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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Neural Phase Simulation

The Neural Phase Simulation (NPS) is a package of codes for simulating microstructure evolution and accelerated molecular dynamics with deep neural-networks based surrogate models. NPS is designed to offer quantitatively accurate and computationally efficient simulation capabilities by leveraging modern machine-learning techniques. The primary intended use cases of NPS are training neural network surrogate models, though performing simulations on a single node is also supported. The NPS surrogate models can be trained from ground truth simulation methods, which are supposed to be accurate but expensive, such as molecular dynamics, phase field methods, kinetic Monte Carlo and discrete dislocation dynamics.

Zhou, Fei↗
DOE OSTI2026

BSMV-mediated genome editing exhibits host-specific heritability: germline transmission in barley and somatic edits in Nicotiana benthamiana

Plant RNA virus–mediated guide RNA (gRNA) delivery represents a transformative advance in genome editing technologies. Unlike conventional transformation methods that rely on labor-intensive tissue culture and regeneration for each individual gRNA delivery, viral vectors can rapidly and systemically transmit gRNAs into pre-established Cas-expressing plants, providing an accelerated route for functional genomics and trait discovery directly in planta . However, key design parameters, including subgenomic promoter choice, transcript architecture, and their effects on viral fitness and editing outcomes, remain to be elucidated for most viral platforms. We developed five Barley stripe mosaic virus (BSMV) vectors, each with distinct subgenomic promoter elements to drive single gRNA expression. These were initially evaluated in Cas9-expressing transgenic Nicotiana benthamiana plants targeting the Phytoene desaturase ( PDS ) gene to compare their editing efficiencies. Single gRNAs expressed under the duplicated γb subgenomic promoter or when fused directly to the γb genome achieved the highest mutation frequencies (up to 90% at 60 days post-inoculation), whereas β1- and β2-driven sgRNAs produced delayed and reduced editing. Thus, promoter selection critically determines gRNA accumulation and the efficacy of BSMV-mediated genome editing. The top-performing design was then applied to Cas9-expressing barley ( Hordeum vulgare ) targeting HvCMF7 (conferring green-white variegation) and HvGW2.1 (impacts grain width and weight). BSMV spread systemically throughout barley, inducing somatic and heritable mutations at frequencies up to 100%, with virus-free edited progeny. In contrast, despite robust somatic editing in N. benthamiana, no heritable mutations were detected indicating species-dependent limitations in germline transmission. Our systematic comparison of subgenomic promoter architectures establishes clear design principles for optimizing viral vector–mediated delivery. Promoter choice and transcript structure critically shape editing efficiency and viral stability. The host-specific boundary for germline editing, defined by efficient heritable editing in barley but not N. benthamiana , highlights where BSMV offers advantages and where alternative vectors or hybrid strategies are required, guiding rational platform selection for diverse crop species and applications. Collectively, these findings establish BSMV as a promising next-generation vector for rapid, tissue culture–free, and transformation-independent genome editing in cereals and other recalcitrant monocots.

barley↗
DOE OSTI2021

Introduction of the PELICAN loop, a Full-Scale Pressure Drop Test Facility

The Versatile Test Reactor (VTR) is a test reactor currently under development by the US Department of Energy. This reactor will rely on fast neutrons enabling novel and wide-ranging experiment to support the development of the various advanced reactor technologies. With the high flux achievable, accelerated testing of fluid and materials will be made possible. To support VTR design efforts [1], an experimental facility has been designed and constructed at Argonne National Laboratory to recreate the hydraulic flow conditions within the VTR’s primary heat transport system (PHTS). This facility, the Pressure drop Experimental Loop for Investigations of Core Assemblies in advanced Nuclear reactors, PELICAN, measures the pressure drop across a full-scale fuel assembly containing prototypic axial reflectors, fuel, and plena components. Here, we first describe the design considerations required to recreate aspects of the VTR. Then we discuss the design and construction of PELICAN to address these design requirements, the design and construction of the test articles placed inside PELICAN’s test section, and finally present some of the first experimental results.

