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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Ionic Interdiffusion at Cathode|Solid-Electrolyte Interface: A Machine Learning–Assisted Multiscale Investigation and Mitigation Strategies

Future lithium batteries are expected to use solid electrolytes to achieve higher energy density and fast charge capabilities. However, most solid electrolytes are thermodynamically unstable against layered oxide cathodes. In this study, the stability of LiCoO2 (LCO) cathode with Li10GeP2S12 (LGPS) solid electrolyte is investigated using ab initio molecular dynamics (AIMD) and machine learning molecular dynamics (MLMD). The propensity of ionic interdiffusion, formation of a passivating interphase layer, and corresponding decay in cell performance is addressed using a continuum model. Large-scale MLMD simulations confirm that the LCO|LGPS interface permits interdiffusion of cobalt (Co) and other ionic species, leading to the formation and growth of a resistive interphase and to dramatic capacity fade even in the first cycle. We examine the literature evidence that incorporating a thin layer of LiNb0.5Ta0.5O3 (LNTO) between LCO and LGPS prevents the interdiffusion of ions. Atomistic simulations suggest that substituting lithium (Li) in LNTO with Co is thermodynamically unfavorable, thereby inhibiting ionic interdiffusion. The stable Nb5+/Ta5+ states form a rigid metal-oxide framework, which consequently also prevents the substitution of niobium (Nb) or tantalum (Ta). However, continuum-level analysis suggests that the higher mechanical stiffness of LNTO can lead to interfacial delamination between the LCO and LNTO. This phenomenon reduces the effectiveness of the protective layer. This paper, therefore, highlights the need to develop novel interlayers that balance low ionic interdiffusion with low mechanical stiffness.

Ncube, Musawenkosi K.↗
DOE OSTI2022

Helium interaction with solutes and impurities in neutron-irradiated nanostructured ferritic alloys: A first principles study

Density functional theory calculations are performed to explore the binding between He and alloying solutes, impurities, and transmutation products expected in neutron irradiated nanostructured ferritic alloys (NFAs), here 14YWT is taken as an example. Elements that exhibit significant binding (attraction) with an interstitial He are Y (binding energy = 0.46 eV), Mg (0.32), O (0.33), Ti (0.16), and C (0.15). Those that provide significant binding to a substitutional He are O (1.44), Y (1.24), N (0.73), H (0.56), Mg (0.52), Ti (0.34), Si (0.34), C (0.33), Al (0.32), Ni (0.26), Ta (0.23), and Mn (0.16). The presence of these elements in Fe matrix could reduce the transport of He towards oxide particles, dislocations, and internal boundaries, and could promote He bubble nucleation in the matrix. For convenience, we compile existing binding energy data of He with He n and He n V (He-vacancy) clusters. Dissociation pathway analysis reveals that, in general, the most likely dissociation of a He n V cluster is by a sequential emission of individual He atoms. Furthermore, larger bubbles are more prone to dissociation than smaller ones. In addition, we estimate the binding energy (segregation energy) of He in bulk Y 2 Ti 2 O 7 (YTO) single crystal, YTO/Fe interface, and YTO particle embedded in Fe, with respect to interstitial He in Fe, from existing formation energies of He in these structures. We also compile available data of He binding with Fe self-interstitial atom (SIA), SIA clusters, and edge and screw dislocations. Note that given the absence of DFT data, the binding with SIA clusters and dislocations are gathered from simulations with empirical potentials. Finally, the data presented in this paper is important to inform multiscale simulations of He bubble accumulation.

36 MATERIALS SCIENCE↗
DOE OSTI2015

An adhesive contact mechanics formulation based on atomistically induced surface traction

In this work, we have developed a novel multiscale computational contact formulation based on the generalized Derjuguin approximation for continua that are characterized by atomistically enriched constitutive relations in order to study macroscopic interaction between arbitrarily shaped deformable continua. The proposed adhesive contact formulation makes use of the microscopic interaction forces between individual particles in the interacting bodies. In particular, the double-layer volume integral describing the contact interaction (energy, force vector, matrix) is converted into a double-layer surface integral through a mathematically consistent approach that employs the divergence theorem and a special partitioning technique. The proposed contact model is formulated in the nonlinear continuum mechanics framework and implemented using the standard finite element method. With no large penalty constant, the stiffness matrix of the system will in general be well-conditioned, which is of great significance for quasi-static analysis. Three numerical examples are presented to illustrate the capability of the proposed method. Results indicate that with the same mesh configuration, the finite element computation based on the surface integral approach is faster and more accurate than the volume integral based approach. In addition, the proposed approach is energy preserving even in a very long dynamic simulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Microstructural impacts on ionic conductivity of oxide solid electrolytes from a combined atomistic-mesoscale approach

