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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Ordered hexagonal columnar liquid crystalline self-assembly of mesogen-free sulfonylated side-chain chiral polyethers and their high dielectric property

To achieve large spontaneous polarization for ferroelectric liquid crystalline polymers, we have designed a series of mesogen-free isotactic polyethers with sulfonylated side chains. Different from prior studies using linear alkyl side chains, in this work a methyl branch was implemented in the alkyl side chains of the chiral polyethers. As a result of the continuous twist from the methyl branches and strong dipole-dipole interactions among neighboring sulfonyl groups, a helical chain conformation with tilted side chains was achieved. When the branched side chains were short (e.g., C5) and double chirality existed in both the main chain and side chains, a crystalline structure was obtained. When the branched side chains were long (e.g., ≥ C8) and single chirality existed in the main chain, an ordered hexagonal columnar liquid crystalline phase was induced. Their high-field dielectric property was studied by electric displacement-electric field (D-E) loop tests; however, no ferroelectric behavior was observed up to 200 MV/m. It was considered that the dipole-dipole interaction among neighboring sulfonyl groups in the helical chains was too strong to allow easy ferroelectric flipping.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Random strain induced correlations in materials with intertwined nematic and magnetic orders

Electronic nematicity is rarely observed as an isolated instability of a correlated electron system. Instead, in iron pnictides and in certain cuprates and heavy-fermion materials, nematicity is intertwined with an underlying spin-stripe or charge-stripe state. As a result, random strain, ubiquitous in any real crystal, creates both random-field disorder for the nematic degrees of freedom and random-bond disorder for the spin or charge ones. Here, we put forward an Ashkin-Teller model with random Baxter fields to capture the dual role of random strain in nematic systems for which nematicity is a composite order arising from a stripe state. Using Monte Carlo to simulate this random Baxter-field model, we find not only the expected break-up of the system into nematic domains, but also the emergence of nontrivial disorder-promoted magnetic correlations. Such correlations enhance and tie up the fluctuations associated with the two degenerate magnetic stripe states from which nematicity arises, leaving characteristic signatures in the spatial profile of the magnetic domains, in the configurational space of the spin variables, and in the magnetic noise spectrum. We discuss possible experimental manifestations of these effects in iron-pnictide superconductors. Furthermore, our work establishes the random Baxter-field model as a more complete alternative to the random-field Ising model to describe complex electronic nematic phenomena in the presence of disorder.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Discerning element and site-specific fluctuations of the charge-orbital order in Fe 3 O 4 below the Verwey transition

Despite countless experimental probes into magnetite's electronic structure across the Verwey transition Fe 3 O 4 , the exact origin of this archetypical metal-insulator transition remains a puzzle. Advanced x-ray diffraction techniques have mostly resolved the monoclinic structure of the insulating phase, including interatomic bond lengths, but the complexity of the charge-orbitally ordered state is difficult to disentangle. Here we combined resonant elastic x-ray scattering and x-ray photon correlation spectroscopy to probe charge-orbital fluctuations in the insulating state of magnetite. By accessing the Bragg forbidden $(00\frac{1}{2})_{c}$ peak at the oxygen K-edge, we complement our previous study on the iron L 3 – edge to reveal the dynamics of the iron 3d and oxygen 2p orbital domains. Our new results reveal a decoupling of the orbital correlation lengths between the oxygen 2p states and site-specific iron 3d states, and we further show charge-orbital domain fluctuations at the iron t 2g orbital sites of trimeron chains. These results also demonstrate an experimental method capable of distinguishing electronic dynamics between the oxygen ligands and the transition metal that underpins emergent behaviors in complex oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Nematic Order, Plasmonic Switching and Self-Patterning of Colloidal Gold Bipyramids

