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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Time-Accurate Unsteady Pressure Loads Simulated for the Space Launch System at Wind Tunnel Conditions

A transonic flow field about a Space Launch System (SLS) configuration was simulated with the Fully Unstructured Three-Dimensional (FUN3D) computational fluid dynamics (CFD) code at wind tunnel conditions. Unsteady, time-accurate computations were performed using second-order Delayed Detached Eddy Simulation (DDES) for up to 1.5 physical seconds. The surface pressure time history was collected at 619 locations, 169 of which matched locations on a 2.5 percent wind tunnel model that was tested in the 11 ft. x 11 ft. test section of the NASA Ames Research Center's Unitary Plan Wind Tunnel. Comparisons between computation and experiment showed that the peak surface pressure RMS level occurs behind the forward attach hardware, and good agreement for frequency and power was obtained in this region. Computational domain, grid resolution, and time step sensitivity studies were performed. These included an investigation of pseudo-time sub-iteration convergence. Using these sensitivity studies and experimental data comparisons, a set of best practices to date have been established for FUN3D simulations for SLS launch vehicle analysis. To the author's knowledge, this is the first time DDES has been used in a systematic approach and establish simulation time needed, to analyze unsteady pressure loads on a space launch vehicle such as the NASA SLS.

Alter, Stephen J.↗
DOE OSTI2020

Computationally Predicted High-Throughput Free-Energy Phase Diagrams for the Discovery of Solid-State Hydrogen Storage Reactions

The design of multinary solid-state material systems that undergo reversible phase changes via changes in temperature and pressure provides a potential means of safely storing hydrogen. However, fully mapping the stabilities of known or newly targeted compounds relative to competing phases at reaction conditions has previously required many stringent experiments or computationally demanding calculations of each compound’s change in Gibbs energy with respect to temperature, G(T). Here, we have extended the approach of constructing chemical potential phase diagrams based on ΔG f (T) to enable the analysis of phase stability at non-zero temperatures. We first performed density functional theory calculations to compute the formation enthalpies of binary, ternary, and quaternary compounds within several compositional spaces of current interest for solid-state hydrogen storage. Temperature effects on solid compound stability were then accounted for using our recently introduced machine learned descriptor for the temperature-dependent contribution G δ (T) to the Gibbs energy G(T). From these Gibbs energies, we evaluated each compound’s stability relative to competing compounds over a wide range of conditions and show using chemical potential and composition phase diagrams that the predicted stable phases and H2 release reactions are consistent with experimental observations. This demonstrates that our approach rapidly computes the thermochemistry of hydrogen release reactions for compounds at sufficiently high accuracy relative to experiment to provide a powerful framework for analyzing hydrogen storage materials. This framework based on G(T) enables the accelerated discovery of active materials for a variety of technologies that rely on solid-state reactions involving these materials.

08 HYDROGEN↗
DOE OSTI2026

Selection of a Pair of Experiments to Optimally Reduce Uncertainty in Targeted Nuclear Data

We propose a novel process to select a pair of differential and integral experiments that best reduce uncertainties in targeted 239 ⁢Pu nuclear data while compressing the current nuclear data pipeline from 20 to 3 years. 239⁢ Pu nuclear data are poorly understood for neutrons in the intermediate energy range due to sparsity and uncertainty in historical experiments. New experiments targeting this range will enable better understanding of these nuclear data, but choosing the ideal experiments to conduct is challenging. Beginning with a prior distribution represented by samples of nuclear data generated from theory, generalized least squares adjustments are made to incorporate data from historical experiments. To quantify potential uncertainty reduction obtainable from a pair of candidate experiments, we compute the D-optimality criterion of the posterior covariance of intermediate energy range nuclear data compared to the equivalent covariance after additional adjustment to the pair of candidate experiments. Repeating the process for each of many candidate pairs facilitates the final selection. Results support 63⁢ Cu total cross section measurements for differential experiments and alumina and alumina/graphite configurations for integral experiments. This analysis enables choosing differential and integral experiments to be executed concurrently while shortening decision times relative to the current nuclear data pipeline.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1994

Theory, Image Simulation, and Data Analysis of Chemical Release Experiments

The final phase of Grant NAG6-1 involved analysis of physics of chemical releases in the upper atmosphere and analysis of data obtained on previous NASA sponsored chemical release rocket experiments. Several lines of investigation of past chemical release experiments and computer simulations have been proceeding in parallel. This report summarizes the work performed and the resulting publications. The following topics are addressed: analysis of the 1987 Greenland rocket experiments; calculation of emission rates for barium, strontium, and calcium; the CRIT 1 and 2 experiments (Collisional Ionization Cross Section experiments); image calibration using background stars; rapid ray motions in ionospheric plasma clouds; and the NOONCUSP rocket experiments.