Abstract Although multiple oxide-based solid electrolyte materials with intrinsically high ionic conductivities have emerged, practical processing and synthesis routes introduce grain boundaries and other interfaces that can perturb primary conduction channels. To directly probe these effects, we demonstrate an efficient and general mesoscopic computational method capable of predicting effective ionic conductivity through a complex polycrystalline oxide-based solid electrolyte microstructure without relying on simplified equivalent circuit description. We parameterize the framework for Li 7- x La 3 Zr 2 O 12 (LLZO) garnet solid electrolyte by combining synthetic microstructures from phase-field simulations with diffusivities from molecular dynamics simulations of ordered and disordered systems. Systematically designed simulations reveal an interdependence between atomistic and mesoscopic microstructural impacts on the effective ionic conductivity of polycrystalline LLZO, quantified by newly defined metrics that characterize the complex ionic transport mechanism. Our results provide fundamental understanding of the physical origins of the reported variability in ionic conductivities based on an extensive analysis of literature data, while simultaneously outlining practical design guidance for achieving desired ionic transport properties based on conditions for which sensitivity to microstructural features is highest. Additional implications of our results are discussed, including a possible connection between ion conduction behavior and dendrite formation.

25 ENERGY STORAGE↗
DOE OSTI2022

Atomistic modeling of LiF microstructure ionic conductivity and its influence on nucleation and plating

We report the formation and degradation of the solid electrolyte interphase (SEI) and its underlying transport properties play an essential role in the overall performance of lithium-ion batteries. This paper presents classical molecular dynamics studies on polycrystalline inorganic lithium fluoride (LiF) layers to model and predict the SEI transport properties. The ionic conductivity is obtained from the lithium-ion diffusivity in polycrystalline structures of LiF using the Nernst-Einstein relation. The predicted molecular dynamics data are used in a continuum scale phase-field model to evaluate the plating kinetics under fast charging conditions. The analysis emphasizes that the SEI ionic conductivity properties impact the plating dynamics, where SEI's low ion conductivity value is prone to large plating and subsequent capacity degradation. The combination of atomic and continuum scale studies shown herein lays a foundation to tune in SEI transport properties to decrease the amount of lithium plating and improve the performance of fast-charging batteries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Baropycnal Work: A Mechanism for Energy Transfer across Scales

The role of baroclinicity, which arises from the misalignment of pressure and density gradients, is well-known in the vorticity equation, yet its role in the kinetic energy budget has never been obvious. Here, we show that baroclinicity appears naturally in the kinetic energy budget after carrying out the appropriate scale decomposition. Strain generation by pressure and density gradients, both barotropic and baroclinic, also results from our analysis. These two processes underlie the recently identified mechanism of “baropycnal work”, which can transfer energy across scales in variable density flows. As such, baropycnal work is markedly distinct from pressure-dilatation into which the former is implicitly lumped in Large Eddy Simulations. We provide numerical evidence from 1024 3 direct numerical simulations of compressible turbulence. The data shows excellent pointwise agreement between baropycnal work and the nonlinear model we derive, supporting our interpretation of how it operates.

42 ENGINEERING↗
DOE OSTI2025

Implications of AI and Data Center Service Demand on Energy, Water, and Economic Systems

This talk focuses on the challenges posed by the rapid growth of data centers (DCs), which rely heavily on electricity, water, land, and mineral resources, and have significant interactions with economic growth. The U.S. leads in DC deployment, but understanding how resource demands and constraints impact DC expansion requires strategic multisector analysis. To address this, the talk introduces a new conceptual framework and modeling capability within GCAM-USA, allowing integrated analysis of DC service demand, electricity, and direct and indirect water needs under varied scenarios. Insights include state-level water and electricity trends for DCs, uncertainty bounds, and the broader implications for resources and economics.

