Search NASA⌕ Search

SEARCH · Search NASA

Results for “Computer interfaces”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Computationally Aided Design of a Small-Scale Waste-Glass Melter - 20452

To provide mission support to the WTP for the vitrification of legacy nuclear tank waste at the Hanford site, a reduced-scale vitrification pilot system is being designed to process simulated and actual radioactive tank wastes. The waste stored in underground tanks will be separated into HLW and LAW fractions, where the majority is LAW by mass (approximately 90%) and HLW by activity (greater than 95%). The first tank waste to be processed at the WTP will be LAW. Because Hanford tank waste and the resultant melter feed compositions are known to vary widely, pilot-scale operations are essential to identify potential problems, provide needed data, confirm assumptions, and determine impacts to full-scale melters and off-gas systems from these different feeds. In addition, the reduced-scale melter system must be capable of quickly providing results to operations without the typically high cost of full-scale radioactive operations and to provide an engineering platform in close proximity to local operations staff. The objectives of this computational modeling effort are to produce process conditions like those encountered in the WTP LAW melters and to minimize the volume of radioactive waste necessary for the evaluations. To this end, melter design features are being explored to increase production without significantly increasing surface area or melter-glass volumes or compromising data quality. In support of these objectives, a set of computational fluid dynamic models was developed to provide insight into the flow patterns within these small melters and evaluate design features to increase production without significantly increasing glass inventory. Computed velocities were evaluated at the interface between the cold cap and the molten-glass pool and within the bulk-glass pool. Since melt rate correlates with the velocity underneath the cold cap, this information can be used to select design features that produce the highest gains. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Two-Phase Turbulent Kinetic Energy Budget Computation in Co-Current Taylor Bubble Flow

Simulation of two-phase flows is relevant for reactor design and safety at normal operation or during accident scenarios. Often, the two-phase flow is in a regime in which slugs are formed or where the flow stratifies. Modeling such situations using standard single-phase Reynolds-averaged Navier-Stokes (RANS) turbulence models fails due to an overestimation of the eddy viscosity at the resolved two-phase interface. To solve this, an ad hoc turbulence damping term has been proposed in the literature that reduces the turbulence production locally at a two-phase interface, analogously to turbulence wall functions. However, this approach must be tailored to the specific setting and does not consider physical contributions such as surface tension or flow topology. Therefore, the problem of two-phase interfacial turbulence must be studied more in-depth. In this work, we consider co-current turbulent Taylor bubble flow using high-fidelity numerical simulation. The Basilisk code is used to simulate a Taylor bubble rising in a vertical pipe. By simulating the bubble in a moving frame of reference, we may study the turbulent kinetic energy (TKE) budgets ahead of the bubble, in its wake, and across the interface. The implementation of the TKE budget computation and the underlying averaging techniques are validated for the single-phase region ahead of the Taylor bubble using reference direct numerical simulation data. The analysis of the TKE budgets in the setting of Taylor bubble flow allows for the study of how turbulence behaves due to the presence of a two-phase interface and, in turn, supports the improvement of two-phase RANS models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

FLIT: A Generic Fortran Library based on Interfaces and Templates

This Fortran code consists of multiple modules with a focus on simplifying array operations, image processing, and numerical computation especially for computational geophysics applications. We intend to use this code to demonstrate the application and usefulness of Fortran interface and templates for generic programming, especially for computational geophysics and seismology applications. The code has several notable features. Firstly, it is based on a modularized structure, where each module contains multiple functions but with a focus of functionality. Secondly, it heavily uses interfaces and templates for improving the genericness and convenience of the resulting code, where a same function interface can enclose a group of functions that perform the same functionality but with inputs/output variables of different data types. Thirdly, it includes a variety of generic functions with an emphasis on array operations, such as rotation, flipping, cropping, padding, fast Fourier transform, Gaussian blurring, interpolation, and so on. We name this package FLIP – a generic Fortran Library based on Interfaces and Templates.

