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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Hybridized Discontinuous Galerkin Methods for Computational Fluid Dynamics

Hybridizable Discontinuous Galerkin (HDG) methods hold promise for any applications with significant advection character, including thermal hydraulics in light water reactors and advanced reactor concepts and fluid models of plasmas in magnetic confinement fusion. Its features include natural upwinding, local element conservation, and extensibility to arbitrarily high order accuracy. In the last fiscal year we have implemented HDG in the Multiphysics Object-Oriented Simulation Environment (MOOSE). We developed a first-of-its-kind automatic static condensation system in MOOSE’s underlying finite element library libMesh which can condense out arbitrarily many internal variables. Finally, we developed the first preconditioner for HDG discretizations of the Navier-Stokes equations which shows robust performance across a wide range of problem sizes and Reynolds numbers. This preconditioner yields solution times that are equivalent to the fastest developed for industry standard finite volume methods. Moreover, the arbitrarily high-order nature of HDG makes it a prime candidate for acceleration via graphical processing units (GPUs). We believe these developments will hold significant importance for future DOE Nuclear Energy (NE) and Fusion Energy Science (FES) programs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Development and Hotfire Testing of Additively Manufactured Copper Combustion Chambers for Liquid Rocket Engine Applications

NASA and industry partners are working towards fabrication process development to reduce costs and schedules associated with manufacturing liquid rocket engine components with the goal of reducing overall mission costs. One such technique being evaluated is powder-bed fusion or selective laser melting (SLM), commonly referred to as additive manufacturing (AM). The NASA Low Cost Upper Stage Propulsion (LCUSP) program was designed to develop processes and material characterization for GRCop-84 (a NASA Glenn Research Center-developed copper, chrome, niobium alloy) commensurate with powder bed AM, evaluate bimetallic deposition, and complete testing of a full scale combustion chamber. As part of this development, the process has been transferred to industry partners to enable a long-term supply chain of monolithic copper combustion chambers. To advance the processes further and allow for optimization with multiple materials, NASA is also investigating the feasibility of bimetallic AM chambers. In addition to the LCUSP program, NASA’s Marshall Space Flight Center (MSFC) has completed a series of development programs and hot-fire tests to demonstrate SLM GRCop-84 and other AM techniques. MSFC’s efforts include a 4,000 pounds-force thrust liquid oxygen/methane (LOX/CH4) combustion chamber. Small thrust chambers for 1,200 pounds-force LOX/hydrogen (H2) applications have also been designed and fabricated with SLM GRCop-84. Similar chambers have also completed development with an Inconel 625 jacket bonded to the GRCop-84 material, evaluating direct metal deposition (DMD) laser- and arc-based techniques. The same technologies for these lower thrust applications are being applied to 25,000-35,000 pounds-force main combustion chamber (MCC) designs. This paper describes the design, development, manufacturing and testing of these numerous combustion chambers, and the associated lessons learned throughout their design and development processes.

Gradl, Paul R.↗
DOE OSTI2024

Response of 11 B enriched ZrB 2 ultra-high temperature ceramic to neutron irradiation at elevated temperatures

ZrB 2 , an ultra-high temperature ceramic (UHTC) is being considered for use in fusion reactor first-wall structures, yet its response to irradiation remains poorly understood. This study employed scanning/transmission electron microscopy (S/TEM), synchrotron X-ray diffraction (XRD), finite element calculations, and thermal property measurements to thoroughly investigate the neutron-irradiation effects on 11 B-enriched ZrB 2 . Neutron irradiations were conducted at 220 °C and 620 °C, with a neutron fluence of 2.2 × 10 25 neutron/m 2 (energy > 0.1 MeV), resulting in 3.9 dpa and 4200 appm He. The study revealed the unusual prevalence of prism loops and a > c anisotropic lattice swelling, likely linked to the low c/a ratio of ZrB 2 , leading to grain boundary microcracking. Reducing the grain sizes was effective in reducing intergranular cracking and macroscopic swelling. The observation of cavities in ZrB 2 irradiated at 620 °C, as opposed to 220 °C, prompts questions about the temperature at which vacancies in ZrB 2 become mobile, and the role of neutron absorption by 10 B in elevating irradiation temperatures. Isotopic enrichment in 11 B proves to be a viable strategy for mitigating helium production in transition-metal diborides, which is a critical consideration for nuclear applications. Irradiation-induced defects reduce the thermal diffusivity and conductivity of ZrB 2 by a factor of 4–9, which has important implications for its role as a plasma-facing material in fusion reactors that drive high heat fluxes through first-wall materials. Here, this comprehensive study lays the foundation for understanding ZrB 2 behavior under neutron irradiation and highlights important phenomena to consider for various material applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predictive scale-bridging simulations through active learning