Data Centers↗
DOE OSTI2013

Multiphysics Simulations: Challenges and Opportunities

We consider multiphysics applications from algorithmic and architectural perspectives, where "algorithmic" includes both mathematical analysis and computational complexity, and "architectural" includes both software and hardware environments. Many diverse multiphysics applications can be reduced, en route to their computational simulation, to a common algebraic coupling paradigm. Mathematical analysis of multiphysics coupling in this form is not always practical for realistic applications, but model problems representative of applications discussed herein can provide insight. A variety of software frameworks for multiphysics applications have been constructed and refined within disciplinary communities and executed on leading-edge computer systems. We examine several of these, expose some commonalities among them, and attempt to extrapolate best practices to future systems. From our study, we summarize challenges and forecast opportunities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

ATF material model development and validation for priority fuel concepts

Development, implementation, and validation of material and behavior models for accident tolerant fuel (ATF) concepts in the Bison fuel performance code began in 2014 in response to the events that occurred at the Fukushima Daichii nuclear power plant in March 2011. Early on the focus was on U 3 Si 2 fuel and FeCrAl cladding as part of a high impact problem through the Nuclear Energy and Advanced Modeling Simulation (NEAMS) program. Then, developments for Cr 2 O 3 -doped UO 2 fuel, and SiC-SiC and Cr-coated zirconium-based claddings began based upon industry interests. In late fiscal year 2018 the Consortium for Advanced Simulation of Light Water Reactors (CASL) took over further ATF work in Bison in support of the Nuclear Regulatory Commission (NRC) engagement. Discussions with the NRC identified their list of priority fuel and cladding concepts, which included Cr 2 O 3 -doped UO 2 and U 3 Si 2 fuels, and Cr-coated zirconium-based and FeCrAl claddings. In particular, the NRC suggested that reports similar in form to NUREG/CR-7024 [1] that was developed for traditional LWR materials UO 2 and zirconium-based claddings (i.e., Zircaloy-4, M5®, ZIRLO) be created for the priority ATF concepts. The approach to ATF capability development in Bison since the beginning has been two-fold: (1) empirical correlations and (2) multiscale model development. Both approaches have uncertainty inherent to them. Uncertainty in empirical correlations is bounded by the experimental data upon which with the correlation was developed. Models developed through a multiscale approach have uncertainty associated with the lower length scale calculations and input parameters that must be propagated to the engineering scale model in Bison. In this report, the recommended models, their range of applicability (e.g., temperature, burnup), and associated uncertainty for the NRC priority fuel concepts Cr 2 O 3 -doped UO 2 and U 3 Si 2 are presented in a manner similar to the aforementioned NUREG. In addition, the Cr 2 O 3 -doped UO 2 models are validated to the Halden Reactor tess IFA-677.1 rods 1 and 5 and IFA-716.1 rod 1. For U 3 Si 2 the models are validated to the recent post-irradiation examination (PIE) data ATF-13 and ATF-15 rods that were irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory. Finally, an uncertainty quantification (UQ) and sensitivity analysis (SA) is completed on a single rod for each fuel concept that takes into account the defined uncertainty in select models. The results of the UQ and SA analyses indicate that given the large uncertainty in some of the input ATF models that the uncertainty on the Bison predictions of fuel performance metrics of interest bound the available experimental data. Correlation coefficients are also reported that identified the uncertain inputs with the strongest correlation (positive or negative) with the outputs of interest. Fuel performance metrics investigated include fuel elongation, rod internal pressure, fuel centerline temperature, and fission gas release.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Investigating the Crosslinking, Degradation, and Adhesion Behavior of Photovoltaic Encapsulants Under Thermal Accelerated Aging

