SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Because of aging reactor fleets, complicated regulatory applications for new reactors, unresolved uranium disposition pathways, and the nonproliferation and minimization efforts spearheaded by the National Nuclear Security Administration (NNSA), non-fission-based production of 99 Mo is becoming an attractive alternative.
One of the objectives of the United States Department of Energy Office of Nuclear Energy’s Office of Spent Fuel and High-Level Waste Disposition is to better understand the technical bases, risks, and uncertainties associated with the safe and secure disposition of spent nuclear fuel and high-level radioactive waste. Domestic defense and research activities have generated a few thousand metric tons of spent nuclear fuel and high-level radioactive waste, much of which has been or will be processed and vitrified into high-level waste glass. The Nuclear Waste Policy Act 1982 makes the Department of Energy responsible for disposal of these materials.
The urgency to deliver fusion power is growing now more than ever, with increasing pressure for both public programs and private companies to meet milestones timelines and overcome significant remaining technical challenges to ensure growth of a nascent fusion industry in time to meet rapidly growing clean energy demands. With incredible advancements in computation and years of investment in fusion model development and validation, integrated modeling is poised to fill a key role in accelerating the timeline to a fusion pilot plant (FPP). Future fusion pilot plants will operate in regimes far beyond current experience, and device design will rely on physics-based prediction and extrapolation. Many concepts will also rely on simulation to assess safety (shielding, tritium management, materials activation and lifetimes), economics and scalability before the decision to build. Importantly, integrated simulation can be used to reveal and solve the complexities of system integration that may otherwise not be apparent in physical components or models developed in isolation. New experimental test facilities that produce relevant conditions to validate and resolve key technical challenges for various subsystems (materials, blankets, fuel cycle, etc.) have been repeatedly called for by the fusion community but are not yet realized. Integrated modeling has an important role in identifying realistic load conditions (thermal, electromagnetic, plasma, neutron and photon loads, etc.) and defining the components and experiments for these test facilities in order to ensure meaningful validation that sufficiently reduces modeling uncertainties and technical risk for the full integrated reactor. The Fusion REactor Design and Assessment (FREDA) SciDAC project is building a component-based integrated modeling framework & data structure to enable self-consistent, multi-fidelity, iterative optimization workflows for the fusion reactor design process. FREDA aims to shorten the time to viable designs by providing a set of flexible workflows to support the various stages of the design process using an integrated model hierarchy, ranging from the simple analytic descriptions to the highest fidelity, theory-based plasma and engineering modeling developed by the fusion and fission communities. These tools are expected to be needed for timely support of FPP design in the milestone program and in the FIRE collaboratives. The plasma simulation backbone of FREDA is IPS-FASTRAN with newly developed coupled Core-Edge Pedestal-SOL (CESOL) workflows, which is being extended to the far-SOL region up to the plasma facing components. FREDA incorporates the FERMI engineering modeling suite and will enable self-consistent evaluation of the thermal shields, limiters, blanket, magnets, and other surrounding structures with predictions of temperatures, erosion, dpa, activation, tritium generation and transport, creep, corrosion, material degradation, etc. Parametric generation of 3D CAD enables rapid iteration of component geometry in response to plasma and loading specifications.
This project investigated the role of defects and dopants in cadmium telluride (CdTe) solar cells to improve efficiency and long-term stability. Advanced X-ray microscopy and spectroscopy techniques were used to map composition, identify defect structures, and measure charge collection at the nanoscale. These experimental results were combined with computational modeling to understand how dopants such as copper and arsenic interact with other elements during processing. The study found that many dopants become electrically inactive due to the formation of complexes with chlorine and oxygen, limiting carrier concentration and device performance. It also showed that selenium, used to enhance efficiency, can migrate within the material during both manufacturing and operation, altering local structure and potentially affecting stability. The project developed new diagnostic and analysis tools for studying photovoltaic materials and provided a mechanistic understanding of key performance limitations. These insights support the development of more efficient, stable, and cost-effective CdTe solar technologies for large-scale energy deployment.
