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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Pressure, temperature, and orientation dependent thermal conductivity of pentaerythritol tetranitrate (PETN)

We use reverse non-equilibrium molecular dynamics to determine the thermal conductivity tensor, κ, of tetragonal pentaerythritol tetranitrate (PETN). The most stable form under standard ambient conditions (PETN I, with space group P4¯2 1 c) is considered in the temperature and pressure intervals 200–500 K and 0–8 GPa, which covers approximately the stability range for this polymorph. We compute κ along the [100] and [001] directions, which is enough to construct the full thermal conductivity tensor for a system with tetragonal symmetry. In addition, we also determine κ along the [010] direction and confirm that κ 100 ~ κ 010 , with less than 4% average absolute error between the two quantities. We observe an anisotropic response for κ, with κ 100 > κ 001 across the whole (T,P) interval, and 37% difference at 300 K, 0 GPa. Furthermore, we provide analytical functions to interpolate κ(P,T) within the fitting interval and serve as input for continuum-scale simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Current-driven transverse domain wall oscillations in perpendicular spin-valve structures

Spin-transfer-driven oscillations of a transverse domain wall confined to a perpendicular spin-valve structure are investigated using a one-dimensional model. The stack consists of a polarizer, nonmagnetic spacer, soft free layer, and pinned magnetic layer. It is found that the domain-wall oscillation frequency is a nonmonotonic, highly asymmetrical function of applied electric current, showing a strong dependence on the current direction and the relative strengths of the interfacial and bulk spin-transfer torques. Micromagnetic analysis reveals that the surprising and atypical oscillator response is due to an interplay between the interfacial spin-transfer torque, the bulk spin-transfer torque, the exchange torque, and the damping torque. The underlying physical and material responses are examined, including the important role of the domain-wall twist. The competitions between the involved torques under different operating conditions suggest that the oscillator could serve as a model system to investigate magnetic and spintronic phenomena at the nanoscale. Finally, the observed current-dependent twisting of the free-layer magnetization about the axis of precession may further be found interesting for investigations of the interaction between spin-polarized current and chiral spin structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

FAST-1.0: A Computer Code for Thermal-Mechanical Nuclear Fuel Analysis under Steady-state and Transients: Developed under NQA-1-2017

Fuel Analysis under Steady-state and Transients (FAST) is the US Nuclear Regulatory Commission (NRC)’s computer code that calculates the steady-state and transient response of nuclear reactor fuel rods during long-term in-reactor burnup, anticipated operational occurrences (AOOs), design basis accidents (DBAs), and dry storage conditions. The code calculates the temperature, pressure, and deformation of a fuel rod as functions of time-dependent fuel rod power and coolant boundary conditions. The phenomena modeled by the code include: heat conduction through the fuel and other materials, heat transfer from the cladding-to-coolant, cladding elastic and plastic deformation, including creep, fuel-cladding mechanical interaction, fission gas release from the fuel, rod internal pressure and void volume, and cladding oxidation. The code contains necessary material and coolant properties, as well as clad-to-coolant heattransfer correlations, for normal operation through postulated accidents for today’s US-based light water reactor (LWR) fuel designs. FAST-1.0 also contains preliminary materials and models for new LWR fuel concepts, such as accident tolerant fuel (ATF), and non-LWR fuel concepts such as metallic fuels for sodium fast reactors (SFRs). FAST has been developed for use on Windows and Linux operating systems. This document describes FAST-1.0, which is the first official version of this code. This document is one of a series of documents on FAST; the other documents detail the material properties used by FAST as well as its integral assessment to experiments and commercial data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

FAST-1.1: A Computer Code for Thermal-Mechanical Nuclear Fuel Analysis under Steady-state and Transients (Developed under NQA-1-2017)

