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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Sim-Situ: A Framework for the Faithful Simulation of in situ Processing

The amount of data generated by numerical simulations in various scientific domains led to a fundamental redesign of how the analysis and visualization of simulation outputs are performed. The throughput and capacity of storage subsystems have not evolved as fast as the computing power in extreme-scale supercomputers, making the classical post-hoc approach highly inefficient. In situ processing has then emerged as a solution in which simulation and data analysis/visualization are intertwined for better performance and greater interactivity.Determining the best allocation, i.e., how many resources to allocate to simulation and analysis respectively, mapping, i.e., where and at which frequency to run the analysis/visualization, and data transfer mode is a complex task whose performance assessment is crucial to the efficient execution of in situ processing. However, such a performance evaluation of different strategies usually relies either on directly running them on the targeted execution environments, which can rapidly become extremely time- and resource-consuming, or on resorting to simplified models of the components of an in situ application, which can lack of realism. In both cases, the validity of the performance evaluation is limited.In this paper, we present Sim-Situ, a simulation-based framework for the faithful performance evaluation of in situ processing strategies. We designed Sim-Situ to reflect the typical features of in situ processing systems. Thanks to its modular design, Sim-situ has the necessary flexibility to easily and faithfully evaluate the behavior and performance of various allocation, mapping, and data transfer strategies. We illustrate the simulation capabilities of Sim-Situ on a Molecular Dynamics use case. We study the impact of different strategies on performance and show how users can leverage Sim-Situ to determine interesting tradeoffs when adding analysis/visualization components to their application.

Honoré, Valentin↗
NASA NTRS1974

Peripheral visual response time and visual display layout

Experiments were performed on a group of 42 subjects in a study of their peripheral visual response time to visual signals under positive acceleration, during prolonged bedrest, at passive 70 deg headup body lift, under exposures to high air temperatures and high luminance levels, and under normal stress-free laboratory conditions. Diagrams are plotted for mean response times to white, red, yellow, green, and blue stimuli under different conditions.

Haines, R. F.↗
NASA NTRS1989

Ice/frost/debris assessment for space shuttle mission STS-27R, December 2, 1988

An Ice/Frost/Debris assessment was conducted for Space Shuttle Mission STS-27R. Debris inspections of the flight elements and launch pad are performed before and after launch. Ice/frost conditions are assessed by the use of computer programs, nomographs, and infrared scanner data during cryogenic loading of the vehicle followed by an on-pad visual inspection. High speed photography is viewed after launch to identify ice/debris sources and evaluate potential vehicle damage. The Ice/Frost/Debris conditions of Mission STS-27R and their effect on the Space Shuttle Program are documented.

Katnik, Gregory N.↗
NASA NTRS1989

The role of flight planning in aircrew decision performance

The role of flight planning in increasing the safety and decision-making performance of the air transport crews was investigated in a study that involved 48 rated airline crewmembers on a B720 simulator with a model-board-based visual scene and motion cues with three degrees of freedom. The safety performance of the crews was evaluated using videotaped replays of the flight. Based on these evaluations, the crews could be divided into high- and low-safety groups. It was found that, while collecting information before flights, the high-safety crews were more concerned with information about alternative airports, especially the fuel required to get there, and were characterized by making rapid and appropriate decisions during the emergency part of the flight scenario, allowing these crews to make an early diversion to other airports. These results suggest that contingency planning that takes into account alternative courses of action enhances rapid and accurate decision-making under time pressure.

Pepitone, Dave↗
NASA NTRSDate not supplied

Overview of Plume-Surface Interaction Data from Subscale Inert Gas Testing at NASA MSFC Test Stand 300 Vacuum Facilities

Maturation and validation of predictive modeling capabilities for plume-surface interaction, and the effects caused by the impingement of rocket engine exhaust on surface regolith, require relevant, highly characterized ground test data. The multi-phase nature of this problem poses challenges to ground testing across physical scaling, test environments and techniques, and diagnostics. NASA has developed a Physics Focused Ground Test (PFGT) of plume-surface interaction to address this need. The PFGT is a subscale, inert-gas experiment at Mars- and Lunar-relevant ambient pressure conditions that will provide critical data for model validation of cratering, ejecta, and plume physics. In this test, the ambient pressure in a vacuum chamber will be varied, along with the height of a supersonic nozzle above a surface, and the mass flow rate through the nozzle. Testing will be performed on a range of regolith simulants of varying composition and will also include instrumented impingement plate tests in place of regolith simulant to characterize gas-only plume physics. Novel high-speed visual diagnostics will capture temporal data on crater formation and ejecta dynamics, and planar laser-induced fluorescence diagnostics will capture plume structure and behavior. This paper will present an overview of the data collected during this test – for both the cratering and ejecta testing and the gas-only plume physics testing. Emphasis will be placed on trends, effects, and interactions observed in the data across the test parameter space.

