology. Here electrocoagulation is considered as a pretreatment prior to membrane distillation. The focus of this work is on understanding the electrocoagulation process in order to design an integrated unit operation. Electrocoagulation is used to remove organic compounds that will foul the membrane leading to membrane failure during membrane distillation. Using aluminum or iron electrodes, half-cell reactions in the electrocoagulation cell and electrode potentials have been calculated. Electrocoagulation was conducted using a continuous electrocoagulation reactor with actual produced water using aluminum, iron or mixed aluminum and iron electrodes. Here, the results obtained here indicate that electrocoagulation can obtain good removal efficiency of total organic carbon (TOC) by using different reaction conditions. Removal of organic compounds is essential to minimize fouling during membrane distillation. Further the performance of the electrocoagulation process depends strongly on the quality of the feed water. Insoluble species were more effectively coagulated than dissolved organic species. Continuous electrocoagulation shows great potential as a scalable unit operation for pretreating hydraulic fracturing produced water.

42 ENGINEERING↗
DOE OSTI2026

Atomic-scale vacancy engineering unlocks basal-plane catalytic activity in metallic WSe 2 for reversible oxygen electrocatalysis

Two-dimensional metallic transition metal dichalcogenides offer high electrical conductivity and large surface areas for electrocatalysis, yet their inherent basal planes are catalytically inert. Here, we present an atomic-scale vacancy engineering strategy to activate the basal surfaces of metallic WSe 2 for reversible oxygen electrocatalysis. This approach, based on intentionally designed substitutional metal doping, promotes the spontaneous formation of selenium vacancies while preserving the metallic 1 T′ phase, thereby creating highly reactive and oxygen-affinitive sites. Density functional theory calculations reveal that these vacancy-mediated metal complexes dramatically lower the energy barriers for initial oxygen adsorption, enabling dissociative oxygen adsorption. Operando and ex-situ spectroscopic analyses confirm that vacancy-mediated metal complexes transform into dynamic Se/W-oxide intermediates under operating conditions. Se/W-oxides on the surface experimentally and theoretically prove electrocatalytic activity and reversibility. Applying this strategy in lithium–oxygen batteries, the basal-plane activated WSe 2 shows high discharge capacities (9868 mA h g −1 , corresponding to 3947 mA h g$^{-1}_{cathode}$), impressive cycle retention over 550 cycles at 1000 mA h g −1 , and outstanding rate–capability over a wide current–density range (100–3000 mA g −1 ) during 256 cycles.

2D materials↗
DOE OSTI2022

Microwave-assisted C–H oxidation of methylpyridylheteroarenes via a Kornblum-Type reaction

Expansion of the operational capacity of soft-Lewis basic complexant scaffolds towards improved physical properties for the chemoselective sequestration of minor actinides from the electronically similar lanthanides necessitates rapid access to synthons for efficient complexant construction for downstream employment in separations assays. Pursuant to the aforementioned, we were interested in exploring the potential utility of advanced, unsymmetric heteroarene constructs for separations which required access to pyridyl carbaldehydes. Limited commercial availability of synthetic precursors inspired our effort to define a chemoselective, microwave assisted, Kornblum-type reaction via C–H functionalization. This efficient reaction sequence uses I 2 as a mild oxidant without acidic or basic additives, in concert with DMSO as the solvent and putative oxygen source to afford a diverse array of heteroaryl products devoid of competitive remote methyl group oxidation, or degradation of the heteroaryl N atom. Finally, method development, substrate scope, and a preliminary mechanistic hypothesis supported by Density Functional Theory are presented herein.

1,2,4-triazine↗
DOE OSTI2022

Understanding Hydrogenation Chemistry at MgB 2 Reactive Edges from Ab Initio Molecular Dynamics

Solid-state hydrogen storage materials often operate via transient, multistep chemical reactions at complex interfaces that are difficult to capture. Here, we use direct ab initio molecular dynamics simulations at accelerated temperatures and hydrogen pressures to probe the hydrogenation chemistry of the candidate material MgB 2 without a priori assumption of reaction pathways. Focusing on highly reactive ($10\overline{1}0$) edge planes where initial hydrogen attack is likely to occur, we track mechanistic steps toward the formation of hydrogen-saturated BH 4 – units and key chemical intermediates, involving H 2 dissociation, generation of functionalities and molecular complexes containing BH 2 and BH 3 motifs, and B–B bond breaking. Further, the genesis of higher-order boron clustering is also observed. Different charge states and chemical environments at the B-rich and Mg-rich edge planes are found to produce different chemical pathways and preferred speciation, with implications for overall hydrogenation kinetics. The reaction processes rely on B–H bond polarization and fluctuations between ionic and covalent character, which are critically enabled by the presence of Mg 2+ cations in the nearby interphase region. Our results provide guidance for devising kinetic improvement strategies for MgB 2 -based hydrogen storage materials, while also providing a template for exploring chemical pathways in other solid-state energy storage reactions.