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Functional Photoresists for Energy Applications. Final Report

Monolithic ultralow-density porous bulk materials have recently attracted much interest due to many emerging applications in the areas of catalysis, energy storage and conversion, and thermal insulation. They are also important components of high energy density (HED) and inertial confinement fusion (ICF) targets. However, despite tremendous progress that has been made in the synthesis of porous materials, deterministic and independent control over microscopic architecture, density and composition remain key issues, and their integration in high precision devices requires cost and time-intensive mechanical machining that not only reduces reproducibility by generating debris but also limits the complexity of the 3D shapes that can be realized. In this project, we overcame these limitations by developing a universal templating capability that provides deterministic and independent control over density, composition, architecture, and macroscopic sample shape. This was achieved by developing the technology to 1) 3D print ultrahigh resolution, ultra-high precision polymeric micro-lattice templates, 2) coat these templates with the desired materials, and 3) removing the template (Fig. 1a). Atomic layer deposition (ALD) provides the atomic scale coating thickness accuracy required for precisely controlling density. While this templating approach had been demonstrated in prior work, limitations in suitable photoresists, 3D print technologies, print design, and template removal techniques did not allow the fabrication of millimeter-sized high-precision parts with sub-micron resolution. To enable this technology, we developed 1) two-photon polymerization (TPP) print designs that enable the fabrication of millimeter-sized, mechanically robust polymeric templates with sub-micron resolution and 2) a continuum level TPP printing simulation capability for additional print design guidance; 3) atomistic models to study photoresist polymerization kinetics and network topography, 4) refractive index matched polymeric and preceramic TPP photoresists, and 5) functional TPP photoresists including porous voxel structures and self-immolative polymer photoresist chemistries; and 6) damage free template removal techniques that enable the fabrication of defect-free high-precision low-density foam components. We also developed a templating approach for pure carbon microlattices with a unique tube-in-tube ligament morphology. As a test platform, we pursued the fabrication of foam liners that promise to further increase the neutron yield in indirect drive ICF experiments by improving implosion symmetry control and coupling between the laser and the deuterium-tritium fuel. This application requires fabrication and integration of a ultra-high precision, millimeter-sized, thin-walled (200-400 micrometer thick), low-density (10-30 mg/cc), high atomic number (high Z) cylindrical foam tube into the gold hohlraum of an indirect drive ICF target (Fig. 1b). While our hohlraum liner test case will mainly find application in HED and ICF experiments, the underlying science will also directly apply to previously developed nanoparticle and additive manufacturing technologies and will advance those techniques as well.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantifying Uncertainties of Microscopic Nuclear Theories