Chad J. Eberhart↗
NASA NTRS1987

An experimental low Reynolds number comparison of a Wortmann FX67-K170 airfoil, a NACA 0012 airfoil and a NACA 64-210 airfoil in simulated heavy rain

Wind tunnel experiments were conducted on Wortmann FX67-K170, NACA 0012, and NACA 64-210 airfoils at rain rates of 1000 mm/hr and Reynolds numbers of 310,000 to compare the aerodynamic performance degradation of the airfoils and to attempt to identify the various mechanisms which affect performance in heavy rain conditions. Lift and drag were measured in dry and wet conditions, a variety of flow visualization techniques were employed, and a computational code which predicted airfoil boundary layer behavior was used. At low angles of attack, the lift degradation in wet conditions varied significantly between the airfoils. The Wortmann section had the greatest overall lift degradation and the NACA 64-210 airfoil had the smallest. At high angles of attack, the NACA 64-210 and 0012 airfoils had improved aerodynamic performance in rain conditions due to an apparent reduction of the boundry layer separation. Performance degradation in heavy rain for all three airfoils at low angles of attack could be emulated by forced boundary layer transition near the leading edge. The secondary effect occurs at time scales consistent with top surface water runback times. The runback layer is thought to effectively alter the airfoil geometry. The severity of the performance degradation for the airfoils varied. The relative differences appeared to be related to the susceptibility of each airfoil to premature boundary layer transition.

Craig, Anthony P.↗
DOE OSTI2026

Understanding failure in austenitic steels: Key considerations for molten salt storage in CSP applications

Owing to their excellent properties, austenitic stainless steels are extensively used in boilers, furnaces, molten salt tanks, and other applications that are subjected to extreme mechanical loads and high-temperature conditions. Their high corrosion and creep resistance make them suitable for high-temperature operating environments. Additionally, good fatigue resistance and favorable mechanical and visual properties are essential. However, the premature failure of several components at elevated temperatures has been previously reported. Although the failure analysis of components in service is complex, processes such as cold work and welding have been identified as contributing factors to the performance degradation of these steels. This study aims to analyze the various documented failure modes and mechanisms in austenitic steels, including creep, cracking, stress relaxation cracking, and fatigue, to better understand the multi-objective design requirements for these alloys as structural materials for high-temperature molten salt tanks in Concentrating Solar Power (CSP) plants. Stabilized austenitic grades, such as AISI 347H, demonstrate superior resistance to creep and corrosion-related degradation when compared to non-stabilized grades like AISI 316L at temperatures relevant to concentrated solar power (CSP) applications. In contrast, nickel-based alloys offer enhanced corrosion resistance, albeit at a higher cost. This review underscores that creep, stress relaxation cracking, and thermo-mechanical fatigue are the predominant long-term failure risks in CSP hot tanks.

14 SOLAR ENERGY↗
DOE OSTI2023

Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation

We evaluate AI-assisted generative capabilities on fundamental numerical kernels in high-performance computing (HPC), including AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG. We test the generated kernel codes for a variety of language-supported programming models, including (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). We use the GitHub Copilot capabilities powered by the GPT-based OpenAI Codex available in Visual Studio Code as of April 2023 to generate a vast amount of implementations given simple + + prompt variants. To quantify and compare the results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. Results suggest that the OpenAI Codex outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general purpose Python can benefit from adding code keywords, while Julia prompts perform acceptably well for its mature programming models (e.g., Threads and CUDA.jl). We expect for these benchmarks to provide a point of reference for each programming model's community. Overall, understanding the convergence of large language models, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.