Wescott, Eugene M.↗
DOE OSTI2025

A tweezer array with 6,100 highly coherent atomic qubits

Optical tweezer arrays have transformed atomic and molecular physics, now forming the backbone for a range of leading experiments in quantum computing, simulation and metrology. Typical experiments trap tens to hundreds of atomic qubits and, recently, systems with around 1,000 atoms were realized without defining qubits or demonstrating coherent control. However, scaling to thousands of atomic qubits with long coherence times and low-loss and high-fidelity imaging is an outstanding challenge and critical for progress in quantum science, particularly towards quantum error correction (QEC). Here we experimentally realize an array of optical tweezers trapping more than 6,100 neutral atoms in around 12,000 sites, simultaneously surpassing state-of-the-art performance for several metrics that underpin the success of the platform. Specifically, while scaling to such a large number of atoms, we demonstrate a coherence time of 12.6(1) s, a record for hyperfine qubits in an optical tweezer array. We show room-temperature trapping lifetimes of about 23 min, enabling record-high imaging survival of 99.98952(1)% with an imaging fidelity of more than 99.99%. We present a plan for zone-based quantum computing and demonstrate necessary coherence-preserving qubit transport and pick-up/drop-off operations on large spatial scales, characterized through interleaved randomized benchmarking. Our results, along with recent developments, indicate that universal quantum computing and QEC with thousands to tens of thousands of physical qubits could be a near-term prospect.

atomic and molecular physics↗
DOE OSTI2025

Quantum computing universal thermalization dynamics in a (2 + 1)D Lattice Gauge Theory

Simulating non-equilibrium phenomena in strongly-interacting quantum many-body systems, including thermalization, is a promising application of near-term and future quantum computation. By performing experiments on a digital quantum computer consisting of fully-connected optically-controlled trapped ions, we study the role of entanglement in the thermalization dynamics of a Z 2 lattice gauge theory in 2+1 spacetime dimensions. Using randomized-measurement protocols, we efficiently learn a classical approximation of non-equilibrium states that yields the gap-ratio distribution and the spectral form factor of the entanglement Hamiltonian. These observables exhibit universal early-time signals for quantum chaos, a prerequisite for thermalization. Our work, therefore, establishes quantum computers as robust tools for studying universal features of thermalization in complex many-body systems, including in gauge theories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .

36 MATERIALS SCIENCE↗
NASA NTRS2013

Swept-Wing Ice Accretion Characterization and Aerodynamics

NASA, FAA, ONERA, the University of Illinois and Boeing have embarked on a significant, collaborative research effort to address the technical challenges associated with icing on large-scale, three-dimensional swept wings. The overall goal is to improve the fidelity of experimental and computational simulation methods for swept-wing ice accretion formation and resulting aerodynamic effect. A seven-phase research effort has been designed that incorporates ice-accretion and aerodynamic experiments and computational simulations. As the baseline, full-scale, swept-wing-reference geometry, this research will utilize the 65% scale Common Research Model configuration. Ice-accretion testing will be conducted in the NASA Icing Research Tunnel for three hybrid swept-wing models representing the 20%, 64% and 83% semispan stations of the baseline-reference wing. Three-dimensional measurement techniques are being developed and validated to document the experimental ice-accretion geometries. Artificial ice shapes of varying geometric fidelity will be developed for aerodynamic testing over a large Reynolds number range in the ONERA F1 pressurized wind tunnel and in a smaller-scale atmospheric wind tunnel. Concurrent research will be conducted to explore and further develop the use of computational simulation tools for ice accretion and aerodynamics on swept wings. The combined results of this research effort will result in an improved understanding of the ice formation and aerodynamic effects on swept wings. The purpose of this paper is to describe this research effort in more detail and report on the current results and status to date. 1

Broeren, Andy P.↗
NASA NTRS2013

Swept-Wing Ice Accretion Characterization and Aerodynamics

NASA, FAA, ONERA, the University of Illinois and Boeing have embarked on a significant, collaborative research effort to address the technical challenges associated with icing on large-scale, three-dimensional swept wings. The overall goal is to improve the fidelity of experimental and computational simulation methods for swept-wing ice accretion formation and resulting aerodynamic effect. A seven-phase research effort has been designed that incorporates ice-accretion and aerodynamic experiments and computational simulations. As the baseline, full-scale, swept-wing-reference geometry, this research will utilize the 65 percent scale Common Research Model configuration. Ice-accretion testing will be conducted in the NASA Icing Research Tunnel for three hybrid swept-wing models representing the 20, 64 and 83 percent semispan stations of the baseline-reference wing. Threedimensional measurement techniques are being developed and validated to document the experimental ice-accretion geometries. Artificial ice shapes of varying geometric fidelity will be developed for aerodynamic testing over a large Reynolds number range in the ONERA F1 pressurized wind tunnel and in a smaller-scale atmospheric wind tunnel. Concurrent research will be conducted to explore and further develop the use of computational simulation tools for ice accretion and aerodynamics on swept wings. The combined results of this research effort will result in an improved understanding of the ice formation and aerodynamic effects on swept wings. The purpose of this paper is to describe this research effort in more detail and report on the current results and status to date.