Gao, Kai↗
DOE OSTI2024

Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation

The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Furthermore, our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Te/CdTe and Al/CdTe Interfacial Energy Band Alignment by Atomistic Modeling

A synergistic approach that incorporates first-principles atomistic modeling with numerical device simulations is used to systematically evaluate the role of heterointerfaces within metal-chalcogenide-based photovoltaic technologies. Two interfaces involving either a tellurium back contact or aluminum back electrode combined with a cadmium telluride absorber layer within cadmium-telluride-based thin-film solar cells are investigated on an atomic scale to determine the mechanisms contributing to variations in device performance. Electronic structures and predicted charge transport behavior with respect to cadmium and tellurium termination of the absorber layer are studied along the polar oriented CdTe{111} facets. The computational methodology reveals a noticeable contrast between the Schottky barrier forming Al/CdTe interface versus the Type I Te/CdTe heterojunction. Greater band bending features are exhibited by the cadmium termination as opposed to the tellurium termination for each interface case. Subsequent device modeling suggests that 3.6% higher photovoltaic conversion efficiency is achievable for the cadmium termination relative to the tellurium termination of the Te/CdTe interface. Based strictly on an idealistic representation, both interface models show the importance of atomic-scale interfacial properties for cadmium telluride solar cell device performance with their bulk properties being validated in comparison to published experimental data. Here, the synergistic approach offers a suitable method to analyze solar cell interfaces through a predictive computational framework for the engineering and optimization of metal-chalcogenide-based thin-film photovoltaic technologies.

Green’s function↗
DOE OSTI2023

Reaction Current Heterogeneity at the Interface between a Lithium Electrode and Polymer/Ceramic Composite Electrolytes

Although lithium metal anodes are expected to increase the energy density of next-generation batteries, dendrite growth during charge remains a major bottleneck preventing widespread implementation. Composite solid electrolytes with ceramic particles embedded in a polymer matrix have the potential to prevent dendrites owing to the higher mechanical stiffness while also possessing the flexibility to maintain contact with the electrode. However, microscopically, the different mechanical and electrochemical properties of the polymer and ceramic domains can cause inhomogeneous charge transfer at the Li/electrolyte interface, which can lead to nonuniform Li deposition and propagation of dendrites. Here, we computationally examine the coupled electrochemical, transport, and mechanical processes at the interface to determine the propensity for dendrite formation and possible approaches to mitigate this issue. Predictions of two possible microstructures at the interface, namely, (i) where both the polymer and ceramic come in contact with Li metal and (ii) when only the polymer comes in contact with Li metal, suggest that the former has a greater tendency for nonuniform plating. In addition, predictions suggest that minimizing the interfacial resistance between polymers and ceramics and incorporating interlayers between the electrode and electrolyte help mitigate current heterogeneity. In conclusion, these predictions provide guidance for experimental approaches to prevent dendrites in composite electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2021

Localizing Clinical Patterns of Blast Traumatic Brain Injury Through Computational Modeling and Simulation

