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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Influences of shock imprinting on mix in a 3D-printed porous media

Mixing of materials in porous media can cause a significant impact on fusion yield as previously demonstrated by the National Ignition Facility (NIF) MARBLE Campaign. Initially, the reactants are separated, with deuterium in the lattice struts and a tritium gas fill in the voids. Lattice parameters such as the strut thickness and relative pitch, provide a control for the mix parameters in the experiment. Los Alamos National Laboratory’s (LANL) BOSQUE project looks to better understand how the mix of the reactants and shell materials impact the fusion burn and resultant yield on various laser platforms. xRAGE’s Eulerian hydrodynamics and adaptive mesh refinement (AMR) provide the unique ability to study the impacts of multiscale features of complex lattice structures. This modeling provides the ability to measure shock front variations as the wave progress’ through a given media. Initial conditions of the lattice are essential to accurately model mix and burn measured by experiment. By varying the initial orientation and densities of these lattice regions the early time dynamics of how the shock is launched into the system is changed and advocates for the study of resulting effects. In this work, we will study the sensitivities of shock effects in varying 3D printed geometric systems and how these shocks alter the structure and mix in the lattice. We will discuss both preliminary experimental results and simulations to help plan and constrain future experiments where we will study the impact of different lattice geometries and lattice bulk densities. This work concludes with the relative impacts of lattice geometries on shock speeds at different bulk densities and the resultant mix widths due to those shock interactions. We see agreement with theory at the higher end of our bulk density study, however, as we approach lower bulk density systems the dynamics of these interactions begin to change.

2 Photon Polymerization↗
DOE OSTI2024

GRCop-42: Comparison between laser powder bed fusion and laser powder direct energy deposition

This study involves a comparative analysis of additively manufactured GRCop-42 specimens produced using two processes: laser-powder bed fusion (L-PBF) and laser powder direct energy deposition (LP-DED). The investigation characterizes a range of material attributes, including surface topography, internal defects, microstructural features, quasi-static mechanical properties, and fractographic characteristics. The findings demonstrate that, despite the specimens being fabricated with the same base material, the resulting material properties vary significantly between the two additive manufacturing processes. As such, material properties cannot be presumed to be uniform across different manufacturing methods. Consequently, material characterization must be conducted for individual manufacturing processes based on specific parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Deep Multimodal Networks for M-type Star Classification with Paired Spectrum and Photometric Image

Abstract Traditional stellar classification methods include spectral and photometric classification separately. Although satisfactory results can be achieved, the accuracy could be improved. In this paper, we pioneer a novel approach to deeply fuse the spectra and photometric images of the sources in an advanced multimodal network to enhance the model’s discriminatory ability. We use Transformer as the fusion module and apply a spectrum–image contrastive loss function to enhance the consistency of the spectrum and photometric image of the same source in two different feature spaces. We perform M-type stellar subtype classification on two data sets with high and low signal-to-noise ratio (S/N) spectra and corresponding photometric images, and the F1-score achieves 95.65% and 90.84%, respectively. In our experiments, we prove that our model effectively utilizes the information from photometric images and is more accurate than advanced spectrum and photometric image classifiers. Our contributions can be summarized as follows: (1) We propose an innovative idea for stellar classification that allows the model to simultaneously consider information from spectra and photometric images. (2) We discover the challenge of fusing low-S/N spectra and photometric images in the Transformer and provide a solution. (3) The effectiveness of Transformer for spectral classification is discussed for the first time and will inspire more Transformer-based spectral classification models.

Astronomy & Astrophysics↗
DOE OSTI2020

Modeling Nondestructive Defect Detection in Additively Manufactured Metallic Structures for Nuclear Applications

