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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

System and method for alfvenic reconnecting plasmoid plasma acceleration

According to various embodiments, a system for using magnetic reconnection to accelerate plasma is disclosed. The system includes a pair of electrodes including two concentric rings separated by an electrode gap and held at different electrostatic potential by applying a voltage to generate an inter-electrode electric field. The system further includes a plurality of magnetic coils configured to produce magnetic field lines that connect the pair of electrodes. The system additionally includes a gas injector configured to inject gas into the electrode gap, the injected gas being partially ionized by the inter-electrode electric field to generate a poloidal current that flows along open magnetic field lines across the electrode gap. A total Lorentz force causes oppositely directed magnetic field lines to be expanded around a region of the gas injector to further create an azimuthal current in the form of an axially elongated current sheet that is unstable such that the axially elongated current sheet reconnects and breaks into plasmoids.

Ebrahimi, Fatima Arezu↗
DOE OSTI2021

Scalable Approaches to Selecting Key Entities in Large Networked Infrastructure Systems

This work aims at bringing advances in discrete optimization algorithms to solving practical engineering problems at scale. Often times, in many engineering design problems, there is a need to select a small set of influential or representative elements from a large ground set of entities in an optimal fashion. Submodular optimization provides for a formal way to solve such problems. Common examples with infrastructure systems involve sensor placement and identification of key entities with certain objectives. However, scaling these approaches to large infrastructure systems can be challenging because of the high computational complexity of the overall framework that include the optimization algorithms as well as high-complexity compute-oracles that provide the necessary objective function values. In this work, we explore a well-studied and widely-applicable paradigm, namely leader-selection in a multi-agent networked setting in the context of scalable methodologies. We demonstrate novel frameworks that utilize variations of accelerated submodular optimization algorithms along with linear-algebraic methods that can help accelerate the oracle computations. We further explore this combination in conjunction with graph partitioning paradigms to take advantage of the accelerated algorithms in a distributed setting. Finally we demonstrate the key findings on a practical problem in an operational setting. For this, we leverage an example road network with approximately 18k nodes and 27k edges in a traffic control application, where we seek a limited number of k=200 key intersections. This problem can be solved in a serial setting in just under 5 hours providing more than 2 orders of magnitude speed-up over methods that do not consider acceleration techniques.

Visweswara Sathanur, Arun↗
DOE OSTI2022

Examination of Ac-225 production from Ra-226 using fast reactor JOYO

In this study, the authors investigated the method of producing the radionuclide Ac{sup 225} used for targeted alpha therapy (TAT). Currently, Ac{sup 225} is mainly generated from ORNL's Th{sup 229} generator, and the annual production amount is limited to about 63 GBq, and methods for generating it using accelerators are under development in each country. The method using an accelerator has the advantage of being able to generate Ac{sup 225} from a small amount of target nuclides with high efficiency but has the disadvantage of not being able to irradiate a large amount of target nuclides at once due to the small irradiation area. Therefore, the authors investigated a method to generate Ac{sup 225} by neutron irradiation of Ra{sup 226} as a target nuclide using the experimental fast reactor JOYO, which has abundant neutrons and a large loading region. Irradiation of Ra{sup 226} with fast neutrons causes a (n, 2n) reaction to generate Ra{sup 225}, and then decay to produce Ac{sup 225}. In addition, although harmful Ac{sup 227} is also produced at the same time by the (n,γ) reaction, first of all the actinium isotope is chemically separated and eliminated. Since the remaining Ra{sup 225} collapses and Ac{sup 225} is produced, pure Ac{sup 225} can be extracted by performing chemical separation again. As a result of the analysis, 1 g of Ra{sup 226} is irradiated with JOYO for 60 days, and milking is performed 4 times every 17.5 days. By doing these three times a year, it was found that about 50 GBq of Ac{sup 225} was generated. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Workshop Report for Next-Gen AI for Proliferation Detection: Accelerating the Development and Use of Explainability Methods to Design AI Systems Suitable for Nonproliferation Mission Applications

