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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Experimental validation of shock propagation through a foam with engineered macro-pores

The engineered macro-pore foam provides a new way to study thermonuclear burn physics by utilizing capsules containing deuterated (D) foam and filling tritium (T) gas in the engineered macro-pores. The implosion of a thermonuclear capsule filled with an engineered macro-pore foam will be complex due to the interaction of a shock wave with the engineered macro-pores. It is our goal to quantify how substantially complex foam structures affect the shape of shock and bulk shock speed. A cylinder-shape shock tube experiment has been designed and performed at the Omega Laser Facility. In order to examine how a foam structure will affect shock propagation, we performed several tests varying (1) engineered macro-pore size, (2) average foam density, and (3) with/without neopentane (C 5 H 12 ) gas. X-ray radiographic data indicate that shock speed through engineered macro-pore foams depends strongly on average foam density and less on pore size. In this work, experimental shock propagation data helped guide two numerical simulation approaches: (1) a 2D simulation with homogenizing foams rather than explicitly simulating engineered macro-pores and (2) a 2D toroidal-pore approximation adopting a toroidal-tube geometry to model engineered macro-pores.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Mineralized sclerites in the gorgonian coral Leptogorgia chilensis as a natural jamming system

The soft corals (Cnidaria, Octocorallia), a diverse group of colonial marine invertebrates, can reversibly tune their body stiffness in response to external stimuli. This capability is attributed to their dynamic skeletal systems, which consist of thousands of mineralized skeletal elements, called sclerites, embedded within a gel-like matrix that swells/deswells and unjams/jams the sclerites, thus modulating skeletal stiffness. While sclerite morphology is widely used for species identification, its role in the mechanical performance of a soft coral’s skeletal system is largely unknown. Here, we investigated structure-jamming relationships in sclerite-based skeletal architectures using the red gorgonian octocoral Leptogorgia chilensis as a model system. The sclerites of L. chilensis exhibit a shaft-like geometry with two axial branches and two sets of triradiate side branches, which are aligned with the crystallographic symmetry of the constituent magnesium-containing calcite. By combining multiscale three-dimensional (3D) structural characterization, parametric geometrical modeling, 3D printing, mechanical testing, and discrete element simulations, we demonstrate how sclerite geometry achieves a balanced jamming performance in terms of stiffness, weight, strength, and fracture resistance in comparison to alternative geometries parametrically modified from the native sclerites (e.g., changes in the length and number of side branches). Here, we also found that these performance metrics are achieved through the effective interlocking among side and axial branches, which is further enhanced by the fractal-like microscopic spikes on the branch tips. The findings in this natural jamming system offer insights for designing synthetic mechanotunable material architectures for a wide range of applications, from soft robotics to mechanical dampeners.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Element differentiation with a Hartmann- based X-ray phase imaging system

Significant efforts are currently ongoing in X-ray imaging to provide multimodal imaging systems, targeting better sensitivity and specificity for both biomedical or non-destructive testing applications. Knowing the elemental composition of specific structures, such as breast microcalcifications in mammography, would help to differentiate malign and benign tumours. Standard X-ray Phase Contrast Imaging techniques provide only qualitative information on elements with similar absorption properties. However, their chemical composition can be determined from the measurement of the phase as it is directly related to the optical index of elemental materials. We will show new experimental results obtained with an X-ray phase imaging system based on a Hartmann mask. Early data treatment succeeded in retrieving both the real and imaginary parts of the refractive index. The system demonstrates the capability to discriminate materials based on elemental composition.

36 MATERIALS SCIENCE↗
DOE OSTI2015

E-ELT constraints on runaway dilaton scenarios

We use a combination of simulated cosmological probes and astrophysical tests of the stability of the fine-structure constant α, as expected from the forthcoming European Extremely Large Telescope (E-ELT), to constrain the class of string-inspired runaway dilaton models of Damour, Piazza and Veneziano. We consider three different scenarios for the dark sector couplings in the model and discuss the observational differences between them. We improve previously existing analyses investigating in detail the degeneracies between the parameters ruling the coupling of the dilaton field to the other components of the universe, and studying how the constraints on these parameters change for different fiducial cosmologies. We find that if the couplings are small (e.g., α{sub b} = α{sub V} ∼ 0) these degeneracies strongly affect the constraining power of future data, while if they are sufficiently large (e.g., α{sub b} ∼> 10{sup −5}−α{sub V} ∼> 0.05, as in agreement with current constraints) the degeneracies can be partially broken. We show that E-ELT will be able to probe some of this additional parameter space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Differential cosmic expansion and the Hubble flow anisotropy