Next, a robust and efficient framework for simulating NSPD in multiple dimensions is developed. The reactive Navier-Stokes equations are extended to include a drift-diffusion plasma-fluid model with a local field approximation (LFA) in a finite-volume solver, which uses an adaptive mesh refinement (AMR) strategy to address the wide separation of length scales in the problem. A two-way coupling strategy is used whereby the plasma-fluid model and reactive Navier-Stokes equations are integrated simultaneously. The oxidation of ethylene/air mixtures mediated by NSPD is simulated in a pin-to-pin configuration. All phases of the plasma discharge are simulated explicitly (including streamer ignition, propagation, and connection, as well as the subsequent spark phase), along with the evolution of the plasma during the inter-pulse period. Temporally and spatially-resolved results are presented, with an emphasis on the analysis of heating and energy deposition, as well as of the evolution of the concentration of active particles generated during the NSPD and their influence on ignition.
Explore the source record for details and available documents.
We constrain the emission mechanisms responsible for the prodigious electromagnetic output generated by active galactic nuclei (AGNs) and their host galaxies with a novel state-of-the-art AGN radio-to-X-ray spectral energy distribution model fitting code (ARXSED). ARXSED combines multiple components to fit the spectral energy distributions (SEDs) of AGNs and their host galaxies. Emission components include radio structures such as lobes and jets, infrared emission from the AGN torus, visible-to-X-ray emission from the accretion disk, and radio-to-ultraviolet emission from the host galaxy. Applying ARXSED to the radio SEDs of 20 3CRR quasars at 1 < z < 2 verifies the need for more than a simple power law when compact radio structures are present. The nonthermal emission contributes 91%–57% of the observed-frame 1.25 mm to 850 μm flux, and this component must be accounted for when using these wavelengths to estimate star formation properties. We predict the presence of strong radio-linked X-ray emission in more than half the sample sources. ARXSED estimates median (and the associated first and third quartile ranges) BH mass of ${2.9}_{1.7}^{6.0}\times {10}^{9}\,{M}_{\odot }$ , logarithm of Eddington ratio of $-{1.0}_{-1.2}^{-0.6}$ , and spin of ${0.98}_{0.94}^{0.99}$ for our sample. The inferred AGN torus and accretion disk parameters agree with those estimated from spectroscopic analyses of similar samples in the literature. We present the median intrinsic SED of the luminous radio-loud quasars at 1 < z ≲ 2; this SED represents a significant improvement in the way each component is modeled.
A system and method (referred to as the system) detects malware, viruses, and/or malicious activity by generating a direct current source power consumption profile by causing a monitored device to execute a fully automated recurrent software operation. The system receives by an automated detection system, the direct current source power consumption profile generated by an intelligent power sensor and generates by a detection engine, a power security profile that identifies suspicious code by profiling direct current consumed by monitored type devices. The system executes a detection engine remote from the monitored device that identifies an infected device.
This paper develops a transactive energy system model to restore electricity to customers in an isolated distribution system after an outage. The model engages a variety of customer types -- prosumers, flexible loads, critical/noncritical customers, and distributed generators -- as active participants in the restoration process. Unlike many existing transactive approaches, the proposed model is developed for service restoration and accounts for various customer types and their \textit{autonomy} and \textit{privacy} through an iterative approach to determine the optimal market price, while maintaining system-level power flow and voltage constraints. The advantages of the proposed approach are numerically validated on a modified IEEE 123-bus test system.
This paper develops a transactive energy system model to restore electricity to customers in an isolated distribution system after an outage. The model engages a variety of customer types -- prosumers, flexible loads, critical/noncritical customers, and distributed generators -- as active participants in the restoration process. Unlike many existing transactive approaches, the proposed model is developed for service restoration and accounts for various customer types and their autonomy and privacy through an iterative approach to determine the optimal market price, while maintaining system-level power flow and voltage constraints. The advantages of the proposed approach are numerically validated on a modified IEEE 123-bus test system.
