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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Experimental measurement of 12 C + 16 O fusion at stellar energies

The total cross section of the 12 C + 16 O fusion reaction has been measured at low energies to investigate the role of this reaction during late stellar evolution burning phases. A high-intensity oxygen beam, produced by the 5 MV pelletron accelerator at the University of Notre Dame, impinged on a thick, ultrapure graphite target. Protons and γ rays were simultaneously measured in the center-of-mass energy range from 3.64 to 5.01 MeV for singles and from 3.73 to 4.84 MeV for coincidence events, using silicon and Ge detectors. Statistical model calculations were employed to interpret the experimental results. The emergence of a new resonance-like broad structure and a decreasing trend in the S-factor data towards lower energies (opposite to previous data) are found for the 12 C + 16 O fusion reaction. Furthermore, based on these results the uncertainty range of the reaction rate within the temperature range of late stellar burning environments is discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Transformer-powered surrogates close the ICF simulation-experiment gap with extremely limited data

Abstract Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Predicting transport of intra-articularly injected growth factor fusion proteins into human knee joint cartilage

There are no drugs or treatment methods known to prevent the development of post-traumatic osteoarthritis (PTOA), a type of osteoarthritis (OA) that is triggered by traumatic joint injuries and accounts for ~12% of the nearly 600 million OA cases worldwide. Lack of effective drug delivery techniques remains a major challenge in developing clinically effective treatments, but cationic delivery carriers can help overcome this challenge. Scaling up treatments that are effective in in vitro models to achieve success in preclinical in vivo models and clinical trials is also a challenging problem in the field. Here we use a cationic green fluorescent protein (GFP) as a carrier to deliver Insulin-Like Growth Factor 1 (IGF-1), a drug considered as a potential therapeutic for PTOA. GFP-IGF-1 conjugates were first synthesized as fusion proteins with different polypeptide linkers, and their transport properties were characterized in human cartilage explants. In vitro experimental data were used to develop a predictive mathematical transport model that was validated using an independent in vitro experimental data set. In this study, the model was used to predict the transport of these fusion proteins upon intra-articular injection into human knee joints. The predictions included results for the rate and extent of fusion protein penetration into cartilage, and the maximum levels of fusion proteins that would escape into systemic circulation through the joint capsule. Together, our transport measurements and model set the stage for translation of such explant culture studies to in vivo preclinical studies and potentially clinical application.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Report on Progress of correlation of in-situ and ex-situ data and the use of artificial intelligence to predict defects

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear components which will be assembled into a fully functional microreactor core. Compared with traditional manufacturing technologies, AM technologies allow (1) real-time observation of the manufacturing process at a much higher resolution using in-situ monitoring technologies to capture the sensor signatures that scientifically describe each event occurring over time and space and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on in-situ and ex-situ data correlation and associated data analytics results. Examples are provided to illustrate progress with respect to laser powder bed fusion (L-PBF), binder jetting, computed tomography (CT) reconstruction, and mechanical testing. Elements of the Digital Thread and data management infrastructure are discussed in the main document, and an extensive supplemental appendix is provided detailing the Digital Platform, as well as its implementation and subcomponents. In conclusion, the path forward for the next fiscal year is also discussed.

42 ENGINEERING↗
DOE OSTI2019

Linking pyrometry to porosity in additively manufactured metals

Porosity in additively manufactured metals can reduce material strength which is generally considered undesirable. Although studies have shown relationships between process parameters and porosity, monitoring strategies for defect detection and pore formation are still needed. In this paper, instantaneous anomalous conditions are detected in-situ via pyrometry during laser powder bed fusion additive manufacturing and correlated with voids observed using post-build micro-computed tomography. Large two-color pyrometry data sets were used to estimate instantaneous temperatures, melt pool orientations and aspect ratios. Machine learning algorithms were then applied to processed pyrometry data to detect outlier images and conditions. It is shown that melt pool outliers are good predictors of voids observed post-build. With this approach, real time process monitoring can be incorporated into systems to detect defect and void formation. Alternatively, using the methodology presented here, pyrometry data can be post processed for porosity assessment.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Absence of hindrance in a microscopic 12 C + 12 C fusion study