Artificial intelligence (AI) promises powerful new capabilities in an expansive array of applications. One area is proliferation detection, where AI can provide transformative tools to achieve objectives currently inaccessible using conventional methods. In particular, AI affords the opportunity to use new indicators and process massive amounts of heterogeneous data that can increase sensitivity to proliferant activities and push proliferation detection to the earliest possible stages. However, existing AI tools are not ready to achieve such potential, falling short in regard to some important considerations involved in proliferation detection. One crucial aspect that requires improvement is the explainability of the most powerful AI algorithms (i.e., understanding how the algorithms actually arrive at their conclusions)-the lack of which prevents widespread adoption in national security missions. The Data Science and AI portfolio within the National Nuclear Security Administration's Office of Defense Nuclear Nonproliferation Research and Development is driving the development of next-generation AI for proliferation detection through the expertise and work of the national laboratories and partners in academia. The "Next-Gen AI for Proliferation Detection: Accelerating the Development and Use of Explainability Methods to Design AI Systems Suitable for Nonproliferation Mission Applications" workshop aimed to further this objective. The meeting was held virtually on Sept. 15-16, 2020 and included more than 170 participants, primarily from the national laboratories with additional contributions from university researchers and mission partners. Each day included a keynote presentation, four technical presentations about cutting-edge research in explainability, and a panel to explore considerations in applying explainability and developing AI systems that can accelerate the transition of emerging AI technologies among partners and end users to solve critical mission questions. This report summarizes the Next-Gen AI for Proliferation Detection workshop's content and findings.

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

Linear stability analysis via simulated annealing and accelerated relaxation

Simulated annealing (SA) is a kind of relaxation method for finding equilibria of Hamiltonian systems. A set of evolution equations is solved with SA, which is derived from the original Hamiltonian system so that the energy of the system changes monotonically while preserving Casimir invariants inherent to noncanonical Hamiltonian systems. The energy extremum reached by SA is an equilibrium. Since SA searches for an energy extremum, it can also be used for stability analysis when initiated from a state where a perturbation is added to an equilibrium. The procedure of the stability analysis is explained, and some examples are shown. Because the time evolution is computationally time consuming, efficient relaxation is necessary for SA to be practically useful. An acceleration method is developed by introducing time dependence in the symmetric kernel used in the double bracket, which is part of the SA formulation described here. An explicit formulation for low-beta reduced magnetohydrodynamics (MHD) in cylindrical geometry is presented. In conclusion, since SA for low-beta reduced MHD has two advection fields that relax, it is important to balance the orders of magnitude of these advection fields.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Accelerated Materials Design for Molten Salt Technologies Using Innovative High-Throughput Methods

The focus of the project is on building an innovative accelerated materials design platform for molten salt technologies using novel high-throughput methods coupled to data analytics. The main objectives is to predict a FeCrMnNi alloy compositional space with better corrosion resistance than stainless steel 316 and identify new molten salt corrosion mechanisms. The project demonstrates the feasibility to use high-throughput methods coupled to data analytics to accelerate alloy design for extreme environments applications. Using a trained and tested machine learning (ML) model, 2000 FeCrMnNi alloy corrosion rate in molten chloride salts were predicted and a compositional field with corrosion rate lower than 316 stainless steel was identified. The ML model interpretability unveiled multiple features of importance in the model prediction. Some features were expected to be of relative significance, such as work function, surface energy and alloy electronegativity, and the ML model interpretability analysis confirmed those. On the other hand, the most important feature is the diffusion coefficient of Ni in the bulk alloy which indicates that a surface diffusion mechanism plays an important role in the overall corrosion mechanism in molten salts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Updated ASME design correlations and qualification plan for powder bed fusion 316H stainless steel

This report provides an update on the Advanced Materials and Manufacturing Technologies (AMMT) program effort to qualify Laser-Powder Bed Fusion (L-PBF) 316H stainless steel for use with the ASME Boiler & Pressure Vessel Code Section III, Division 5 rules. The report summarizes progress in testing and characterizing L-PBF material at elevated temperatures by providing preliminary design data for L-PBF 316H and by comparing the elevated temperature performance of the L-PBF material to wrought and conventional fusion welded 316H. The report then updates the initial AMMT qualification plan for L-PBF 316H, originally developed in 2023, to update the accelerated qualification strategy adopted in that plan to account for the new high temperature test data. The report also explores a few methods for further accelerating the qualification process using machine learning techniques to supplement the more conventional, empirical analysis methods typically used by ASME to correlate and extrapolate time-dependent material test data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A flexible linear diffusion acceleration to k-eigenvalue neutron transport with SN discontinuous finite element method