The Universe on scales 10–100 h {sup −1}Mpc is dominated by a cosmic web of voids, filaments, sheets and knots of galaxy clusters. These structures participate differently in the global expansion of the Universe: from non-expanding clusters to the above average expansion rate of voids. In this paper we characterize Hubble expansion anisotropies in the COMPOSITE sample of 4534 galaxies and clusters. We concentrate on the dipole and quadrupole in the rest frame of the Local Group. These both have statistically significant amplitudes. These anisotropies, and their redshift dependence, cannot be explained solely by a boost of the Local Group in the Friedmann-Lemaitre-Robertson-Walker (FLRW) model which expands isotropically in the rest frame of the cosmic microwave background (CMB) radiation. We simulate the local expansion of the Universe with inhomogeneous Szekeres solutions, which match the standard FLRW model on ∼> 100 h {sup −1}Mpc scales but exhibit nonkinematic relativistic differential expansion on small scales. We restrict models to be consistent with observed CMB temperature anisotropies, while simultaneously fitting the redshift variation of the Hubble expansion dipole. We include features to account for both the Local Void and the 'Great Attractor'. While this naturally accounts for the Hubble expansion and CMB dipoles, the simulated quadrupoles are smaller than observed. Further refinement to incorporate additional structures may improve this. This would enable a test of the hypothesis that some large angle CMB anomalies result from failing to treat the relativistic differential expansion of the background geometry; a natural feature of solutions to Einstein's equations not included in the current standard model of cosmology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Kinetic modeling of hot tail runaway electron generation during plasma disruptions using the JOREK code

The generation of runaway electrons (REs) during disruptions poses a significant challenge for the operation of tokamaks. The production of these high-energy electrons can cause substantial damage, particularly when the plasma current is high, making it a critical concern for ITER. For the high-temperature plasmas anticipated in ITER, the primary generation of REs may be dominated by the hot tail mechanism, which consists of the acceleration of hot electrons from the pre-disruption population which have not yet thermalized with the bulk following the rapid cooling of the plasma. To account for the significant 3D effects on RE production, a hot tail modeling framework has been developed within the non-linear 3D extended MHD code JOREK. This paper presents the structure of this framework, which is based on test electrons evolving in MHD fields. The verification of the method shows good agreement with the reference DREAM code for 0D test cases, as well as for axisymmetric simulations of 15 MA ITER H-mode disruption scenarios. Furthermore, a proof-of-principle application to a DIII-D case demonstrates the framework’s capability to capture for the first time the hot tail generation in 3D MHD simulations in realistic geometry. Preliminary results suggest that the production of REs is significantly reduced by stochastic losses.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening ∗

Abstract Interface structures in complex oxides remain an active area of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D STEM datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO 3 and SrTiO 3 , using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We test the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and create a DCNN regression model to predict step positions. We quantify the applicability of the model to different thicknesses and the transferability of the approach. The method shown here is general and can be applied for any inverse imaging problem where forward models are present.

42 ENGINEERING↗
DOE OSTI2018

Broad-band trajectory mechanics

We present here a trajectory-based solution to the elastodynamic equation of motion that is valid across a wide range of seismic frequencies. That is, the derivation of the solution does not invoke a high-frequency assumption or require that the medium have smoothly varying properties. The approach, adopted from techniques used in quantum dynamics, produces a set of coupled ordinary differential equations for the trajectory, the slowness vector and the elastic wave amplitude along the ray path. The trajectories may be determined by a direct solution of the governing equations or derived as the by-product of a numerical wavefield simulation. Synthetic tests with interfaces and layers containing increasingly narrow transition zones indicate that the conventional high-frequency trajectories associated with the eikonal equation bend too sharply into high-velocity regions as the wavelength exceeds the transition zone width. Tests in a velocity model, based upon mapped structural surfaces from the Geysers geothermal field in California, indicate that discrepancies between high-frequency and broad-band trajectories can exceed several hundred metres at wavelengths of 1 Hz. An application to a crosswell tomographic imaging experiment demonstrates that the technique provides a basis for the seismic monitoring of fluid flow along narrow features such as fracture zones.

