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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

ISA-TAB-Nano: A Specification for Sharing Nanomaterial Research Data in Spreadsheet-based Format

Background and motivation: The high-throughput genomics communities have been successfully using standardized spreadsheet-based formats to capture and share data within labs and among public repositories. The nanomedicine community has yet to adopt similar standards to share the diverse and multi-dimensional types of data (including metadata) pertaining to the description and characterization of nanomaterials. Owing to the lack of standardization in representing and sharing nanomaterial data, most of the data currently shared via publications and data resources are incomplete, poorly-integrated, and not suitable for meaningful interpretation and re-use of the data. Specifically, in its current state, data cannot be effectively utilized for the development of predictive models that will inform the rational design of nanomaterials. Results: We have developed a specification called ISA-TAB-Nano, which comprises four spreadsheet-based file formats for representing and integrating various types of nanomaterial data. Three file formats (Investigation, Study, and Assay files) have been adapted from the established ISA-TAB specification; while the Material file format was developed de novo to more readily describe the complexity of nanomaterials and associated small molecules. In this paper, we have discussed the main features of each file format and how to use them for sharing nanomaterial descriptions and assay metadata. Conclusion: The ISA-TAB-Nano file formats provide a general and flexible framework to record and integrate nanomaterial descriptions, assay data (metadata and endpoint measurements) and protocol information. Like ISA-TAB, ISA-TAB-Nano supports the use of ontology terms to promote standardized descriptions and to facilitate search and integration of the data. The ISA-TAB-Nano specification has been submitted as an ASTM work item to obtain community feedback and to provide a nanotechnology data-sharing standard for public development and adoption.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Shock compression modeling of metallic single crystals: comparison of finite difference, steady wave, and analytical solutions

Background: The shock response of metallic single crystals can be captured using a micro-mechanical description of the thermoelastic-viscoplastic material response; however, using a such a description within the context of traditional numerical methods may introduce a physical artifacts. Advantages and disadvantages of complex material descriptions, in particular the viscoplastic response, must be framed within approximations introduced by numerical methods. Methods: Three methods of modeling the shock response of metallic single crystals are summarized: finite difference simulations, steady wave simulations, and algebraic solutions of the Rankine-Hugoniot jump conditions. For the former two numerical techniques, a dislocation density based framework describes the rate- and temperature-dependent shear strength on each slip system. For the latter analytical technique, a simple (two-parameter) rate- and temperature-independent linear hardening description is necessarily invoked to enable simultaneous solution of the governing equations. For all models, the same nonlinear thermoelastic energy potential incorporating elastic constants of up to order 3 is applied. Results: Solutions are compared for plate impact of highly symmetric orientations (all three methods) and low symmetry orientations (numerical methods only) of aluminum single crystals shocked to 5 GPa (weak shock regime) and 25 GPa (overdriven regime). Conclusions: For weak shocks, results of the two numerical methods are very similar, regardless of crystallographic orientation. For strong shocks, artificial viscosity affects the finite difference solution, and effects of transverse waves for the lower symmetry orientations not captured by the steady wave method become important. The analytical solution, which can only be applied to highly symmetric orientations, provides reasonable accuracy with regards to prediction of most variables in the final shocked state but, by construction, does not provide insight into the shock structure afforded by the numerical methods.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Artificial neural network correction for density-functional tight-binding molecular dynamics simulations

In this paper, the authors developed a Behler–Parrinello-type neural network (NN) to improve the density-functional tight-binding (DFTB) energy and force prediction. The Δ-machine learning approach was adopted and the NN was designed to predict the energy differences between the density functional theory (DFT) quantum chemical potential and DFTB for a given molecular structure. Most notably, the DFTB-NN method is capable of improving the energetics of intramolecular hydrogen bonds and torsional potentials without modifying the framework of DFTB itself. This improvement enables considerably larger simulations of complex chemical systems that currently could not easily been accomplished using DFT or higher level ab initio quantum chemistry methods alone.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Stochastic fracture generation and thermo-hydro-mechanical modeling in an equivalent continuum framework for enhanced geothermal systems