Grannan, A. M.↗
DOE OSTI2026

Raptor: A Python Library for Porosity Predictions in Additive Manufacturing

Raptor is an efficient Python library for simulating stochastic lack-of-fusion (sLoF) defects in additive manufacturing (AM) processes. These defects arise from stochastic variations in the melt pool boundary leading to undermelting from insufficient overlap between adjacent melt pools or successive layers (Grasso & Colosimo, 2017; Khairallah et al., 2016). Performance variability of AM parts is a pressing challenge in qualification and certification of AM parts; this is in part due to the poorly understood formation rate and statistics of sLoF defects. Raptor is designed to capture the explicit morphologies of sLoF defects and their statistics to accelerate qualification and certification efforts of AM parts in critical applications.

Subraveti, Vamsi [Vanderbilt University]↗
DOE OSTI2022

PyEmittance: A General Python Package for Particle Beam Emittance Measurements With Adaptive Quadrupole Scans [Poster]

We present PyEmittance: a new Python package for general particle beam emittance measurements that offers adaptive quadrupole scans and is designed for robust and flexible integration with Python-based software, with online accelerators, and with simulation software. It is open source software, and can be installed via the command pip install pyemittance.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Estimation of Electromechanical Modes of Oscillation in the Eastern Interconnection from Ambient PMU Data

A power system's inter-area electromechanical modes of oscillation determine if the system is small-signal stable. Mode meters are tools that estimate the modes based on ambient synchrophasor measurements, allowing continuous monitoring of the system's stability margin. This paper reports on the application of mode meter algorithms to 21 months of synchrophasor measurements from the United States Eastern Interconnection (EI). Estimates for three modes were updated every minute, providing a much more comprehensive set of results than previous studies based on disturbance recordings. The detailed signal designs and mode meter configurations provided in this paper can be used to accelerate mode meter deployments in the EI. Analysis of the mode estimates reveal for the first time how seasonal load changes and system reconfiguration impact the EI system's dynamics.

Follum, James D.↗
DOE OSTI2021

Binary Complex Neural Network Acceleration on FPGA

Being able to learn from complex data with phase information is imperative for many signal processing applications. Today’s real-valued deep neural networks (DNNs) have shown efficiency in latent information analysis but fall short when applied to the complex domain. Deep complex networks (DCN) , in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision making requirements of many deployable systems dealing with short observations or short signal bursts. Recent, Binarized Complex Neural Network (BCNN), which integrates DCNs with binarized neural networks (BNN), shows great potential in classifying complex data in real-time. In this paper, we propose a structural pruning based accelerator of BCNN, which is able to provide more than 5000 frames/s inference throughput on edge devices. The high performance comes from both the algorithm and hardware sides. On the algorithm side, we conduct structural pruning to the original BCNN models and obtain 20 × pruning rates with negligible accuracy loss; on the hardware side, we propose a novel 2D convolution operation accelerator for the binary complex neural network. Experimental results show that the proposed design works with over 90% utilization and is able to achieve the inference throughput of 5882 frames/s and 4938 frames/s for complex NIN-Net and ResNet-18 using CIFAR-10 dataset and Alveo U280 Board.

Peng, Hongwu↗
DOE OSTI2023

Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning

Modern astronomical experiments are designed to achieve multiple scientific goals, from studies of galaxy evolution to cosmic acceleration. These goals require data of many different classes of night-sky objects, each of which has a particular set of observational needs. These observational needs are typically in strong competition with one another. This poses a challenging multi-objective optimization problem that remains unsolved. The effectiveness of Reinforcement Learning (RL) as a valuable paradigm for training autonomous systems has been well-demonstrated, and it may provide the basis for self-driving telescopes capable of optimizing the scheduling for astronomy campaigns. Simulated datasets containing examples of interactions between a telescope and a discrete set of sky locations on the celestial sphere can be used to train an RL model to sequentially gather data from these several locations to maximize a cumulative reward as a measure of the quality of the data gathered. We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO). We combine multiple improvements on the DQN and adjustments to the dataset, showing that DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward in each state on the test set. This is the first comparison of offline RL algorithms for a particular astronomical challenge and the first open-source framework for performing such a comparison and assessment task.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Process modeling guides operational variables that affect CO 2 utilization during the accelerated carbonation of concrete