58 GEOSCIENCES↗
DOE OSTI2020

Capturing multireference excited states by constrained-density-functional theory

The computation of excited electronic states with commonly employed (approximate) methods is challenging, typically yielding states of lower quality than the corresponding ground state for a higher computational cost. In this work, we present a mean-field method that extends the previously proposed excited constrained-density-functional theory (XCDFT) from single Slater determinants to ensemble one-body reduced density matrices for computing low-lying excited states. The method still retains an associated computational complexity comparable to a semilocal density functional theory (DFT) calculation while at the same time it is capable of approaching states with multireference character. Further, we benchmark the quality of this method on well-established test sets, finding good descriptions of the electronic structure of multireference states and maintaining an overall accuracy for the predicted excitation energies comparable to semilocal time-dependent DFT.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Global calculation of two-neutrino double- β decay within the finite amplitude method in nuclear density functional theory

Two-neutrino double-beta (2νββ) decay has been used to constrain the neutron-proton part of effective interactions, which in turn is used to compute the nuclear matrix elements for neutrinoless double-beta decay, the observation of which would have important consequences for fundamental physics. We carefully examine 2νββ matrix elements within the proton-neutron quasiparticle random-phase approximation with nuclear energy density functionals. Here we work with functionals that are fit globally to single-beta-decay half-lives and charge-exchange giant-resonance energies, but not to 2νββ half-lives themselves, to evaluate the 2νββ nuclear matrix elements for all important nuclei, including those whose half-lives have not yet been measured. Such a comprehensive evaluation in large model spaces without configuration truncation requires an efficient computational scheme; we employ a double contour integration within the finite amplitude method. The results generally reproduce the nuclear matrix element extracted from half-lives well, without the use of any of those half-lives in the fitting procedure. We present predictions of the matrix elements in a total of 27 nuclei with half-lives that are still unmeasured.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

One-proton and one-neutron knockout reactions from N = Z = 28 56 Ni to the A = 55 mirror pair 55 Co and 55 Ni

Here, we present a high-resolution in-beamγ-ray spectroscopy study of excited states in the mirror nuclei 55 Co and 55 Ni following one-nucleon knockout from a projectile beam of 56 Ni. The newly determined partial cross sections and theγ-decay properties of excited states provide a test of state-of-the-art nuclear structure models and probe mirror symmetry in unique ways. The new experimental data are compared to large-scale shell-model calculations in the full pf space and including charge-dependent contributions. A mirror asymmetry for the partial cross sections leading to the two lowest 3/2 – states in the A = 55 mirror pair was identified as well as a significant difference in the E 1 decays from the 1/2 + 1 state to the same two 3/2 – states. The mirror asymmetry in the partial cross sections cannot be reconciled with the present shell-model picture or small mixing introduced in a two-state model. The observed mirror asymmetry in theE1 decay pattern, however,points at stronger mixing between the two lowest 3/2 – states in 55 Co than in its mirror 55 Ni.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Ferroelectric phase transition in group-IV monochalcogenides from an equivariant machine learned force field

Group-IV monochalcogenides are a class of layered ferroelectric semiconductors that have demonstrated spontaneous intrinsic polarization above room temperature. Here, in this study, we use the multi-atomic cluster expansion (MACE) machine learning architecture to train and test a force field capable of modeling the structural properties and second-order ferroelectric-to-paraelectric phase transition in a Group-IV monochalcogenide, GeSe. The model captures the double-well potential energy surface associated with the onset of macroscopic polarization in bulk GeSe within 12.5 meV/atom, as well as near-equilibrium properties like the phonon dispersion. The development of this quantitatively accurate force field enables long-time molecular dynamics simulations, which predict the critical temperature of the ferroelectric-to-paraelectric phase transition in bulk GeSe to be T c = 600 K. This study demonstrates the capabilities of equivariant force-fields to accurately describe phenomena associated with structural symmetry breaking.

ferroelectricity↗
DOE OSTI2024

DOC-DICAM: Domain Aware One Class Defect Identification in Composite Aerostructure Material

Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.