Fuel Analysis under Steady-state and Transients (FAST) is the U.S. Nuclear Regulatory Commission (NRC)’s computer code that calculates the steady-state and transient response of nuclear reactor fuel rods during long-term in-reactor burnup, anticipated operational occurrences (AOOs), design basis accidents (DBAs), and dry storage conditions. The code calculates the temperature, pressure, and deformation of a fuel rod as functions of time-dependent fuel rod power and coolant boundary conditions. The phenomena modeled by the code include: * heat conduction through the fuel and other materials * heat transfer from the cladding-to-coolant * cladding elastic and plastic deformation, including creep * fuel-cladding mechanical interaction * fission gas release from the fuel * rod internal pressure and void volume * cladding oxidation The code contains necessary material and coolant properties, as well as clad-to-coolant heat transfer correlations, for normal operation through postulated accidents and AOOs for today’s U.S.-based light water reactor (LWR) fuel designs. FAST-1.1 also contains preliminary materials and models for new LWR fuel concepts, such as accident tolerant fuel (ATF), and non-LWR fuel concepts such as metallic fuels for sodium fast reactors (SFRs). FAST has been developed for use on Windows and Linux operating systems. This document describes FAST-1.1 and is one of a series of documents on the code; the other documents detail the material properties used by FAST as well as its integral assessment to experiments and commercial data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

FAST-1.2: A Computer Code for Thermal-Mechanical Nuclear Fuel Analysis under Steady-state and Transients

Fuel Analysis under Steady-state and Transients (FAST) is the U.S. Nuclear Regulatory Commission (NRC)’s computer code that calculates the steady-state and transient response of nuclear reactor fuel rods during long-term in-reactor burnup, anticipated operational occurrences (AOOs), design basis accidents (DBAs), and dry storage conditions. The code calculates the temperature, pressure, and deformation of a fuel rod as functions of time-dependent fuel rod power and coolant boundary conditions. The phenomena modeled by the code include: • heat conduction through the fuel and other materials • heat transfer from the cladding-to-coolant • cladding elastic and plastic deformation, including creep • fuel-cladding mechanical interaction • fission gas release from the fuel • rod internal pressure and void volume • cladding oxidation The code contains necessary material and coolant properties, as well as clad-to-coolant heat-transfer correlations, for normal operation through postulated accidents and AOOs for today’s U.S.-based light water reactor (LWR) fuel designs. FAST-1.2 also contains preliminary materials and models for new LWR fuel concepts, such as accident tolerant fuel (ATF), and non-LWR fuel concepts such as metallic fuels for sodium fast reactors (SFRs). FAST has been developed for use on Windows and Linux operating systems. This document describes FAST-1.2 and is one of a series of documents on the code; the other documents detail the material properties used by FAST as well as its integral assessment to experiments and commercial data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

FAST-1.2.2: A Computer Code for Thermal-Mechanical Nuclear Fuel Analysis under Steady-state and Transients

Fuel Analysis under Steady-state and Transients (FAST) is the U.S. Nuclear Regulatory Commission (NRC)’s computer code that calculates the steady-state and transient response of nuclear reactor fuel rods during long-term in-reactor burnup, anticipated operational occurrences (AOOs), design basis accidents (DBAs), and dry storage conditions. The code calculates the temperature, pressure, and deformation of a fuel rod as functions of time-dependent fuel rod power and coolant boundary conditions. The phenomena modeled by the code include heat conduction through the fuel and other materials, heat transfer from the cladding-to-coolant, cladding elastic and plastic deformation (including creep), fuel-cladding mechanical interaction, fission gas release from the fuel, rod internal pressure, void volume, and cladding oxidation. The code contains necessary material and coolant properties, as well as clad-to-coolant heat transfer correlations, for normal operation through postulated accidents and AOOs for today’s U.S.-based light water reactor (LWR) fuel designs. FAST-1.2.2 also contains preliminary materials and models for new LWR fuel concepts, such as accident tolerant fuel (ATF), and non-LWR fuel concepts such as metallic fuels for sodium fast reactors (SFRs). FAST has been developed for use on Windows and Linux operating systems. This document describes FAST-1.2.2 and is one of a series of documents on the code; the other documents detail the material properties used by FAST as well as its integral assessment to experiments and commercial data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Perovskite Quantum Dot Solar Cells: Mapping Interfacial Energetics for Improving Charge Separation