In this paper, we derive a flexible linear diffusion acceleration (LDA) for k-eigenvalue neutron transport discretized with discontinuous finite element method (DFEM) and discrete ordinates(SN). This LDA is based on our two pieces of previous works: the flexible non linear diffusion acceleration (NDA) for DFEM-SN and LDA for k-eigenvalue neutron transport using pre-conditioned Jacobian-free Newton-Krylov with self-adjoint angular flux (SAAF), continuous finite element method(CFEM), and SN. We point out the differences between LDA and NDA for DFEM-SN and the difference between DFEM-SN and SAAF-CFEM-SN for LDA. Numerical tests are presented to compare the convergence behaviour of NDA and LDA. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Map reduce using coordination namespace hardware acceleration

A system and method for supporting data MapReduce operations in a tuple space/coordinated namespace (CNS) extended memory storage architecture. The system-wide CNS provides an efficient means for storing and communicating data generated by local processes running at the nodes, and coordinated to provide MapReduce operations in a multi-nodal system. A hardware accelerated mechanism supports map reduce sorting/shuffle operations and reduce operations according to an aggregate function. Local processes running at a node generate a tuple corresponding to data generated by a process, each tuple having a tuple name and tuple data value corresponding to the generated data. Each tuple is processed and stored at the node or another node, dependent upon its tuple name. Tuple records associated with a tuple name are accumulated at one or more nodes according to a linked list structure at each that is accessible via a hash table index pointer at the node.

Jacob, Philip↗
DOE OSTI2022

Computational Methods to Characterize Panel Loading Conditions for Accelerated Testing

Panel cracking and degradation due to wind loading are known to have a detrimental effect on power output. Recreating these damaging conditions in a controlled experimental setting requires an understanding of the loads being generated at the panel surface as a function of both wind speed, wind direction, and panel orientation. To better understand these relationships, a computational fluid dynamics (CFD) simulation package was constructed using an open-source Python library for solving partial differential equations with the finite element method. This CFD package allows the simulated wind speed and panel orientation of a multi-panel array to be easily changed and provides the capability to measure the traction forces at discrete points along the sun-facing and ground-facing surface of an interior panel residing in the wake of one or more upstream panels. A parameter exploration was performed in which the panel angle was varied in even increments from -70 deg to 70 deg and the wind speed was varied from 2 to 30 m/s. During post processing, the measured traction along the panel surface was averaged spatially and interpreted as a time-varying signal, where further processing of this signal yielded the root mean square amplitude and a characteristic frequency associated with the loading. This study found that higher wind speeds are generally associated with increased amplitude of loading and that the panel orientation angle can significantly exacerbate or mitigate this loading. These outcomes are presented along with current work on higher-fidelity verification simulations and recommendations for performing accelerated experimental testing.

loading↗
DOE OSTI2025

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation

Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method achieves comparable reconstruction accuracy for interpolation tasks, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability. These results suggest that while the model offers a computationally efficient alternative for datasets with temporal stability, its current formulation is best suited for interpolation rather than for predicting future states in time-evolving systems. This study thus lays the groundwork for further refinement of neural network-based approaches to low-rank matrix factorization in high-dimensional plasma simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

From Femtoseconds to Gigaseconds: The SolDeg Platform for the Performance Degradation Analysis of Silicon Heterojunction Solar Cells

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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Accelerated Creep Test Qualification of Creep-Resistance Using the Wilshire–Cano–Stewart Constitutive Model and Stepped Isostress Method