Dispersing inorganic colloidal nanoparticles within nematic liquid crystals provides a versatile platform both for forming new soft matter phases and for predefining physical behavior through mesoscale molecular-colloidal self-organization. However, owing to formation of particle-induced singular defects and complex elasticity-mediated interactions, this approach has been implemented mainly just for colloidal nanorods and nanoplatelets, limiting its potential technological utility. Here, orientationally ordered nematic colloidal dispersions are reported of pentagonal gold bipyramids that exhibit narrow but controlled polarization-dependent surface plasmon resonance spectra and facile electric switching. Bipyramids tend to orient with their C 5 rotation symmetry axes along the nematic director, exhibiting spatially homogeneous density within aligned samples. Topological solitons, like heliknotons, allow for spatial reorganization of these nanoparticles according to elastic free energy density within their micrometer-scale structures. With the nanoparticle orientations slaved to the nematic director and being switched by low voltages ≈1 V within a fraction of a second, these plasmonic composite materials are of interest for technological uses like color filters and plasmonic polarizers, as well as may lead to the development of unusual nematic phases, like pentatic liquid crystals.

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

Semi-inclusive single-jet production in DIS at next-to-leading order in the Color Glass Condensate

Within the Color Glass Condensate (CGC) effective field theory, we derive the next-to-leading order (NLO) cross-section for the single-jet semi-inclusive cross-section in deep inelastic scattering (DIS) at small x, for both longitudinally and transversely polarized virtual photons. We provide analytic expressions, valid at finite N c and suitable for numerical evaluation, for both the cross-section differential in rapidity and transverse momentum and the cross-section differential in rapidity only. Our NLO formulae demonstrate that the very forward rapidity regime is plagued by large double logarithmic corrections coming from phase space constraints on soft gluons close to the kinematic threshold for jet production. A joint resummation of small-x and threshold logarithms at single logarithmic accuracy is proposed to remedy the instability of the cross-section in this regime. By integrating over the single-jet phase space, we recover known results for the NLO DIS structure functions at small x, previously obtained using the optical theorem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning

One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an AM model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, ultimately reducing the time/cost of AM control and optimization processes. Our adoption of the operator learning (OL) approach enabled us to learn a family of differential equations produced by altering process variables in the laser’s Gaussian point heat source. More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses. Furthermore, we benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM. Ultimately, we found that OL methods offer comparable performance and, in terms of accuracy and generalizability, even outperform DNN at predicting scalar model responses. The DNN-based ROM afforded the fastest training time. Furthermore, all the ROMs were faster than the original MOOSE model yet still provided accurate predictions. FNO had a smaller mean prediction error than DeepONet, with a larger variance for time-dependent responses. Unlike DNN, both FNO and DeepONet were able to simulate time series data without the need for dimensionality reduction techniques. Finally, the present work can help facilitate the AM optimization process by enabling faster execution of simulation tools while still preserving evaluation accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Competing Interactions between Mesoscale Length-Scales, Order-Disorder, and Martensitic Transformation in Ferromagnetic Shape Memory Alloys

In the present study, the effects of composition and heat treatments and the resulting microstructural changes on the martensitic transformation and ferromagnetic transition have been investigated in NiCoMnIn metamagnetic shape memory alloys. In this shape memory alloy system, it is observed that upon heat treatments at a wide temperature range, the onset temperature of the martensitic transformation follows a non-monotonic behavior with respect to heat treatment time, and the nature of the non-monotonic behavior is also a function of composition. This behavior cannot be attributed to well-known factors such as precipitation, change in local composition due to the precipitation and/or global degree of order. In this work, a systematic investigation through synthesis, thermal processing and characterization via thermo-physical measurements, transmission electron microscopy and in-situ synchrotron x-ray diffraction experiments has been used to correlate the non-monotonic dependence of the martensitic transformation and ferromagnetic transition temperatures to the evolution of L2 1 domains arising from a order/disorder phase transition. A thermodynamic model for the magneto-structural transition is combined with classical nucleation theory to further ascertain the role of microstructural length-scales on the onset of the martensitic transformation. This work thus provides understanding of the thermodynamic and kinetic factors that can be controlled to tune the coupled magneto-structural transformations in NiCoMnIn metamagnetic shape memory alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Ordering effects on deformation substructures and strain hardening behavior of a CrCoNi based medium entropy alloy