08 HYDROGEN↗
DOE OSTI2026

Stable Co-valorization of Carbon Dioxide and Methane via Dynamic Reconstruction of a Metal Oxide Solid Solution Catalyst

Dry reforming of methane (DRM) is a process that converts two greenhouse gases (methane and carbon dioxide) into syngas, a mixture of H 2 and CO, that can lead to a variety of value-added chemicals. Owing to its endothermic nature, high reaction temperatures up to 800 °C are typically required and the grand challenge lies in developing robust catalysts without sintering and coking-induced deactivation during the long-term on-stream operation. Towards this aim, herein, a robust complex oxide-supported NiCu alloy catalyst was generated in situ during DRM. By leveraging the configurational stability of a solid oxide solution precursor, tightly anchored NiCu bimetallic nanoparticles were in situ exsoluted and acted as the active sites in DRM. The as-afforded catalyst exhibited stable performance for DRM due to the ability to repel coke off the surface as the reaction proceeds. Kinetic experiments along with top surface characterization detail the reconstruction behavior of the solid oxide solution under DRM reaction conditions. In conclusion, the fundamental insights from this work provide guidance on generating resistant and flexible catalysts via in situ active sites formation from easily synthesized metal oxide solid solutions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Durability Research Is Pivotal for Perovskite Photovoltaics

Metal halide perovskite solar cells have shown promising power conversion efficiencies, but commercialization requires that decent durability is also demonstrated. Under normal operation, solar cells are subject to a complex combination of stressors, such as visible light, ultraviolet light, heat, humidity, mechanical stress and electric potential, which complicates the understanding of failure mechanisms. Existing stress tests do not act as a time machine. In new materials systems such as perovskite photovoltaics, the tests have no known relationship to field service. In this Perspective we recommend following a durability learning cycle that interleaves photovoltaic module engineering with field testing; accelerated testing; and preconditioning and performance engineering. We advocate for field testing to demonstrate real-world performance and identify field-relevant failure modes, and urge the community to develop accelerated and qualification tests that account for device metastability, variations in material composition and different/various processing methods. In conclusion, these practices are more difficult, but more important, than the simple pursuit of higher initial efficiencies.

14 SOLAR ENERGY↗
DOE OSTI2020

Let’s get our hands dirty: a comprehensive evaluation of DAQDB, key-value store for petascale hot storage

Data acquisition systems are a key component for successful data taking in any experiment. The DAQ is a complex distributed computing system and coordinates all operations, from the data selection stage of interesting events to storage elements. For the High Luminosity upgrade of the Large Hadron Collider, the experiments at CERN need to meet challenging requirements to record data with a much higher occupancy in the detectors. The DAQ system will receive and deliver data with a significantly increased trigger rate, one million events per second, and capacity, terabytes of data per second. An effective way to meet these requirements is to decouple real-time data acquisition from event selection. Data fragments can be temporarily stored in a large distributed key-value store. Fragments belonging to the same event can be then queried on demand, by the data selection processes. Implementing such a model relies on a proper combination of emerging technologies, such as persistent memory, NVMe SSDs, scalable networking, and data structures, as well as high performance, scalable software. In this paper, we present DAQDB (Data Acquisition Database) — an open source implementation of this design that was presented earlier, with an extensive evaluation of this approach, from the single node to the distributed performance. Furthermore, we complement our study with a description of the challenges faced and the lessons learned while integrating DAQDB with the existing software framework of the ATLAS experiment.