In this paper, a qualification of accelerated creep-resistance of Inconel 718 is assessed using the novel Wilshire–Cano–Stewart (WCS) model and the stepped isostress method (SSM) and predictions are made to conventional creep data. Conventional creep testing is a long-term continuous process; in fact, the ASME B&PV III requires that 10,000+ h of experiments must be conducted to each heat for materials employed in boilers and/or pressure vessel components. This process is costly and not feasible for rapid development of new materials. As an alternative, accelerated creep testing techniques have been developed to reduce the time needed to characterize the creep resistance of materials. Most techniques are based upon the time-temperature-stress superposition principle that predicts minimum-creep-strain-rate (MCSR) and stress-rupture behaviors but lack the ability to predict creep deformation and consider deformation mechanisms that occur for experiments of longer duration. The SSM has been developed, which enables the prediction of creep deformation response as well as reduce the time needed for qualification of materials. The SSM approach has been successful for polymer, polymeric composites, and recently has been introduced for metals. In this study, the WCS constitutive model, calibrated to SSM test data, qualifies the creep resistance of Inconel 718 at 750 °C and predictions are compared to conventional creep testing data. The WCS model has proven to make long-term predictions for stress-rupture, MCSR, creep deformation, and damage in metallic materials. The SSM varies stress levels after time interval adding damage to the material, which can be tracked by the WCS model. The SSM data is calibrated into the model and the WCS model generates realistic predictions of stress-rupture, MSCR, damage, and creep deformation. The calibrated material constants are used to generate predictions of stress-rupture and are postaudit validated using the National Institute of Material Science database. Similarly, the MCSR predictions are compared from previous studies. Finally, the creep deformation predictions are compared with real data and is determined that the results are well in between the expected boundaries. Material characterization and mechanical properties can be determined at a faster rate and with a more cost-effective method. This is beneficial for multiple applications such as in additive manufacturing, composites, spacecraft, and industrial gas turbines.

42 ENGINEERING↗
DOE OSTI2023

Three-dimensional nanoscale reduced-angle ptycho-tomographic imaging with deep learning (RAPID)

X-ray ptychographic tomography is a nondestructive method for three dimensional (3D) imaging with nanometer-sized resolvable features. The size of the volume that can be imaged is almost arbitrary, limited only by the penetration depth and the available scanning time. Here we present a method that rapidly accelerates the imaging operation over a given volume through acquiring a limited set of data via large angular reduction and compensating for the resulting ill-posedness through deeply learned priors. The proposed 3D reconstruction method “RAPID” relies initially on a subset of the object measured with the nominal number of required illumination angles and treats the reconstructions from the conventional two-step approach as ground truth. It is then trained to reproduce equal fidelity from much fewer angles. After training, it performs with similar fidelity on the hitherto unexamined portions of the object, previously not shown during training, with a limited set of acquisitions. In our experimental demonstration, the nominal number of angles was 349 and the reduced number of angles was 21, resulting in a x140 aggregate speedup over a volume of 4.48 x 93.18 x 3.92 μm 3 and with (14nm) 3 feature size, i.e. ~ 10 8 voxels. RAPID’s key distinguishing feature over earlier attempts is the incorporation of atrous spatial pyramid pooling modules into the deep neural network framework in an anisotropic way. We found that adjusting the atrous rate improves reconstruction fidelity because it expands the convolutional kernels’ range to match the physics of multi-slice ptychography without significantly increasing the number of parameters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Replacement of Cobalt in Medical Device Sterilization: Current Trends, Opportunities and Barriers to Adoption of X-ray and E-Beam Within the Medical Device Sterilization Market

Radioactive sources, specifically Co-60, are crucially important to the medical device sterilization industry; however, their benefits must be balanced against the need to ensure that the materials are secure. Accelerator-based sterilization methods, including X-ray and E-Beam, can in many cases sufficiently replace Co-60 and ethylene oxide, the dominant modalities currently in use; however, a lack of hands-on experience with accelerators, regulations, and a lack of capacity have hampered faster adoption of these technologies. The market is likely to change, however, given public pressures against ethylene oxide and concerns over the cost and long-term supply chain robustness of Co-60. This may provide an opportunity for growth for accelerator capabilities. Current research at Sandia National Laboratories and Argonne National Laboratory, on behalf of the Department of Energy’s National Nuclear Security Administration Office of Radiological Security, is focused on developing an understanding of the medical device sterilization marketplace, the different modalities that are used to sterilize goods, and the drivers that affect the actors within it. Research at Pacific Northwest National Laboratory, in conjunction with the medical device sterilization industry, has focused on expanding the industry’s understanding of the capabilities of X-ray sterilization and developing data that can inform the industry’s consideration of a transition towards accelerator-based devices.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Convergence acceleration of Monte Carlo many-body perturbation methods by direct sampling