Enhanced geothermal systems (EGS) involve fracturing low permeability material to establish well connectivity and then injecting and circulating fluid into the fractured subsurface for geothermal power production. Changes in fracture aperture from contraction of the cooling matrix rock may alter network connectivity and risk thermal short-circuiting. Thermo-hydro-mechanical (THM) models are a useful tool to study these processes. However, as fracture networks are complex, and data may be limited, fracture networks in THM models are often stochastically generated. Given reliance on stochastic fracture networks and THM modeling to represent the subsurface and assess productivity of EGS, increased understanding of the influence of such statistically derived fracture networks on flow and heat transport in THM models is needed. Here, a new fracture process model is developed in the reactive transport code PFLOTRAN to stochastically generate fracture families and simulate changes in fracture aperture over time due to temperature changes of the rock matrix. Sixty-four different fracture networks ranging from well to poorly-connected, are modeled in PFLOTRAN with and without mechanical processes (THM vs TH). Results indicate that for well-connected fracture networks, thermal short-circuiting is less of a concern due to the abundance of available alternative flowpaths. For poorly-connected fracture networks, inclusion of mechanical processes showed steep thermal drawdown coincident with increase in fracture aperture along developing colder flowpaths, demonstrating the risk of thermal short-circuiting. Simulations with additional, larger fractures engineered to establish connectivity in a poorly-fractured subsurface, indicate that while stochastic variation of fracture orientation of the background network had limited influence, such variation in the engineered fractures significantly affected flow and heat transport.

Discrete fracture networks (DFN)↗
DOE OSTI2024

Device response principles and the impact on energy resolution of epitaxial quantum dot scintillators with monolithic photodetector integration

Abstract Epitaxial quantum dot (QD) scintillator crystals with picosecond-scale timing and high light yield have been created for medical imaging, high energy physics and national security applications. Monolithic photodetector (PD) integration enables the sensing of photons generated within the waveguiding crystal and allows a wide range of scintillator-photodetector coupling geometries. Until recently, these doubly novel devices have suffered from complex, high variance responses to monoenergetic sources which significantly reduces their precision and accuracy. The principles governing the overall device response have now been discerned and embodied by an expression derived within a geometrical optics framework which considers optical properties, surface roughness and photodetector coupling geometry. Response variation due to these factors was sufficiently reduced to obtain material-related energy resolution values of 2.4% with alpha particles. These findings place energy resolution alongside luminescence timescale, photon yield, and radiation hardness as outstanding properties of these engineered materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Designing a quantum-accurate machine-learning potential to enable large-scale simulations of deuterium under shock

Large-scale molecular dynamics of deuterium under shock can elucidate kinetic processes vital to the target design in inertial confinement fusion and high-energy-density experiments. However, modeling the complex evolution of this material from an insulating molecular state at ambient pressure to an ionized, atomic fluid under strong shock is beyond the capability of simple pair and even bond order potentials. We thus train a quantum-accurate and broadly transferable machine-learning interatomic potential for deuterium using the Chebyshev Interaction Model for Efficient Simulations framework. We show that due to an improved description of the molecular-to-atomic transition, our model is able to better reproduce the ab initio equation of state, radial distribution functions, and principal Hugoniot than bond order potentials. This represents an important step toward large-scale quantum-accurate and nonequilibrium simulations of complicated systems under dynamic changes including phase transitions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

A Multi-Phase Modeling Framework Suitable for Dynamic Applications

Under dynamic loading conditions and the associated extreme conditions many metals will undergo phase transformations. The change in crystal structure associated with solid–solid phase transformations can significantly alter the subsequent mechanical response of the material. For the interpretation of experiments involving dynamic loading it is beneficial to have a modeling framework that captures key features of the material response while remaining relatively simple. We introduce a candidate framework and apply it to the metal tin to highlight a range of behaviors that are captured by the model. We also discuss potential extensions to capture additional behaviors that could be important for certain materials and loading scenarios. The model is useful for analysis of results from dynamic experiments and offers a point of departure for more complex model formulations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