Blast traumatic brain injury is ubiquitous in modern military conflict with significant morbidity and mortality. Yet the mechanism by which blast overpressure waves cause specific intracranial injury in humans remains unclear. Reviewing of both the clinical experience of neurointensivists and neurosurgeons who treated service members exposed to blast have revealed a pattern of injury to cerebral blood vessels, manifested as subarachnoid hemorrhage, pseudoaneurysm, and early diffuse cerebral edema. Additionally, a seminal neuropathologic case series of victims of blast traumatic brain injury (TBI) showed unique astroglial scarring patterns at the following tissue interfaces: subpial glial plate, perivascular, periventricular, and cerebral gray-white interface. The uniting feature of both the clinical and neuropathologic findings in blast TBI is the co-location of injury to material interfaces, be it solid-fluid or solid-solid interface. This motivates the hypothesis that blast TBI is an injury at the intracranial mechanical interfaces. In order to investigate the intracranial interface dynamics, we performed a novel set of computational simulations using a model human head simplified but containing models of gyri, sulci, cerebrospinal fluid (CSF), ventricles, and vasculature with high spatial resolution of the mechanical interfaces. Simulations were performed within a hybrid Eulerian—Lagrangian simulation suite (CTH coupled via Zapotec to Sierra Mechanics). Because of the large computational meshes, simulations required high performance computing resources. Twenty simulations were performed across multiple exposure scenarios—overpressures of 150, 250, and 500 kPa with 1 ms overpressure durations—for multiple blast exposures (front blast, side blast, and wall blast) across large variations in material model parameters (brain shear properties, skull elastic moduli). All simulations predict fluid cavitation within CSF (where intracerebral vasculature reside) with cavitation occurring deep and diffusely into cerebral sulci. These cavitation events are adjacent to high interface strain rates at the subpial glial plate. Larger overpressure simulations (250 and 500kPa) demonstrated intraventricular cavitation—also associated with adjacent high periventricular strain rates. Additionally, models of embedded intraparenchymal vascular structures—with diameters as small as 0.6 mm—predicted intravascular cavitation with adjacent high perivascular strain rates. The co-location of local maxima of strain rates near several of the regions that appear to be preferentially damaged in blast TBI (vascular structures, subpial glial plate, perivascular regions, and periventricular regions) suggest that intracranial interface dynamics may be important in understanding how blast overpressures leads to intracranial injury.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Quantum defects in silicon

The dataset used in the publication titled “High-throughput identification of spin-photon interfaces in silicon. Using an automated first-principles computational workflow, we construct a dataset that details simple charged defects in silicon that are relevant for spin-photon interface applications. It encompasses properties vital for quantum defect design, including charged defect stability, excitation energy (determined using the single-shot hybrid functional approach), emission brightness, electronic structures, and more. The dataset encompasses all charged defects stable within the energy gap. We emphasized the importance of defect-bound excitons as telecom band emitters and proposed promising candidates for spin-photon interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Entrapment Behavior of Solid Surrogate Fission Products at Engineered UN Nano‐Hetero‐Interfaces Within Metallic Nuclear Fuels

Nanometric hetero-interfaces provide a wealth of scientific and engineering opportunities due to their complex and often misunderstood properties that can differ from their respective bulk constituents. In this work, the ability for engineered nanostructures within a bulk U─Mo alloy to arrest simulant fission products is investigated experimentally and computationally. Nanostructured 90 wt% U/ 10 wt% Mo (U-10Mo) with 7.1 at% Nd is consolidated using spark-plasma- sintering (SPS) techniques and is heat-treated at 500 °C under vacuum for 24, 100, 500, and 1000 h. Analysis on the sintered and heat-treated U-10Mo reveals rapid kinetics in Nd diffusion to nanocluster sites, with evidence of Nd diffusion occurring during sintering and during the following heat-treatment. The segregation behavior of Nd at two different U─Mo/UN interfaces is computationally verified using density functional theory (DFT) to reinforce experimental data.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Effects of ion adsorption on graphene oxide films and interfacial water structure: A molecular-scale description

Herein we discuss graphene oxide as a promising, emerging separation material, as it is durable, dispersible in water, and has naturally forming functional groups. Bulk studies using graphene oxide flakes have demonstrated impressive metal adsorption. However, little interfacial information about water and metal organization near graphene oxide is available. A mechanistic understanding of water and ions interactions with graphene oxide films is critical toward advanced separations, including improved sorption efficiency and membrane regeneration. We study metal ion and local water organization near graphene oxide thin films formed at the air/water interface. These films are not typical membranes and allow us to determine nanoscale information about the graphene oxide-water interface. We accomplish this with x-ray reflectivity (XR), x-ray fluorescence near total reflection (XFNTR), and vibrational sum frequency generation spectroscopy (SFG). These interface-specific techniques provide the electron density profile normal to the interface, number of adsorbed ions, and information about the orientational ordering and hydrogen-bonding network of interfacial water, respectively. Via XFNTR and SFG, we find that trivalent yttrium ions preferentially adsorb to graphene oxide and affect its structure, compared to divalent strontium and monovalent cesium ions. Two different interfacial water populations can be described, based on their hydrogen bonding strength, and the adsorbed ions affect these populations differently. These results provide fundamental information about ion and water organization at the interface and help address the large computational-experimental agreement gap for graphene oxide systems. Additionally, they are relevant for improved soft-scaffold graphene oxide membranes and downstream applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Prediction of Solute Segregation at Metal/Oxide Interfaces Using Machine Learning Approaches