The future of quickly, economically produced metallic nuclear reactor parts with minimal supply-chain dependence lies in Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM): a 3D printing method involving laser melting and net shaping stainless steel and Inconel metallic powder into a solid structure. However, intrinsic features in LPBF frequently leads to the formation of materials defects, such as pores, within 3D printed structures. As safe long-term use in energy applications requires knowledge of all relevant defects before deployment in a reactor, we must develop methods for nondestructive detection of these defects. We are investigating Pulsed Thermal Tomography (PTT), which is a non-contact nondestructive imaging method scalable to arbitrary structure size. Thermal tomography (TT) is a computational method for 3D spatial reconstruction of material thermal effusivity from flash or pulsed thermography temperature data cube. Thermography data cube consists of 2D surface temperature measurements at different times. The objective of the present work is to investigate limits on defect detection in AM metallic structures with PTT. To this effect, we modeled PTT with COMSOL heat transfer computer simulations. We developed a layered media COMSOL simulation consisting of a Stainless Steel 316 (SS316) plate with an internal layer of un-sintered SS316 powder. Thermophysical properties of the powder layer were modeled with equivalent volume mixing model. To account for partial sintering at the boundary of the defect, the transition between solid and powder layers was modeled as a Gaussian. Using data from COMSOL simulations, we reconstructed depth-dependent thermal effusivity, which allowed defect visibility estimation. A series of parametric studies determined that at 1mm depth, 50µm is the smallest detectable defect. In addition, classification of the defects which can lead to early fatigue of the metallic structure in a reactor is briefly discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

X-ray Computed Tomography as a Metrology Technique for the Analysis of Additively Manufactured Material

X-ray computed tomography (X-ray CT) is an analytical technique used in materials science to non-destructively characterize features in a variety materials like polymer, metals, composites, and explosives. It also has the capability of imaging additively manufacture, machine and assembled parts. The non-destructive imaging allows for the analysis of features (voids and cracks), which give a fundamental understanding of the material characteristics. Additionally, X-ray CT can obtain accurate measurements of dimensional and topographic variations due to different stimuli and assess the accuracy of material production. This study focuses on parts manufactured via metal additive manufacturing (AM). Although AM produces parts faster and easier, the printing process can produce defects (pores and surface roughness) that undermine the part’s mechanical properties and performance. The analysis of 3D printed objects has an asset in that the material has an STL file from which the item was printed, which is not available in many manufactured materials (i.e., foams) due to stochastic structures. For this study, the print accuracy of four additively manufactured cylinders will be assessed via X-ray CTto approximate the surface roughness and visualize any major morphological changes to assess the dimensional accuracy of complex additively manufactured parts. It was concluded that using X-ray CT to measure surface roughness was affective because reasonable surface roughness values were measured. Additionally, itwas determined that small-scale features can be produced via additive manufacturing with strong dimensional accuracy so long as the features are highly complex with sharp grooves.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Testing Capabilities for Superconducting Magnets at Fermilab

This presentation will focus on the current testing capabilities for superconducting magnets at Fermilab, highlighting existing systems, recent upgrades, and future plans. The Vertical Magnet Test Facility (VMTF) remains a cornerstone for testing superconducting magnets in R&D applications, maintaining its established capabilities. Stand 4 continues to support the Hi-Lumi AUP production line of magnets in cryostats and has achieved significant improvements through a redesign of lead connections, resolving issues with liquid helium levels and resistance, culminating in a successful endurance test without interruption. Similarly, Stand 7 underwent commissioning and a lead redesign to reduce heat load, while its core testing capabilities remain unchanged. Looking forward, Stand 3 is undergoing a redesign, with plans to restart and begin commissioning by the end of 2025, introducing new features such as background magnetic field testing and upgraded power supplies. Fermilab is also advancing the High Field Vertical Magnet Test Facility (HFVMTF), designed to support the fusion and magnet R&D program, with commissioning scheduled for 2025 and critical components, including the cryostat and power supplies, being delivered. Enhancing all operations, the newly commissioned IB1 cryoplant now offers automated overnight helium production and efficient transfer to a 10,000-liter dewar, streamlining support across all test stands. This talk will provide a comprehensive overview of testing capabilities, and showcasing how Fermilab is advancing its capabilities to meet the evolving demands of superconducting magnet testing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Photodiode-based machine learning for optimization of laser powder bed fusion parameters in complex geometries

