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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 OSTI2022

Language models for the prediction of SARS-CoV-2 inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Blanchard, Andrew E.↗
DOE OSTI2021

Moving Beyond the Standard of Care: Accelerate Testing of Radiation-Drug Combinations

Radiation therapy is a major treatment modality used in > 60% of cancer patients as definitive local treatment for inoperable locoregionally confined tumors and as palliative therapy. Although cytotoxic chemotherapy enhances the effectiveness of treatment, the benefit over radiation therapy alone is modest. There is a need to enhance the effectiveness of local tumor control over what sequentially or concurrently administered cytotoxic chemotherapy provides. Although many biological pathways are known to enhance the effectiveness of radiation therapy, there is currently a paucity of drugs approved for use in combination. Several clinical trials have tested the effectiveness of combining targeted agents or immunotherapies with radiation therapy, but the results of these trials have been negative, likely stemming from the relative lack of preclinical evidence using appropriate experimental standardization or model systems. Accelerating the identification of agents tested in an appropriate clinical context and experimental systems or models would greatly enhance the potential to bring forward early testing of drugs that would not only be safe but also more effective. This article provides an overview of the opportunities and challenges of developing therapeutics to combine with radiation therapy, and some guidance toward preclinical and early clinical testing to improve the chance that advanced phase testing of drug-radiation combinations would be successful in the long term.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Crack-Free Joining of High-Strength AA7055 Sheets by Friction Based Self-Piercing Riveting with the Aid of Numerical Design

Unique friction-based self-piercing riveting (F-SPR) was employed to join high-strength, low-ductility aluminum alloy 7055 for lightweight vehicle applications. This study aimed to maximize the joint strength of the AA7055 F-SPR joint while avoiding cracking issues due to low ductility at room temperature. A fully coupled Eulerian–Lagrangian (CEL) model was employed to predict the process temperature during F-SPR, and the temperature field was then mapped onto a 2D axisymmetric equivalent model for accelerated numerical analysis. The geometry, dimensions, and material strength of the rivet, as well as the depth of the die cavity and plunging depth, were investigated to enhance joint formation. Also, a static finite-element analysis model was developed to predict and analyze the stress distribution in the rivet under different mechanical testing loading conditions. Overall, the numerical model showed good agreement with the experiment results, such as joint formation and mechanical joint strength. With the aid of virtual fabrication through numerical modeling, the joint design iterations and process development time of F-SPR were greatly reduced regarding the goal of lightweight, high-strength aluminum joining.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Evaluation and prediction of long-term environmental effects of nonmetallic materials

The properties of a number of nonmetallic materials were evaluated experimentally in simulated space environments in order to develop models for accelerated test methods useful for predicting such behavioral changes. Graphite-epoxy composites were exposed to thermal cycling. Adhesive foam tapes were subjected to a vacuum environment. Metal-matrix composites were tested for baseline data. Predictive modeling designed to include strength and aging effects on composites, polymeric films, and metals under such space conditions (including the atomic oxygen environment) is discussed. The Korel 8031-00 high strength adhesive foam tape was shown to be superior to the other two tested.

Papazian, H.↗
NASA NTRS1991

Plasma motion in the Venus ionosphere - Transition to supersonic flow

A remarkable feature of the ionosphere of Venus is the presence of nightward supersonic flows at high altitude near the terminator. In this paper it is shown that the relatively high pressure dayside plasma wells up slowly, and at high altitude it is accelerated horizontally through a relatively constricted region near the terminator toward the low-density nightside. In effect, the plasma flows through a 'nozzle' that is first converging, then diverging, permitting the transition to supersonic flow. The model plasma accelerates to supersonic speeds, reaching sonic speed just behind the terminator. The ion transport rates are sufficient to produce and maintain the nightside ionosphere.

Whitten, R. C.↗
DOE OSTI2022

Single electron in a storage ring: a probe into the fundamental properties of synchrotron radiation and a powerful diagnostic tool

An experimental study of undulator radiation generated by a single electron was carried out at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab. Photons were detected with a single-photon avalanche diode (SPAD) at an average rate of 20 kHz, corresponding to one detection every few hundred revolutions. Events were continuously recorded by a picosecond event timer for as long as 5 minutes at a time. The collected data were used to directly test the predictions of classical Poisson photostatistics. The study was motivated in part by a previous observation of sub-Poissonian statistics in a similar experiment with synchrotron radiation. In addition, we show that the time series of recorded events can be used to study the synchrotron motion of a single electron, yielding dynamical parameters such as the phase jitter of the radiofrequency cavity and the dependence of the synchrotron period on amplitude. Research plans for further experiments are also discussed.

