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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

High Performance N2O4/Amine Elements

An analytical and experimental investigation was conducted to develop an understanding of the mechanisms that cause reactive stream separation, commonly called blowapart, for hypergolic propellants. The investigation was limited to the N2O4/MMH propellant combination and to a range of engine-operating conditions applicable to the space tug and space shuttle attitude control and orbital maneuvering engines. Primary test variables were: chamber pressure (1 to 20 atm), fuel injection temperature (283 to 400 K)m and propellant injection velocity (9 to 50 m/s). The injector configuration studied was the unlike doublet. The reactive stream separation experiments were conducted using special combustors designed to permit photography of the near-injector spray combustion flow field. Analysis of color motion pictures provided the means of determining the occurrence of reactive stream separation.

Falk, A. Y.↗
NASA NTRS2008

Rotor Performance at High Advance Ratio: Theory versus Test

Five analytical tools have been used to study rotor performance at high advance ratio. One is representative of autogyro rotor theory in 1934 and four are representative of helicopter rotor theory in 2008. The five theories are measured against three sets of well documented, full-scale, isolated rotor performance experiments. The major finding of this study is that the decades spent by many rotorcraft theoreticians to improve prediction of basic rotor aerodynamic performance has paid off. This payoff, illustrated by comparing the CAMRAD II comprehensive code and Wheatley & Bailey theory to H-34 test data, shows that rational rotor lift to drag ratios are now predictable. The 1934 theory predicted L/D ratios as high as 15. CAMRAD II predictions compared well with H-34 test data having L/D ratios more on the order of 7 to 9. However, the detailed examination of the selected codes compared to H-34 test data indicates that not one of the codes can predict to engineering accuracy above an advance ratio of 0.62 the control positions and shaft angle of attack required for a given lift. There is no full-scale rotor performance data available for advance ratios above 1.0 and extrapolation of currently available data to advance ratios on the order of 2.0 is unreasonable despite the needs of future rotorcraft. Therefore, it is recommended that an overly strong full-scale rotor blade set be obtained and tested in a suitable wind tunnel to at least an advance ratio of 2.5. A tail rotor from a Sikorsky CH-53 or other large single rotor helicopter should be adequate for this exploratory experiment.

Harris, Franklin D.↗
NASA NTRS1988

High temperature furnace modeling and performance verifications

Analytical, numerical and experimental studies were performed on two classes of high temperature materials processing furnaces. The research concentrates on a commercially available high temperature furnace using zirconia as the heating element and an arc furnace based on a ST International tube welder. The zirconia furnace was delivered and work is progressing on schedule. The work on the arc furnace was initially stalled due to the unavailability of the NASA prototype, which is actively being tested aboard the KC-135 experimental aircraft. A proposal was written and funded to purchase an additional arc welder to alleviate this problem. The ST International weld head and power supply were received and testing will begin in early November. The first 6 months of the grant are covered.

Smith, James E., Jr.↗
NASA NTRS1992

High temperature furnace modeling and performance verifications

Analytical, numerical, and experimental studies were performed on two classes of high temperature materials processing sources for their potential use as directional solidification furnaces. The research concentrated on a commercially available high temperature furnace using a zirconia ceramic tube as the heating element and an Arc Furnace based on a tube welder. The first objective was to assemble the zirconia furnace and construct parts needed to successfully perform experiments. The 2nd objective was to evaluate the zirconia furnace performance as a directional solidification furnace element. The 3rd objective was to establish a data base on materials used in the furnace construction, with particular emphasis on emissivities, transmissivities, and absorptivities as functions of wavelength and temperature. A 1-D and 2-D spectral radiation heat transfer model was developed for comparison with standard modeling techniques, and were used to predict wall and crucible temperatures. The 4th objective addressed the development of a SINDA model for the Arc Furnace and was used to design sample holders and to estimate cooling media temperatures for the steady state operation of the furnace. And, the 5th objective addressed the initial performance evaluation of the Arc Furnace and associated equipment for directional solidification. Results of these objectives are presented.