anomaly detection↗
DOE OSTI2018

Prescriptive provenance for streaming analysis of workflows at scale

Abstract- We extend our approach capturing and relating the provenance and performance metrics of computational workflows as a diagnostic tool for runtime optimization and placement. One important challenge is the volume of extracted data, both for performance metrics and provenance, even when specifying filters and focusing on quantities of interest in a simulation. We reduce this data by performing anomaly detection on streaming data and store provenance for the detected anomalies, an approach we call prescriptive provenance. This paper discusses the Chimbuko architecture enabling the approach. We present the use of a protein structure propagation workflow based on NWChemEx. We are testing algorithms for anomaly detection and present preliminary results here obtained with Local Outlier Factor. While scaling remains a challenge, these results show that our robust Chimbuko architecture for streaming analysis with prescriptive provenance is a promising approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Tailoring Thermal and Mechanical Performance Through Multimaterial Laser Powder Directed Energy Deposition of Copper and 17-4PH Stainless Steel

This study investigates the additive manufacturing (AM) processing, microstructural evolution, and resulting mechanical and thermal properties of multimaterial components combining 17-4PH stainless steel and pure copper (Cu) fabricated via laser powder directed energy deposition (LP-DED). Conventional tooling steels exhibit limited thermal conductivity, significantly constraining production throughput in high-volume processes. Incorporating Cu, with its superior thermal conductivity, could significantly enhance tool performance, though Cu and steel present metallurgical incompatibilities when processed via AM. A systematic investigation was conducted across compositions ranging from 0 to 100 wt% Cu, revealing critical thresholds influencing solidification behavior, defect formation, microstructure, hardness, and thermal transport. Optical microscopy, electron backscatter diffraction (EBSD), hardness testing, and thermal conductivity measurements provided comprehensive process–structure–property correlations. Severe hot cracking occurred at low-Cu contents (6–25 wt%), aligning generally well with crack susceptibility modeling, with an unexpected discrepancy at 25 wt%. Porosity remained low (≥99% dense) throughout the compositional spectrum. EBSD analysis revealed a transformation from columnar martensitic structures at low-Cu contents to equiaxed FCC Cu-dominated structures at higher Cu concentrations, highlighting the complex microstructural transitions driven by Cu-induced changes in solidification and phase stability. Hardness decreased from 330 HV (pure 17-4PH) to 62 HV (pure Cu), consistent with microstructural changes. Concurrently, thermal conductivity improved substantially from 13.5 W/m K to 367.9 W/m K, emphasizing Cu’s dominant role in thermal transport. The findings highlight the feasibility of leveraging compositional gradients between 17-4PH and Cu to achieve tailored tooling with optimized thermal and mechanical performance.

17-4PH↗
DOE OSTI2016

Sci-Fri AM: Quality, Safety, and Professional Issues 01: CPQR Technical Quality Control Suite Development including Quality Control Workload Results

A close partnership between the Canadian Partnership for Quality Radiotherapy (CPQR) and the Canadian Organization of Medical Physicist’s (COMP) Quality Assurance and Radiation Safety Advisory Committee (QARSAC) has resulted in the development of a suite of Technical Quality Control (TQC) Guidelines for radiation treatment equipment, that outline specific performance objectives and criteria that equipment should meet in order to assure an acceptable level of radiation treatment quality. The framework includes consolidation of existing guidelines and/or literature by expert reviewers, structured stages of public review, external field-testing and ratification by COMP. The adopted framework for the development and maintenance of the TQCs ensures the guidelines incorporate input from the medical physics community during development, measures the workload required to perform the QC tests outlined in each TQC, and remain relevant (i.e. “living documents”) through subsequent planned reviews and updates. This presentation will show the Multi-Leaf Linear Accelerator document as an example of how feedback and cross-national work to achieve a robust guidance document. During field-testing, each technology was tested at multiple centres in a variety of clinic environments. As part of the defined feedback, workload data was captured. This lead to average time associated with testing as defined in each TQC document. As a result, for a medium-sized centre comprising 6 linear accelerators and a comprehensive brachytherapy program, we evaluate the physics workload to 1.5 full-time equivalent physicist per year to complete all QC tests listed in this suite.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a super-parameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Additionally, even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s -1 . In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.

54 ENVIRONMENTAL SCIENCES↗