We report the quality of parts produced through laser powder bed fusion additive manufacturing can be irregular, with complex geometries sometimes exhibiting dimensional inaccuracies and defects. For optimal part quality, laser process parameters should be selected carefully prior to printing and adjusted during the print if necessary. This is challenging since approaches to control and optimize the build parameters need to take into account the part geometry, the material, and the complex physics of laser powder bed fusion. This work describes a data-driven approach using experimental diagnostics for the optimization of laser process parameters prior to printing. A training dataset is generated by collecting high speed photodiode signal data while printing simple parts containing key geometry features with various process parameter strategies. Supervised learning approaches are employed to train both a forward model and an inverse model. The forward model takes as inputs track-wise geometry features and laser parameters and outputs the photodiode signal along the scan path. The inverse model takes as inputs the geometry features and photodiode signal and predicts the laser parameters. Given the part geometry and a desired photodiode signal, the inverse model can thus determine the required laser parameters. Two test parts which contain defect-prone features are used to assess the validity of the inverse model. The use of the model leads to improved part quality (higher dimensional accuracy, reduced dross, reduced distortion) for both test geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A systematic study and framework of fringe projection profilometry with improved measurement performance for in-situ LPBF process monitoring

Fringe Projection Profilometry (FPP) is a cost-effective and non-invasive technology that has been shown to measure finer features. Here, in this work, we developed an in-situ FPP method to measure the dynamic topography of powder bed and printed layer during Laser Powder Bed Fusion (LPBF) additive manufacturing (AM) process. A systematic study towards developing a comprehensive framework of LPBF-specific FPP is demonstrated to enhance and evaluate the performance of applying FPP for in-situ LPBF monitoring, including 1) a modified sensor model with localized correction; 2) improved phase unwrapping with FFT filtering 3) quantitative uncertainty analysis; and 4) experimental validation with ex-situ characterization. The developed LPBF-specific FPP system and methods are implemented on a commercial LPBF-AM machine, achieving better accuracy, more robustness, and increased field of view while maintaining sufficient measurement range and decent resolution, in contrast to literature methods. The established FPP framework will facilitate the development of closed-loop control strategies for advancing LPBF based AM.

42 ENGINEERING↗
DOE OSTI2025

Rapid Solidification Effects in Additively Manufactured Si and SiGe Compositions

SiGe alloys have a proven track record as robust high-temperature thermoelectric materials, powering NASA missions like SNAP-10A, LES-9, and Voyager 1 and 2. Enhancing thermoelectric efficiency hinges on minimizing thermal conductivity while preserving electrical conductivity. Rapid solidification via LPBF can generate microstructural features such as subgrain cellular boundaries and twinning, which may help reduce thermal conductivity while preserving semiconducting behavior. Here, this study investigates the potential of laser powder bed fusion (LPBF) additive manufacturing to fabricate nanostructured Si and SiGe thermoelectric materials. High cooling rates (10 5 to 10 7 K/s) rates inherent to the LPBF process are conducive to forming such nanostructures. Moreover, this fabrication technique could also be suitable for fabricating complex geometries needed to achieve improved device level performance. Process mapping of commercial Si powder with irregular morphology was first performed to understand the LPBF processing behavior of this semiconductor material. Subsequent studies included in-house synthesized B-doped (p-type) Si7 8 Ge 22 spherical powder that was produced via ultrasonic atomization. Scan strategies involved multiple laser exposures to mitigate solidification cracking: a high density of >98% was achieved, but solidification cracking could not be fully eliminated. Subsequently, a high electrical resistivity (i.e., low conductivity) was observed, but the measured Seebeck coefficient, ∼230 μV/K @ 500 °C, proved that good semiconductor material was being fabricated. A subgrain cellular structure (5–10 μm) was observed as defined by Ge segregation to the intercellular boundaries. The remelting strategies helped lower the cooling rates in processing SiGe, but this still resulted in high residual stresses, which induced a remarkably high density of twins (78–95%) to accommodate the deformation. This unique grain structure offers an avenue for phonon scattering and potential improvements in thermoelectric performance.

figure of merit↗
DOE OSTI2026

Revealing key structures for reversible sulfur redox in amorphous polymeric sulfur

Amorphous polymeric sulfur cathodes, such as sulfurized polyacrylonitrile (SPAN), enable high-energy lithium-sulfur batteries without cobalt or nickel, leveraging abundant sulfur. However, the limited in situ understanding of their synthesis and electrochemistry has impeded targeted optimization. Here, in this study, we integrate operando high-energy total scattering with sulfur K-edge X-ray absorption spectroscopy to monitor SPAN's formation and cycling in real time. Our results show that S-C bond formation halts further fusion of cyclized polyacrylonitrile, fostering π-π stacking and a transition from long-chain to short-chain sulfur-critical for reversible sulfur redox. These features synergistically minimize polysulfide dissolution and charge-transfer resistance, enabling optimized SPAN to achieve high capacity retention over 1,000 cycles. Operando X-ray absorption spectroscopy reveals that residual protons drive thiol-thione tautomerism, with lithium replacement during the first discharge causing ~20% irreversible capacity loss. To enhance performance, minimizing -NH groups and expanding pyridine networks are key. These findings transform SPAN optimization from empirical tuning to mechanism‑guided engineering and point the way towards sulfur loadings and energy densities competitive with state‑of‑the‑art Li‑ion cathodes.