Studies of low-energy fusion of light nuclei are important in astrophysical modeling, with small variations in reaction rates having a large impact on nucleosynthesis yields. Due to the lack of experimental data at astrophysical energies, extrapolation and microscopic methods are needed to model fusion probabilities. To investigate deep sub-barrier 12 C+ 12 C fusion cross sections and establish trends for the S factor. Microscopic methods based on static Hartree-Fock and time-dependent Hartree-Fock (TDHF) mean-field theory are used to obtain 12 C+ 12 C ion-ion fusion potentials. Fusion cross sections and astrophysical S factors are then calculated using the incoming wave boundary condition method. Both density-constrained frozen Hartree-Fock (DCFHF) and density-constrained TDHF (DC-TDHF) predict a rising S factor at low energies, with DC-TDHF predicting a slight damping in the deep sub-barrier region (≈1 MeV). Comparison between DC-TDHF calculations and maximum experimental cross sections in the resonance peaks are good. However, the discrepancy in experimental low-energy results inhibits interpretation of the trend. Using the fully microscopic DCFHF and DC-TDHF methods, no S factor maximum is observed in the 12 C+ 12 C fusion reaction. In addition, no extreme sub-barrier hindrance is predicted at low energies. Furthermore, the development of a microscopic theory of fusion including resonance effects, as well as further experiments at lower energies must be done before the deep sub-barrier behavior of the reaction can be established.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2020

Novel Nuclear Reactions Observed in Bremsstrahlung-Irradiated Deuterated Metals

d-D nuclear fusion events were observed in an electronscreened, deuterated metal lattice by reacting cold deuterons with hot deuterons (d*) produced by elastically scattered neutrons originating from bremsstrahlung photodissociation (where “d” and “D” denote 2H). Exposure of deuterated materials (ErD3 and TiD2) to photon energies in the range of 2.5 to 2.9 MeV resulted in photodissociation neutrons that were below 400 keV and also the 2.45-MeV neutrons, consistent with 2H(d, n)3He fusion. Additionally, neutron energies of approximately 4 and 5 MeV for TiD2 and ErD3 were measured, consistent with either boosted neutrons from kinetically heated deuterons or Oppenheimer-Phillips stripping reactions in the highly screened environment. Neutron spectroscopy was conducted using calibrated lead shielded liquid (EJ-309) and plastic (stilbene) scintillator detectors. The data support the theoretical analysis in a companion paper, predicting fusion reactions and subsequent reactions in the highly screened environment.

Bruce M Steinetz↗
DOE OSTI2019

Bayesian Spectral Moment Estimation and Uncertainty Quantification

We present a Bayesian spectral fitting method developed for spectroscopic data analysis, particularly (but not solely) in the context of fusion energy research. The presented techniques are particularly valuable to estimating moments and corresponding uncertainties whenever the spectra result from line-integrated measurements in nonuniform plasmas, for which the approximation of atomic line shapes being ideal Gaussians gives poor estimates. We decompose multiple, potentially overlapping spectral lines into a sum of Gauss-Hermite polynomials, whose properties allow efficient truncation and uncertainty quantification, often with only three free parameters per atomic emission line. Tests with both synthetic and experimental data demonstrate the effectiveness and robustness where more standard nonlinear fitting routines may experience difficulties. A parallelized version of our implementation is publicly released under an open source license.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗
DOE OSTI2015

The center for multiscale plasma dynamics

This final report describes research performed in Princeton University, led by Professor Yannis G. Kevrekidis, over a period of six years (August 1, 2014 to July 31, 2010, including a one-year, no-cost extension) as part of the Center for Multiscale Plasma Dynamics led by the University of Maryland. The work resulted in the development and implementation of several multiscale algorithms based on the equation-free approach pioneered by the PI, including its applications in plasma dynamics problems. These algoriithms include coarse projective integration and coarse stability/bifurcation computations. In the later stages of the work, new links were made between this multiscale, coarse-graining approach and advances in data mining/machine learning algorithms.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

NewLife Nuclear - An Environmentally and Economically Minded Solution for Fusion Energy Waste Handling

