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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Scientific Case for Concurrent Neutron and X-ray Scattering and Spectroscopy

The interrogation of materials with X-rays or neutrons to determine the structure, energetics, and dynamics of materials is fundamental to advancing materials' physical and chemical science and developing innovative material technologies. A transcending challenge in developing novel materials is that progress hinges on understanding the structure and dynamics across multiple time and length scales in complex materials that feature multiple components, interfaces, and compositions. Despite the ever-growing demands on materials’ characterization, existing approaches are almost exclusively based on isolated X-ray or neutron scattering, i.e., an approach commensurate with the more narrowly defined needs of fifty years ago. A three-day workshop sponsored by the U.S. National Science Foundation (NSF) analyzed the demand for concurrent neutron and X-ray (NeX) experiments. It was held at the Spring Hill Suites, San Jose, California, from June 2 to 4, 2022. In this workshop, 70 national and international experts ascertained the crucial need to establish NeX capabilities to advance the science of complex materials and systems in the US. Here, we illustrate the need for NeX scattering and spectroscopy experiments by showcasing examples that span areas as diverse as biomaterials, energy science, soft matter, and nanomaterials. To provide NeX capability will require new instrumentation that enables concurrent experiments. Affected areas include chemistry, soft matter, quantum materials, pure and applied chemistry, bioscience, geoscience, and applied materials. NeX benefits research outcomes due to the complementarity of the two techniques, which is essential for better model refinement. While joint refinement of data from separate neutron and X-ray experiments is critical to avoid ambiguities, especially in multiphase-multicomponent materials, concurrent experiments overcome scientific and technical barriers associated with single measurements, separated by location and, thus, time. Among all the examples, these factors introduce uncertainties in the results that complicate data analysis. [1,2] [3] While models are strongly sample-dependent, the principles of joint refinement are generally applicable to these disciplines, including the development of advanced parameterization, modeling, and analysis techniques that also consider the temporal and spatial resolutions of the two methods, leading to unambiguous data interpretation. Solutions for technical barriers must be found to realize NeX experiments, including developing robust sample environments that meet the optical requirements of neutrons and X-rays.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Short-range order parameter expansions for simple configurational energetics

Local chemical environments of multicomponent crystals are parametrized according to basis vectors of their irreducible invariant subspaces. These short-range order parameters enable Taylor expansions of configurational properties that easily incorporate additional couplings. As one example, expansions including long-range composition can describe the energies of many alloys more accurately than conventional cluster expansions while using far fewer neighbors and basis functions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Flavour-selective localization in interacting lattice fermions

Abstract A large repulsion between particles in a quantum system can lead to their localization, an effect responsible for the Mott insulator phases in strongly correlated materials. In a system with multiple orbitals, an orbital-selective Mott insulator can form, where electrons in some orbitals are predicted to localize while others remain itinerant. Here we demonstrate a more general version of this phenomenon by observing flavour-selective localization in an atom-based quantum simulator. Our experiment realizes Fermi–Hubbard models with an SU(3) symmetry that can be broken using a tunable coupling between flavours. We observe an enhancement of the localization associated with a selective Mott transition and the emergence of flavour-dependent correlations. Our realization of flavour-selective Mott physics demonstrates the potential of cold atoms to simulate interacting multicomponent materials such as superconductors and topological insulators.

Physics↗
DOE OSTI2026

Prognostic simulations of mixed-phase clouds with model AC-1D v1.0: the impact of aerosol types and freezing parameterizations on ice crystal budgets