47 OTHER INSTRUMENTATION↗
NASA NTRS2017

Applications of Fracture Mechanics to Quantitative Accelerated Life Testing of Plastic Encapsulated Microelectronics

Accelerated testing must address the failure mechanisms active within the devices undergoing tests in order to assess lifetimes in a meaningful way. The assumption of constant temperature, thermally activated lifetime, based upon the Arrhenius assumptions, does not always provide the necessary understanding to interpret accelerated tests in microelectronics. Plastic encapsulants, dielectric polymers, and underfill materials are subject to delamination and cracking with thermal cycling. Crack propagation during use environment exposure, drives the potential for failure of microelectronic devices and is therefore a necessary focal point in qualification and life testing. This paper reviews the available research in the application of fracture mechanics to this class of problems in microelectronics including relevant test data. In addition, useful acceleration factor models are derived for polymer crack propagation based on principles of linear elastic fracture mechanics. Further, a simple approach to estimating the minimum temperature cycling ranges, necessary to propagate a crack, is also presented. Finally, a methodology of applying acceleration factors to develop testing plans is shown, with an example in spaceflight for a cubesat in low Earth orbit. Overall, this is a paper that shows a useful and appropriate process for creating physics of failure based life testing for delamination and cracking failures in microelectronic polymers in a temperature cycling environment.

Accelerated testing↗
DOE OSTI2020

Automated detection of part quality during two-photon lithography via deep learning

Two-photon lithography (TPL) is an additive manufacturing technique for fabricating three-dimensional objects with nanoscale features. A main challenge of TPL is the routine and labor-intensive task of finding suitable light dosage parameters, i.e. writing speed and laser intensity that induce photo-polymerization within a wide variety of candidate photo-curing polymers. Another challenge is the monitoring required during fabrication. In this work, we apply machine learning (ML) models to accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. We curate TPL videos of different parts fabricated under a range of light dosage parameters using different resins and train spatial-temporal ML models on this data. Our results show that ML models can detect TPL part quality with a 95.1% accuracy in milliseconds. We also evaluate classification failures and identify two operating modes: parameter optimization and part quality detection. Last but not least, we publicly release this labelled dataset so that it may serve as a useful benchmark to the community. Our approach to process optimization and part quality detection addresses important aspects of TPL industrialization, is applicable beyond TPL and should benefit other additive manufacturing techniques with similar barriers to operating at industrial scale.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Prioritization of Existing Reactor Materials

The Advanced Materials and Manufacturing Technologies (AMMT) Program is aiming at the faster incorporation of new materials and manufacturing technologies into complex nuclear-related systems. An integrated approach, combining advanced characterization, high-throughput and accelerated testing, modeling and simulation, including machine learning and artificial intelligence, will be employed. Although 316H (Fe–[16–18]Cr–[10–14]Ni–[2–3]Mo–[0.04–0.1]C) has been identified as a key alloy to be integrated into the AMMT accelerated alloy qualification approach because of its relevance for many current and future nuclear energy reactors, many other alloys could be considered for the advanced fabrication of innovative, high-performance nuclear components. Argonne National Laboratory (ANL), Idaho National Laboratory (INL), Oak Ridge National Laboratory (ORNL), and Pacific Northwest National Laboratory (PNNL) are collaborating on identifying the most promising alloy candidates relevant for the AMMT Program. A selection criteria matrix was established to evaluate the alloys considering their relative importance and technological readiness levels for nuclear energy applications, with a focus on laser powder bed fusion (LPBF). Because of the broad range of potential candidate alloys, ORNL and INL focused on nickel-based alloys, and ANL and PNNL mainly evaluated iron-based alloys. PNNL previously published material scorecards reports on several key alloys, and this report provides a broader overview of iron- and nickel-based candidate alloys, expending beyond alloys well-known to the nuclear community.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Preliminary Feasibility of Printing, Microstructure Analysis and Mechanical Performance of a Down Selected Ni Alloy