Here, a CrCoNi based medium entropy alloy with small additions of Ti, Al and Nb (denoted as (CrCoNi) 93 Al 4 Ti 2 Nb) in the as-quenched condition, exhibits tensile properties comparable to those of the equiatomic CrCoNi alloy at room temperature. Dark field transmission electron microscopy (TEM), atomic resolution high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) together with atom probe tomography (APT) show that spatially-localized long range ordering (LRO) L1 2 domains exist in this alloy. The evolution of deformation substructure with plastic deformation in this alloy was characterized using electron backscatter diffraction (EBSD), electron channeling contrast imaging (ECCI) and STEM based techniques including the recently developed weak beam dark field STEM imaging. Plastic deformation occurs by the slip of a/2<110>dislocations, which are narrowly dissociated into Shockley partial dislocations on {111} slip planes. Their dissociation distances in the (CrCoNi) 93 Al 4 Ti 2 Nb alloy are much smaller than the widths of the corresponding partials in the equiatomic CrCoNi alloy due to one or more of the minor alloying elements (Al, Ti, Nb). Dislocation slip in this alloy has a pronounced planar character. The leading dislocations in slip bands glide as pairs due to the existence of LRO domains. Multipoles were formed through the slip of dislocations with opposite signs on adjacent {111} slip planes. Those multipoles serve as building blocks for the formation of subgrain structures consisting of fine slip bands. The distances between slip bands were continuously refined during plastic deformation and dynamic refinement of slip bands plays a crucial role in strain hardening. The effects of LRO domains on planar dislocation slip, the deactivation of deformation twinning and strain hardening of this alloy are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Rigorous computation of short-range order unifies its controversial effects in complex concentrated alloys

Direct experimental observations of chemical short-range order (SRO) in complex concentrated alloys (CCAs) have triggered high interest. However, the reported effects of SRO on yield stresses are controversial, and their atomic-scale mechanisms are elusive, which limits our ability to utilize SRO in alloy design. Here we tackle this challenge using an advanced computational approach that rigorously takes into account the critical lattice distortion in CCAs and further verify our theoretical predictions with experiments. We show that the CoCrNi model alloy has a narrow temperature window around 670 °C for SRO formation. This explains why the mechanical effect of SRO is observed in some experiments but not in others. Here, we propose an effective alloy-doping method to control SRO and reveal atomic-bonding types that dominate SRO formation for different alloys. The strategies and insights generally apply to a broad spectrum of alloys, laying the foundation for designing advanced alloys by manipulating their SRO.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Reduced-order modeling for efficient cross section library development in high-temperature gas reactor pebble-bed depletion analysis

Accurate modeling of running-in and equilibrium conditions in pebble-bed reactors (PBRs) requires precise microscopic multigroup neutron cross sections. In Griffin, deterministic neutronics calculations rely on multivariate interpolation over large cross section libraries, resulting in significant memory usage and performance bottlenecks. This work, together with a companion paper on Griffin integration, explores reduced-order models (ROMs) to replace interpolation with lightweight surrogates. Several ROM techniques are benchmarked, with deep neural networks (DNNs) demonstrating superior memory efficiency, scalability, and predictive accuracy. A total of 295 DNNs were trained to build a comprehensive isotope library, integrated into Griffin through a custom LibTorch interface for depletion analysis. Initial results demonstrate that DNN-based ROMs drastically reduce memory demands while preserving accuracy, enabling finer tabulations and additional state variables without overhead. In conclusion, the framework also supports online cross section generation and real-time DNN updates through transfer learning, improving fidelity by capturing self-shielding and evolving nuclide compositions during burnup.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