In the Monte Carlo many-body perturbation (MC-MP) method, the conventional correlation-correction formula, which is a long sum of products of low-dimensional integrals, is first recast into a short sum of high-dimensional integrals over electron-pair and imaginary-time coordinates. These high-dimensional integrals are then evaluated by the Monte Carlo method with random coordinates generated by the Metropolis–Hasting algorithm according to a suitable distribution. The latter algorithm, while advantageous in its ability to sample nearly any distribution, introduces autocorrelation in sampled coordinates, which in turn increases the statistical uncertainty of the integrals and thus the computational cost. It also involves wasteful rejected moves and an initial “burn-in” step as well as displays hysteresis. Here, an algorithm is proposed that directly produces a random sequence of electron-pair coordinates for the same distribution used in the MC-MP method, which is free from autocorrelation, rejected moves, a burn-in step, or hysteresis. Furthermore, this direct-sampling algorithm is shown to accelerate second- (MC-MP2) and third-order Monte Carlo many-body perturbation (MC-MP3) calculations by up to 222% and 38%, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

ACCELERATED CREEP TEST (ACT) QUALIFICATION OF CREEP RESISTANCE USING THE WCS CONSTITUTIVE MODEL AND STEPPED ISOSTRESS METHOD (SSM)

In this study, a qualification of accelerated creep-resistance of Inconel 718 is assessed using the novel Wilshire-Cano-Stewart (WCS) model and the stepped isostress method (SSM) and predictions are made to conventional creep data. Conventional creep testing (CCT) is a long-term continuous process, in fact, the ASME B&PV III requires that 10,000+ hours of experiments must be conducted to each heat for materials employed in boilers and/or pressure vessel components. This process is costly and not feasible for rapid development of new materials. As an alternative, accelerated creep testing techniques have been developed to reduce the time needed to characterize the creep resistance of materials. Most techniques are based upon the time-temperature-stress superposition principle (TTSSP) that predicts minimum-creep-strain-rate (MCSR) and stress-rupture behaviors but lack the ability to predict creep deformation and consider deformation mechanisms that occur for experiments of longer duration. The stepped isostress method (SSM) has been developed which enables the prediction of creep deformation response as well as reduce the time needed for qualification of materials. The SSM approach has been successful for polymer, polymeric composites, and recently has been introduced for metals. In this study, the WCS constitutive model, calibrated to SSM test data, qualifies the creep resistance of Inconel 718 at 750°C and predictions are compared to CCT data. The WCS model has proven to make long-term predictions for stress-rupture, minimum-creep-strain-rate (MCSR), creep deformation, and damage in metallic materials. The SSM varies stress levels after time interval adding damage to the material, which can be tracked by the WCS model. The SSM data is calibrated into the model and the WCS model generates realistic predictions of stress-rupture, MSCR, damage, and creep deformation. The calibrated material constants are used to generate predictions of stress-rupture and are post-audit validated using the National Institute of Material Science (NIMS) database. Similarly, the MCSR predictions are compared from previous studies. Finally the creep deformation predictions are compared with real data and is determined that the results are well in between the expected boundaries. Material characterization and mechanical properties can be determined at a faster rate and with a more cost-effective method. This is beneficial for multiple applications such as in additive manufacturing, composites, spacecraft, and Industrial Gas Turbines (IGT).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electron diffraction intensity from single crystal silicon in a photoinjector

A method includes simulating diffraction in a transmission geometry of relativistic electron bunches from a crystallographic structure of a crystal thereby simulating diffraction of the relativistic electron bunches into a plurality of Bragg peaks. The method includes selecting a range of angles between a direction of propagation of the relativistic electron bunches and a normal direction of crystal including an angle at which a diffraction portion is maximized. The method includes sequentially accelerating a plurality of physical electron bunches to relativistic energies toward a physical crystal having the crystallographic structure and diffracting the plurality of physical electron bunches off the physical crystal at different angles and measuring the diffraction portion into the respective Bragg peak at the different angles. The method includes selecting a final angle based on the measured diffraction portion into the respective Bragg peak at the different angles and generating a pulse of light.

Graves, William↗