Colloidal halide perovskite nanocrystals or quantum dots (QDs) show similar defect tolerance as thin film perovskite materials with added nanoscale phenomena. Perovskite QD solar cells have demonstrated efficiencies of 16.6%, greater than that of any other QD material system. While the efficiency lags behind the best thin-film perovskite devices, these solar cells could have advantages over the thin-film versions in terms of processability, phase stability, and high open-circuit voltages. However, some operating principles behind perovskite quantum dot device stacks and the associated electric field properties are still unknown. Here, we characterize the junction structure within perovskite QD solar cells, by exposing functioning cross-sections and using nanometer-scale Kelvin probe force microscopy to offer insight into the selection and performance of charge selective contacts. We also evaluated various solar cell device architectures with different selective contacts to isolate the role of each junction in device performance. We show that in high-performance n-i-p architectures, both electron- and hole-transport layer (HTL) interfaces possess a strong electric field, but in the case of the inverted p-i-n architecture, we find that high interfacial recombination at the HTL/QD junction is responsible for subpar device performance. Perovskite QD and thin film materials can synergistically be combined to offer more design flexibility in PV devices, and here we demonstrate that the interface between perovskite thin films and QDs are relatively benign and amenable for synergistic device design.

14 SOLAR ENERGY↗
DOE OSTI2021

Machine learned features from density of states for accurate adsorption energy prediction

Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Diffractive optical computing in free space

Abstract Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Generalizable Approach to Direct the Self-Assembly of Functional Blue-Phase Liquid Crystals

Blue phases (BPs) are soft and stimuli-responsive photonic crystals that are interesting for sensing, display, and lasing applications. Polycrystallinity has, however, limited both the study and applicability of BPs. Continuum simulations, which lack molecule-specific details, predict that striped chemical patterns, consisting of alternating homeotropic and planar regions, can be used to nucleate single crystals of BPs. Here it is experimentally demonstrated that, independent of the chemistry and complexity of the BP forming material, chemical patterns direct the self-assembly of BPs; these results indicate that a general, thermodynamics-based continuum description of BP self-assembly is adequate for a broad range of materials. When the pattern periodicity equals the BPII unit cell size, single crystals with (100) orientation form for all studied materials. Chemical patterns also promote the growth of BPI crystals with (110) orientation. Importantly, the self-assembly of a photopolymerizable BP is directed and thermally stable, UV-polymerized single crystals are prepared. As a result, the ability to create arbitrarily large, polymeric photonic single crystals with uniform lattice orientation, may have broad implications for optical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Photoluminescence in Tagging: A Glowing Review

This literature review explores the options of using photoluminescence for tagging uranium fuel and zirconium cladding in order to easily and quickly associate interdicted material to its facility of origin or pathway after loss of institutional control. Photoluminescence is the spontaneous emission of radiation by a substance when excited by interaction with light. The source of photoluminescence can be from organic, inorganic, and organometallic compounds. Each luminophore, or compound responsible for luminescent properties, has individual strengths and weaknesses, depending on the intended use. Many factors can affect the brightness, lifetime, color, and detectability of luminophores, such as humidity, temperature, oxygen availability, noise from radiation–induced luminescence, and the presence of quenching substances like iron. Application of luminescent compound to the surface of an item can be done using paint, dye, thin film, thick film, or electrochemical coating. Several of the application techniques may have secondary functions such as corrosion resistance and detection, heat resistance, protective properties, and more. When considering cladding, there are various successful studies on the application of luminescent coatings on zirconium. However, significant research will be necessary to explore which luminophore and which application technique would best fit the intended use. In order to proceed with this research, a baseline of the natural luminescence of the intended base material (nuclear fuel or cladding) is necessary. While there may be issues with background noise, quenching from the environment, quenching from the substrate, signal to noise weakness, etc. the multitude of options may make photoluminescent tagging an option. In summary, this review has demonstrated that photoluminescence is a potential option to tag nuclear fuel and cladding, but additional paper–studies, testing, and research is required to understand the compatibility of luminescent tags in nuclear reactor environments, manufacturability, and detectability.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Structural and magnetic properties of Pt/Co/Mn-based multilayers

