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At least 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Multimodal Instrument Concepts for Chemical Sciences at Second Target Station

The development of advanced chemical and energy systems is inherently complex, relying heavily on their unique pathways. Because no two catalysts or batteries follow exactly the same kinetic trajectories, conventional sequential measurements often obscure crucial relationships between structure and dynamics through statistical averaging. This report introduces two neutron scattering instrument concepts for the Second Target Station (STS) specifically designed to overcome this barrier: the Extended Q-range Diffractometer (EQD) and the Multimodal Chemical Spectrometer (MCS).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Atomic Manipulation of Single Erbium on GaAs(110): From Adatom Motion to Creation and Control of Bistable Substitutions

We demonstrate atomic-scale control over the position and configuration of single erbium (Er) atoms at the GaAs(110) surface using scanning tunneling microscopy (STM). By applying voltage pulses from the STM tip, we drive Er adatoms to move sequentially among three distinct adsorption sites and controllably substitute into Ga lattice sites. The resulting substitutional Er atoms exhibit two isoelectronic configurations that differ by less than one angstrom in lateral position and can be reversibly switched by additional voltage pulses. Differential conductance spectroscopy shows differences in the unoccupied local electronic states for the two substitutional configurations, and an absence of mid-gap states. Preliminary density functional theory (DFT) calculations support these findings, identifying the most stable adsorption and substitutional sites, their relative binding energies, charge states, and local density of states (LDOS) characteristics. Together, the experimental and theoretical results establish a method for precise, reversible manipulation of rare-earth dopants at semiconductor surfaces, opening a pathway to engineer atomic-scale quantum and optoelectronic functionalities.

Poche, Steven [Ohio State University]↗
DOE OSTI2025

CRN Modeling of Ammonia RQL Combustion using a Partially-Stirred Reactor Approach

Ammonia is a promising alternative to hydrogen with high energy density and favorable storage and transport characteristics. However low flammability and a propensity for high nitrogen oxide (NOx) emissions make direct utilization challenging. Recently, two-stage rich-quench-lean (RQL) combustion strategies have shown promise in achieving low NOx emissions with ammonia. In this approach, the rich stage serves to oxidize a portion of the fuel, while thermally decomposing as much of the remaining ammonia as possible, generating hydrogen. In the second (lean) stage, air is rapidly introduced, burning out the hydrogen and residual ammonia. Two-stage RQL combustion of ammonia has been investigated in the open literature both experimentally and numerically. In general, idealized chemical reactor network (CRN) models predict NOx concentrations below that of 2D/3D computational fluid dynamics models and experiments. The primary drivers of these discrepancies may be largely attributed to finite rate mixing non-adiabatic operation. The typical CRN model is comprised of a perfectly-stirred-reactor (PSR), followed by a plug-flow-reactor (PFR), meant to represent the flame, and post-flame zones, respectively. In the two-stage RQL approach two PSR-PFR networks are arranged sequentially, corresponding to the rich and lean stages, with secondary air injection in between. In the authors’ past work, this arrangement has demonstrated the significant sensitivity of exit NOx to the rich stage equivalence ratio, while the amount of secondary air injection was shown to be less critical. In this paper, the CRN model is extended to (1) include the impacts of heat loss and (2) utilize a partially-stirred-reactor (PaSR) approach to study the impacts of mixing on emissions performance. Varying amounts of heat loss are applied to the rich relaxation zone to understand emissions performance and changes to optimization of equivalence ratio and residence time. Premixed and non-premixed configurations are considered in the rich stage PaSR, with varying degrees of mixing intensity to study the interaction between mixing, transport, and kinetic timescales. Critically, the impact of mixing between hot products and secondary air injection is studied to understand practical injector needs. Results show unburnt ammonia leaving the rich stage as a primary contributor to NOx emissions – driven both by increased heat loss and reduced mixing rates. Furthermore, heat losses have shown to create conditions which are conducive to increased N2O formation in the lean stage. The results of this study will be considered in the context of developing optimized two-stage RQL combustors for ammonia..