QES-Plume v1.0: a Lagrangian dispersion model

Low-cost simulations providing accurate predictions of transport of airborne material in urban areas, vegetative canopies, and complex terrain are demanding because of the small-scale heterogeneity of the features influencing the mean flow and turbulence fields. Common models used to predict turbulent transport of passive scalars are based on the Lagrangian stochastic dispersion model. The Quick Environmental Simulation (QES) tool is a low-computational-cost framework developed to provide high-resolution wind and concentration fields in a variety of complex atmospheric-boundary-layer environments. Part of the framework, QES-Plume, is a Lagrangian dispersion code that uses a time-implicit integration scheme to solve the generalized Langevin equations which require mean flow and turbulence fields. Here, QES-Plume is driven by QES-Winds, a 3D fast-response model that computes mass-consistent wind fields around buildings, vegetation, and hills using empirical parameterizations, and QES-Turb, a local-mixing-length turbulence model. In this paper, the particle dispersion model is presented and validated against analytical solutions to examine QES-Plume’s performance under idealized conditions. In particular, QES-Plume is evaluated against a classical Gaussian plume model for an elevated continuous point-source release in uniform flow, the Lagrangian scaling of dispersion in isotropic turbulence, and a non-Gaussian plume model for an elevated continuous point-source release in a power-law boundary-layer flow. In these cases, QES-Plume yields a maximum relative error below 6 % when compared with analytical solutions. In addition, the model is tested against wind-tunnel data for a uniform array of cubical buildings. QES-Plume exhibits good agreement with the experiment with 99 % of matched zeros and 59 % of the predicted concentrations falling within a factor of 2 of the experimental concentrations. Furthermore, results also emphasize the importance of using high-quality turbulence models for particle dispersion in complex environments. Finally, QES-Plume demonstrates excellent computational performance.

58 GEOSCIENCES↗
DOE OSTI2016

A multifunctional chemical sensor based on a three-dimensional lanthanide metal-organic framework

A 3D lanthanide MOF with formula [Sm{sub 2}(abtc){sub 1.5}(H{sub 2}O){sub 3}(DMA)]·H{sub 2}O·DMA (1) has been successfully synthesized via solvothermal method. Luminescence studies reveal that 1 exhibits dual functional detection benzyl alcohol and benzaldehyde among different aromatic molecules. In addition, 1 displays a turn-on luminescence sensing with respect to ethanol among different alcohol molecules, which suggests that 1 is also a promising luminescent probe for high selective sensing of ethanol. - Highlights: • A three-dimensional lanthanide metal-organic framework has been synthesized. • Complex 1 exhibits dual functional detection benzyl alcohol and benzaldehyde among different aromatic molecules. • Complex 1 displays a turn-on luminescence sensing with respect to ethanol among different alcohol molecules.

36 MATERIALS SCIENCE↗
DOE OSTI2021

CFD/FEA Co-Simulation Framework for Analysis of the Thermal Barrier Coating Design and Its Impact on the HD Diesel Engine Performance

Thermal barrier coatings (TBCs) have been investigated both experimentally and through simulation for mixing controlled combustion (MCC) concepts as a method for reducing heat transfer losses and increasing cycle efficiency, but it is still a very active research area. Early studies were inconclusive, with different groups discovering obstacles to realizing the theoretical potential. Nuanced papers have shown that coating material properties, thickness, microstructure, and surface morphology/roughness all can impact the efficacy of the thermal barrier coating and must be accounted for. Adding to the complexities, a strong spatial and temporal heat flux inhomogeneity exists for mixing controlled combustion (diesel) imposed onto the surfaces from the impinging flame jets. In support of the United States Department of Energy SuperTruck II program goal to achieve 55% brake thermal efficiency on a heavy-duty diesel engines, this study sought to develop a deeper insight into the inhomogeneous heat flux from mixing controlled combustion on thermal barrier coatings and to infer concrete guidance for designing coatings. To that end, a co-simulation approach was developed that couples high-fidelity computational fluid dynamics (CFD) modeling of in-cylinder processes and combustion, and finite element analysis (FEA) modeling of the thermal barrier-coated and metal engine components to resolve spatial and temporal thermal boundary conditions. The models interface at the surface of the combustion chamber; FEA modeling predicts the spatially resolved surface temperature profile, while CFD develops insights into the effect of the thermal barrier coating on the combustion process and the boundary conditions on the gas side. The paper demonstrates the capability of the framework to estimate cycle impacts of the temperature swing at the surface, as well as identify critical locations on the piston/thermal barrier coating that exhibit the highest charge temperature and highest heat fluxes. In addition, the FEA results include predictions of thermal stresses, thus enabling insight into factors affecting coating durability. An example of the capability of the framework is provided to illustrate its use for investigating novel coatings and provide deeper insights to guide future coating design.