Magnetic multilayers are a rich class of material systems with numerous highly tunable physical parameters that determine both their magnetic and electronic properties. Here, we present a comprehensive experimental study of the system Pt/Co/Mn, which extends the group of Pt / Co / X ( X = metal) multilayers that have been investigated thus far. We demonstrate that an increasing Co layer thickness changes the magnetic anisotropy from out of plane to in plane, whereas the deposition of thicker Mn layers leads to a decrease in the saturation magnetization. Temperature-dependent magnetometry measurements reinforce the hypothesis of antiferromagnetic coupling at the Co/Mn interfaces being responsible for the observed Mn thickness dependence of the magnetization reversal. Moreover, magneto-optical imaging experiments indicate systematic changes in magnetic domain patterns as a function of the Co and Mn layer thickness, suggesting the existence of bubblelike domains—potentially even magnetic skyrmions—in the case of sufficiently thick Mn layers, which are expected to contribute to a sizable Dzyaloshinskii-Moriya interaction in the multilayer stacks. As a result, we identify Pt/Co/Mn as a highly complex multilayer system with strong potential for further fundamental studies and possible applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Increased damping through captured powder in additive manufacturing

This paper describes the retention of metal powder within additively manufactured structures to achieve increased damping. In this work, solid and hollow (powder filled) aluminum cylinders were fabricated in using laser powder bed fusion where the internal, captured powder in the hollow cylinders served as an energy dissipation mechanism. Impact testing was completed to compare the frequency response functions of three cylinder geometries: one solid cross-section and two hollow cross-sections with different inner diameters and, therefore, different powder volumes. Increased damping with larger inner diameter was observed. The damping was quantified using a structural damping model.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exchange bias in La 0.7 Sr 0.3 CrO 3 /La 0.7 Sr 0.3 MnO 3 /La 0.7 Sr 0.3 CrO 3 heterostructures

In the recent past, heterostructures of magnetic oxide thin films have attracted a great deal of research excitement due to very interesting physical properties such as antiferromagnetic interlayer coupling, tunable exchange-bias, interfacial driven magnetic properties and high mobility electron gas across the interfaces. In this work, we report on the comprehensive magnetic properties observed from the heterostructures of (2 unit cells) La 0.7 Sr 0.3 CrO 3 /(8 unit cells) La 0.7 Sr 0.3 MnO 3 /(2 unit cells) La 0.7 Sr 0.3 CrO 3 , which are epitaxially deposited on SrTiO 3 substrate by plasma-assisted oxide molecular beam epitaxy. Using SQUID magnetometer, the magnetic properties are studied when the magnetic field was applied both in plane and out of plane. The Curie temperature of this structure is found to be at 290 K. Most significantly, at 2 K, we observed a complete up/down shift (along magnetization axis) of hysteresis loop when the sample was cooled under a magnetic field of ± 5000 Oe in the in-plane configuration. We believe that the strong antiferromagnetic (super) exchange coupling of Mn-Cr across the two interfaces is responsible for the observed exchange bias. We will present and discuss our in-detailed experimental findings collected on this heterostructure as a function of temperature and magnetic field.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Unified, Geometric Framework for Nonequilibrium Protocol Optimization

Controlling thermodynamic cycles to minimize the dissipated heat is a long-standing goal in thermodynamics, and more recently, a central challenge in stochastic thermodynamics for nanoscale systems. Here, we introduce a theoretical and computational framework for optimizing nonequilibrium control protocols that can transform a system between two distributions in a minimally dissipative fashion. These protocols optimally transport a system along paths through the space of probability distributions that minimize the dissipative cost of a transformation. Furthermore, we show that the thermodynamic metric—determined via a linear response approach—can be directly derived from the same objective function that is optimized in the optimal transport problem, thus providing a unified perspective on thermodynamic geometries. As a result, we investigate this unified geometric framework in two model systems and observe that our procedure for optimizing control protocols is robust beyond linear response.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chapter 4: Physically informed deep learning networks for simulating microstructure evolution of 3D polycrystals