Abed Abud, Adam↗
DOE OSTI2023

Identifying native point defect configurations in α-alumina

Intimately intertwined atomic and electronic structures of point defects govern diffusion-limited corrosion and underpin the operation of optoelectronic devices. For some materials, complex energy landscapes containing metastable defect configurations challenge first-principles modeling efforts. Here, we thoroughly reevaluate native point defect geometries for the illustrative case of α-Al 2 O 3 by comparing three methods for sampling candidate geometries in density functional theory calculations: displacing atoms near a naively placed defect, initializing interstitials at high-symmetry points of a Voronoi decomposition, and Bayesian optimization. We find symmetry-breaking distortions for oxygen vacancies in some charge states, and we identify several distinct oxygen split-interstitial geometries that help explain literature discrepancies involving this defect. We also report a surprising and, to our knowledge, previously unknown trigonal geometry favored by aluminum interstitials in some charge states. Importantly, these new configurations may have transformative impacts on our understanding of defect migration pathways in aluminum-oxide scales protecting metal alloys from corrosion. Overall, the Voronoi scheme appears most effective for sampling candidate interstitial sites because it always succeeded in finding the lowest-energy geometry identified in this study, although no approach found every metastable configuration. Finally, we show that the position of defect levels within the band gap can depend strongly on the defect geometry, underscoring the need to conduct careful searches for ground-state geometries in defect calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Measurement of a radial flow profile with eddy current flow meters and deep neural networks

Eddy current flow meters (ECFMs) measure flows of conductive fluids. Recent interest in ECFMs has increased due to applications in advanced nuclear reactors. ECFMs are well suited for such applications, as they can provide non-invasive measurements of flow in fluids that are often difficult to measure. Traditionally, ECFMs are operated using an alternating current at a single frequency, limiting ECFMs to measure average fluid velocities, blockages, or voids. Here, we expand the capabilities of ECFMs by measuring the fluid radial velocity profile of liquid mercury. To accomplish this, we made several ECFM sensitivity measurements at a range of frequencies. Different frequencies vary the electromagnetic skin depth of the device. By adjusting frequencies, we probed the fluid velocity at various radial locations and constructed a flow-velocity profile. The relationship between the ECFM measurements and velocity profile is nonlinear and requires solving an inverse problem. Using electromagnetic finite-element simulations to train a deep neural network (DNN), we created a model that provides a stable general relationship between the sensitivity measurements of an ECFM and the fluid velocity profile. Using ECFM measurements of liquid mercury, our DNN model calculates a flow profile that agrees well with computational fluid dynamics (CFD) simulations. This technique has potential to improve flow monitoring for optimization, safe operation of conductive fluid loops, and/or validating complex CFD models.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Non-Hermitian Quantum Mechanics Approach for Extracting and Emulating Continuum Physics Based on Bound-State-like Calculations

Here, this Letter introduces a unified emulation framework for studying continuum physics in finite quantum systems. Using a reduced basis method, we construct powerful emulators for the inhomogeneous Schrödinger equation that operate in a combined parameter space of complex energy (𝐸) and other inputs (𝜽). Within the space, the emulators simultaneously perform analytical continuation in 𝐸—extracting continuum physics from numerically simpler bound-state-like calculations—and interpolate this entire process across 𝜽. This yields a small, non-Hermitian system whose properties (e.g., resonances and scattering observables) can be rapidly predicted for any 𝜽. Crucially, the complex-𝐸 emulation provides a pathway to compute continuum observables for complex systems where advanced bound-state methods exist but direct continuum calculations are yet to be developed, while the 𝜽 emulation enables rapid parameter-space exploration and can be adapted to accelerate other existing continuum calculations. Demonstrations with two- and three-body systems highlight the method’s effectiveness and suggest its connection to (near-)optimal rational approximation. This Letter presents the key results, with further details reserved for a companion paper.

ab initio calculations↗
DOE OSTI2023

A Review of Visualization Methods for Cyber-Physical Security: Smart Grid Case Study

Cyber-Physical Systems (CPSs) are becoming increasingly complex and interconnected as they attempt to meet the demands of evolving society. As a result, monitoring and maintaining them becomes a more complex and demanding task for control system operators and cyber defenders. While the literature on visualization techniques in the context of cybersecurity is extensive, the same cannot be said for studies on visualization for the security of cyber-physical systems. This paper aims to fill that gap by: 1) defining the main features of a visualizations workflow for security visualizations in cyber-physical systems. The workflow includes the acquisition of cyber and physical data, processing of data, selection, and configuration of both visualization tools and end-user interactions. 2) Providing an overview of cyber-physical security visualization systems, with a focus on smart grids as a case study. Finally, we use the perspectives gained from this analysis to provide insights and directions for future research and design of cyber-physical visualization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Design and Challenges of Edge Computing ASICs on Front-End Electronics

In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted

47 OTHER INSTRUMENTATION↗