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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

FY22 Year End Report of Delivery Environments Predictive Capabilities Use in Analysis of SRP Programs

As was established in a previous milestone report for FY20, W-13 continues to be tasked with analyses that require knowledge of in-flight environments and require an in-house capability to generate such environments. To this end, FY21 work included obtaining and benchmarking the use of such tools for an in-house capability. With these tools available for analysis tasks, FY22 work has been to continue the development of analysis for Responsive Development Experiment (ReDX) flights in the Stockpile Responsiveness Program (SRP). While no flights were executed in FY22 that leveraged internal flight analysis., this report shows that both CBAERO and TAOS were used in W-13 under the Delivery Environments Program for the FY22 milestone in order to predict the flight environments of ReDX flight 3 and determine preliminary environments of the Calypso flight vehicle which includes a deployable heat shield.

42 ENGINEERING↗
DOE OSTI2023

FY23 Year End Report of Delivery Environments Predictive Capabilities Use in Analysis of SRP Programs

As was established in a previous milestone report for FY20, W-13 continues to be tasked with analyses that require knowledge of in-flight environments and require an in-house capability to generate such environments. To this end, FY21 work included obtaining and benchmarking the use of such tools for an in-house capability. With these tools available for analysis tasks, ongoing work has been to continue the development of analysis capabilities and understanding while specifically using the tools for Responsive Development Experiment (ReDX) flights in the Stockpile Responsiveness Program (SRP). In FY23, design and analysis of ReDX flight 3A continued while targeting an FY24 launch date. This report shows that a variety of vehicle characterization and flight analysis tools were used in W-13, under the Delivery Environments Program. This work was performed in order to aid in vehicle design by predicting flight stability, trajectory, and induced environments while adapting to a series of changing internal payload configurations that resulted in significant changes to mass properties and associated flight stability.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1994

Teleoperator performance with varying force and visual feedback

An experimental study was conducted to determine the effects of various forms of visual and force feedback on human performance for several 'peg-in-hole'-type telemanipulation tasks. Each of six human test subjects used a master/slave manipulator during two experimental sessions. In one session the subjects performed the tasks with direct vision, where subtended visual angle, force feedback, task difficulty, and the interaction of subtended visual angle and force feedback made signigicant differences in task completion times. During the other session the tasks were performed using a video monitor for visual feedback, and video frame rate, force feedback, task difficulty and the interaction of frame rate and force feedback were found to make significant differences in task times. An analysis between the direct and video viewing environments showed that apart from subtended visual angle and reduced frame rate, the video medium itself did not significantly affect task times relative to direct viewing.

Massimino, Michael J.↗
NASA NTRS1997

Sulfur in Cometary Dust

The computer-intensive project consisted of the analysis and synthesis of existing data on composition of comet Halley dust particles. The main objective was to obtain a complete inventory of sulfur containing compounds in the comet Halley dust by building upon the existing classification of organic and inorganic compounds and applying a variety of statistical techniques for cluster and cross-correlational analyses. A student hired for this project wrote and tested the software to perform cluster analysis. The following tasks were carried out: (1) selecting the data from existing database for the proposed project; (2) finding access to a standard library of statistical routines for cluster analysis; (3) reformatting the data as necessary for input into the library routines; (4) performing cluster analysis and constructing hierarchical cluster trees using three methods to define the proximity of clusters; (5) presenting the output results in different formats to facilitate the interpretation of the obtained cluster trees; (6) selecting groups of data points common for all three trees as stable clusters. We have also considered the chemistry of sulfur in inorganic compounds.

Fomenkova, M. N.↗
DOE OSTI2011

Screening Analysis for the Environmental Risk Evaluation System Task 2.1.1.2: Evaluating Effects of Stressors Fiscal Year 2011 Progress Report - Environmental Effects of Marine and Hydrokinetic Energy

Potential environmental effects of marine and hydrokinetic (MHK) energy development are not well understood, and yet regulatory agencies are required to make decisions in spite of substantial uncertainty about environmental impacts and their long-term consequences. An understanding of risks associated with interactions between MHK installations and aquatic receptors, including animals, habitats, and ecosystems, can help define key uncertainties and focus regulatory actions and scientific studies on interactions of most concern. As a first step in developing the Pacific Northwest National Laboratory (PNNL) Environmental Risk Evaluation System (ERES), PNNL scientists conducted a preliminary risk screening analysis on three initial MHK cases. During FY 2011, two additional cases were added: a tidal project in the Gulf of Maine using Ocean Renewable Power Company TidGenTM turbines and a wave project planned for the coast of Oregon using Aquamarine Oyster surge devices.

Environmental Risk Evaluation System↗
DOE OSTI2012

Scripted Building Energy Modeling and Analysis (Presentation)

Building energy analysis is often time-intensive, error-prone, and non-reproducible. Entire energy analyses can be scripted end-to-end using the OpenStudio Ruby API. Common tasks within an analysis can be automated using OpenStudio Measures. Graphical user interfaces (GUI's) and component libraries reduce time, decrease errors, and improve repeatability in energy modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1974

Shuttle user analysis (study 2.2). Volume 1: Executive summary

The following tasks related to the FY 74 Shuttle Users Analysis were completed: (1) STS user charge analysis, (2) business risk and value of operations in space (BRAVO) analysis, and (3) standardized subsystem module analysis. The first task was required to initiate the process of furnishing transportation charge estimates for STS payloads to STS users.