Mixed-phase clouds at high latitudes contribute to the uncertainty in predicting cloud feedbacks and climate sensitivity, mainly due to the complexity of microphysical processes that influence the partitioning between the supercooled liquid and ice phases, and hence, cloud radiative effects on regional scales. Particularly in Arctic mixed-phase clouds, the activation of ice-nucleating particles (INPs) from various aerosol populations remains a leading source of uncertainty. We developed an aerosol-cloud one-dimensional (AC-1D) model, which provides a novel framework to prognostically treat INP and ice crystal budgets while explicitly accounting for polydisperse and multicomponent aerosol that activate INPs following different freezing parameterizations. The AC-1D model is informed by large-eddy simulations to probe the impact of INP representation on predicted ice crystal number concentrations (N i ) and ice crystal budgets in mixed-phase Arctic stratus. We apply three immersion freezing (IMF) parameterizations, two time-independent (singular) and one time-dependent (classical nucleation theory), to predict the evolution of the INP reservoir and resulting ice crystal budget from polydisperse mineral dust, organic (humic-like substances), and sea spray aerosol particle size distributions. Our analysis focuses on how variations in aerosol number concentration and cloud system parameters such as cloud cooling rate, cloud-top entrainment rate, and ice crystal fall speed influence the INP reservoir and ice crystal budgets. Furthermore, this study investigates the competitive ice nucleation dynamics in mixed aerosol environments and provides a process-level quantification of the INP budget terms, which directly controls ice crystal budgets. For all studied case scenarios, the aerosol types and associated particle size distributions significantly impact INP and N i , and the choice between a time-dependent and a singular freezing description yields orders-of-magnitude differences in the predicted INP and N i over the 10 h simulation time, reflecting typical cloud lifetimes. Our results show that the influence of cloud cooling, INP entrainment, and sedimentation varies significantly depending on the chosen freezing parameterization. These findings underscore the critical need for robust IMF parameterizations and precise cloud system observations to enhance the accuracy of models in predicting mixed-phase cloud structure and evolution.

Arctic clouds↗
NASA NTRS1989

Fuel-rich catalytic combustion of Jet-A fuel-equivalence ratios 5.0 to 8.0

Fuel-rich catalytic combustion (E.R. greater than 5.0) is a unique technique for preheating a hydrocarbon fuel to temperatures much higher than those obtained by conventional heat exchangers. In addition to producing very reactive molecules, the process upgrades the structure of the fuel by the formation of hydrogen and smaller hydrocarbons and produces a cleaner burning fuel by removing some of the fuel carbon from the soot formation chain. With fuel-rich catalytic combustion as the first stage of a two stage combustion system, enhanced fuel properties can be utilized by both high speed engines, where time for ignition and complete combustion is limited, and engines where emission of thermal NO sub x is critical. Two-stage combustion (rich-lean) has been shown to be effective for NO sub x reduction in stationary burners where residence times are long enough to burn-up the soot formed in the first stage. Such residence times are not available in aircraft engines. Thus, the soot-free nature of the present process is critical for high speed engines. The successful application of fuel-rich catalytic combustion to Jet-A, a multicomponent fuel used in gas turbine combustors, is discusssed.

Brabbs, Theodore A.↗
DOE OSTI2022

CASM — A software package for first-principles based study of multicomponent crystalline solids

CASM is a software package that enables first-principles based studies of crystalline materials. It has been designed to treat coupled chemical, mechanical, vibrational, and magnetic degrees of freedom to determine ground state and finite temperature properties of crystals. The symmetry of the underlying parent crystal structure is used to enumerate perturbations of the parent crystal structure and generate derivative structures which can be input to first-principles calculations in order to explore the ground state energy landscape. CASM algorithmically constructs cluster expansions that fully couple discrete and continuous degrees of freedom, and generates highly efficient code to evaluate the cluster expansion basis functions. Widely used machine learning methods are integrated for fitting expansion coefficients to first-principle calculations. The fully parameterized cluster expansions can be combined with (kinetic) Monte Carlo methods to calculate finite temperature thermodynamic and kinetic properties. CASM Alloy Manager identifies distinct parent crystal structures in an alloy system, creating individual projects for each, and integrating the results. The integrated infrastructure facilitates the linkage between first-principles statistical mechanics predictions with higher length scale computational methods, such as phase field simulations. In conclusion, CASM projects are designed to be easy to share in repositories, to re-use, and to extend to include additional chemical species or types of degrees of freedom.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data-Efficient Methods for Determining Flory–Huggins χ Parameters in Multicomponent Polymer Formulations