High-Order Mesh Morphing for Boundary and Interface Fitting to Implicit Geometries

Here, we propose a method that morphs high-order meshes such that their boundaries and interfaces coincide/align with implicitly defined geometries. Our focus is particularly on the case when the target surface is prescribed as the zero isocontour of a smooth discrete function. Common examples of this scenario include using level set functions to represent material interfaces in multimaterial configurations, and evolving geometries in shape and topology optimization. The proposed method formulates the mesh optimization problem as a variational minimization of the sum of a chosen mesh-quality metric using the Target-Matrix Optimization Paradigm (TMOP) and a penalty term that weakly forces the selected faces of the mesh to align with the target surface. The distinct features of the method are use of a source mesh to represent the level set function with sufficient accuracy, and adaptive strategies for setting the penalization weight and selecting the faces of the mesh to be fit to the target isocontour of the level set field. We demonstrate that the proposed method is robust for generating boundary- and interface-fitted meshes for curvilinear domains using different element types in 2D and 3D.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Data-driven variational multiscale reduced order models

We propose a new data-driven reduced order model (ROM) framework that centers around the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data to increase the ROM accuracy at a modest computational cost. The VMS methodology is a natural fit for the hierarchical structure of the ROM basis: In the first step, we use the ROM projection to separate the scales into three categories: (i) resolved large scales, (ii) resolved small scales, and (iii) unresolved scales. In the second step, we explicitly identify the VMS–ROM closure terms, i.e., the terms representing the interactions among the three types of scales. In the third step, we use available data to model the VMS–ROM closure terms. Thus, instead of phenomenological models used in VMS for standard numerical discretizations (e.g., eddy viscosity models), we utilize available data to construct new structural VMS–ROM closure models. Specifically, we build ROM operators (vectors, matrices, and tensors) that are closest to the true ROM closure terms evaluated with the available data. We test the new data-driven VMS–ROM in the numerical simulation of four test cases: (i) the 1D Burgers equation with viscosity coefficient v = 10 -3 ; (ii) a 2D flow past a circular cylinder at Reynolds numbers, Re = 100, Re = 500, and Re= 1000; (iii) the quasi-geostrophic equations at Reynolds number Re = 450 and Rossby number Ro = 0.0036; and (iv) a 2D flow over a backward facing step at Reynolds number Re = 1000. The numerical results show that the data-driven VMS–ROM is significantly more accurate than standard ROMs.

42 ENGINEERING↗
DOE OSTI2021

Second-order invariant domain preserving approximation of the compressible Navier–Stokes equations

Here, we present a fully discrete approximation technique for the compressible Navier–Stokes equations that is second-order accurate in time and space, semi-implicit, and guaranteed to be invariant domain preserving. The restriction on the time step is the standard hyperbolic CFL condition, i.e. τ ≲ O ( h ) / V where V is some reference velocity scale and h the typical meshsize.

42 ENGINEERING↗
DOE OSTI2022

A comparison of neural network architectures for data-driven reduced-order modeling

The popularity of deep convolutional autoencoders (CAEs) has engendered new and effective reduced-order models (ROMs) for the simulation of large-scale dynamical systems. Despite this, it is still unknown whether deep CAEs provide superior performance over established linear techniques or other network-based methods in all modeling scenarios. To elucidate this, the effect of autoencoder architecture on its associated ROM is studied through the comparison of deep CAEs against two alternatives: a simple fully connected autoencoder, and a novel graph convolutional autoencoder. Through benchmark experiments, it is shown that the superior autoencoder architecture for a given ROM application is highly dependent on the size of the latent space and the structure of the snapshot data, with the proposed architecture demonstrating benefits on data with irregular connectivity when the latent space is sufficiently large.