Recently, electrochemical hydrogen peroxide (H 2 O 2 ) generation from oxygen molecules has been extensively studied. Thus far, the best peroxide activity under alkaline conditions has been reported at the surface of a mild reduced graphene oxide annealed at 600 °C (mrGO-600). However, the detailed material information, such as chemical functionality and structural morphology, is unknown, which results in ambiguous debates on its catalytic active sites. Therefore, to solve this problem, we intensively characterize the structure of mrGO-600 to clarify the origin of its catalytic activity. Various characterizations, including X-ray photoelectron spectroscopy, Raman spectroscopy, infrared spectroscopy, near-edge X-ray absorption fine spectroscopy, and high-resolution transmittance electron microscopy coupled with in situ infrared spectroelecrochemistry, reveal that the annealing process generates not only various hole edge defects that are related to the ring ether group but also numerous point defects that result in a small-sized disconnected graphitic carbon region. These defects are believed to form a unique atomic level configuration in mrGO-600, which enables it to facilitate high peroxide-generated activity from oxygen molecules in an alkaline electrolyte.
Not Available
Incorporating socio-demographic and behavioral considerations into decision-support tools is crucial for identifying gaps and addressing consumer needs to ensure reliable and affordable energy solutions. In energy simulation models, the correlation between socio-demographics and time-use behavior is not well-captured. Thus, we developed a large-scale simulation workflow to generate schedules for 10 residential activities across 24 population segments defined by age, income, and employment status. Using pre-pandemic 2015-2019 American Time Use Survey (ATUS) data, we used ANOVA to confirm the correlation between demographic factors and time use. We explored three k-modes clustering methods-backward, forward, and a new hybrid approach-to delineate the occupancy patterns based on demographics. Using the probability of cluster membership for each population segment and a time inhomogeneous Markov chain to generate activity transition probabilities for each cluster, we simulated 50,000 schedules per segment and validated them against the ATUS data. The hybrid method produced the most socio-demographically differentiated clusters while demonstrating comparable performance to other approaches, with an overall root mean square error of 0.12 for both weekday and weekend schedules. Thus, the hybrid method, where each cluster is dominated by certain demographic segments and occupancy patterns, offers more modeling versatility in terms of scenario analysis. The new workflow improves the socio demographic differentiation of energy consumption by considering differences in time use. This approach enables future research on demographically segmented time of use (TOU) energy consumption, including impacts of TOU utility bills and rate analysis, long-run marginal emissions, and energy retrofits.
The formylglycine-generating enzyme (FGE) catalyzes the selective oxidation of a peptidyl-cysteine to form formylglycine, a critical cotranslational modification for type I sulfatase activation and a useful bioconjugation handle. Previous studies have shown that the substrate peptidyl-cysteine binds to the linear bis-thiolate Cu(I) site of FGE to form a trigonal planar tris-thiolate Cu(I) structure that activates O 2 for the oxidation of the C β –H of the cysteine substrate via an unknown mechanism. Here, we employed a combination of stopped-flow kinetic, spectroscopic (UV–vis absorption, XAS, and EPR), and computational (DFT/TD-DFT calculations) methods to observe and characterize the key intermediates in this reaction for FGE from Streptomyces coelicolor. Our results define the reaction coordinate of FGE, which involves H-atom abstraction from the C β –H bond of the cysteine substrate by a reactive Cu(II)–O 2 •– species to form the now experimentally observed Cu(I)–OOH intermediate bound to a peptidyl-thioaldehyde, which proceeds to oxidize one of the protein-derived cysteine residues to a sulfenate that is end-on O-coordinated to Cu(I). These results provide fundamental insights into how the unusual mononuclear Cu(I) site of FGE activates O 2 for cysteine oxidation and stores oxidizing equivalents during catalysis by employing a Cu(I)–sulfenate intermediate with an end-on O-coordination that is unprecedented in biology.