Abstract Accelerated concrete carbonation is an expanding option for decarbonizing construction. Factors such as concrete mixture design and carbonation environment can influence the maximum CO 2 utilization that can be achieved during such a process. A carbonation process designed to utilize a water‐saturated dilute CO 2 source wherein 2 < CO 2 concentration (v/v%) < 16, was modeled in AspenPlus©. A regression model was developed to correlate CO 2 uptake, relative humidity (11%–100%), CO 2 concentration ([CO 2 ] = 2—16 v/v%), and temperature ( T = 11–74°C) conditions within a carbonation reactor. It was determined that [CO 2 ] was the most significant variable as higher concentrations enhanced CO 2 transport through the concrete. The energy use intensity per mass of CO 2 utilized (kWh/kgCO 2 ) was determined across a range of processing conditions. As a function of the operational conditions, accelerated carbonation provides a net CO 2 reduction of up to 28 kgCO 2 /tonne of concrete; a reduction of up to ~45% compared to typical formulations.

Prentice, Dale P.↗
DOE OSTI2021

Development of High-efficiency and Cost-effective Forged Ingot Niobium Technology for Science Frontiers and Accelerator Applications

Development of Forged Ingot Niobium Technology: Worlds science frontier programs and SRF accelerator applications demand high performance and cost-effective SRF accelerator technology [1-8]. Fine-grain (FG) and Large-grain (LG) ingot niobium technologies have been very well developed and implemented in all the present-day accelerator projects. However, forged ingot niobium technology which is the focus of this development proposal will be much more cost-effective and expected to have several technical advantages. FG niobium sheet production is very complex involving more than ten processing steps making them prone to contamination. As a result, they are very expensive to produce and require stringent QA procedure to be ready for SRF cavity production. The accelerating cavity process steps are also numerous and require strict procedures in order to achieve high accelerating gradients and quality factors needed for science frontier programs. LG niobium disc production, directly sliced from the ingot, is relatively simple and straight forward to keep surface cleanness. The disc production cost is significantly low compared to FG niobium sheet production. However, there are (some) draw backs due to non-homogeneity of the grain boundary distribution, resulting in non-uniform mechanical properties and complex cavity fabrication, although the LG cavities achieve the expected high-gradient performance goals with lower cost. Medium-grain (MG) niobium disk production may be realized with a new approach/process, the disc directly sliced from the forged ingot, involves a simpler process steps contributing major production cost reduction [9]. These discs are expected to be superior as they tend to be homogenous with uniform sub millimeter grains and mechanical properties. We are eagerly looking forward to developing the forged ingot niobium SRF accelerator technology for the benefit of the world-wide science frontier programs, green energy subcritical nuclear energy systems and a wide variety of industrial applications including the production of radio isotopes and nuclear transmutation applications. Measurement of thermal characteristics of the forged ingot niobium: Measurement of the thermal diffusivity, D, of superconducting MG niobium is important to be understood in comparison with FG sheet and LG disc, as well as that of advanced composite material of Nb3Sn film sputtered on forged ingot Nb, which will be determined using transient pump-probe thermo-modulation [10,11]. Transient thermo-modulation is based on using an ultrafast laser pulse to heat the superconducting materials by a few K, then a synchronized laser pulse probes the reflectance of the heated material. For thin films on a substrate (e.g, 100-nm Nb3Sn on Nb), it takes <100 ps for the heat to reach the substrate