Degradation of photovoltaic (PV) module encapsulant characteristics that lead to mechanical embrittlement and delamination remains a cause of failure in solar installations. A multiscale reliability model connecting the encapsulant mechanical and fracture properties to the degraded molecular structure and interfacial bonding to adjacent solar cell and glass substrates was previously published. The model, developed primarily for poly(ethylene-co-vinyl acetate) acetate (EVA) encapsulants, remains to be experimentally validated. Determining the degradation and crosslinking kinetics of alternative encapsulants, such as polyolefin elastomer (POE) and EVA/POE/EVA composites (EPE), can generalize the model. In this work, we subject fully cured EVA, POE, and EPE encapsulants to accelerated thermal aging to determine how high temperatures impact reaction kinetics. An increase in gel content (crosslinking) and decrease in crystallinity of the encapsulants under hot-aerobic (90 degrees C, 22% RH) and hot-anaerobic (90 degrees C, sealed in N 2 air) aging were observed, even in the absence of UV and crosslinking initiators. Fourier transform infrared spectroscopy (FTIR)-attenuated total reflectance analysis showed insignificant encapsulant degradation, demonstrating the critical role of UV and moisture in accelerating degradation. Adhesion testing performed on coupon-level specimens (cell/encapsulant/glass laminates) showed decreases in adhesion energy, Gc , from 5000 h of hot-dry (90 degrees C, ~1% RH) and hot-humid (90 degrees C, 60% RH) aging. POE coupons demonstrated the best stability, followed by EPE then EVA. For EVA and POE, hot-humid aged coupons experienced a larger decrease in Gc due to enhanced hydrolytic degradation. Hot-dry aging condition demonstrated that thermal degradation of the interface could be significant even if the encapsulant experiences negligible degradation in the absence of UV and elevated humidity.

accelerated aging↗
DOE OSTI2020

A Unified Theory of Fractional Nonlocal and Weighted Nonlocal Vector Calculus

Nonlocal and fractional-order models capture effects that classical partial differential equations cannot describe; for this reason, they are suitable for a broad class of engineering and scientific applications that feature multiscale or anomalous behavior. This has driven a desire for a vector calculus that includes nonlocal and fractional gradient, divergence and Laplacian type operators, as well as tools such as Green's identities, to model subsurface transport, turbulence, and conservation laws. In the literature, several independent definitions and theories of nonlocal and fractional vector calculus have been put forward. Some have been studied rigorously and in depth, while others have been introduced ad-hoc for specific applications. The goal of this work is to provide foundations for a unified vector calculus by (1) consolidating fractional vector calculus as a special case of nonlocal vector calculus, (2) relating unweighted and weighted Laplacian operators by introducing an equivalence kernel, and (3) proving a form of Green's identity to unify the corresponding variational frameworks for the resulting nonlocal volume-constrained problems. The proposed framework goes beyond the analysis of nonlocal equations by supporting new model discovery, establishing theory and interpretation for a broad class of operators, and providing useful analogues of standard tools from the classical vector calculus.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Towards a Unified theory of Fractional and Nonlocal Vector Calculus

Nonlocal and fractional-order models capture effects that classical partial differential equations cannot describe; for this reason, they are suitable for a broad class of engineering and scientific applications that feature multiscale or anomalous behavior. This has driven a desire for a vector calculus that includes nonlocal and fractional gradient, divergence and Laplacian type operators, as well as tools such as Green’s identities, to model subsurface transport, turbulence, and conservation laws. In the literature, several independent definitions and theories of nonlocal and fractional vector calculus have been put forward. Some have been studied rigorously and in depth, while others have been introduced ad-hoc for specific applications. The goal of this work is to provide foundations for a unified vector calculus by (1) consolidating fractional vector calculus as a special case of nonlocal vector calculus, (2) relating unweighted and weighted Laplacian operators by introducing an equivalence kernel, and (3) proving a form of Green’s identity to unify the corresponding variational frameworks for the resulting nonlocal volume-constrained problems. Here, the proposed framework goes beyond the analysis of nonlocal equations by supporting new model discovery, establishing theory and interpretation for a broad class of operators, and providing useful analogues of standard tools from the classical vector calculus.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Design principles from multiscale simulations to predict nanostructure in self-assembling ionic liquids