42 ENGINEERING↗
DOE OSTI2025

Data-driven reduced-order models for port-Hamiltonian systems with operator inference

Hamiltonian operator inference has been developed in Sharma et al. (2022) to learn structure-preserving reduced-order models (ROMs) for Hamiltonian systems. The method constructs a low-dimensional model using only data and knowledge of the functional form of the Hamiltonian. The resulting ROMs preserve the intrinsic structure of the system, ensuring that the mechanical and physical properties of the system are maintained. In this work, we extend this approach to port-Hamiltonian systems, which generalize Hamiltonian systems by including energy dissipation, external input, and output. Based on snapshots of the system’s state and output, together with the information about the functional form of the Hamiltonian, reduced operators are inferred through optimization and are then used to construct data-driven ROMs. To further alleviate the complexity of evaluating nonlinear terms in the ROMs, a hyper-reduction method via discrete empirical interpolation is applied. Accordingly, we derive error estimates for the ROM approximations of the state and output. Lastly, we demonstrate the structure preservation, as well as the accuracy of the proposed port-Hamiltonian operator inference framework, through numerical experiments on a linear mass–spring-damper problem and a nonlinear Toda lattice problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Bayesian discovery of optimal reduced order models from mechanistic and experimental data: A case study of Pd penetration in TRISO fuels using BISON

TRistructural ISOtropic (TRISO) particles rely on a silicon carbide (SiC) layer as the primary structural material and barrier to metallic fission products (FPs) release. Accurate prediction of palladium (Pd) transport and penetration is therefore critical for qualifying TRISO fuels for advanced reactors. The empirical correlation for Pd penetration in BISON is derived from historical particle-fuel data, but cannot explain the large scatter in the experimental data that arises from varying experimental conditions. To aid fuel qualification, we previously developed a mechanistic reduced order model (ROM) using BISON that resolves these dependencies. Here, in this work we build on that mechanistic ROM and perform validation and quantify its uncertainty using Bayesian uncertainty quantification (UQ). calibration against a suite of in-pile and out-of-pile experiments spanning particle compositions, geometries, and operating conditions, and we benchmark it against the empirical correlation. Bayesian UQ identifies influential parameters, calibrates them to data, and yields predictive intervals. Results show that while the empirical correlation can be tuned to fit a single experiment type, it transfers poorly; the mechanistic ROM sustains accuracy with credible uncertainty across disparate conditions. This demonstrates a practical path—via Bayesian UQ applied to mechanistic ROMs—to leverage single-effect experiments for inferring in-reactor behavior and supporting TRISO fuel qualification.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A high-order explicit Runge-Kutta approximation technique for the shallow water equations

Here, we introduce a high-order space–time approximation of the Shallow Water Equations with sources that is invariant-domain preserving (IDP), well-balanced with respect to rest states, and employs a novel explicit Runge–Kutta (ERK) introduced in Ern and Guermond (SIAM J. Sci. Comput. 44(5), A3366–A3392, 2022) for systems of non-linear conservation equations. The resulting method is then numerically illustrated through verification and validation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

HTR solver: An open-source exascale-oriented task-based multi-GPU high-order code for hypersonic aerothermodynamics

In this study, the open-source Hypersonics Task-based Research (HTR) solver for hypersonic aerothermodynamics is described. The physical formulation of the code includes thermochemical effects induced by high temperatures (vibrational excitation and chemical dissociation). The HTR solver uses high-order TENO-based spatial discretization on structured grids and efficient time integrators for stiff systems, is highly scalable in GPU-based supercomputers as a result of its implementation in the Regent/Legion stack, and is designed for direct numerical simulations of canonical hypersonic flows at high Reynolds numbers. Additionally, the performance of the HTR solver is tested with benchmark cases including inviscid vortex advection, low- and high-speed laminar boundary layers, inviscid one-dimensional compressible flows in shock tubes, supersonic turbulent channel flows, and hypersonic transitional boundary layers of both calorically perfect gases and dissociating air.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