Source record↗
DOE OSTI2016

SU-F-T-246: Evaluation of Healthcare Failure Mode And Effect Analysis For Risk Assessment

Purpose: To evaluate the differences between the Veteran Affairs Healthcare Failure Modes and Effect Analysis (HFMEA) and the AAPM Task Group 100 Failure and Effect Analysis (FMEA) risk assessment techniques in the setting of a stereotactic radiosurgery (SRS) procedure were compared respectively. Understanding the differences in the techniques methodologies and outcomes will provide further insight into the applicability and utility of risk assessments exercises in radiation therapy. Methods: HFMEA risk assessment analysis was performed on a stereotactic radiosurgery procedure. A previous study from our institution completed a FMEA of our SRS procedure and the process map generated from this work was used for the HFMEA. The process of performing the HFMEA scoring was analyzed, and the results from both analyses were compared. Results: The key differences between the two risk assessments are the scoring criteria for failure modes and identifying critical failure modes for potential hazards. The general consensus among the team performing the analyses was that scoring for the HFMEA was simpler and more intuitive then the FMEA. The FMEA identified 25 critical failure modes while the HFMEA identified 39. Seven of the FMEA critical failure modes were not identified by the HFMEA and 21 of the HFMEA critical failure modes were not identified by the FMEA. HFMEA as described by the Veteran Affairs provides guidelines on which failure modes to address first. Conclusion: HFMEA is a more efficient model for identifying gross risks in a process than FMEA. Clinics with minimal staff, time and resources can benefit from this type of risk assessment to eliminate or mitigate high risk hazards with nominal effort. FMEA can provide more in depth details but at the cost of elevated effort.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Characterization and identification of HPC applications at leadership computing facility

High Performance Computing (HPC) is an important method for scientific discovery via large-scale simulation, data analysis, or artificial intelligence. Leadership-class supercomputers are expensive, but essential to run large HPC applications. The Petascale era of supercomputers began in 2008, with the first machines achieving performance in excess of one petaflops, and with the advent of new supercomputers in 2021 (e.g., Aurora, Frontier), the Exascale era will soon begin. However, the high theoretical computing capability (i.e., peak FLOPS) of a machine is not the only meaningful target when designing a supercomputer, as the resources demand of applications varies. A deep understanding of the characterization of applications that run on a leadership supercomputer is one of the most important ways for planning its design, development and operation. In order to improve our understanding of HPC applications, user demands and resource usage characteristics, we perform correlative analysis of various logs for different subsystems of a leadership supercomputer. This analysis reveals surprising, sometimes counter-intuitive patterns, which, in some cases, conflicts with existing assumptions, and have important implications for future system designs as well as supercomputer operations. For example, our analysis shows that while the applications spend significant time on MPI, most applications spend very little time on file I/O. Combined analysis of hardware event logs and task failure logs show that the probability of a hardware FATAL event causing task failure is low. Combined analysis of control system logs and file I/O logs reveals that pure POSIX I/O is used more widely than higher level parallel I/O. Based on holistic insights of the application gained through combined and co-analysis of multiple logs from different perspectives and general intuition, we engineer features to "fingerprint" HPC applications. We use t-SNE (a machine learning technique for dimensionality reduction) to validate the explainability of our features and finally train machine learning models to identify HPC applications or group those with similar characteristic. To the best of our knowledge, this is the first work that combines logs on file I/O, computing, and inter-node communication for insightful analysis of HPC applications in production.

Liu, Zhengchun↗
DOE OSTI2021

Long-duration Energy Storage Technology Analysis and Development

This project develops a new low-cost and flexible long-duration energy storage (LDES) technology based on simultaneous regeneration technology. In lieu of the technology development, the project team also conducts a complete cost analysis for LDES based on various currently available technologies, as well as grid economic and environmental benefits analysis for LDES. There are three tasks in this project: Task 1. Fe-H2 flow cell fabrication, testing, demonstration; Task 2. Dynamic modeling, simulation and control architecture of power system; Task 3. Cost and grid analysis.