advanced gas turbines↗
DOE OSTI2026

Assessment of BPM options for the EIC Beam Accumulator Ring

The electron injection system for the Electron-Ion Collider (EIC) at BNL is designed to provide a beam of polarized electrons, which is crucial for studying the structure of protons and atomic nuclei. The Beam Accumulator Ring (BAR) is a part of the injection chain between the 750 MeV linear accelerator and the Rapid Cycling Synchrotron (RCS), which accelerates the beam up to full energy (5–18 GeV). The functional role of the BAR is to accumulate the charge injected from the linear accelerator in order to achieve the high intensity of the polarized electron beam required by the specifications for injection into the RCS. This objective will be realized through the sequential injection of bunches with a charge of 1.1 nC at a repetition rate of 30 Hz. Once the charge of a single bunch reaches 28 nC, the beam will be extracted from the BAR and injected into the RCS. The beam instrumentation needs to provide reliable measurements with the required accuracy over the dynamic range from 0.1 nC (one tenth of a typical injected bunch charge) to 32 nC – the maximum expected accumulated current. There are 10 Beam Position Monitors (BPMs) in the ring, 1 extra button-electrode assembly for the RF system, and 8 BPMs in the beam transport lines. The BPM locations are shown in Fig. 1, marked by blue rectangles. The ring BPMs will be capable of both average orbit and turn-by-turn measurement modes. Accurate measurement of the beam position with a large horizontal offset requires polynomial correction of the BPM nonlinearity.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2026

Thermal Reservoir Networks for Modularly Expandable Thermal Microgrids

The Department of Defense (DoD) faces the substantial challenge of cost-effectively retrofitting one to two installations per month, each comprising approximately 1,000 buildings, to improve resilience, reduce energy consumption, and enhance energy supply security. Achieving these objectives requires optimal system selection and effective risk mitigation during system integration. To address this need, we introduce Platform-Based Design (PBD), a structured, hierarchical methodology adapted from other industrial sectors to the domain of energy system retrofits. We demonstrate the effectiveness of PBD through a techno-economic feasibility study comparing geothermal-coupled thermal energy networks (TENs) with conventional energy systems for heating, cooling, and powering 17 buildings at Joint Base Andrews (JBA) in Maryland. Our analysis illustrates that the PBD approach enables rigorous, data-driven, sequential decision making, resulting in a family of Pareto-optimal systems, among which the TEN emerged as the most promising solution. The selected TEN design integrates geothermal borefields, heat recovery heat pumps, photovoltaic (PV) arrays, and battery storage. Compared to the baseline system – gas heating combined with air-source chillers – the proposed TEN reduces annual imported energy by 74% and peak electricity demand by 45%, achieves a levelized cost of energy of $\$0.210$/kWh, and substantially enhances resilience. Life-cycle costs increase by approximately 6%, and initial investment costs are about 2.5 times higher than the baseline. However, if central plant infrastructure, district loops, and utility-scale PV and battery systems are privately funded and operated, the initial investment would fall below the baseline system cost. Critical to achieving these significant performance improvements were detailed nonlinear dynamic simulations coupling geothermal heat transfer, energy system operation, and realistic feedback control logic. These simulations identified essential design modifications and control strategy refinements that substantially reduced energy use, peak demand, and compressor shortcycling, thereby improving durability and reliability—issues that would have been significantly more expensive to resolve during operation. Additionally, the verification step highlighted sensitivities to key design parameters that could reduce initial investment by approximately $\$2$ million and reduce annual life-cycle costs more than $\$300,000$. We recommend adopting the PBD methodology for future feasibility studies and TEN pilot projects to gain valuable operational experience. Furthermore, we recommend that DoD invest in transferring and scaling the PBD methodology to other installations. This entails developing standardized computational frameworks and component libraries as well as training industry in conducting PBD. Such investments would enable rapid, robust, reliable, and cost-effective retrofits, supporting DoD’s ambitious energy system modernization goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses

Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that near term algorithms can be performed with proximate accuracy (like chemical accuracy for quantum chemistry problems). While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development. Demonstrations of these algorithms over the past few years, coupled with the idea that imperfect algorithm performance can be caused by several dominant noise sources in the quantum processor, which can be measured and calibrated during algorithm execution or in post-processing, has led to the use of noise mitigation to improve typical computational results. Conversely, benchmark algorithms coupled with noise mitigation can help diagnose the nature of the noise, whether systematic or purely random. Here, we outline the use of coherent noise mitigation techniques as a characterization tool in trapped-ion testbeds. We perform model-fitting of the noisy data to determine the noise source based on realistic physics focused noise models and demonstrate that systematic noise amplification coupled with error mitigation schemes provides useful data for noise model deduction. Further, in order to connect lower level noise model details with application specific performance of near term algorithms, we experimentally construct the loss landscape of a variational algorithm under various injected noise sources coupled with error mitigation techniques. This type of connection enables application-aware hardware codesign, in which the most important noise sources in specific applications, like quantum chemistry, become foci of improvement in subsequent hardware generations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Partitioned Quantum Subspace Expansion

We present an iterative generalisation of the quantum subspace expansion algorithm used with a Krylov basis. The iterative construction connects a sequence of subspaces via their lowest energy states. Diagonalising a Hamiltonian in a given Krylov subspace requires the same quantum resources in both the single step and sequential cases. We propose a variance-based criterion for determining a good iterative sequence and provide numerical evidence that these good sequences display improved numerical stability over a single step in the presence of finite sampling noise. Implementing the generalisation requires additional classical processing with a polynomial overhead in the subspace dimension. By exchanging quantum circuit depth for additional measurements the quantum subspace expansion algorithm appears to be an approach suited to near term or early error-corrected quantum hardware. Our work suggests that the numerical instability limiting the accuracy of this approach can be substantially alleviated in a parameter-free way.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Efficient Distributed Sequence Parallelism for Transformer-Based Image Segmentation

We introduce an efficient distributed sequence parallel approach for training transformer-based deep learning image segmentation models. The neural network models are comprised of a combination of a Vision Transformer encoder with a convolutional decoder to provide image segmentation mappings. The utility of the distributed sequence parallel approach is especially useful in cases where the tokenized embedding representation of image data are too large to fit into standard computing hardware memory. To demonstrate the performance and characteristics of our models trained in sequence parallel fashion compared to standard models, we evaluate our approach using a 3D MRI brain tumor segmentation dataset. We show that training with a sequence parallel approach can match standard sequential model training in terms of convergence. Furthermore, we show that our sequence parallel approach has the capability to support training of models that would not be possible on standard computing resources.

Lyngaas, Isaac↗
DOE OSTI2022

Real-Time On-Ramp Merging Control of Connected and Automated Vehicles using Pseudospectral Convex Optimization

Highway on-ramp merging can be a challenging task for human drivers due to the complex vehicle negotiations and interactions in limited time and space. Connected and automated vehicles (CAVs) have great potential to address the problem and offer many benefits in terms of safety, traffic efficiency, and fuel economy. However, real-time optimal control of CAVs still faces many challenges, including nonlinear dynamics, complex inter-vehicle interactions, and a highly dynamic and uncertain traffic environment. To address these challenges, we develop a novel control approach that balances the solution optimality and computational efficiency to determine optimal merging speed profiles in real time. Specifically, by employing a pseudospectral method and a sequential convex programming approach, two algorithms are proposed and implemented within the model predictive control (MPC) framework to enable real-time generation of optimal solutions for potential on-vehicle applications. The convergence and optimality of the proposed algorithms are validated by comparing with a general-purpose solver under different traffic scenarios.