Broeren, Andy P.↗
NASA NTRS2023

Turbulence Model Validation Through Joint Experimental /Computational Studies of Separated Flow Over A Three-Dimensional Tapered Bump: Part I - Experimental Investigation

The results of a three-year experimental campaign aimed at comprehensively documenting the separated flow over a three-dimensional bump are presented with the purpose of generating a benchmark experimental database useful in validating computational fluid dynamics (CFD) flow simulations. The bump model geometry was designed to provide well-defined and repeatable smooth-body flow separation conditions that were suitable for both experiments and simulations. The bump had a Gaussian streamwise profile with a constant height equal to 8.5% of its width over the central 60% of the test section width. The remaining 40% were outboard spanwise portions that gradually taper to zero using an error function profile to minimize side-wall boundary layer effects. The model was immersed in a turbulent boundary layer that was developed on a suspended flat plate in the Notre Dame Mach 0.6 Wind Tunnel. In order to document the effect of the incoming boundary layer thickness on the flow separation, the bump model could be located at two streamwise positions. The mean velocity and turbulence intensity of the wind tunnel freestream flow field and approaching turbulent boundary layer were fully documented. The measurements of the flow separation region included surface visualization, wall shear stress using oil-film interferometry, mean and dynamic surface pressure, and planar and stereoscopic particle image velocimetry. The experiments were conducted over a range of Mach numbers from 0.05 to 0.2 corresponding to a range of Reynolds numbers based on the test section spanwise dimension (0.914m) of 1.0 × 10 6 ≤ Re L = U ∞ L/ν ≤ 4.0 × 10 6 . The bulk of the results are presented for the higher Mach number conditions of 0.1 and 0.2 with Re L = 2.0 × 10 6 and 4.0 × 10 6 , respectively. Extensive uncertainty analysis of the data was performed. The data is archived in the NASA Langley Turbulence Modeling Resource website at https://turbmodels.larc.nasa.gov/Other_exp_Data/speedbump_sep_exp.html. In addition to the experiments, a computational effort was made in parallel by the CFD group at Boeing Research & Technology highlighting the usefulness of the data set, which is outlined in the accompanying CFD report.

Flow separation↗
DOE OSTI2020

Multiscale computational understanding and growth of 2D materials: a review

Abstract The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general.

Momeni, Kasra↗
NASA NTRS2007

Synthetic Jets in Quiescent Air

An oscillatory jet with zero net mass flow is generated by a cavity-pumping actuator. Among the three test cases selected for the Langley CFD validation workshop to assess the current CFD capabilities to predict unsteady flow fields, this basic oscillating jet flow field is the least complex and is selected as the first test case. Increasing in complexity, two more cases studied include jet in cross flow boundary layer and unsteady flow induced by suction and oscillatory blowing with separation control geometries. In this experiment, velocity measurements from three different techniques, hot-wire anemometry, Laser Doppler Velocimetry (LDV) and Particle Image Velocimetry (PIV), documented the synthetic jet flow field. To provide boundary conditions for computations, the experiment also monitored the actuator operating parameters including diaphragm displacement, internal cavity pressure, and internal cavity temperature.

Yao, C. S.↗
DOE OSTI2023

Diffusion-assisted growth of periodic patterns on metal surfaces

Periodic patterns formed on various solid surfaces could seriously affect the properties of materials. Understanding the underlying mechanism is a key step to apply and control pattern formation in many fields. In this work, periodic patterns induced by low energy plasma exposures on tungsten surfaces are observed and related new formation mechanisms are developed. Results from experiments and computer simulations demonstrate that atom diffusion accelerated by low energy and high flux exposures is the dominant mechanism for pattern formation. Additionally, a similar pattern formation has been confirmed by plasma exposure experiments on molybdenum. Based on this mechanism, patterns with controllable size and shape have been successfully prepared on tungsten surface by adjusting plasma exposure parameters, which may provide a new understanding of pattern formation and related surface damage in tungsten induced by plasma exposures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Validation of Jezebel Reactivity Coefficients and Sensitivity Analysis