The goal of this project was to produce modernized nuclear data libraries of light ion fusion reactions and actinide fission product yields. Such libraries are essential input in both stockpile science but also fundamental science (nucleosynthesis, superheavy nuclei). When such libraries are used to model complex processes involving exotic, very short-lived nuclei that have not been measured experimentally, the underlying nuclear data come from theoretical models, the predictive power of which needs to be quantified, together with its uncertainties. By teaming up nuclear theorists and statisticians, we built comprehensive theoretical frameworks based on the most fundamental theories of fusion and fission that we coupled for the first time with a Bayesian framework with machine learning techniques for uncertainty quantification. Our results include: (1) statistical emulators for neutron-alpha scattering, and the fission product mass yields of neutron-induced fission of 239 Pu, which will enable developers to generate random realizations of the corresponding data for uncertainty quantification (UQ) in applications; (2) the first complete and most accurate ab initio prediction of the S-factor for proton capture on 7 Be, which – combined with UQ – may become the recommended value at solar energies in the next review of solar fusion cross sections; (3) statistical emulators for fission mass yields in the neutron-induced fission of 239 Pu; (4) the first quantification of how uncertainties in nuclear forces impact fission properties that are relevant in nucleosynthesis simulations to explain how heavy elements are formed in the Cosmos.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Application of an energy-dependent instrument response function to analysis of nTOF data from cryogenic DT experiments

Neutron time-of-flight (nTOF) detectors are used to diagnose the conditions present in inertial confinement fusion (ICF) experiments and basic laboratory physics experiments performed on an ICF platform. The instrument response function (IRF) of these detectors is constructed by convolution of two components: an x-ray IRF and a neutron interaction response. The shape of the neutron interaction response varies with incident neutron energy, changing the shape of the total IRF. Analyses of nTOF data that span a broad range of energies must account for this energy-dependence in order to accurately infer plasma parameters and nuclear properties in ICF experiments. This work briefly reviews a matrix multiplication approach to convolution which allows for an energy-dependent change in the shape of the IRF. This method is applied to synthetic data resembling symmetric cryogenic DT implosions to examine the effect of the energy-dependent IRF on the inferred areal density. Here, results of forward fits that infer ion temperatures and areal densities from nTOF data collected during cryogenic DT experiments on OMEGA are also discussed.

47 OTHER INSTRUMENTATION↗
NASA NTRS1985

Explosive Welding of Tubular Configurated Joints for Critical Applications

Explosive welding can provide the answer to problems of permanently joining metals typically used in the aerospace industry. The explosive bonding process is a solid state bonding process enabling material incompatibility problems associated with fusion welding to be overcome. In addition, heat affected zones are eliminated thus, enhancing joint strength, properties and performance. The process requires the parts being joined to be impelled, by means of explosives, to collide with each other. Certain critical collision parameters must be met and controlled and these parameters are defined. Various component geometries which satisfy the collision parameters are described. Examples of transition joints used in the aerospace industry are described and illustrated.

Hardwick, R.↗
DOE OSTI2021

System-on-Chip approach microwave diagnostics development for burning plasma

System-on-chip (SoC) millimeter wave integrated circuit technology has been developed and employed in chip heterodyne radiometers in Electron Cyclotron Emission Imaging (ECEI) and Microwave Imaging Reflectometer (MIR) on the DIII-D tokamak for 2D electron temperature and density fluctuation diagnostics. The W-band (75-110 GHz) ECEI system has been employed with 20 poloidal horn receiver modules with customized monolithic integrated circuit chips and internal local oscillator coupling path. The new SoC approach demonstrates > 30 dB additional gain on the receiver and 95% electronics noise suppression compared to the previous Schottky diode mixer array . An F-band (110-140 GHz) receiver chip has been developed to measure MHD and turbulence behavior on the DIII-D high-field side, such as Alfven eigenmodes. Furthermore, it can image pedestal structure evolution on fusion facilities with stronger magnetic fields, such as ASDEX-U, EAST, HL-2M, and LHD. The F-band chip performance has been tested in the laboratory. Also, V-band (55-75 GHz) transmitter and receiver modules (55-75 GHz) have been developed for DIII-D and NSTX-U Furthermore, microwave Imaging Reflectometer (MIR) application suitability for 2D density fluctuation imaging on the pedestal region.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