Godoy, William↗
DOE OSTI2019

Galaxy morphological classification in deep-wide surveys via unsupervised machine learning

ABSTRACT Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology (e.g. as a prior for photometric-redshift measurements and as contextual data for transient light-curve classifications). While a rich literature exists on morphological-classification techniques, the unprecedented data volumes, coupled, in some cases, with the short cadences of forthcoming ‘Big-Data’ surveys (e.g. from the LSST), present novel challenges for this field. Large data volumes make such data sets intractable for visual inspection (even via massively distributed platforms like Galaxy Zoo), while short cadences make it difficult to employ techniques like supervised machine learning, since it may be impractical to repeatedly produce training sets on short time-scales. Unsupervised machine learning, which does not require training sets, is ideally suited to the morphological analysis of new and forthcoming surveys. Here, we employ an algorithm that performs clustering of graph representations, in order to group image patches with similar visual properties and objects constructed from those patches, like galaxies. We implement the algorithm on the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously reduce the galaxy population to a small number (160) of ‘morphological clusters’, populated by galaxies with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications (which we release publicly) exhibit a high level of purity, and reproduce known trends in key galaxy properties as a function of morphological type at z < 1 (e.g. stellar-mass functions, rest-frame colours, and the position of galaxies on the star-formation main sequence). Our study demonstrates the power of unsupervised machine learning in performing accurate morphological analysis, which will become indispensable in this new era of deep-wide surveys.

Martin, G.↗
NASA NTRS1999

Experimental and Computational Investigation of a Translating-Throat Single-Expansion-Ramp Nozzle

An experimental and computational study was conducted on a high-speed, single-expansion-ramp nozzle (SERN) concept designed for efficient off-design performance. The translating-throat SERN concept adjusts the axial location of the throat to provide a variable expansion ratio and allow a more optimum jet exhaust expansion at various flight conditions in an effort to maximize nozzle performance. Three design points (throat locations) were investigated to simulate the operation of this concept at subsonic-transonic, low supersonic, and high supersonic flight conditions. The experimental study was conducted in the jet exit test facility at the Langley Research Center. Internal nozzle performance was obtained at nozzle pressure ratios (NPR's) up to 13 for six nozzles with design nozzle pressure ratios near 9, 42, and 102. Two expansion-ramp surfaces, one concave and one convex, were tested for each design point. Paint-oil flow and focusing schlieren flow visualization techniques were utilized to acquire additional flow data at selected NPR'S. The Navier-Stokes code, PAB3D, was used with a two-equation k-e turbulence model for the computational study. Nozzle performance characteristics were predicted at nozzle pressure ratios of 5, 9, and 13 for the concave ramp, low Mach number nozzle and at 10, 13, and 102 for the concave ramp, high Mach number nozzle.

Deere, Karen A.↗
DOE OSTI2021

Machine learning elastic constants of multi-component alloys

The present manuscript explores application of machine learning methods for determining elastic constants and other derived mechanical properties of multi-component alloys. Here, a number of machine learning models, including linear regression, neural network and random forest based models, are trained and tested on a dataset of binary alloys generated using density functional theory (DFT) calculations and spanning over a large number of elemental species in the periodic table. Starting with a wide range of simple and easily accessible compositionally-averaged elemental features, a correlation-based feature selection strategy was used to systematically down-select a set of most relevant features towards the prediction of the elasticity tensor components. The true predictive performance and the associated uncertainties of the models were established by testing on unseen data and bootstrapping, respectively. A single and pair-wise feature partial dependence analysis was performed to visualize the average property trends in the multi-dimensional feature space in order to further understand the achieved predictive performance. The utility of the trained model is further demonstrated by obtaining sufficiently accurate yet highly efficient approximations for bulk modulus, Young’s modulus, shear modulus and Poisson’s ratio for alloys beyond the binary space (i.e., two-component alloys) on which the model was originally trained. More importantly, we test and validate the predictive performance of the developed model directly against the experimentally measured elastic constants of technologically relevant multi-component alloys (such as, Ni- and Ti-based alloys). Finally, utility of such a data-enabled route is demonstrated by predicting the possible range of various elastic properties for vast composition space available within the five component Ni-Cr-Fe-Mo-W alloy system in a high-throughput manner.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Framework for Analysis and Visualization of Simulation Data (VizAly)

As supercomputing resources increase, cosmological scientists are able to run more detailed and larger simulations generating massive amounts of data. Analyzing these simulations with an available open-source toolkit is important for collaborative Department of Energy scientific discovery across labs, universities, and other partners. Developed software as a part of this collection include: comparing data with other existing simulations, verifying and validating results with observation databases, new halo finder algorithms, and using analytical tools to get insights into the physics of the cosmological universe. The goal of this software project is to provide a set of open-source libraries, tools, and packages for large-scale cosmology that allows scientists to visualize, analyze, and compare large-scale simulation and observational data sets. Developed software will provide a variety of methods for processing, visualization, and analysis of astronomical observation and cosmological simulation data. These tools are intended for deployment on multiple scientific computing platforms, including but not limited to personal computers, cloud computing, experimental sites (telescopes) and high performance supercomputers.