25 ENERGY STORAGE↗
DOE OSTI2022

ASDFL: An adaptive super‐pixel discriminative feature‐selective learning for vehicle matching

Abstract There are a large number of cameras in modern transportation system that capture numerous vehicle images continuously. Therefore, automatic analysis of these vehicle images is helpful for traffic flow management, criminal investigations and vehicle inspections. Vehicle matching, which aims to determine whether two input images depict an identical vehicle, is one of the core tasks in vehicle analysis. Recent relevant studies have focused on local feature extraction instead of global extraction, since local details can provide crucial cues to distinguish between cars. However, these methods do not select local features; that is, they do not assign weights to local features. Therefore, in this research, we systematically study the vehicle matching task, and present a novel annotation‐free local‐based deep learning method called Adaptive super‐pixel discriminative feature‐selective learning (ASDFL) to address this issue. In ASDFL, vehicle images are segmented into clusters of super‐pixels of similar size by considering the location and colour similarities of pixels without using any component‐level annotation. These super‐pixels are deemed to be the virtual components of vehicles. Moreover, a convolutional neural network is used to extract the deep features of these virtual components. Thereafter, an instance‐specific mask generation module driven by the extracted global features is enhanced to produce a mask to select the most distinctive virtual components of each vehicle image pair in the feature space. Finally, the vehicle matching task is accomplished by classifying the selected virtual component features of each imaged vehicle pair. Extensive experiments on two popular vehicle identification benchmarks demonstrate that our method is 1.57% and 0.8% more accurate than the previous baselines in a vehicle matching task on the VeRi and VehicleID datasets, respectively, which demonstrates the effectiveness of our method.

Qin, Rong↗
DOE OSTI2016

GazeAppraise v. 0.1

GazeAppraise advances the state of the art of gaze pattern analysis using methods that simultaneously analyze spatial and temporal characteristics of gaze patterns. GazeAppraise enables novel research in visual perception and cognition; for example, using shape features as distinguishing elements to assess individual differences in visual search strategy. Given a set of point-to-point gaze sequences, hereafter referred to as scanpaths, the method constructs multiple descriptive features for each scanpath. Once the scanpath features have been calculated, they are used to form a multidimensional vector representing each scanpath and cluster analysis is performed on the set of vectors from all scanpaths. An additional benefit of this method is the identification of causal or correlated characteristics of the stimuli, subjects, and visual task through statistical analysis of descriptive metadata distributions within and across clusters.

Wilson, Andrew↗
DOE OSTI2024

DLSIA: Deep Learning for Scientific Image Analysis

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Preliminary Tests For Development Of A Non-Pertechnetate Analysis Method

The objective of this task was to develop a non-pertechnetate analysis method that 222-S lab could easily implement. The initial scope involved working with 222-S laboratory personnel to adapt the existing Tc analytical method to fractionate the non-pertechnetate and pertechnetate. SRNL then developed and tested a method using commercial sorbents containing Aliquat ® 336 to extract the pertechnetate (thereby separating it from non-pertechnetate), followed by oxidation, extraction, and stripping steps, and finally analysis by beta counting and Mass Spectroscopy. Several additional items were partially investigated, including impacts of a 137 Cs removal step. The method was initially tested on SRS tank waste samples to determine its viability. Although SRS tank waste does not contain non-pertechnetate, testing with it was useful to investigate the compatibility, separation efficiency, interference removal efficacy, and method sensitivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Identification and interpretation of patterns in rocket engine data

A prototype software system was constructed to detect anomalous Space Shuttle Main Engine (SSME) behavior in the early stages of fault development significantly earlier than the indication provided by either redline detection mechanism or human expert analysis. The major task of the research project is to analyze ground test data, to identify patterns associated with the anomalous engine behavior, and to develop a pattern identification and detection system on the basis of this analysis. A prototype expert system which was developed on both PC and Symbolics 3670 lisp machine for detecting anomalies in turbopump vibration data was checked with data from ground tests 902-473, 902-501, 902-519, and 904-097 of the Space Shuttle Main Engine. The neural networks method was also applied to supplement the statistical method utilized in the prototype system to investigate the feasibility in detecting anomalies in turbopump vibration of SSME. In most cases the anomalies detected by the expert system agree with those reported by NASA. On the neural networks approach, the results are given the successful detection rate higher than 95 percent to identify either normal or abnormal running condition based on the experimental data as well as numerical simulation.

Lo, C. F.↗
NASA NTRS1978

Collection, processing and dissemination of data for the national solar demonstration program

A national solar data system developed for the DOE by IBM provides for automatic gathering, conversion, transfer, and analysis of demonstration site data. NASA requirements for this system include providing solar site hardware, engineering, data collection, and analysis. The specific tasks include: (1) solar energy system design/integration; (2) developing a site data acquisition subsystem; (3) developing a central data processing system; (4) operating the test facility at Marshall Space Flight Center; (5) collecting and analyzing data. The systematic analysis and evaluation of the data from the National Solar Data System is reflected in a monthly performance report and a solar energy system performance evaluation report.

Day, R. E.↗
DOE OSTI2013

Acquisition, Sharing and Processing of Large Datasets for Strain Imaging: an Example of an Indented Ni3Al/Mo Composite

The local effects of stress from a mechanical indentation have been studied on a Ni3 Al single crystal containing submicron inclusions of molybdenum fibers. Polychromatic X ray Microscopy (PXM) was used to measure elastic and plastic deformations near the indents. Analysis of freshly acquired massive sets of PXM data has been carried out over the Science Studio network using parallel processing software FOXMAS. This network and the FOXMAS software have greatly improved the efficiency of the data processing task.. The analysis was successfully applied to study lattice orientation distribution and strain tensor components for both the Ni3 Al and the Mo phases, particularly around 8 indents patterned at the longitudinal section of the alloy.

data processing↗