DLIO: A DATA-CENTRIC BENCHMARK FOR DEEP LEARNING APPLICATIONS

SF-22-136 Deep learning has been shown as a successful method for various tasks, and its popularity results in numerous open-source deep learning software tools. Deep learning has been applied to a broad spectrum of scientific domains such as cosmology, particle physics, computer vision, fusion, and astrophysics. Scientists have performed a great deal of work to optimize the computational performance of deep learning frameworks. However, the same cannot be said for I/O performance. As deep learning algorithms rely on big-data volume and variety to effectively train neural networks accurately, I/O is a significant bottleneck on large-scale distributed deep learning training. DLIO, is a novel representative benchmark suite built based on the I/O profiling of the selected workloads. DLIO can be utilized to accurately emulate the I/O behavior of modern deep learning applications. Using DLIO, application developers and system software solution architects can identify potential I/O bottlenecks in their applications and guide optimizations to boost the I/O performance leading to lower training times. The storage vendor can also use DLIO as a guide for designing and optimize the storage and filesystem targeting at deep learning application.

ZHENG, HUIHUO↗
DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Linking microstructure to creep behavior in vertically and horizontally built LPBF Haynes 282 compared with wrought material via θ -projection

Laser Powder Bed Fusion (LPBF) has emerged as a promising route for fabricating intricate geometries in high-performance alloys. Haynes 282 (H282) is a strong candidate for applications such as heat exchangers or engines due to its excellent creep strength and thermal stability; however, the long-term creep behavior of LPBF-processed H282 remains poorly understood. In this study, the θ -projection method is used to analyze and extrapolate the creep behavior of vertically built LPBF, horizontally built LPBF, compared to wrought H282 tested at 816 °C. Vertically built LPBF H282 exhibits the lowest minimum creep rate (MCR), while the horizontally built condition shows a higher MCR comparable to that of wrought H282. Despite these differences, both LPBF conditions exhibit significantly shorter rupture life and reduced rupture strain than the wrought material, with the most severe degradation observed in the horizontal builds, consistent with an earlier onset of tertiary creep and accelerated strain-rate evolution. Microstructural characterization reveals that both LPBF and wrought H282 exhibit abundant twin-related boundary character; however, their grain boundary topologies differ markedly. The wrought alloy contains a higher fraction of low-angle grain boundaries and continuous twin lamellae, whereas the LPBF microstructure is characterized by a suppressed low-angle boundary population and fragmented twin-related boundaries embedded within irregular high-angle grain boundary networks. Fractographic analysis further reveals predominantly intergranular cracking in LPBF H282, accompanied by grain-boundary-decorated carbides, Al 2 O 3 inclusions, and high-aspect-ratio pores. These results demonstrate that grain boundary topology, rather than minimum creep rate alone, plays a critical role in governing creep damage accumulation and rupture behavior in LPBF and wrought H282.

Creep↗
DOE OSTI2021

Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules

Advances in machine learning provide the ability to leverage data from expensive simulations of high-energy-density experiments to significantly cut down on computational time and costs associated with the search for optimal target designs. This study presents an application of cutting-edge Bayesian optimization methods to the one-dimensional (1D) design optimization of double shell graded layer targets for inertial confinement fusion experiments. This investigation attempts to reduce hydrodynamic instabilities while retaining high yields for future NIF experiments. Machine learning methods can use predictive physics simulations to identify graded layer designs from within the vast design space that demonstrate high predicted performance, including novel designs with high uncertainty in performance that may hold unexpected promise. By applying machine learning tools to the simulation design, we map the trade-off between 1D yield and instability, specifically isolating parameter ranges, which maintain high performance while showing significantly improved Rayleigh–Taylor stability over the point design. Furthermore, the groundwork laid in this study will be a useful design tool for future NIF experiments with graded layer targets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Dynamic response of 17-4 stainless steel as a function of manufacturing method and heat treatment