by diffusion. Therefore, an ultrafast method is needed to probe D of the studied material. D will be measured from room temperature to liquid helium temperatures. Fundamental Research: The increase of rf loss in SRF cavities is related to trapping of residual magnetic field during the cavity cool-down. The study suggested that the micro structure plays the role in flux trapping sensitivity. The research includes the flux trapping and expulsion study as forged ingot niobium goes through several mechanical deformations, crystallization leading to the optimal SRF cavity performance. Furthermore, the cavity made from FG, MG, and LG sheet/disc will be compared to understand the optimal re-crystallization temperature that cavity needed to be heat treated which minimize the flux trapping and increase SRF cavity performance. References: 1. S. Belomestnykh, Overview of recent SRF developments for ERLs, presented at the SRF 2015, Whistler, BC, Canada (2015). 2. A. Yamamoto, M. Yamanaka and G. Myneni, Ingot Nb based SRF Technology for the International Linear Collider, in Science and Technology of Ingot Niobium for Superconducting Radio Frequency Applications, AIP Conf. Proc. 1687, 030005-1 ? 03005-6, 2015 3. Report of the Workshop on Energy and Environmental Applications of Accelerators, DOE Workshop Report. https://science.osti.gov/-/media/hep/pdf/Reports/2020/CASM_WorkshopReport.pdf? la=en&hash=AEB0B318ED0436B1C5FF4EE0FDD6DEB84C2F15B2 4. G. Ciovati, et al., Design of a cw, low-energy, high-power superconducting linac for environmental applications, Phys. Rev. Accel. Beams 21, 091601 (2018). 5. P. Dhakal, et al., Effect of high temperature heat treatments on the quality factor of a large-grain superconducting radio-frequency niobium cavity, Phys. Rev. ST Accel. Beams 16, 042001 (2013). 6. P. Kneisel et al., Review of ingot niobium as a material for superconducting radio frequency accelerating cavities, Nuclear Instruments and Methods in Physics Research A 774, 133 (2015). 7. G. Ciovati, P. Dhakal, and G. R, Myneni, Superconducting radio-frequency cavities made from medium and low-purity niobium ingots, Supercond. Sci. Technol. 29, 064002 (2016). 8. M. Drury et al., commissioning of the prototype C75 cavities in a CEBAF cryomodule, in Proc. of IPAC 2018, Vancouver, BC, Canada (2018) 9. Feasibility of forged-ingot niobium disc and SRF cavity fabrication technology recently demonstrated in cooperation of ATI, BSCE, and KEK (2020). 10. Hani E. Elsayed-Ali, ?Measurements of heat transport in thin films by ultrafast laser-based techniques,? 3rd International Conference on Thermal Issues in Emerging Technologies Theory and Applications, Cairo, Egypt, pp. 347 ? 350 (2010). DOI: 10.1109/ThETA17616.2010 11. W. M. G. Ibrahim, H. E. Elsayed-Ali, M. Schinn, and C. A. Bonner, Jr., ?Ultrafast investigation of electron dynamics in multilayer metals,? Int. J. Heat and Mass Transfer, 47(10?11), 2261?2268 (2004).

Myneni, G.↗
DOE OSTI2026

Insights from Initial Engineering Designs of Point Source Capture at Industrial Facilities

Initial engineering design studies examining the application of state-of-the-art carbon capture technology at industrial plants contain generally overlooked real-world design considerations for near-term deployment of point source capture (PSC). The implementation of PSC across a wide range of industrial applications presents unique challenges associated with fluctuating CO<sub>2</sub> concentrations, flue gas composition, and utility and land availability. In this article, seven recent industrial retrofit PSC projects are reviewed to investigate the impact of site-specific factors on project design and cost. Across the seven projects, three capture technology classes and four industrial applications are considered, allowing insight into industry-specific opportunities for PSC technology synergy. Common challenges across projects and proposed design solutions are highlighted to propagate ideas and solutions to close the technology gaps and accelerate learning rates.