The atomic structure and chemistry at metal/oxide interfaces play a crucial role in determining their properties. However, studying semi-coherent metal/oxide interfaces that include misfit dislocations through density functional theory (DFT) is often computationally expensive due to the large number of atoms involved, ranging from hundreds to thousands. In this study, we explore solute segregation behavior at the Fe/Y 2 O 3 interface—an important model interface for cladding applications in nuclear fission reactors—by combining DFT calculations with a machine learning (ML) approach. ML models are trained using DFT-calculated segregation energies (𝐸 𝑆𝑒𝑔 ) to identify the key chemical and geometric factors influencing solute segregation at metal/oxide interfaces, revealing the competition between these features in determining 𝐸 𝑆𝑒𝑔 . Moreover, the segregation behavior at a specific Fe/Y 2 O 3 interface is predicted with high accuracy using ML models trained on data from this interface. Furthermore, it is found that the ML models could also predict solute segregation at a different Fe/Y 2 O 3 interface with a new orientation relationship (OR), at a computational cost of less than 1/45 of that required for similar DFT calculations.

36 - MATERIALS SCIENCE↗
DOE OSTI2022

Development of the uncertainty quantification toolkit's python interface and surrogate construction tutorial

The uncertainty quantification toolkit (UQTk) is a collection of c++ libraries that assess the confidence of numerical models. Surrogate approximations, often polynomial chaos expansions (PCEs), lessen the computational cost of these assessments. I developed a Python interface in UQTk for regression and Bayesian compressive sensing to add to the existing Galerkin projection method. These methods receive an object containing the polynomial basis information and NumPy arrays of sample points, call c++ methods, and return the PCE coefficients in a NumPy array. To demonstrate these methods, I wrote a tutorial in which I use them to construct surrogates for Genz functions and calculate the resulting error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The Future of Computing: Integrating Scientific Computation on Neuromorphic Systems

Neuromorphic computing is known for its integration of algorithms and hardware elements that are inspired by the brain. Conventionally, this nontraditional method of computing is used for many neural or learning inspired applications. Unfortunately, this has resulted in the field of neuromorphic computing being relatively narrow in scope. In this paper we discuss two research areas actively trying to widen the impact of neuromorphic systems. The first is Fugu, a high-level programming interface designed to bridge the gap between general computer scientists and those who specialize in neuromorphic areas. The second aims to map classical scientific computing problems onto these frameworks through the example of random walks. This elucidates a class of scientific applications that are conducive to neuromorphic algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments

The annual Workshop on Workflows in Support of Large-Scale Science (WORKS) is a premier venue for the scientific workflow community to present the latest advances in research and development on the many facets of scientific workflows throughout their life-cycle. The Lightning Talks at WORKS focus on describing a novel tool, scientific workflow, or concept, which are work-in-progress and address emerging technologies and frameworks to foster discussion in the community. This paper summarizes the lightning talks at the 2023 edition of WORKS, covering five topics: leveraging large language models to build and execute workflows; developing a common workflow scheduler interface; scaling uncertainty workflow applications on exascale computing systems; evaluating a transcriptomics workflow for cloud vs. HPC systems; and best practices in migrating legacy workflows to workflow management systems.