25 ENERGY STORAGE↗
DOE OSTI2024

Quasi-monoenergetic ion acceleration and neutron generation from laser-driven transverse collisionless shocks

Experiments using the OMEGA EP laser system were performed to study collisionless shock acceleration of ions driven by the interaction of a relativistically intense laser pulse with underdense plasma. The energy spectrum of accelerated ions in the direction transverse to laser propagation is measured to have several narrow-band peaks which are quasi-monoenergetic with a typical energy bandwidth of 3%. In deuterium plasmas, these ions generate a significant number of fast fusion neutrons. Particle-in-cell simulations confirm that these ions were accelerated by the interaction of transverse shocks and that the appearance of quasi-monoenergetic spectral features depends on the growth of an ion-electron two-stream instability during the interaction.

Kordell, P. (ORCID:0000000321456317)↗
DOE OSTI2026

Spontaneously Broken Noninvertible Symmetries in Transverse-Field Ising Qudit Chains

Recent developments have revealed that symmetries need not form a group, but instead can be noninvertible. Here we use analytical arguments and numerical evidence to illuminate how spontaneous symmetry breaking of a noninvertible symmetry is similar yet distinct from ordinary, invertible, symmetry breaking. We consider one-dimensional chains of group-valued qudits, whose local Hilbert space is spanned by elements of a finite group 𝐺 (reducing to ordinary qubits when 𝐺=ℤ 2 ). We construct Ising-type transverse-field Hamiltonians with Rep⁡(𝐺) symmetry whose generators multiply according to the tensor product of irreducible representations (irreps) of the group 𝐺 . For non-Abelian 𝐺 , the symmetry is noninvertible. In the symmetry broken phase there is one ground state per irrep on a closed chain. The symmetry breaking can be detected by local order parameters but, unlike the invertible case, different ground states have distinct entanglement patterns. We show that for each irrep of dimension greater than one the corresponding ground state exhibits string order, entanglement spectrum degeneracies, and has gapless edge modes on an open chain—features usually associated with symmetry-protected topological order. Consequently, domain wall excitations behave as one-dimensional non-Abelian anyons with nontrivial internal Hilbert spaces and fusion rules. Our Letter identifies properties of noninvertible symmetry breaking that existing quantum hardware can probe.

1-dimensional spin chains↗
DOE OSTI2023

A Graphical Model for Fusing Diverse Microbiome Data

This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high-dimensional features, in this case, transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each corresponds to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. Here, the inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Construction of 3D MHD pressure drop correlation and flow characterization in the contraction region of a fusion blanket manifold

Inlet and outlet manifolds are typical components of liquid metal (LM) blanket designs of a fusion power reactor to be used to distribute the LM flow into breeding channels and collect it at the exit of the blanket. High pressure loss in the magnetohydrodynamic (MHD) flows featuring abrupt geometrical changes is one of the main feasibility issues of such designs. Recently, optimization studies were conducted to construct 3D MHD pressure drop correlations for a LM flow in an electrically insulating manifold with gradual expansion. Here, the 3D computational approach developed in that study is applied to the outlet manifold featuring gradual contraction. A systematic analysis was performed with a total number of 135 flow cases computed with COMSOL Multiphysics for Hartmann numbers 1000 < Ha < 10,000, Reynolds numbers 100 < Re < 12,000, and contraction angles 45° < θ < 75° for a fixed contraction ratio of 4. The effects of Ha, Re and θ on the flow recirculation, development length and the total pressure drop were carefully examined. A linear regression analysis was used to determine the power rule of pressure drop coefficient k related to Ha and Re, demonstrating a good match with the Ludford layer theory. Eventually, a correlation for the 3D MHD pressure drop coefficient was constructed as a function of Ha, Re and θ. Further, the results were compared against the inlet manifold. It was found that the flow in the inlet manifold exhibits larger recirculation zones. In the investigated range of Ha, Re and θ, the pressure drop coefficient k of the LM MHD flow in the gradual contraction is only slightly lower (< 8 %) than that in the gradual expansion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Correlations between thermal history and keyhole porosity in laser powder bed fusion