Wendelberger, Joanne↗
DOE OSTI2023

Spectral Peak Enhancement by Combining Trusted Response Elements via Machine Learning (SPECTRE-ML) v0.8.0

SPECTRE-ML (Spectral Peak Enhancement by Combining Trusted Response Elements via Machine Learning) is a machine learning based program for finding optimal clusters of radiation detector segments (i.e., pixels or voxels) in order to improve spectral performance. It provides facilities for pre-processing and analyzing training datasets, running ML algorithms, and evaluating and visualizing outputs. SPECTRE-ML outperforms simpler ad-hoc segmentation methods such as uniform depth clusters, learning detector performance trends such as dead layers, edge effects, and gain shifts. Although extensible to arbitrary highly-segmented spectroscopic radiation detectors, SPECTRE-ML currently focuses on improving spectral performance in highly-segmented CdZnTe (CZT) detectors for International Atomic Energy Agency (IAEA) non-destructive assay (NDA) safeguards tasks.

Vavrek, Jayson↗
DOE OSTI2020

ATDM AD projects to ECP Report (Q3 FY2020)

The SPARC team has completed much of the workflow development needed for the coupled and uncoupled analyses needed to support our flight test validation work. The SPARC team has continued to make progress on development activities to support unsteady, full reentry vehicle aero analysis, which have focused on turbulence modeling, uniform mesh refinement, in-situ visualization (with the Paraview/Catalyst team), and performance analysis. The SPARC team is making solid progress towards our Q4 goal of demonstrating an improved Wall Modeled Large Eddy Simulation (WMLES) capability for standard geometries using high-order finite difference, discontinuous Galerkin, and low-dissipation finite volume methods. The SPARC team has met the minimum completion criteria for parts of the L1, including documenting performance and scaling of SPARC on Trinity, Sierra and Astra, and performing runs for the flight test validation case.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

J-FLiC UAS Flights for Acoustic Testing Research

The jet-powered flying testbed (J-FLiC) unmanned aircraft system (UAS) successfully completed twenty-six flights at Fort AP Hill, VA, from 27 August until September 3 2015, supporting tests of a microphone array system for aircraft noise measurement. The test vehicles, J-FLiC NAVY2 (N508NU), and J-FLiC 4 (N509NU), were flown under manual and autopiloted control in a variety of test conditions: clean at speeds ranging from 80 to 150 knots; and full landing configuration at speeds ranging from 50 to 95 knots. During the test campaign, autopilot capability was incrementally improved to ultimately provide a high degree of accuracy and repeatability of the critical test requirements for airspeed, altitude, runway alignment and position over the microphone array. Manual flights were performed for test conditions at the both ends of the speed envelope where autopiloted flight would have required flight beyond visual range and more extensive developmental work. The research objectives of the campaign were fully achieved. The ARMD Integrated Systems Research Program (ISRP) Environmentally Responsible Aviation (ERA) Project aims to develop the enabling capabilities/technologies that will allow prediction/reduction of aircraft noise. A primary measurement tool for ascertaining and characterizing empirically the effectiveness of various noise reduction technologies is a microphone phased array system. Such array systems need to be vetted and certified for operational use via field deployments and overflights of the array with test aircraft, in this case with sUAS aircraft such as J-FLiC.

Motter, Mark A.↗
NASA NTRS1997

Human heading estimation during visually simulated curvilinear motion

Recent studies have suggested that humans cannot estimate their direction of forward translation (heading) from the resulting retinal motion (flow field) alone when rotation rates are higher than approximately 1 deg/sec. It has been argued that either oculomotor or static depth cues are necessary to disambiguate the rotational and translational components of the flow field and, thus, to support accurate heading estimation. We have re-examined this issue using visually simulated motion along a curved path towards a layout of random points as the stimulus. Our data show that, in this curvilinear motion paradigm, five of six observers could estimate their heading relatively accurately and precisely (error and uncertainty < approximately 4 deg), even for rotation rates as high as 16 deg/sec, without the benefit of either oculomotor or static depth cues signaling rotation rate. Such performance is inconsistent with models of human self-motion estimation that require rotation information from sources other than the flow field to cancel the rotational flow.