Polymer formulations are essential in diverse applications including personal care products, coatings, paints, adhesives, and plastic materials. Designing these formulations requires navigating large, complex design spaces, where phase and self-assembly behavior critically impact performance. The Flory–Huggins χ parameter, which quantifies segmental miscibility, is widely used to parametrize the excess free energy of mixing in formulation models. In this work, we introduce two data-efficient, top-down methods for estimating χ parameters using the Random Phase Approximation (RPA): (i) Boundary Nonlinear Regression (Boundary-NLR), which fits theoretical spinodal boundaries to experimental phase boundaries, and (ii) Surrogate Model Inverse Parameter Estimation (SMIPE), which uses a Gaussian Process Classifier to fit sparse phase maps via a surrogate model. Both methods allow rapid parametrization of polymer field-theoretic models without the need for additional experiments. We evaluate these approaches on data sets involving polymer–solvent–nonsolvent ternary mixtures and block copolymer–solvent systems, demonstrating their robustness to experimental noise and their relevance for real-world formulation design.

copolymers↗
DOE OSTI2024

Vacancy diffusion barrier spectrum and diffusion correlation in multicomponent alloys

Vacancy diffusion serves a crucial role in many important kinetic behaviors and properties of multicomponent alloys. Essential questions, however, persist regarding how chemical complexity affects diffusion and what unique characteristics, if any, set these alloys apart from traditional metals. Using neural network kinetics model, we study vacancy diffusion in NbMoTa alloy across a broad temperature range (2600 to 800 K). Unlike pure metals, the two key diffusion parameters—diffusion correlation factor f and activation energy ΔG m —are not constant in alloys, but instead substantially decrease with decreasing temperature. This temperature dependence arises from a reduced number of active vacancy jump pathways at lower temperatures, leading to more correlated diffusion. Upon examining vacancy diffusion throughout the entire compositional space of the Nb-Mo-Ta system, we discover that the slowest vacancy diffusion surprisingly occurs in the non-equimolar region, rather than the equimolar concentration where the configurational entropy is highest. The diffusion barrier spectrum, characterizing the diffusion energy landscape, is an intrinsic material characteristic, which controls both f and ΔG m and, thereby, the diffusivity. Lastly, we find that the vacancy diffusion rate drops noticeably in the presence of local chemical order in the NbMoTa system, particularly for MoTa alloys with long-range B2 order.

Defects↗
DOE OSTI2021

A coupled thermo-hydro-mechanical model for simulating leakoff-dominated hydraulic fracturing with application to geologic carbon storage

A potential risk of injecting CO2 into storage reservoirs with marginal permeability (≲ 10 mD (1 mD = 10 –15 m 2 )) is that commercial injection rates could induce fracturing of the reservoir and/or the caprock. Such fracturing is essentially fluid-driven fracturing in the leakoff-dominated regime. Recent studies suggested that fracturing, if contained within the lower portion of the caprock complex, could substantially improve the injectivity without compromising the overall seal integrity. Modeling this phenomenon entails complex coupled interactions among the fluids, the fracture, the reservoir, and the caprock. Here, we develop a simple method to capture all these interplays in high fidelity by sequentially coupling a hydraulic fracturing module with a coupled thermal-hydrological-mechanical (THM) model for nonisothermal multiphase flow. The model was made numerically tractable by taking advantage of self-stabilizing features of leakoff-dominated fracturing. The model is validated against the PKN solution in the leakoff-dominated regime. Moreover, we employ the model to study thermo-poromechanical responses of a fluid-driven fracture in a field-scale carbon storage reservoir that is loosely based on the In Salah project's Krechba reservoir. The model reveals complex yet intriguing behaviors of the reservoir-caprock-fluid system with fracturing induced by cold CO 2 injection. We also study the effects of the in situ stress contrast between the reservoir and caprock and thermal contraction on the vertical containment of the fracture. The proposed model proves effective in simulating practical problems on length and time scales relevant to geological carbon storage.