Additive manufacturing has the potential to revolutionize the production of metallic components as it yields near net shape parts with complex geometries and minimizes waste. At the present day, additively manufactured components face qualification and certification challenges due to the difficulty in controlling defects. This has driven a significant research effort aimed at better understanding and improving processing controls – yielding a plethora of in-situ measurements aimed at correlating defects with material quality metrics of interest. In this work, we develop machine-learning methods to learn correlations between thermal history and subsurface porosity for a variety of print conditions in laser powder bed fusion. Un-normalized surface temperatures (in the form of black-body radiances) are obtained using high-speed infrared imaging and porosity formation is observed in the sample cross-section through synchrotron x-ray imaging. To demonstrate the predictive power of these features, we present four statistical machine-learning models that correlate temperature histories to subsurface porosity formation in laser fused Ti-6Al4V powder.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Method for Rapid Modeling of Distortion in Laser Powder Bed Fusion Metal Additive Manufacturing Parts

The simulation and modeling of part-level distortion and residual stress in diverse metal additive manufacturing (AM) geometries has great potential to enable the rapid adoption of this technology in engineering design. Moreover, the use of additive manufacturing component libraries (CLs) offer a computationally efficient means of quantifying these part-level defects resultant from AM processing. We report on how the individual simulations of simple shapes, potential entries in a CL, can be superimposed to provide an indication of distortion and residual stresses in complex geometries. Laser powder bed fusion AM was used to construct test geometries of varied shapes and their combinations in the form of CLs in an effort to characterize location-dependent and feature-dependent distortion distributions. Blue light scanning was used to experimentally measure 3D distortions in order to investigate the interaction between the component shapes and local boundary conditions. Overall, part-level distortions were highly dependent on test component geometry, local boundary conditions, and shape combination. Commercial finite element software was used to verify experimental trends and to make predictions of distortion. The use of CLs resulted in over 20 times savings in computational cost while reproducing overall trends in distortion for test geometry assemblies. Therefore, it is anticipated that the use of CLs for L-PBF AM geometries has demonstrated potential to facilitate efficient simulations of full component AM assemblies, thereby reducing the need for costly trial-and-error-type experimental analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep Koopman Neural Network for Analyzing High-Energy-Density Simulations of Electrical Wire Explosions

Megaampere-scale electrical wire experiments (EWEs) provide a platform for studying magnetohydrodynamic (MHD) instability growth in magneto-inertial fusion (MIF) devices. Even when nonlinear simulations of these experiments can digitally reproduce much of the experimentally observed instability growth, interpreting the results and understanding mode growth and evolution can be non-trivial. As a first step toward providing better interpretation of these simulation features, this work investigates the use of a deep neural network that uses Koopman operator theory to analyze the dynamics of pulsed-power-driven explosions of EWEs. This deep neural network is trained on 1-D resistive MHD simulations of EWEs. This neural network learns to transform the nonlinear data into a lower-dimensional representation where the time dynamics are linear. Layers of this neural network are shown to learn features of the simulations, including the locations of shock waves and different physical regimes of the simulation. Using the learned features, the network can compress a time state of the simulation consisting of 5120 data point into a 36-parameter lower-dimensional latent space embedding. Furthermore, these embeddings are shown to be clustered in the latent space by initial radius and time state.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Study of x-ray fluorescence spectroscopy from high-energy-density plasmas (Final Report)

The primary objective of this subcontract was to support the development of x-ray fluorescence spectroscopy (XFS) as a diagnostic tool for high-energy-density (HED) plasmas. Several experimental campaigns were completed to develop and benchmark XFS at various plasmas conditions of interest for inertial confinement fusion and HED science. The experimental measurements at the Omega laser facility used x-ray absorption spectroscopy (XAS) to determine the plasma temperature from the shape of the K-edge to benchmark XFS data. During the course of these experiments, it was found that XAS can also constrain ionization from bound-bound absorption features, and it became a primary diagnostic in this study. Several experimental campaigns were conducted at the Omega laser facility to refine XAS and XFS measurements of warm dense copper.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