NASA Center ARC↗
DOE OSTI2022

Electrical Analysis of Pulsed Laser Annealed Poly-Si:Ga/SiOx Passivating Contacts

High-efficiency single crystalline silicon (c-Si) solar cells require precise control of dopant diffusion profiles and highly active doping concentrations through thermal annealing. Conventional furnace annealing has been successfully employed for dopant diffusion in aluminum back-surface field (Al-BSF), passivated emitter and rear contact (PERC), and Topcon cells. However, furnace annealing limits some next-generation polycrystalline silicon (poly-Si) on SiOx passivating contacts. This limitation largely affects p-type passivating contacts by not being able to: 1) control the dopant diffusion accurately to prevent B segregation at the SiOx and c-Si interface; and 2) provide highly activated doping concentrations for low contact resistivity. In this contribution, we use a nanosecond excimer laser to examine the passivation quality and electrical performance of both B- and Ga-doped poly-Si/SiOx passivating contacts. The core of this method is to take advantage of the nonequilibrium nature of the anneal through rapid melting and recrystallizing the poly-Si in a short timescale and to achieve doping concentrations above the solid solubility limit. Simulations were performed on polished surfaces to visualize the doping diffusion profiles under different laser conditions, and secondary ion mass spectrometry (SIMS) was used to verify the experimental diffusion profiles post pulsed laser melting (PLM). The results show the dopant diffusion profiles can be tuned precisely through PLM. The electrical analysis using VdP-Hall measurements reveals that the active doping concentration reached 10^21 cm-3 for B and ~2×10^21 cm-3 for Ga in poly-Si, far exceeding their solid solubility limit in Si, with a nearly 100% dopant activation achieved for B, and 20% for Ga. This highly activated dopant profile results in a low contact resistivity of <15 mO·cm^2 for B-doped contacts and <40 mO·cm^2 for Ga-doped contacts. We demonstrate that the passivation quality of the laser annealed contacts shows a linear dependency with the laser energy density and the number of pulses. PLM was also performed on random pyramid textured samples, and we found that preferential melting exists for the tips of pyramids, which can be pursued further for selective pinhole opening. Additionally, we perform thermal stability tests of these hyperdoped passivating contacts and show that under certain laser conditions, the B-doped samples exhibit good thermal stability with near full activation for temperatures up to 800°C. Lastly, we transfer this technique to a larger area by overlapping laser spots and show uniform passivation quality and electrical performance. We are currently implementing this PLM annealed p-type passivating contacts into double-side back-junction poly-Si/SiOx passivating contact devices. Our results show that PLM can not only enable the next generation of high-efficiency c-Si photovoltaics technology but can also benefit the c-Si integrated circuit industry through hyperdoping other semiconductors with atoms that may otherwise have a low solubility.

doping diffusion profile↗
NASA NTRSDate not supplied

Microbial Vessel for Impedance Spectroscopy and Electrochemistry (Mvise): an Extensible, Interoperable Data Acquisition Platform for Liquid Culture Studies in Space Biology Research

The White House Office of Science and Technology Policy (OSTP) has declared 2023 to be the Year of Open Science following an initiative to democratize scientific knowledge. Simultaneously, new sensor technologies have broadened the experimental space available to bioastronautics research. With these open-science goals and technological advances in mind, we have designed and constructed a data acquisition platform for high-precision, real-time monitoring of liquid culture systems. The vessel rig is fitted with six Atlas Scientific probes (micro pH, electrical conductivity, dissolved oxygen, oxidation-reduction potential, liquid temperature, air CO2) and a custom optical density probe similar to the one on BioSentinel’s BioSensor payload. A custom dielectric spectroscopy probe is also planned. The structure of the vessel is resin 3-D printed on a hobbyist-level machine, reducing the production cost and iteration time by over 60% each while increasing extensibility. Data acquisition and storage is controlled with a standalone C state machine-based program running on a Raspberry Pi 3 Model B. When not running headless, an additional program automatically generates and updates plots for live data visualization. Validation of the rig as a data collection system was performed with a yeast liquid culture experiment. While the vessel rig is currently used for standalone experiments, it can also be used as the base perception unit in a self-driving laboratory (SDL). SDLs are high-throughput data collection systems that employ automation and artificial intelligence to conduct and manage routine experiments. Here, we envision an SDL driven by several vessel rigs in which an automated script compares key results, informing the design of future experiments. A vessel rig SDL would streamline many operations, including 1) strain selection for the Lunar Explorer Instrument for space biology Applications (LEIA) investigation and 2) the study of bioregenerative life support systems (BLSS). Ultimately, the datasets that can now be acquired will provide crucial information for accelerating bioastronautics application development in the era of commercial space.

Stephen Lantin↗