58 GEOSCIENCES↗
DOE OSTI2025

A generalized and adaptable tensor-contraction-based cluster expansion formalism for multicomponent solids

Density functional theory (DFT)-based simulations of materials have first-principles accuracy, but are very computationally expensive. For simulating various properties of multi-component alloys, the cluster expansion (CE) technique has served as the standard workaround to improve computational efficiency. However, the standard CE technique is difficult to extend to exotic and/or low-symmetry lattices, often implemented via iteration over particular cluster types, which must be enumerated per lattice structure. In this work, we introduce the tensor cluster expansion (TCE), implemented in the open-source code tce-lib, which maps correlation functions to mixed tensor contractions, eliminating the need to iterate over cluster types and additionally making the calculation of correlation functions well-suited for massively parallel architectures like GPUs. We show that local interaction energies are an immediate consequence of the TCE formalism, yielding nearly $\mathcal{O}$(1) energy difference calculations. We then use this formalism to fit CE models for the TaW and CoNiCrFeMn systems, and use these models to respectively compute the enthalpy of mixing curve and Cowley short-range order parameters, showing excellent agreement with ground truth data.

Cluster expansion↗
NASA NTRS1972

Vlf/elf radiation patterns of arbitrarily oriented electric and magnetic dipoles in a cold lossless multicomponent magnetoplasma.

With the use of a power integral formulation, a study is made of the vlf/elf radiation patterns of arbitrarily oriented electric and magnetic dipoles in a cold lossless multicomponent magnetoplasma. Expressions for the ray patterns are initially developed that apply for arbitrary values of driving frequency, static magnetic-field strength, plasma density, and composition. These expressions are subsequently specialized to vlf/elf radiation in a plasma modeled on the magnetosphere. A series of representative pattern plots are presented for frequencies between the proton and electron gyrofrequencies. These patterns illustrate the fact that focusing effects that arise from the geometrical properties of the refractive index surface tend to dominate the radiation distribution over the entire range from the electron gyrofrequency to 4.6 times the proton gyrofrequency. It is concluded that focusing effects should be of significant importance in the design of a vlf/elf satellite transmitting system in the magnetosphere.

Wang, T. N. C.↗
NASA NTRSDate not supplied

Modeling Electrolytic O2 Recovery from Metabolic CO2 for Advanced Closed Loop Life Support Systems in Extraterrestrial Human Missions

The International Space Station (ISS) is currently equipped with a complex, heavy, and power consuming system that recovers approximately 50% of O2 from metabolic CO2. Future long duration human missions to the Moon and Mars will necessitate a sustainable and highly efficient metabolic oxygen recovery system capable of yielding a minimum of 75% O2 recovery. A Macrofluidic Electrochemical Reactor (MFECR) technology development effort is currently underway at NASA Marshall Space Flight Center (MSFC) to significantly increase current metabolic O2 recovery efficiency, expand mission sustainability, and reduce complexity of the system. The novel design combines CO2 conversion to O2 along with C2H4 as byproduct and water electrolysis (currently conducted in two separate units) into a single compact unit that runs at standard conditions and is theoretically capable of generating O2 with a theoretical maximum metabolic CO2 conversion of 73% while consuming less than metabolic water. This paper presents a comprehensive multi-physic 3D model developed at MSFC on CO2 conversion to O2 and C2H4 at standard conditions via MFECR. The 3D spatial domain of the model is a replica of the actual MFECR’s 3D drawing generated for the MFECR fabrication and operated to recover O2 from CO2 yielding C2H4 as byproduct. Electrochemical (EC) physics that includes EC multicomponent reaction mechanisms, mass transport, and electrical current density distributions is coupled in the model with all the other physics phenomena involved in the MFECR’s process, such as two-phase flow, free and porous fluid regimes, multicomponent mass transfer, heat transfer, and DC electrical current generation along with Joule heating effect. The EC reaction sections of the MFECR consists of two porous gas diffusion electrodes (GDE) and an electrolyte serpentine channel sandwiched in the middle. The CO2 feeds the cathode serpentine channel and part of the O2 product is fed back to the anode serpentine chamber. An alkaline solution feeds the electrolyte serpentine chamber wetting the GDEs of both, the anode and cathode allowing the OH- ionic transport between them. The EC reactions in the cathode’s GDE yield C2H4 from CO2 and H2 from water while the EC reaction in the anode’s GDE yields O2. The authors will present in this paper the validation of the model using experimental data and the utilization of the validated model in building a reliable simulator that will not only assist the authors on the MFECR design but also the optimization of its operation in the ISS and future spatial human missions.