Titov, Mikhail↗
DOE OSTI2024

Diffusion-Limited Kinetics in Reactive Systems

A proper representation of chemical kinetics is vital to understanding, modeling, and optimizing many important chemical processes. In liquid and surface phases, where diffusion is slow, the rate at which the reactants diffuse together limits the overall rate of many elementary reactions. Commonly, the textbook Smoluchowski theory is utilized to estimate effective rate coefficients in the liquid phase. On surfaces, modelers commonly resort to much more complex and expensive Kinetic Monte Carlo (KMC) simulations. Here, in this study, we extend the Smoluchowski model to allow the diffusing species to undergo chemical reactions and derive analytical formulas for the diffusion-limited rate coefficients for 3D, 2D, and 2D/3D interface cases. With these equations, we are able to demonstrate that when species react faster than they diffuse they can react orders of magnitude faster than predicted by Smoluchowski theory, through what we term “the reactive transport effect”. We validate the derived steady-state equations against particle Monte Carlo (PMC) simulations, KMC simulations, and non-steady-state solutions. Furthermore, using PMC and KMC simulations, we propose corrections that agree with all limits and the computed data for the 2D and 2D/3D interface steady-state equations, accounting for unique limitations in the associated derived equations. Additionally, we derive equations to handle couplings between diffusion-limited rate coefficients in reaction networks. We believe these equations should make it possible to run much more accurate mean-field simulations of liquids, surfaces, and liquid–surface interfaces accounting for diffusion limitations and the reactive transport effect.

Johnson, Matthew Sean↗
DOE OSTI2024

Boosting RDataFrame performance with transparent bulk event processing

RDataFrame is ROOT’s high-level interface for Python and C++ data analysis. Since it first became available, RDataFrame adoption has grown steadily and it is now poised to be a major component of analysis software pipelines for LHC Run 3 and beyond. Thanks to its design inspired by declarative programming principles, RDataFrame enables the development of highperformance, highly parallel analyses without requiring expert knowledge of multi-threading and I/O: user logic is expressed in terms of self-contained, small computation kernels tied together by a high-level API. This design completely decouples analysis logic from its actual execution, and opens several interesting avenues for workflow optimization. In particular, in this work we explore the benefits of moving internal data processing from an event-by-event to a bulkby-bulk loop. This refactoring dramatically reduces the framework’s runtime overheads; in collaboration with the I/O layer it improves data access patterns; it exposes information that optimizing compilers might use to auto-vectorize the invocation of user-defined computations; finally, while existing user-facing interfaces remain unaffected, it becomes possible to additionally offer interfaces that explicitly expose bulks of events, useful e.g. for the injection of GPU kernels into the analysis workflow. In order to inform similar future R&D, design challenges will be presented, as well as an investigation of the relevant timememory trade-off backed by novel performance benchmarks.

Guiraud, Enrico↗
DOE OSTI2021

Cement sensors with acoustic bandgaps using carbon nanotubes

Cement is widely used in wellbores to stabilize the steel casing used in wellbore operations for oil and gas production, enhanced geothermal systems and carbon sequestration, and to limit fluid movement between sub-surface strata. Flaws such as microcracks in wellbore cement can lead to leakage along the wellbore compromising wellbore integrity. There is an increasing need for methods to monitor cement crack propagation in wellbore environments. In this study, we develop and report the first cementitious sensors capable of exhibiting high frequency acoustic bandgaps (ABGs) using carbon nanotubes (CNTs). Computational simulations of a sensor unit cell are used to design cement-multi walled carbon nanotubes (MWCNTs) sensors that show a wide bandgap. When the cement-MWCNTs sensors is embedded in cement specimens, bandgaps were measured experimentally under 300 kHz and under 600 kHz, consistent with the computationally predicted bandgaps in the range of 290–360 kHz, 410–460 kHz and 515–585 kHz. These bandgap features were absent in homogeneous cement specimens. X-ray tomographic reconstructions showed microscopic debonding at cement-MWCNTs sensor interface. Frequency response analysis of a three-dimensional computational model indicated a shift of frequency of minimum transmission due to the interface debonding, but no perturbation of bandgap response was observed. Here, the cement-MWCNTs sensors developed in this study show the potential of a packed CNT inclusion material in cementitious matrix to create ABGs in a cement matrix.

36 MATERIALS SCIENCE↗