42 ENGINEERING↗
DOE OSTI2022

Optimal high-throughput virtual screening pipeline for efficient selection of redox-active organic materials

As global interest in renewable energy continues to increase, there has been a pressing need for developing novel energy storage devices based on organic electrode materials that can overcome the shortcomings of the current lithium-ion batteries. One critical challenge for this quest is to find materials whose redox potential (RP) meets specific design targets. In this study, we propose a computational framework for addressing this challenge through the effective design and optimal operation of a high-throughput virtual screening (HTVS) pipeline that enables rapid screening of organic materials that satisfy the desired criteria. Starting from a high-fidelity model for estimating the RP of a given material, we show how a set of surrogate models with different accuracy and complexity may be designed to construct a highly accurate and efficient HTVS pipeline. We demonstrate that the proposed HTVS pipeline construction and operation strategies substantially enhance the overall screening throughput.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Atomic Movies of Laser-Induced Structural and Phase Transformations from Molecular Dynamics Simulations

Molecular dynamics (MD) simulations of laser-materials interactions are playing an important role in investigation of complex and highly non-equilibrium processes involved in short pulse laser processing and surface modification. This role is defined by the ability of MD simulations to reveal in-depth information on the structural and phase transformations induced by the laser excitation and, at the same time, to provide clear visual representations, or “atomic movies,” of laser-induced dynamic processes. This chapter provides a brief overview of recent progress in the description of laser coupling and relaxation of photo-excited states in metals, semiconductors, insulators and molecular systems within the general framework of the classical MD technique and presents several examples of MD simulations of laser melting, generation of crystal defects, photomechanical spallation, explosive boiling and molecular entrainment in laser ablation. Possible directions of future progress in atomistic modeling of laser-materials interactions and the potential role of MD simulations in the design of an integrated multiscale computational model capable of accounting for interrelations between processes occurring at different time- and length-scales are discussed.

Classical Molecular Dynamic↗
DOE OSTI2016

Macro Supply Chain Lifecycle Decision Analytics

This report summarizes a two-year LDRD project that investigated the problem of representing complex supply chains, identifying the worst risks and evaluating mitigation options. Our team developed a framework that includes a representation for business processes, risk assessment questions, risk indicators and methods for analyzing and summarizing the data. In our approach, the Process Matrix represents an overall supply chain for an end product in a high-level, tabular form. It connects the various touch-points of a business process including people, external vendors, tools, storage locations and transportation services while capturing the flow of both physical and intellectual artifacts. We believe these direct connections are exactly the things that a process owner can typically control. These material flows (both physical and intellectual) are also represented in a graph. This enables us to use graph-oriented analysis such as fault tree analysis and attack graph generation. Our approach is top-down, which helps users to get a more holistic understanding for a given amount of resources. Understanding the provenance of materials is difficult and it is easy to exhaust the analysts' resources. Rather than a tool to do vendor analysis or product comparison, our framework enables an enterprise-level analysis. The risk assessment questionnaires have a varying levels of detail and cover various aspects of the supply chain such as process steps, artifacts, suppliers, etc. and connections between these aspects such as artifact-storage, artifact-supplier, etc. Each question is further associated with one of seven risk indicators which can be used to summarize the risk. These risk indicators can also be weighted to reflect a user's concerns. We have successfully applied our framework to several use cases in various stages of its development and provided valuable insights to our partners, but it can also be applied to other complex systems outside of the supply chain security problem.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Growth, structure, and properties of GaAs-based (GaAs){sub 1–x–y}(Ge{sub 2}){sub x}(ZnSe){sub y} epitaxial films