Smith, James E., Jr.↗
DOE OSTI2025

Enhanced biochemical sensing with high- Q transmission resonances in free-standing membrane metasurfaces

Optical metasurfaces provide solutions to label-free biochemical sensing by localizing light resonantly beyond the diffraction limit, thereby selectively enhancing light–matter interactions for improved analytical performance. However, high-Q resonances in metasurfaces are usually achieved in the reflection mode, which impedes metasurface integration into compact imaging systems. Here, we demonstrate a metasurface platform for advanced biochemical sensing based on the physics of the bound states in the continuum (BIC) and electromagnetically induced transparency (EIT) modes, which arise when two interfering resonances from a periodic pattern of tilted elliptic holes overlap both spectrally and spatially, creating a narrow transparency window in the mid-infrared spectrum. We experimentally measure these resonant peaks observed in transmission mode (Q ~ 734 at λ ~ 8.8 µm) in free-standing silicon membranes and confirm their tunability through geometric scaling. We also demonstrate the strong coupling of the BIC-EIT modes with a thinly coated PMMA film on the metasurface, characterized by a large Rabi splitting (32 cm -1 ) and biosensing of protein monolayers in transmission mode. Our new photonic platform can facilitate the integration of metasurface biochemical sensors into compact and monolithic optical systems while being compatible with scalable manufacturing, thereby clearing the way for on-site biochemical sensing in everyday applications.

Rosas, Samir [Univ. of Wisconsin, Madison, WI (Uni↗
DOE OSTI2021

A Conceptual Framework for HPC Operational Data Analytics

This paper provides a broad framework for under- standing trends in Operational Data Analytics (ODA) for High- Performance Computing (HPC) facilities. The goal of ODA is to allow for the continuous monitoring, archiving, and analysis of near real-time performance data, providing immediately actionable information for multiple operational uses. In this work, we combine two models to provide a comprehensive HPC ODA framework: one is an evolutionary model of analytics capabilities that consists of four types, which are descriptive, diagnostic, predictive and prescriptive, while the other is a four- pillar model for energy-efficient HPC operations that covers facility, system hardware, system software, and applications. This new framework is then overlaid with a description of current development and production deployments of ODA within leading- edge HPC facilities. Finally, we perform a comprehensive survey of ODA works and classify them according to our framework, in order to demonstrate its effectiveness.

Netti, Alessio↗
DOE OSTI2021

Visual HPC Workflows for the Analysis of System Dynamics Models

Visual analytics supported by high performance computing (HPC) accelerates and enhances the discovery, exploration, and analysis of causal patterns in complex system dynamics (SD) models. We present a suite of visualization-assisted ensemble-based techniques for hypothesis generation and testing, and for sensitivity analysis. By employing HPC to provide parallel, on-demand simulation of SD models, one can “steer” an ensemble of simulated scenarios in real time as one first formulates and then informally tests those hypotheses: this provides rapid feedback for analysts to refine their understanding of the causal relationships emergent from a model. Such understandings can be followed and augmented by rigorous application of statistical methods, namely global variance-based sensitivity analysis, Monte-Carlo filtering, adaptive regional sensitivity analysis, and self-organized maps: here timely computation relies on HPC, while effective presentation emphasizes high-dimensional multivariate data visualization. Immersive visualization in virtual 3D environments provides an excellent adjunct to the traditional 2D graphics typically used for SD models, as it generates an embodied understanding of model behavior and facilitates an active, collaborative critique of model structure and output. Finally, we summarize prospects for HPC-enabled visual analytics applied to SD modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2014