As discussed in the previous chapter, high energy diffraction microscopy (HEDM) is used to study the micromechanical evolution of a material during in situ loading. HEDM experiments have been used to verify crystal plasticity (CP) simulations [119, 91, 90, 120], for experimental planning, material design, and to further analyze experimental results. However, Fast Fourier transform-based CP (CP-FFT) or finite element-based CP (CP-FE) methods are often too slow to be used in real-time during an experiment. CP-FFT is faster than CP-FE simulations due to the absence of meshing, but can still take hours to simulate the response of a single volume depending on the size and number of strain steps [127]. Reducing computation time would create a larger exploration space in planning and design, and enable faster analysis of experimental results and real-time feedback during an experiment. This research expands upon previous works to develop a workflow for predicting the full-field evolution of a 3D polycrystal. The workflow is simplified from previous works to predict only orientation and elastic strain tensors (from which stress tensors are calculated). The network is physically informed through loss functions and network architecture for a more robust model. The orientation predictions are informed about the cubic crystal symmetry of the material by incorporating disorientation and misorientation information into the network architecture and loss. The Von Mises stress is used to enforce the correct stress-strain trends in the strain tensor predictions. Additional total strain steps from the elastic and elastoplastic region are included to better capture the stress-strain evolution at smaller total strain steps. Material and hardening parameters are additional inputs into the networks to further inform the network and to study the network’s ability to predict different materials other than those used for training.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Light-Induced Decoupling of Electronic and Magnetic Properties in Manganites

The strongly correlated material La 0.7 ⁢Sr 0.3 ⁢Mn⁢O 3 (LSMO) exhibits metal-to-insulator and magnetic transition near room temperature. Although the physical properties of LSMO can be manipulated by strain, chemical doping, temperature, or magnetic field, they often require large external stimuli. Further, to include additional flexibility and tunability, we developed a hybrid optoelectronic heterostructure that uses photocarrier injection from cadmium sulfide (Cd⁢ S) to an LSMO layer to change its electrical conductivity. LSMO exhibits no significant optical response; however, the Cd ⁢S/LSMO heterostructures show an enhanced conductivity, with a resistance drop of about 37%, at the transition temperature under light stimuli. This enhanced conductivity in response to light is comparable to the effect of a 9 T magnetic field in pure LSMO. Surprisingly, the optical and magnetic responses of Cd⁢ S/LSMO heterostructures are decoupled and exhibit different effects when both stimuli are applied. This unexpected behavior shows that heterostructuring strongly correlated oxides may require a new understanding of the coupling of physical properties across the transitions and provide the means to implement new functionalities.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Distilling physical origins of hardness in multi-principal element alloys directly from ensemble neural network models

Abstract Despite a plethora of data being generated on the mechanical behavior of multi-principal element alloys, a systematic assessment remains inaccessible via Edisonian approaches. We approach this challenge by considering the specific case of alloy hardness, and present a machine-learning framework that captures the essential physical features contributing to hardness and allows high-throughput exploration of multi-dimensional compositional space. The model, tested on diverse datasets, was used to explore and successfully predict hardness in Al x Ti y (CrFeNi) 1- x - y , Hf x Co y (CrFeNi) 1- x - y and Al x (TiZrHf) 1- x systems supported by data from density-functional theory predicted phase stability and ordering behavior. The experimental validation of hardness was done on TiZrHfAl x . The selected systems pose diverse challenges due to the presence of ordering and clustering pairs, as well as vacancy-stabilized novel structures. We also present a detailed model analysis that integrates local partial-dependencies with a compositional-stimulus and model-response study to derive material-specific insights from the decision-making process.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