Shi, Yang↗
DOE OSTI2021

Applying Time-Parallelization to Turbulent Flows

Parallelization of the temporal domain is explored for the solution of turbulent flows. Multigrid reduction-in-time (MGRIT) is used to advance the large-scale fluid dynamics in time sequentially on the coarsest space-time grid but propagate the information in time parallel on all other levels. The goal of this process is to accurately and efficiently resolve the coarse-scale turbulence structure and use that to drive the fine-scales of the turbulent flow. The extra forcing from nonlinear multigrid facilitates the coupling and interaction between fine and coarse scales, through which the multiscale nonlinear physics is properly captured. Adaptive mesh refinement is employed to finely resolve only the regions with strong gradients, which provides further computational efficiency. The underlying computational fluid dynamics solver is a fourth-order finite-volume scheme with the standard 4-stage Runge-Kutta method. An advanced approach is devised and implemented to enable MGRIT to solve highly turbulent flows successfully. Furthermore, the method is applied to solve a Taylor-Green vortex problem and a doubleshear-layer turbulent mixing flow. Results are promising, validating that MGRIT with the filtering approach has the potential to efficiently solve general turbulent flows.

Computational Fluid Dynamics↗
DOE OSTI2020

Supplementary material for manuscript: Microcomputed X-ray Tomographic Imaging and Image Processing for Microstructural Characterization of Explosives

This data contains the supplemental information that will be accessible to the public from the paper “Microcomputed X-ray Tomographic Imaging and Image Processing for Microstructural Characterization of Explosives”. The data set contains the reconstructed slices for the 3D images of three different high explosives including: HMX-HTPB, PBX 9501 and PBX 9502. The data sets are folders of reconstructed tiffs showing the microstructure (crystals, binder, voids) and a segmented data set of each. Finally, a .gif movie is also present that plays the slices sequentially. The folders contain the voxel size information. See the manuscript for more details.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Gulf of Mexico blue hole harbors high levels of novel microbial lineages

Exploration of oxygen-depleted marine environments has consistently revealed novel microbial taxa and metabolic capabilities that expand our understanding of microbial evolution and ecology. Marine blue holes are shallow karst formations characterized by low oxygen and high organic matter content. They are logistically challenging to sample, and thus our understanding of their biogeochemistry and microbial ecology is limited. We present a metagenomic and geochemical characterization of Amberjack Hole on the Florida continental shelf (Gulf of Mexico). Dissolved oxygen became depleted at the hole’s rim (32 m water depth), remained low but detectable in an intermediate hypoxic zone (40–75 m), and then increased to a secondary peak before falling below detection in the bottom layer (80–110 m), concomitant with increases in nutrients, dissolved iron, and a series of sequentially more reduced sulfur species. Microbial communities in the bottom layer contained heretofore undocumented levels of the recently discovered phylum Woesearchaeota (up to 58% of the community), along with lineages in the bacterial Candidate Phyla Radiation (CPR). Thirty-one high-quality metagenome-assembled genomes (MAGs) showed extensive biochemical capabilities for sulfur and nitrogen cycling, as well as for resisting and respiring arsenic. One uncharacterized gene associated with a CPR lineage differentiated hypoxic from anoxic zone communities. Overall, microbial communities and geochemical profiles were stable across two sampling dates in the spring and fall of 2019. The blue hole habitat is a natural marine laboratory that provides opportunities for sampling taxa with under-characterized but potentially important roles in redox-stratified microbial processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Early Inference of Nuclear Technology-Directed Research Activities of Authors from Scientific Publications

Nuclear research articles can provide information about early nuclear proliferation indicators such as influential research entities and technology capability levels of a country, but detection of nuclear activities typically occurs after they have started. We investigate the extent to which nuclear research articles can be used to infer whether a research entity will acquire or develop a nuclear technology before it happens. Early detection of nuclear proliferation or technology development indicators from data is challenging due to partial observability, sparse and unlabeled information, and confounding signals from multiple concurrent activities. This paper presents the early detection problem as a sequential decision-making, goal inference problem, where the objective is to characterize and predict an individual’s, organization’s, or a country’s intent (unobserved goal-directed behavior) towards developing a nuclear capability from partially observed sequences of their research publications, using inverse reinforcement learning and Bayesian goal inference methods. A computational framework is presented, and its application demonstrated using 29,196 Scopus records for a case study related to a civil nuclear capability. The case study results serve as a proof-of-concept demonstration for inference of technology-directed research activity of authors who publish in the nuclear domain. The inference method, combined with advanced computing, may be used to assess and monitor activities pertaining to early developmental stages of a nuclear technology or capability, which in turn can help to identify and prioritize activities with nuclear proliferation potential for further investigation.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Coincidence Anticipation Timing Responses with Head Tracking and Eye Tracking