The possibility of growing the (GaAs){sub 1–x–y}(Ge{sub 2}){sub x}(ZnSe){sub y} alloy on GaAs substrates by the method of liquid-phase epitaxy from a tin solution–melt is shown. X-ray diffraction shows that the grown film is single-crystal with the (100) orientation and has the sphalerite structure. The crystal-lattice parameter of the film is a{sub f} = 0.56697 nm. The features of the spectral dependence of the photosensitivity are caused by the formation of various complexes of charged components. It is established that the I–V characteristic of such structures is described by the exponential dependence I = I{sub 0}exp(qV/ckT) at low voltages (no higher than 0.4 V) and by the power dependence J ∼ V{sup α}, where the exponent α varies with increasing voltage at high voltages (V > 0.5 V). The results are treated within the framework of the theory of the drift mechanism of current transfer taking into account the possibility of the exchange of free carriers within the recombination complex.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Multivalent Electrochemistry of Spinel Mg x Mn 3– x O 4 Nanocrystals

Oxides undergoing reversible electrochemical cycling of Mg 2+ ions would enable novel battery concepts beyond Li + , capable of storing large amounts of energy. However, materials showing this chemical reactivity are scarce. Suitable candidates require small particles to shorten transport lengths, together with chemically complex structures that promote cation mobility, such as spinel. These goals pose a challenge for materials chemists. Here, nanocrystals of spinel-type Mg 0.5 Mn 2.5 O 4 were prepared using colloidal synthesis, and their electrochemical activity is presented. Cycling in an aqueous Mg 2+ electrolyte led to a reversible transformation between a reduced spinel and an oxidized layered framework. This reaction involves large amounts of capacity because of the full oxidation to Mn 4+ , through the extraction of both Mg 2+ and, in the first cycle, Mn 2+ ions. Re-formation of the spinel upon reduction resulted in enrichment with Mg 2+ , indicating that its insertion is more favorable than that of Mn 2+ . Incorporation of water into the structure was not indispensable for the transformation, as revealed by experiments in non-aqueous electrolytes and infrared spectroscopy. Lastly, the findings open the door for the use of similar nanocrystals in Mg batteries provided that electrolytes with suitable anodic stability are discovered, thereby identifying novel routes toward electrode materials for batteries with high energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Physical Sciences Vistas (Issue 3 2020)

In this issue, highlights of our outstanding R&D supporting the Laboratory’s nuclear security mission include the following. 1. The essential nuclear materials science contributions of MST’s Materials Properties Team to a range of Lab missions, operations, and initiatives. 2. Work in the Sigma Complex supporting NNSA’s Advanced Manufacturing Development milestones by members of Sigma Division and colleagues across the Laboratory and other NNSA sites. 3. Greg Dale’s role in the Lab’s molybdenum program and today as a technical lead on the Scorpius project. 4. Measurements that reveal the role thermal interfaces play in dynamic compression experiments. 5. The first-ever synthesis of an actinide framework, which offers opportunities for understanding these structures as potential radioactive waste forms and provides new models of actinide species transport in the environment. This issue also showcases, as part of our commitment to simultaneous excellence in mission operations and community relations, the successful high-hazard repair of the LANSCE accelerator, which resulted in a large team Laboratory Distinguished Performance Award, and a virtual Summer Physics Camp for Young Women that brought together participants from around the world, even as COVID kept them socially distant in their homes.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Anion Exchange in Cationic Frameworks: Structures of Channel-Forming Triarylpyrylium Tetrafluoroborate Salts

The crystal structure of a co-crystal of 2,4,6-tris(4-methylphenyl)pyridine and 2,4,6-tris(4-methylphenyl)pyrylium revealed a pseudohexagonal motif assembled from π-stacked helices that enclose 1-D channels containing disordered tetrafluoroborate counterions and solvent molecules. Though convoluted by disorder, aperiodicity, and complex twinning patterns, further investigation was motivated by the unique packing arrangement of this material, which enables solid-state anion diffusion, while resisting dissolution in water. Such properties are ideal for the development of anion exchange materials that can be employed for the remediation of agricultural and nuclear waste. Here, to develop a more complete understanding of the intermolecular interactions that give rise to this helical motif, we have compiled and analyzed the local interactions and extended structural motifs of 19 pore- and channel-forming 2,4,6-triarylpyrylium crystal structures. Here, three conserved local interactions were identified, including C–H···X– interactions, the rhombic binding pocket, and four-ring antiparallel dimerization. Development of this library of interaction motifs rationalizes the packing arrangement of the co-crystal and contributes to the informed design of anion exchange materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