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

Data Analytics↗
DOE OSTI2024

Tandem Predictions for HPC Jobs

At the core of the predictive analytics applied to High Performance Computing (HPC), the most prominent tasks are the prediction of job runtimes and the prediction of job queue times, both of which have the potential for informing HPC users during their every-day decision making. Accurate runtime predictions can help users better choose so-called wallclock times at job submission, decreasing the odds of their jobs waiting in queues longer than necessary. The accurate and timely queue time predictions offered for the available partitions can inform the favorable selection of partitions for running jobs. This potential is well understood as we see in the abundance of research studies that propose solutions for these tasks, including the work published in the last several years. These tasks are seemingly receptive to the Machine Learning (ML) solutions, considering that there is no shortage of training data where HPC centers over time run millions and millions of jobs. However, we study the existing research literature, as well as look for examples in the toolchains supported on the exemplar HPC facilities, and, surprisingly, do not find any practical solutions that are ready to be adopted. We interpret this as a manifestation of the shortage of UX/UI efforts that support HPC analytics and also as a sign that the research has not come to the consensus on solving these tasks. In this study, we aim to shed new light on the long-running task of job queue time prediction by exploring the utility of runtime predictions in improving prediction accuracy and, actually, predicting these two metrics together, in tandem. In other words, we show how runtime predictions become valuable input in the queue time modeling. We challenge the existing approaches to feature engineering for the queue time prediction and describe promising results we obtained for a large dataset of HPC jobs from a supercomputer at the National Renewable Energy Laboratory.

HPC↗
DOE OSTI2025

Tandem Predictions for HPC Jobs: Preprint

At the core of the predictive analytics applied to High Performance Computing (HPC), the most prominent tasks are the prediction of job runtimes and the prediction of job queue times, both of which have the potential for informing HPC users during their every-day decision making. Accurate runtime predictions can help users better choose so-called wallclock times at job submission, decreasing the odds of their jobs waiting in queues longer than necessary. The accurate and timely queue time predictions offered for the available partitions can inform the favorable selection of partitions for running jobs. This potential is well understood as we see in the abundance of research studies that propose solutions for these tasks, including the work published in the last several years. These tasks are seemingly receptive to the Machine Learning (ML) solutions, considering that there is no shortage of training data where HPC centers over time run millions and millions of jobs. However, we study the existing research literature, as well as look for examples in the toolchains supported on the exemplar HPC facilities, and, surprisingly, do not find any practical solutions that are ready to be adopted. We interpret this as a manifestation of the shortage of UX/UI efforts that support HPC analytics and also as a sign that the research has not come to the consensus on solving these tasks. In this study, we aim to shed new light on the long-running task of job queue time prediction by exploring the utility of runtime predictions in improving prediction accuracy and, actually, predicting these two metrics together, in tandem. In other words, we show how runtime predictions become valuable input in the queue time modeling. We challenge the existing approaches to feature engineering for the queue time prediction and describe promising results we obtained for a large dataset of HPC jobs from a supercomputer at the National Renewable Energy Laboratory.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

High-Performance X-ray Detection in a New Analytical Electron Microscope

X-ray detection by energy-dispersive spectrometry in the analytical electron microscope (AEM) is often limited by low collected X-ray intensity (P), modest peak-to-background (P/B) ratios, and limitations on total counting time (tau) due to specimen drift and contamination. A new AFM has been designed with maximization of P. P/B, and tau as the primary considerations. Maximization of P has been accomplished by employing a field-emission electron gun, X-ray detectors with high collection angles, high-speed beam blanking to allow only one photon into the detector at a time, and simultaneous collection from two detectors. P/B has been maximized by reducing extraneous background signals generated at the specimen holder, the polepieces and the detector collimator. The maximum practical tau has been increased by reducing specimen contamination and employing electronic drift correction. Performance improvments have been measured using the NIST standard Cr thin film. The 0-3 steradian solid angle of X-ray collection is the highest value available. The beam blanking scheme for X-ray detection provides 3-4 times greater throughput of X-rays at high count rates into a recorded spectrum than normal systems employing pulse-pileup rejection circuits. Simultaneous X-ray collection from two detectors allows the highest X-ray intensity yet recorded to be collected from the NIST Cr thin film. The measured P/B of 6300 is the highest level recorded for an AEM. In addition to collected X-ray intensity (cps/nA) and P/B measured on the standard Cr film, the product of these can be used as a figure-of-merit to evaluate instruments. Estimated minimum mass fraction (MMF) for Cr measured on the standard NIST Cr thin film is also proposed as a figure-of-merit for comparing X-ray detection in AEMs. Determinations here of the MMF of Cr detectable show at least a threefold improvement over previous instruments.

Lyman, C. E.↗
DOE OSTI2022

pnnl/COMET

COMET: Domain Specific Compilation in Multi-level IR COMET is a compiler for dense and sparse tensor algebra and a domain-specific-language (DSL) that facilitates development and implementation of high-performance computing, graph analytics, and artificial intelligence (AI) applications.