This report is submitted as completion of a Milestone 3 deliverable under work package ORNL CT-23OR1304051 in support of the Advanced Materials and Manufacturing Technologies (AMMT) program. The AMMT program is aiming at the faster incorporation of new materials and manufacturing technologies into complex nuclear-related systems. An integrated approach, combining advanced characterization, high-throughput and accelerated testing, modeling and simulation including machine learning and artificial intelligence will be employed. While 316H (Fe-16-18Cr-10-14Ni-2-3Mo-0.04-0.1C) has been identified as a key alloy to be integrated into the AMMT accelerated alloy qualification approach due its relevance for many current and future nuclear energy reactors, many other alloys could be considered for the advanced fabrication of innovate high-performance nuclear components. ANL, INL, ORNL and PNNL are collaborating on identifying the most promising alloy candidates relevant for the AMMT program. A selection criteria matrix was established to evaluate the alloys considering their relative importance and technological readiness levels for nuclear energy applications, with a focus on laser powder bed fusion (LPBF). Due to the broad range of potential candidate alloys, ORNL and INL focused on Ni-based alloys, while ANL and PNNL mainly evaluated Fe-based alloys. PNNL previously published materials scorecards reports on several key alloys and this report is providing a broader overview of Ni-based candidate alloys, expending beyond alloys well-known to the nuclear community. Of particular interest are alloys that are currently commercially available in powder form due to the growing demand from other industries that have invested heavily in additive manufacturing. Among these alloys, Haynes 282 (Fe-20Cr-20Co-8Mo) was selected due to its superior strength at high temperature compared to the code-qualified alloy 617 (Ni-20-24Cr-10-15Co-8-10Mo). To assess the integration of this new alloy into the AMMT digital manufacturing framework, we performed the rapid optimization of alloy 282 printing parameters on a Renishaw 250 machine, the fabrication of sufficient materials for extensive characterization and mechanical testing both at ORNL and INL, and added the printing data into the digital platform via the Peregrine software. A detailed analysis of the LPBF 718 alloy was also conducted, with creep specimens being tested at 600-650°C and characterized by advanced electron microscopy. The alloy superior mechanical strength and great printability associated with the extensive database that has already been generated highlight the promising potential of LPBF 718 as a candidate alloy for the AMMT program. INL, ANL and PNNL have generated similar reports and all the information will be compiled into a final M2 milestone report to provide the AMMT leadership team with clear recommendations on the down selection of reactor materials, as well as establish a roadmap for the qualification of these selected alloys.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Electron acceleration in solar flares and the transition from nonthermal to thermal hard X-ray phases

Observations are reviewed which indicate that hard X-rays during the impulsive phase of a flare typically start with a primarily nonthermal phase which undergoes a transition to a primarily thermal phase as the flare progresses. Recent theoretical work on the modified two-stream instability as an efficient electron accelerator and modeling of thermal hard X-ray sources is considered. A scenario which is termed the dissipative thermal model is proposed to explain the observations. Fast tearing modes occurring in a loop give rise to cross-field ion motion. This in turn excites the modified two-stream instability which converts about 50 percent of the ion energy into accelerated electrons along the loop as long as the plasma beta is less than 0.3. These electrons impact the chromosphere and boil off a part of it which rises up the loop. This density increase coupled with the temperature increase due to tearing causes the beta to increase beyond 0.3 and efficient electron acceleration ceases. This leads to the primarily thermal phase.

Smith, D. F.↗
NASA NTRS1991

Consequences of wave-particle interactions on chaotic acceleration

The recent model of Ashour-Abdalla et al. (1991) has proposed that the earth's plasma sheet can be formed by chaotic acceleration in a magnetotail-like field configuration. The ion velocity distributions created by chaotic acceleration have unstable features and represent robust free energy sources for kinetic plasma waves that can modify the original distributions. In the plasma sheet boundary layer, field-aligned ion beamlets are formed which drive a host of instabilities creating a broadbanded noise spectrum and cause thermal spreading of the beamlets. In addition, there is strong heating of any cold background plasma that may be present. In the central plasma sheet, ion antiloss cone distributions are created which are unstable to very low frequency waves that saturate by filling the antiloss cone.

Schriver, David↗
DOE OSTI2021

Deep reinforcement learning for dynamic control of fuel injection timing in multi-pulse compression ignition engines

Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Preliminary Plan to Inform Testing of a Heat Exchanger Test Article