Jesus A Dominguez↗
NASA NTRS1992

Navier-Stokes solutions with surface catalysis for Martian atmospheric entry

In this study numerical solutions have been obtained for two-dimensional axisymmetric hypersonic nonequilibrium CO2 flow over a high angle blunt cone with appropriate surface boundary conditions to account for energy and mass conservation at the body surface. The flowfield is described by the Navier-Stokes equations and multicomponent conservation laws which account for both translational and internal vibrational nonequilibrium effects. Complete forebody solutions have been obtained for the peak heating point of the Mars entry trajectory specified in the proposed NASA MESUR (Mars Environmental Survey) project. In these solutions, radiative equilibrium wall temperature and surface heating distributions are determined over the MESUR aeroshell forebody for entry velocity equal to 7 km/sec with varying degrees of surface catalysis. The effects of gas kinetics, surface catalysis, transport properties, and vibrational relaxation times on the surface heating are examined. The results identify some important issues in the prediction of surface heating for flows in thermochemical nonequilibrium and show that the Navier-Stokes code used herein is effective for thermal protection system design and materials selection.

Chen, Y.-K.↗
DOE OSTI2024

Multiphysics Time-Integration for Turbulent Combustion at the Exascale

Turbulent reacting flow systems are often modeled with coupled time-dependent partial differential equations (PDEs). Solving such equations can easily tax the world's largest supercomputers. One pragmatic strategy for attacking such problems is to split the PDEs into components that can more easily be solved in isolation. This generic operator-splitting strategy leads to a set of ordinary differential equations (ODEs) that need to be solved as part of an "outer-loop" time-stepping approach. In many combustion applications, the ODEs to be solved can be very stiff, exhibiting timescales that span many orders of magnitude. The SUNDIALS library provides a plethora of robust time integration algorithms for solving these ODEs on exascale-capable computing hardware, yet for many complex applications (such multicomponent fuels or emissions predictions), the chemical models remain too complex to solve using reasonable resources. The Quasi-Steady State Approximation (QSSA) can be an effective tool for reducing the size and stiffness of the simulations. In this talk, I will discuss the use of the SUDIALS library of ODE solvers together with automatic code generation tools to solve complex turbulent reacting flow problems using QSSA models.

chemistry↗
DOE OSTI2021

Coupling between bulk thermal defects and surface segregation dynamics

Surface segregation is a phenomenon that depends on the delicate interplay between thermodynamic driving forces and kinetic obstacles, for which elevated temperature is often needed to enhance the atom mobility and reach equilibrium. Using the classic system of Cu 3 Au ( 100 ) under nonisothermal conditions, in this study, we show an adatom process underlying transient surface segregation dynamics through the temperature-change-driven creation and annihilation of thermal vacancies in the bulk and the resulting bulk-surface mass exchanges. This is demonstrated by monitoring the surface composition evolution of Cu 3 Au ( 100 ) with temperature changes between 250 and 500 ° C , showing that the increase in temperature decreases monotonically the surface Au concentration as a result of the transfer of more Cu than Au from the bulk to the surface to form Cu-rich clusters of adatoms. Such a bulk thermal defect effect is expected to be universal in inducing the disparity in the bulk-surface mass exchanges of dissimilar atoms in multicomponent materials because of the inherent differences in the vacancy formation energies of the constituent atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Controlling Exsolution Dynamics in High‐Entropy Oxides for Highly Active and Selective Acetylene Semi‐Hydrogenation