Kestor, Gokcen↗
NASA NTRS1972

Conical-reflector antennas.

The mechanical advantages of a singly curved conical reflector are demonstrated by the experimental test of a furlable 1.83 m conical-Gregorian antenna at 16.33 GHz. The measured gain of 47.5 dB corresponds to a net efficiency of over 57%. A ray-optics analysis of conical-reflector antennas is presented, and data useful in the design of conical antennas are given. The conical-Gregorian antenna, in which a subreflector is used in conjunction with a conventional horn feed, is considered in detail. A physical-optics analysis of the conical-Gregorian antenna is used to investigate diffraction and other effects, and to analytically confirm the high performance of the antenna.

Ludwig, A. C.↗
NASA NTRS1984

Development of acceptance criteria for batches of silane primer for external tank thermal protection system bonding applications

Silane primers for use as thermal protection on external tanks were subjected to various analytic techniques to determine the most effective testing method for silane lot evaluation. The analytic methods included high performance liquid chromatography, gas chromatography, thermogravimetry (TGA), and fourier transform infrared spectroscopy (FTIR). It is suggested that FTIR be used as the method for silane lot evaluation. Chromatograms, TGA profiles, bar graphs showing IR absorbances, and FTIR spectra are presented.

Mikes, F.↗
NASA NTRS1999

Sensitive Procedures for Determining the Permeation Resistance of Chlorinated Polyethylene Against Liquid Propellants

The permeation resistance of chlorinated polyethylene (CPE) used in totally encapsulating chemical protective suits against the aerospace fuels hydrazine, monomethylhydrazine, and uns-dimethylhydrazine was determined by measuring the breakthrough time (BT) and time-averaged vapor transmission rate (VTR) using procedures consistent with ASTM F 739 and ASTM F 1383. Two exposure scenarios were simulated: a 2 hour (h) fuel vapor exposure, and a liquid fuel "splash" followed by a 2 h vapor exposure. To simulate internal suit pressure during operation, a positive differential pressure of 0.3 in. water (75 Pa) on the collection side of the permeation apparatus was used. Using the available data, a model was developed to estimate propellant concentrations inside an air-line fed, totally encapsulating chemical protective suit. Concentrations were calculated under simulated conditions of fixed vapor transmission rate, variable breathing air flow rate, and variable splash exposure area. Calculations showed that the maximum allowable permeation rates of hydrazine fuels through CPE were of the order of 0.05 to 0.08 ng/sq cm min for encapsulating suits with low breathing air flow rates (of the order of 5 scfm or 140 L min-1). Above these permeation rates, the 10 parts-per-billion (ppb) threshold limit value time-weighted average could be exceeded. To evaluate suit performance at 10 ppb threshold-limiting value/time-weighted average level concentrations, use of a sensitive analytical method such as cation exchange high performance liquid chromatography with amperometric detection was found to be essential. The analytical detection limit determines the lowest measurable VTR, which in turn governed the lowest per meant concentration that could be calculated inside the totally encapsulating chemical protective suit.

Waller, Jess M.↗
NASA NTRS1997

Integrated Requirements Analysis and Technology Roadmaps

In fiscal year 1997, Strategic Insight performed analytical studies for NASA's Highly Reusable Space Transportation (HRST) program, creating program documents which illuminated technical requirements and critical research opportunities. Studies were performed to structure and confirm HRST's evolving technical requirements, building on Marshall's Phase 1 work, which defined HRST system concepts, analytical tools and high-level issues for assessment in Phase 2. Specifically, Strategic Insight: (1) Performed a requirements analysis to update HRST: An Advanced Concepts Study, Study Guidelines, Version 2.0 of January 22, 1996; only minor changes were recommended for the given parameters of interest to concept designers; (2) Conducted mini-workshops during HRST Working Group meetings on April 14-15, 1997 and July 22-24, 1997; and (3) Created structures for technology road maps of candidate HRST concepts, both subsystem and end-to-end concepts, emerging from the 13 cooperative agreement projects.

Source record↗