FEED Studies↗
DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The fast neutron source at UTK: a project with Massimo Salvatores

This article is dedicated to the memory of Massimo Salvatores and offers an overview of the history of the design of the Fast Neutron Source (FNS) flexible experiment system at the University of Tennessee. Our collaboration with Massimo set much of the over-arching design goals and inspired the concept that we have today. The decision to design the FNS as a subcritical assembly rather than a critical one stems back to discussions with Massimo Salvatores and his advocacy for subcritical Accelerator Driven Systems (ADS) which coincides with lower licensing time, costs and greater safety for a university-operated nuclear experimental facility. The second contribution of Massimo to the design concept of the FNS was the direct instruction to design the FNS as an extremely flexible facility. This article provides an update on the current design concept and discusses the future innovations in design optimization of flexible, re-configurable nuclear experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

Abstract This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one‐to‐many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root‐mean‐squared‐error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser‐matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Design Considerations for Fast Charging Lithium Ion Cells for NMC/MCMB Electrode Pairs

Lithium ion cells that can be quickly charged are of critical importance for the continued and accelerated penetration of electric vehicles (EV) into the consumer market. Considering this, the U.S. Department of Energy (DOE) has set a cell recharge time goal of 10–15 min. The following study provides an investigation into the effect of cell design, specifically negative to positive matching ratio (1.2:1 vs. 1.7:1) on fast charging performance. By using specific charging procedures based on negative electrode performance, as opposed to the industrial standard constant current constant voltage procedures, we show that the cells with a higher N:P ratio can be charged to ~16% higher capacity in the ten-minute time frame. Cells with a higher N:P ratio also show similar cycle life performance to those with a conventional N:P ratio, despite the fact that these cells experience a much higher irreversible capacity loss, leading to a lower reversible specific capacity.

25 ENERGY STORAGE↗
DOE OSTI2021

Parametric analysis of anodic degradation mechanisms for fast charging lithium batteries with graphite anode

In this work, we report the impact of the temperature-driven synergistically-coupled anodic degradation mechanisms on the electrochemical performance of lithium batteries with graphite anode over multiple cycles. Temperature dependence of electrochemical reactions and damage mechanisms, such as solid electrolyte interface (SEI) growth, lithium plating/stripping, dead lithium storage/dissolution, and film cracking are incorporated into the degradation model. Results of a parametric analysis are presented, evaluating the effects of charging rates (1–6 C), operating temperatures (-15 – 45°C) and electrode design parameters, on the relative performance fade in the lithium-ion battery. Thermo-electrochemical process maps are developed to provide insights into the relationship between electrode performance and failure mechanisms. The simulation results predict a severe capacity loss due to lithium plating at low temperatures, which is further aggravated at high charging rates. A common strategy for mitigating lithium plating, through charging at high temperatures, also results in rapid capacity loss due to accelerated SEI formation. Simulation results are used to identify the combination of operating conditions and electrode design parameters that improve the electrochemical performance of the battery. These results demonstrate an opportunity to design safe and high-performance lithium-ion batteries, guided by anodic degradation models.

Battery design↗
DOE OSTI2026

Current status of the electron transport line from RCS to ESR: RTE line

The electron injection system of the U.S. Electron-Ion Collider (EIC) is located outside of the RHIC tunnel. Electrons beams accelerated by the Rapid Cycling Synchrotron (RCS) must be transported to the Electron Storage Ring (ESR), which resides within the RHIC tunnel. To accomplish this, a dedicated beam transport line, referred to as RTE (RCS-to-ESR) line is being designed. The proposed conceptual design comprises three main sections; RCS extraction, a vertical bend and dispersion suppression region, and ESR injection matching. The extraction section uses pulsed kickers and septum magnets to achieve a total deflection angle of 3 degrees. To align the injection section with ESR, the beamline must provide a vertical elevation of 1.68 m, and an array of FODO cells is used to suppress the vertical dispersion. The total length of the RTE line is approximately 133 m, and this paper presents the current design status and considerations for this transport line.

Accelerator Physics↗