The chemistry and the foundation for a novel 99 Mo/ 99m Tc purification technology for low-specific activity (LSA) 99 Mo is described here in detail. This concept could potentially be engineered to yeild a new 99 Mo/ 99m Tc generator for LSA- 99 Mo.
The grid-forming inverter is regarded as the solution for integrating high levels of renewable resources into future power systems. Ensuring the stable operation of grids necessitates that grid-forming inverters offer fast frequency response. This report introduces an optimal control method that coordinates the embedded storage within the grid-forming control model with conventional synchronous generators. The optimized active power reference for the embedded storage is generated using receding horizon optimization control, aiming to keep the center of inertia frequency within acceptable limits. The effectiveness of the proposed control is verified through testing in the IEEE 39-bus system. In addition, with grid-forming capability, we will also investigate the application of using mobile embedded storages as black-start units to provide cranking power to energize non-blackstart generators in a black-start process.
Heterojunction Si solar cells exhibit notable performance degradation. Here, we modeled this degradation by electronic defects getting generated by thermal activation across energy barriers over time. To analyze the physics of this degradation, we developed the SolDeg platform to simulate the dynamics of electronic defect generation. First, femtosecond molecular dynamics simulations were performed to create a-Si/c-Si stacks, using the machine learning-based Gaussian approximation potential. Second, we created shocked clusters by a cluster blaster method. Third, the shocked clusters were analyzed to identify which of them supported electronic defects. Fourth, the distribution of energy barriers that control the generation of these electronic defects was determined. Fifth, an accelerated Monte Carlo method was developed to simulate the thermally activated time-dependent defect generation across the barriers. Our main conclusions are as follows. (1) The degradation of a-Si/c-Si heterojunction solar cells via defect generation is controlled by a broad distribution of energy barriers. (2) We developed the SolDeg platform to track the microscopic dynamics of defect generation across this wide barrier distribution and determined the time-dependent defect density N(t) from femtoseconds to gigaseconds, over 24 orders of magnitude in time. (3) We have shown that a stretched exponential analytical form can successfully describe the defect generation N(t) over at least 10 orders of magnitude in time. (4) We found that in relative terms, Voc degrades at a rate of 0.2%/year over the first year, slowing with advancing time. (5) We developed the time correspondence curve to calibrate and validate the accelerated testing of solar cells. We found a compellingly simple scaling relationship between accelerated and normal times t normal ∝ t accel T(accel)/T(normal) . (6) We also carried out experimental studies of defect generation in a-Si:H/c-Si stacks. We found a relatively high degradation rate at early times that slowed considerably at longer time scales.
The integrin heterodimer is a transmembrane protein critical for driving cellular process and is a therapeutic target in the treatment of multiple diseases linked to its malfunction. Activation of integrin involves conformational transitions between bent and extended states. Some of the conformations that are intermediate between bent and extended states of the heterodimer have been experimentally characterized, but the full activation pathways remain unresolved both experimentally due to their transient nature and computationally due to the challenges in simulating rare barrier crossing events in these large molecular systems. An understanding of the activation pathways can provide new fundamental understanding of the biophysical processes associated with the dynamic interconversions between bent and extended states and unveil new putative therapeutic targets. In this work, we apply nonlinear manifold learning to coarse-grained molecular dynamics simulations of bent, extended, and two intermediate states of αI I b β3 integrin to learn a low-dimensional embedding of the configurational phase space. We then train deep generative models to learn an inverse mapping between the low-dimensional embedding and high-dimensional molecular space and use these models to interpolate the molecular configurations constituting the activation pathways between the experimentally characterized states. Furthermore, this work furnishes plausible predictions of integrin activation pathways and reports a generic and transferable multi-scale technique to predict transition pathways for biomolecular systems.