Molecular dynamics simulations (up to the nanoscale) were performed on the 3-methyl-1-pentylimidazolium ionic liquid cation paired with three anions; chloride, nitrate, and thiocyanate as aqueous mixtures, using the effective fragment potential (EFP) method, a computationally inexpensive way of modeling intermolecular interactions. The simulations provided insight (preferred geometries, radial distribution functions and theoretical proton NMR resonances) into the interactions within the ionic domain and are validated against 1 H NMR spectroscopy and small- and wide-angle X-ray scattering experiments on 1-decyl-3-methylimidazolium. Ionic liquids containing thiocyanate typically resist gelation and form poorly ordered lamellar structures upon mixing with water. Conversely, chloride, a strongly coordinating anion, normally forms strong physical gels and produces well-ordered nanostructures adopting a variety of structural motifs over a very wide range of water compositions. Nitrate is intermediate in character, whereby upon dispersal in water it displays a range of viscosities and self-assembles into nanostructures with considerable variability in the fidelity of ordering and symmetry, as a function of water content in the binary mixtures. The observed changes in the macro and nanoscale characteristics were directly correlated to ionic domain structures and intermolecular interactions as theoretically predicted by the analysis of MD trajectories and calculated RDFs. Specifically, both chloride and nitrate are positioned in the plane of the cation. Anion to cation proximity is dependent on water content. Thiocyanate is more susceptible to water insertion into the second solvent shell. Experimental 1 H NMR chemical shifts monitor the site-specific competition dependence with water content in the binary mixtures. As a result, thiocyanate preferentially sits above and below the aromatic ring plane, a state disallowing interaction with the protons on the imidazolium ring.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Multiscale multiphysics and multidomain models—Flexibility and rigidity

The emerging complexity of large macromolecules has led to challenges in their full scale theoretical description and computer simulation. Multiscale multiphysics and multidomain models have been introduced to reduce the number of degrees of freedom while maintaining modeling accuracy and achieving computational efficiency. A total energy functional is constructed to put energies for polar and nonpolar solvation, chemical potential, fluid flow, molecular mechanics, and elastic dynamics on an equal footing. The variational principle is utilized to derive coupled governing equations for the above mentioned multiphysical descriptions. Among these governing equations is the Poisson-Boltzmann equation which describes continuum electrostatics with atomic charges. The present work introduces the theory of continuum elasticity with atomic rigidity (CEWAR). The essence of CEWAR is to formulate the shear modulus as a continuous function of atomic rigidity. As a result, the dynamics complexity of a macromolecular system is separated from its static complexity so that the more time-consuming dynamics is handled with continuum elasticity theory, while the less time-consuming static analysis is pursued with atomic approaches. We propose a simple method, flexibility-rigidity index (FRI), to analyze macromolecular flexibility and rigidity in atomic detail. The construction of FRI relies on the fundamental assumption that protein functions, such as flexibility, rigidity, and energy, are entirely determined by the structure of the protein and its environment, although the structure is in turn determined by all the interactions. As such, the FRI measures the topological connectivity of protein atoms or residues and characterizes the geometric compactness of the protein structure. As a consequence, the FRI does not resort to the interaction Hamiltonian and bypasses matrix diagonalization, which underpins most other flexibility analysis methods. FRI's computational complexity is of O(N{sup 2}) at most, where N is the number of atoms or residues, in contrast to O(N{sup 3}) for Hamiltonian based methods. We demonstrate that the proposed FRI gives rise to accurate prediction of protein B-Factor for a set of 263 proteins. We show that a parameter free FRI is able to achieve about 95% accuracy of the parameter optimized FRI. An interpolation algorithm is developed to construct continuous atomic flexibility functions for visualization and use with CEWAR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Mechanistic materials modeling for nuclear fuel performance

Fuel performance codes are critical tools for the design, certification, and safety analysis of nuclear reactors. However, their ability to predict fuel behavior under abnormal conditions is severely limited by their considerable reliance on empirical materials models correlated to burn-up (a measure of the number of fission events that have occurred, but not a unique measure of the history of the material). In this paper, we propose a different paradigm for fuel performance codes to employ mechanistic materials models that are based on the current state of the evolving microstructure rather than burn-up. In this approach, a series of state variables are stored at material points and define the current state of the microstructure. The evolution of these state variables is defined by mechanistic models that are functions of fuel conditions and other state variables. The material properties of the fuel and cladding are determined from microstructure/property relationships that are functions of the state variables and the current fuel conditions. Multiscale modeling and simulation is being used in conjunction with experimental data to inform the development of these models. Finally, this mechanistic, microstructure-based approach has the potential to provide a more predictive fuel performance capability, but will require a team of researchers to complete the required development and to validate the approach.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

Currently, Constraint-Based Reconstruction and Analysis (COBRA) is the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Furthermore, standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We also developed a quadrupleprecision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.

59 BASIC BIOLOGICAL SCIENCES↗