Energy demand is rising as a result of innovative and increasingly more energy intensive processes coming to fruition, particularly through the recent interest in the development of AI data centers as well as manufacturing with the push towards increasing domestic manufacturing interest. Fusion energy can provide virtually limitless energy to support this increase in energy demand. Fusion energy concepts, largely classified as magnetic fusion energy (MFE) and inertial fusion energy (IFE) are being pursued, each having unique challenges to overcome before the successful deployment of electricity to the grid. Achieving fusion ignition on the National Ignition Facility, first in December 2022, and eight times since, has demonstrated the scientific viability of the IFE approach. Meanwhile, MFE test stands continue to improve confinement times, making meaningful strides in progressing towards experimental scientific viability. In each of these approaches, an emphasis is placed on generating more power out of the system than what is required to power the system. An under-researched area applicable to both IFE and MFE is handling activated waste coming out of fusion energy systems, both in the course of normal daily operations, as well as in intermittent periods as structural materials may need to be replaced. In the context of an IFE plant system, commonly discussed plant designs suggest targets are ignited within a chamber at a rate of up to one million targets per day. Between each shot, the chamber housing the ignition event will clear a portion of the chamber – resulting in a mixture of vaporized target gas, target debris, and other materials being expelled from the chamber [source]. Additionally, IFE system concepts typically discuss the modularization of plant designs, which are expected to be replaced periodically as the components degrade over time. This would result in the irradiated chamber structure materials, likely metals and alloys, needing to be removed and safely stored. In MFE plant systems, while targets are not ignited at a repetition rate with the frequent chamber clearing as is expected in IFE plant systems, it is anticipated that portions of the confinement area interfacing with the hot plasma will need to be replaced periodically. In each system, without additional investment and research into alternative processing and recycling methods, the result is storing irradiated materials, and other elements in a safe containment area until they are no longer activated. – resulting in significant waste both economic and environmental.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1991

Sensor fusion for synthetic vision

Display methodologies are explored for fusing images gathered by millimeter wave sensors with images rendered from an on-board terrain data base to facilitate visually guided flight and ground operations in low visibility conditions. An approach to fusion based on multiresolution image representation and processing is described which facilitates fusion of images differing in resolution within and between images. To investigate possible fusion methods, a workstation-based simulation environment is being developed.

Pavel, M.↗
DOE OSTI2015

Reliability and Maintainability Data for Liquid Metal Cooling Systems

One of the coolants of interest for future fusion breeding blankets is lead-lithium. As a liquid metal it offers the advantages of high temperature operation for good station efficiency, low pressure, and moderate flow rate. This coolant is also under examination for use in test blanket modules to be used in the ITER international project. To perform reliability, availability, maintainability and inspectability (RAMI) assessment as well as probabilistic safety assessment (PSA) of lead-lithium cooling systems, component failure rate data are needed to quantify the system models. RAMI assessment also requires repair time data and inspection time data. This paper presents a new survey of the data sets that are available at present to support RAMI and PSA quantification. Recommendations are given for the best data values to use when quantifying system models.

maintainability data↗
NASA NTRS1995

Sensor Fusion in Telerobotic Task Controls

Model-based telerobotic task control and automation is typically limited to motion control of robot arms in free space, and contact events under predictable conditions. A display and control method- ology is described for combining different multidimensional sensor data to guide the performance of telerobotic contact or near-contact tasks successfully in manual or supervised automatic modes of control.

telerobotics robotic task controllers sensor fusio↗
DOE OSTI2025

Strength and ductility of additively manufactured 316L stainless steel: Impact of neutron irradiation and data variability