Nuclear data validation is often performed today using criticality measurements. The gold standard for criticality measurements is the International Criticality Safety Benchmark Experiment Project (ICSBEP). The validation specifically focuses on the effective multiplication factor ($k_{eff}$). $K_{eff}$ is a relatively easy parameter to infer and has reduced uncertainty due to being at or above critical. However, while $k_{eff}$ is the most documented parameter and its uncertainties and sensitivities have been evaluated in great detail, it cannot be used as a standalone metric to determine inaccuracies in nuclear data (e.g., cross section data, PFNS, nu), which is based on theory, physics, and differential measurements. The Experiments Underpinned by Computational Learning for Improvements in nuclear Data (EUCLID) project aims to identify compensating errors in specific isotope nuclear data by optimally designing experiments that are, or are not sensitive to a suite of measurement parameters beyond $k_{eff}$. By identifying parameters that are sensitive to each other, oppositely sensitive, or have substantial magnitude differences in sensitivity, experiments can be designed to constrain questionable nuclear data. One sensitivity that is of particular interest to this project includes the sensitivity of reactivity coefficients. Reactivity coefficients compare reactivity, which is related to $k_{eff}$ at two different states therefore being sensitive to small changes in the system. The most common type of reactivity coefficient measurements is comparison to void for a small sample within the assembly. It is key that the sample sizes are small enough to not affect the flux of the full system. Reactivity coefficients were evaluated for many early experiments to better understand transport corrected cross sections. In fact, ICSBEP includes reactivity coefficient results as “Supplemental Measurements” in appendices for a handful of older benchmarks. One of those benchmarks is Jezebel, the bare Pu critical assembly. This paper compares new simulations of reactivity coefficients for Jezebel, and explores the sensitivity of reactivity coefficients to small changes in nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1995

Sound For Animation And Virtual Reality

Sound is an integral part of the experience in computer animation and virtual reality. In this course, we will present some of the important technical issues in sound modeling, rendering, and synchronization as well as the "art" and business of sound that are being applied in animations, feature films, and virtual reality. The central theme is to bring leading researchers and practitioners from various disciplines to share their experiences in this interdisciplinary field. The course will give the participants an understanding of the problems and techniques involved in producing and synchronizing sounds, sound effects, dialogue, and music. The problem spans a number of domains including computer animation and virtual reality. Since sound has been an integral part of animations and films much longer than for computer-related domains, we have much to learn from traditional animation and film production. By bringing leading researchers and practitioners from a wide variety of disciplines, the course seeks to give the audience a rich mixture of experiences. It is expected that the audience will be able to apply what they have learned from this course in their research or production.

Hahn, James K.↗
NASA NTRS1992

Navier-Stokes simulation of flow through a highly contoured subsonic diffuser

The flow through a highly offset subsonic diffuser with cross-sectional profiles that varied from rectangular at the duct entrance to circular at the engine face was numerically simulated. A multizonal approach combined with a two-grid topology was used to represent both the internal and external flowfields, and an implicit, approximately-factored, partially flux-split finite-difference algorithm was used to solve the three-dimensional thin-layer Navier-Stokes equations. The computed static pressures along the inlet wall and total pressures on the engine face were compared with experimental data. In addition, the overall flowfield within the duct was examined in detail. Good agreement is shown between experiment and computations, with the limiting factor being the lack of a reliable turbulence model for internal flow problems.

Chyu, W. J.↗
DOE OSTI2024

Bridging the gap between experiments and simulations using machine learning

The physics of inertial confinement fusion is rich and complex. Simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this work we use deep learning to build a fast emulator of experiments. To facilitate the development of the deep-learning model, an autoencoder is used to reduce the dimensionality of the input space. Two deep learning models are developed. One model is trained on a vast array of simulation data and is subsequently calibrated to expensive and limited experimental data using a technique known as “transfer learning.” The other model is trained on a statistical model and is subsequently calibrated using experimental data. A comparative study of the two predictive models is carried out. The models potentially reproduce key experimental observables with high accuracy and unprecedented inference times relative to those achieved with simulation codes. These models facilitate rapid exploration of a high dimensional input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

Newton in Space

In this 'Liftoff to Learning' series video, astronauts (Charles Veach, Gregory Harbaugh, Donald McMonagle, Michael Coats, L. Blaine Hammond, Guion Bluford, Richard Hieb) from the STS-39 Mission use physical experiments and computer animation to explain how weightlessness and gravity affects everything and everyone onboard the Space Shuttle. The physics behind the differences between weight and mass, and the concepts of 'free fall', are demonstrated along with explanations and experiments of Sir Issac Newton's three laws of motion.

Herbert, Dexter↗