Head tracking movements are common in interceptive tasks. The benefits of these movements are unclear. The purpose of this study was to compare coincidence anticipation timing (CAT) responses for a simulated approaching object when the eyes were used in tracking the object and when the head was used in tracking the object.A total of 29 subjects participated. A Bassin Anticipation Timer consisting of a track of sequentially illuminated lights was used to simulate an approaching object at velocities of 223 cm · s -1 to 894 cm · s -1 . Each velocity was used 10 times under 2 conditions. In one condition, subjects were told to turn the eyes with the stimulus. In the other condition, subjects viewed the stimulus through apertures and were told to turn the head with the stimulus. Subjects pushed a button to coincide with illumination of the final light on the track. Signed CAT errors, unsigned CAT errors, and variable CAT errors were compared between the head movement (HM) and eye movement (EM) conditions. No significant differences were noted for the signed errors (mean signed error at 894 cm · s -1 ; 10.3 ± 75.4 ms (HM), -16.1 ± 51.0 ms (EM). However, the unsigned and variable errors were significantly larger at some stimulus velocities in the head movement condition [mean unsigned error at 894 cm · s -1 : 82.6.0 ± 45.9 ms (HM), 59.0 ± 22.4 ms (EM); mean variable error at 894 cm · s -1 ; 78.0 ± 37.8 ms (HM), 49.2 ± 17.1ms (EM)]. DISCUSSION: Head movement did not result in improved CAT performance compared to eye movements. Further work will be required to determine whether these results are generalizable to situations where head tracking is required but apertures are not worn.

Biophysics↗
DOE OSTI2024

Submerged Fatigue Testing of Marine Energy Advanced Materials

Marine energy structures are typically made of advanced composite materials and are subjected to extreme ocean environments in service. In extreme ocean environments, seawater currents and waves load structures repeatedly, which cause two environmental conditions: water intrusion and mechanical fatigue. In prior research, the two environmental conditions were applied sequentially, where composite specimens were aged and then mechanically tested. To understand the combined effects of dynamic loading and water intrusion on composite materials, the present study involves the static and fatigue four-point bend testing of composite coupons in a water tank. The water tank was designed and built to fit either a 100 kN or a 250 kN load frame. Flexural strength value, cycles to failure, and failure mode results from submerged fatigue testing will be used to inform marine energy structure designs. The coupon-scale test method will be used to scale up to and inform methods for subsequent subcomponent testing and standards development. The benefits of designing marine energy structures to informed standards are decreased lifetime costs and increased reliability and energy production, ultimately leading to a sustainable and low-carbon energy system.

composite materials↗
DOE OSTI2024

Submerged Fatigue Testing of Marine Energy Advanced Materials

Marine energy structures are typically made of advanced composite materials and are subjected to extreme ocean environments in service. In extreme ocean environments, seawater currents and waves load structures repeatedly, which cause two environmental conditions: water intrusion and mechanical fatigue. In prior research, the two environmental conditions were applied sequentially, where composite specimens were aged and then mechanically tested. To understand the combined effects of dynamic loading and water intrusion on composite materials, the present study involves the static and fatigue four-point bend testing of composite coupons in a water tank. The water tank was designed and built to fit either a 100 kN or a 250 kN load frame. Flexural strength value, cycles to failure, and failure mode results from submerged fatigue testing will be used to inform marine energy structure designs. The coupon-scale test method will be used to scale up to and inform methods for subsequent subcomponent testing and standards development. Finally, the benefits of designing marine energy structures to informed standards are decreased lifetime costs and increased reliability and energy production, ultimately leading to a sustainable and low-carbon energy system.

13 HYDRO ENERGY↗