We present a series of plate-impact experiments on 17-4 stainless steel to study the effect of manufacturing method and heat treatment on the Hugoniot elastic limit (HEL), Hugoniot, phase transformation stress, and spallation strength. Two traditional manufacturing methods were considered, wrought processing and casting, as well as two additive manufacturing methods, laser powder-bed fusion (LPBF) and wire-fed electron beam (EBAM). For both LPBF and EBAM 17-4 stainless steel variants, two billets were printed, enabling the application of two unique heat treatments. The HEL stress depended heavily on the thermal history, with the HEL increasing after the formation of Cu-rich precipitates via heat treatment. The Hugoniot response both below and above the phase transition was unaffected by the manufacturing method or heat treatment. The phase transition stress depended heavily on the thermal history, with its variation being attributed to the presence of various microstructural features. This is supported by a marked increase in the phase transition stress after precipitation hardening. These results suggest that the notion of the phase transition stress being dictated by bulk composition is an oversimplification and the stress fields generated by the meso-scale structure are a dominant force. The spallation strength was lower in the cast material compared to all other 17-4 stainless steel variants due to the presence of brittle δ-ferrite inclusions. Additionally, a drop in the tensile strain-rate was observed in the spallation response above the phase transition stress, which was hypothesized to stem from the kinetics of the reversion to the low-pressure phase during spall.

Compressive stress↗
DOE OSTI2025

Planar shock-induced bubble collapse and jetting in water captured via X-ray phase contrast imaging

Shock wave–bubble interactions in water manifest rich dynamics driven by a combination of strong pressure and density mismatches. They have a wide variety of applications, including the injection of pharmaceuticals, and through scaling, enable the exploration of various aspects of high-energy-density systems such as inertial confinement fusion. In this work, the interaction between a micrometric nitrogen bubble and a planar shock wave, characterized by a Mach number of M = 1.24 and a peak pressure of p max = 0.57 , is experimentally recorded using ultra-high-speed x-ray phase contrast imaging. Highly resolved radiographs provide access to all phase discontinuities along the beam path, offering quantities such as the time-varying bubble size, the speed of a jet produced during the bubble collapse, and the time evolution of the shock wave front, which are critical benchmark data for numerical scheme validation. This study addresses the lack of well-characterized, repeatable, and high spatiotemporal resolution experiments at negative Atwood numbers by providing shock–bubble visualization and corresponding numerical simulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Summary of Methodology for Mitigating Risks Associated with Licensing and Qualifying AM Nuclear Materials

The US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program focuses on accelerating the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. Laser powder bed fusion (LPBF) is one of the most popular additive manufacturing (AM) processes for fabricating components with intrinsically complex geometries. LPBF was extensively explored for nuclear applications under the previous Transformational Challenge Reactor program. Additionally, Oak Ridge National Laboratory developed and licensed the Peregrine software and larger digital platform that couples machine learning and in situ data collection during AM to detect anomalies and any evolved defects. The digital platform will be critical to (1) the qualification of AM components for nuclear applications that link location-specific data to macroscopic properties and (2) predict final component performance. Current in situ process monitoring tools are valuable for observing the formation of stochastic flaws, but additional data are needed to predict the resulting microstructures and associated material performance. Rapid cooling rates and large thermal gradients have caused large heterogeneities in the microstructure, which cause anisotropy in mechanical performance. The AMMT program is evaluating the best approaches for addressing these heterogeneities and their effect on component performance using a combination of multiscale modeling, enhanced in situ process monitoring, and high throughput experimental testing. This report summarizes strategies for mitigating the risks associated with qualifying AM components, including developing new sensing capabilities for in situ process monitoring and characterizing melt pool solidification and residual stresses to inform multiscale modeling efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1982

Metals processing

The metals processing effort is directed towards improvement of performance and usefulness of materials through modification and control of shape and internal structure. Interaction of reactive gases, introduced into a plasma arc, with iron alloys were analyzed. The technology of magnesium production was assessed. The study of fast fluidized bed reactors is continued. Aspects of separation processes are being investigated. The development of high field superconducting composites for use in large magnetic fusion devices is also discussed. Processing, structure, and property relationships of superconducting materials and the substitution of precious metals in standard electrical contact and connector applications are tested. The influence of processing procedures on the structure of zircaloy and nickel base alloys with a goal of improving mechanical properties and performance is investigated. Investigation of the potential of metal insulator semiconductor junctions as photovoltaic devices is reported.

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