Here, this article presents the mechanical properties of additively manufactured (AM) 316L stainless steel processed via the laser powder bed fusion (LPBF) method, focusing on the effects of neutron irradiation on mechanical properties and the variability in strength and ductility data. The rapid melting-solidification process and multiple heating-cooling cycles inherent in LPBF typically result in a fine, metastable microstructure with significant local variability. AM 316L builds of varying thicknesses were fabricated, and SS-J3 miniature tensile specimens were machined from six different locations. These specimens were irradiated with fast neutrons to doses of 2 and 10 dpa at target temperatures of 300 °C and 600 °C. Post-irradiation tensile tests were conducted at room temperature, 300 °C, and 600 °C. Compared to conventional 316L stainless steel, AM 316L exhibited higher initial strength but lower ductility. Irradiation at 300 °C caused significant hardening and prompt necking at yield, with limited uniform ductility, although embrittlement was not observed up to 10 dpa. While neutron irradiation, particularly at 600 °C, increased the variability in strength and ductility data, no clear dependence of mechanical properties on build thickness or sampling location was found—contrary to the conventional perception that AM materials may exhibit high property variability. Furthermore, we observed that the variability in property data for LPBF-processed 316L was relatively low compared to that of wrought 316L stainless steel. This reduced variability in AM 316L steel may be attributed to its highly metastable, stress-containing microstructure, which is discussed in the context of general tensile property variations.

Additively manufactured 316L stainless steel↗
DOE OSTI2015

ND 2 AV: N-dimensional data analysis and visualization analysis for the National Ignition Campaign

Here, one of the biggest challenges in high-energy physics is to analyze a complex mix of experimental and simulation data to gain new insights into the underlying physics. Currently, this analysis relies primarily on the intuition of trained experts often using nothing more sophisticated than default scatter plots. Many advanced analysis techniques are not easily accessible to scientists and not flexible enough to explore the potentially interesting hypotheses in an intuitive manner. Furthermore, results from individual techniques are often difficult to integrate, leading to a confusing patchwork of analysis snippets too cumbersome for data exploration. This paper presents a case study on how a combination of techniques from statistics, machine learning, topology, and visualization can have a significant impact in the field of inertial confinement fusion. We present the $\mathrm{ND}^2\mathrm{AV}$: N-dimensional data analysis and visualization framework, a user-friendly tool aimed at exploiting the intuition and current workflow of the target users. The system integrates traditional analysis approaches such as dimension reduction and clustering with state-of-the-art techniques such as neighborhood graphs and topological analysis, and custom capabilities such as defining combined metrics on the fly. All components are linked into an interactive environment that enables an intuitive exploration of a wide variety of hypotheses while relating the results to concepts familiar to the users, such as scatter plots. $\mathrm{ND}^2\mathrm{AV}$ uses a modular design providing easy extensibility and customization for different applications. $\mathrm{ND}^2\mathrm{AV}$ is being actively used in the National Ignition Campaign and has already led to a number of unexpected discoveries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

First results from gamma ray diagnostics in EAST Tokamak

Gamma ray diagnostics has been developed in the EAST tokamak recently. Six BGO scintillator detectors are arranged on the down-half cross-section and pointed at the up-half cross-section of plasma, with space resolution about 15 cm and energy range from 0.3 MeV to 6 MeV. Three main gamma ray peaks in the energy spectra have been observed and are identified as the results of nuclear reactions {sup 207}Pb(n, n′){sup 207m}Pb, H(n, γ) D, and D(p, γ){sup 3}He, respectively. Upgrading of the system is in progress by using LaBr3(Ce) scintillator, fast photo-multiplier tubes, and a fully digital data acquisition system based on high sample frequency digitizers with digital pulse processing algorithms.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

On the upper bound in the Bohm sheath criterion

The question is discussed about the existence of an upper bound in the Bohm sheath criterion, according to which the Debye sheath at the interface between plasma and a negatively charged electrode is stable only if the ion flow velocity in plasma exceeds the ion sound velocity. It is stated that, with an exception of some artificial ionization models, the Bohm sheath criterion is satisfied as an equality at the lower bound and the ion flow velocity is equal to the speed of sound. In the one-dimensional theory, a supersonic flow appears in an unrealistic model of a localized ion source the size of which is less than the Debye length; however, supersonic flows seem to be possible in the two- and three-dimensional cases. In the available numerical codes used to simulate charged particle sources with a plasma emitter, the presence of the upper bound in the Bohm sheath criterion is not supposed; however, the correspondence with experimental data is usually achieved if the ion flow velocity in plasma is close to the ion sound velocity.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