This report presents a preliminary plan to guide the qualification testing of advanced heat exchanger (HX) components for nuclear-to-industrial heat transfer applications. The objective is to establish a defensible, physics-based methodology that integrates computational modeling, targeted experimentation, and in-service inspection considerations to demonstrate component performance and reliability under representative reactor conditions. The analysis identifies Sodium-cooled Fast Reactor (SFR) and High-Temperature Gas-cooled Reactor (HTGR) systems as reference configurations in terms of temperature, pressure, and chemical environment. Within these operating envelopes, dominant degradation mechanisms— including creep–fatigue interaction, flow-induced vibration, corrosion, and diffusion-bond deterioration—were evaluated to define test requirements. A comprehensive computationalexperimental framework is proposed to support life prediction and qualification activities. The framework couples high-fidelity structural-mechanics, thermal-hydraulic, and fluid-structure interaction models with accelerated degradation testing to produce a traceable linkage between microstructural evolution, mechanical performance, and remaining useful life (RUL). The approach adheres to established Verification, Validation, and Uncertainty Quantification (VVUQ) standards (ASME V&V 10/20; NUREG-2152) and incorporates a digital-twin architecture for continuous model refinement through data assimilation. The plan further outlines testing methodologies, including pre-test analyses, test-loop design parameters, and sensor placement strategies that maximize information yield while maintaining mechanistic fidelity. Complementary sections describe in-service inspection (ISI), on-line monitoring (OLM), and structural-health-monitoring (SHM) techniques applicable to compact HX geometries typical of advanced reactors. Collectively, these activities establish the technical foundation for demonstrating 40-60-year equivalent service life of advanced heat exchangers in support of the U.S. Department of Energy’s Advanced Reactor and Integrated Energy Systems programs. The forthcoming phase will execute the defined pre-test analyses, initiate hardware fabrication, and implement the integrated testing campaign to validate the proposed qualification methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A buoyancy–shear–drag–scalar-based turbulence model for power-law acceleration-driven Rayleigh–Taylor, reshocked Richtmyer–Meshkov, and Kelvin–Helmholtz mixing

A previously developed phenomenological turbulence model for Rayleigh–Taylor, reshocked Richtmyer–Meshkov, and Kelvin–Helmholtz instability-induced mixing based on a general buoyancy–shear–drag model [O. Schilling, “A buoyancy–shear–drag-based turbulence model for Rayleigh–Taylor, reshocked Richtmyer–Meshkov, and Kelvin–Helmholtz mixing,” Physica D 402, 132238 (2020)] is extended to include active or passive scalar mixing and power-law acceleration-driven Rayleigh–Taylor mixing. The buoyancy–shear–drag equations are coupled to a scalar variance equation that is used to define the molecular mixing parameter θ m , and when the scalar is active, modifies the Rayleigh–Taylor and Kelvin–Helmholtz mixing layer growth parameters to depend on the asymptotic value of this parameter, θ mol . Here, the scalar variance equation is closed by algebraically or differentially modeling the scalar variance dissipation rate. Nonlinear analytical solutions of the model are obtained in the total and separate bubble and spike mixing layer width formulations with the algebraic scalar variance dissipation rate for each instability, which are then used to calibrate the mechanical and scalar equation coefficients to predict specific values of physical observables and molecular mixing parameters. Surrogate mechanical and scalar turbulent fields can be constructed by multiplying a presumed self-similar spatial profile by appropriate functions of the width and its time derivative, and of the scalar obtained by solving the ordinary differential model equations. The explicit modeling and solution of turbulent transport equations are not required. The bubble and spike mixing layer width and scalar variance equations are then solved numerically for constant-acceleration Rayleigh–Taylor, impulsively reshocked Richtmyer–Meshkov, and Kelvin–Helmholtz mixing, confirming that the prescribed level of molecular mixing is correctly predicted and illustrating the spatiotemporal evolution of the scalar fields.

Buoyancy–drag↗
DOE OSTI2021

Rock Valley Accelerated Weight Drop Preliminary P-wave Tomographic Model

An active source experiment using an accelerated weight drop was conducted in Rock Valley, Nevada National Security Site, during the spring of 2021 in order to characterize the shallow seismic structure of the region. P-wave first arrival travel times picked from this experiment were used to construct a preliminary 3-D compressional wave speed model over an area that is roughly 4 km wide east-west and 8 km north-south to a depth of about 500-600 m below the surface, but with primary data concentration along the transects of the experimental lines. The preliminary model shows good correlation with basic geology and surface features, but geological interpretation is not the focus of this report. We describe the methods used in the tomographic inversion of the data and show results from this preliminary P-wave model.

42 ENGINEERING↗
DOE OSTI2020

Accelerating nuclear fuel development and qualification: Modeling and simulation integrated with separate-effects testing

In this work, an approach to transform and accelerate nuclear fuel development and qualification is outlined. The approach exploits advanced modeling and simulation at the outset to inform constituent and system selection and to enable integral fuel performance analyses. Analyses using these tools identify and prioritize the most important fuel performance parameters and phenomena for subsequent targeted characterization with separate-effects tests. Separate-effects testing spans out-of-pile and in-pile tests and is meant to iterate with and inform engineering-scale integral fuel performance analyses throughout the development process. Exercising this cycle in an agile fashion will increase confidence in the integral fuel performance predictions while reducing uncertainties. This process sets the stage for executing a much more limited set of well-defined integral irradiation tests designed to validate engineering-scale fuel performance codes and to confirm the performance and safety of the fuel system under prototypic conditions. This approach will reduce the time for development and qualification of a new fuel system, and it will also reduce associated costs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

Abstract Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science.

36 MATERIALS SCIENCE↗