Exsolution-derived catalysts feature robust metal–support interactions that enhance catalytic performance; yet achieving precise control over exsolution dynamics in multicomponent oxides remains challenging. In this study, we demonstrate that exsolution behavior in high-entropy oxides (HEOs) can be rationally tuned through coupled lattice- and valence-engineering to create a highly active and selective catalyst for acetylene semi-hydrogenation. Incorporation of Li + into a rock salt-structured HEO (LiNiMgCuZnCoO x and LiHEO) induces local lattice distortion, generates oxygen vacancies, and partially oxidizes Co sites from Co 2+ to Co 3+ , collectively modulating local charge redistribution. This strategy enables facilitated Cu nanoparticle exsolution and alters the exsolution sequence from Cu 0 > Ni 0 > Co 0 in pristine HEO to Cu 0 > Co 0 > Ni 0 in the LiHEO. The resulting catalyst via controlled exsolution exhibits superior activity and ethylene selectivity, outperforming state-of-the-art transition metal systems. This work establishes entropy-enabled lattice and valence engineering as a facile route to programmable exsolution for enhanced catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Electroprecipitation Mechanism Enabling Silica and Hardness Removal through Aluminum-Based Electrocoagulation

We evaluate the effectiveness of an aluminum-based electrocoagulation pretreatment system to remove dissolved silica and hardness. Silica and hardness limit water recovery during membrane-based desalination applications when silica and hardness exceed the solubility limit and generate scale on the membrane surface. We show that simultaneous removal of nearly all silica (95 ± 4%) and a significant amount of hardness (40–60%) occurs with a hydraulic residence time of 2 h and a charge loading between 0 and 1200 C/L. Increasing the residence time maximized the hardness removal (58 ± 8%) via the formation of larger flocs, which allowed for more constituent removal by gravity settling. We highlight the trade-offs between improved energy efficiency at lower charge loadings and an improved removal rate at a higher charge loading. We further compare the percentage of silica and hardness removed in multicomponent solutions and compare this to single component feed solution. We discuss the implications that operational considerations have in terms of cost and treatment capacity. Finally, a cost–benefit analysis comparing chemical coagulation with electrocoagulation indicates that electrocoagulation could be half the cost of chemical coagulation and could produce more stable effluent pH and conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine learning pipeline to predict defect behavior in metallic alloy systems

The interaction between defect and solute atoms is critical to the thermodynamic and kinetic behavior of metallic alloys under exposure to high-energy radiation, causing irradiation damage in materials. Radiation can generate non-equilibrium concentrations of point defects such as vacancies and interstitials. The excess point defects not only accelerate diffusional processes such as precipitation that cause radiation embrittlement, but also change the pathway of phase transformations, including nucleation processes. Understanding these defect behaviors is complicated by the challenge and complexity of addressing each possible local and discrete distribution of environments and chemical interactions around targeted defects-solute or solute-solute complexes. To resolve the challenge, machine learning regression techniques have emerged as powerful tools that can train and construct an energy model to accurately describe the chemical interactions of solutes and defects. In Fiscal Year 2022, the work focused on the workflow development and demonstration using machine learning regression, density functional theory, cluster expansion, and Monte Carlo simulation to predict the effects of ternary solute elements (e.g., aluminum and molybdenum) and point defects on the Cr-rich $\alpha^{\prime}$ precipitation in multicomponent FeCr model alloys. The computational outcomes include the prediction of the ternary phase diagram, vacancy formation energy for different compositions, and the effect of vacancies on the nucleation of Cr-rich clusters. The simulations predict a pronounced change of Cr solubility in bcc Fe by the addition of Al and the rejection of Al atoms from $\alpha^{\prime}$ precipitates. Additionally, the simulations show the formation of Cr-vacancy clusters as the initial nuclei for stable nucleation and growth of $\alpha^{\prime}$ particles. The results demonstrate important outcomes and applications of using machine learning pipeline to study model or commercial alloys with multicomponent solute species and point defects.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