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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

United States Air Force Wipe Solvent Testing

The Wright-Patterson Air Force Base (WPAFB), as part of the Air Force Material Command, requested that NASA Johnson Space Center (JSC) White Sands Test Facility (WSTF) conduct testing and analyses in support of the United States Air Force Wipe Solvent Development Project. The purpose of the wipe solvent project is to develop an alternative to be used by Air Force flight line and maintenance personnel for the wipe cleaning of oxygen equipment. This report provides material compatibility, liquid oxygen (LOX) mechanical impact, autogenous ignition temperature (AIT), and gauge cleaning test data for some of the currently available solvents that may be used to replace CFC-113 and methyl chloroform. It provides data from previous WSTF test programs sponsored by the Naval Sea Systems Command, the Kennedy Space Center, and other NASA programs for the purpose of assisting WP AFB in identifying the best alternative solvents for validation testing.

Hornung, Steven D.↗
NASA NTRS2016

Scaling Techniques for Combustion Device Random Vibration Predictions

This work presents compares scaling techniques that can be used for prediction of combustion device component random vibration levels with excitation due to the internal combustion dynamics. Acceleration and unsteady dynamic pressure data from multiple component test programs are compared and normalized per the two scaling approaches reviewed. Two scaling technique are reviewed and compared against the collected component test data. The first technique is an existing approach developed by Barrett, and the second technique is an updated approach new to this work. Results from utilizing both techniques are presented and recommendations about future component random vibration prediction approaches are given.

Kenny, R. J.↗
NASA NTRS2020

Scaling and Similitude in Single Nozzle Supersonic Retropropulsion Aerodynamics Interference

Retropropulsion, or the firing of rocket engines or motors into the direction of flight, is a method of spacecraft deceleration and soft landing that dates back to the early 1960s. Current conceptual designs for landing humans on the surface of Mars require supersonic retropropulsion, or initiation of retropropulsion at supersonic freestream conditions, as part of an extended powered descent phase of flight. The objective of this work is to identify the design parameters and flow condition bounds for self-similar behavior of powered descent aerodynamic interference in relevant flight environments. In applications of sub-scale test data, an “unknown” uncertainty lies in scaling to and from full-scale environments and systems. The issue of scaling for the opposing flows characteristic of powered descent is the focus of the following analysis, using data from wind tunnel testing of figurations with a single, central nozzle as a point of departure.

Korzun, Ashley M.↗
NASA NTRS2021

A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks

Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDN(A)). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDN(A) model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDN(A) model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDN(B)) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρ(s)). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDN(B) models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing.

Brandon Smith↗
DOE OSTI2025

The Importance of Being Adaptable: An Exploration of the Power and Limitations of Domain Adaptation for Simulation-Based Inference with Galaxy Clusters

The application of deep machine learning methods in astronomy has exploded in the last decade, with new models showing remarkably improved performance on benchmark tasks. Not nearly enough attention is given to understanding the models' robustness, especially when the test data are systematically different from the training data, or "out of domain." Domain shift poses a significant challenge for simulation-based inference, where models are trained on simulated data but applied to real observational data. In this paper, we explore domain shift and test domain adaptation methods for a specific scientific case: simulation-based inference for estimating galaxy cluster masses from X-ray profiles. We build datasets to mimic simulation-based inference: a training set from the Magneticum simulation, a scatter-augmented training set to capture uncertainties in scaling relations, and a test set derived from the IllustrisTNG simulation. We demonstrate that the Test Set is out of domain in subtle ways that would be difficult to detect without careful analysis. We apply three deep learning methods: a standard neural network (NN), a neural network trained on the scatter-augmented input catalogs, and a Deep Reconstruction-Regression Network (DRRN), a semi-supervised deep model engineered to address domain shift. Although the NN improves results by 17% in the Training Data, it performs 40% worse on the out-of-domain Test Set. Surprisingly, the Scatter-Augmented Neural Network (SANN) performs similarly. While the DRRN is successful in mapping the training and Test Data onto the same latent space, it consistently underperforms compared to a straightforward Yx scaling relation. These results serve as a warning that simulation-based inference must be handled with extreme care, as subtle differences between training simulations and observational data can lead to unforeseen biases creeping into the results.

Ntampaka, Michelle [Baltimore, Space Telescope Sci↗
NASA NTRS1984

A preliminary analysis of flight data from the AFTI/F-16 airplane

Flight test data from the AFTI/F-16 airplane are analyzed. Two flight control system modes (Independent Backup Unit and Standard Normal Mode) are considered. Estimated stability and control derivatives are compared with values from the wind tunnel and F-16A flight tests. Modeling difficulties are shown to arise due to the near-neutral static stability of the airplane and the number of coordinated control surface movements commanded in the Standard Normal Mode.

Batterson, J. G.↗
NASA NTRS2015

Thermal Protection Test Bed Pathfinder Development Project

In order to increase thermal protection capabilities for future reentry vehicles, a method to obtain relevant test data is required. Although arc jet testing can be used to obtain some data on materials, the best method to obtain these data is to actually expose them to an atmospheric reentry. The overprediction of the Orion EFT-1 flight data is an example of how the ground test to flight traceability is not fully understood. The RED-Data small reentry capsule developed by Terminal Velocity Aerospace is critical to understanding this traceability. In order to begin to utilize this technology, ES3 needs to be ready to build and integrate heat shields onto the RED-Data vehicle. Using a heritage Shuttle tile material for the heat shield will both allow valuable insight into the environment that the RED-Data vehicle can provide and give ES3 the knowledge and capability to build and integrate future heat shields for this vehicle.

Snapp, Cooper↗
NASA NTRS1974

Correlation of full-scale drag predictions with flight measurements on the C-141A aircraft : Phase 2: Wind tunnel tests, analysis, and prediction techniques. Volume 2: Wind tunnel test and basic data

A research program has been conducted to determine the degree of cruise drag correlation on the C-141A aircraft between predictions based on wind tunnel test data, and flight test results. Information is presented on the wind tunnel test program and basic aerodynamic data on the C-141A wind-tunnel model used in the correlation studies.

Macwilkinson, D. G.↗
DOE OSTI2019

Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience

Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox—called seqNMF—with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Resistive AC-Coupled Silicon Detectors: principles of operation and first results from a combined analysis of beam test and laser data

This paper presents the principles of operation of Resistive AC-Coupled Silicon Detectors (RSDs) and measurements of the temporal and spatial resolutions using a combined analysis of laser and beam test data. RSDs are a new type of n-in-p silicon sensor based on the Low-Gain Avalanche Diode (LGAD) technology, where the n+ implant has been designed to be resistive, and the read-out is obtained via AC-coupling. The truly innovative feature of RSD is that the signal generated by an impinging particle is shared isotropically among multiple read-out pads without the need for floating electrodes or an external magnetic field. Careful tuning of the coupling oxide thickness and the n+ doping profile is at the basis of the successful functioning of this device. Several RSD matrices with different pad width-pitch geometries have been extensively tested with a laser setup in the Laboratory for Innovative Silicon Sensors in Torino, while a smaller set of devices have been tested at the Fermilab Test Beam Facility with a 120 GeV/c proton beam. The measured spatial resolution ranges between 2.5μm for 70–100 pad-pitch geometry and 17μm with 200–500 matrices, a factor of 10 better than what is achievable in binary read-out (bin size/12). Beam test data show a temporal resolution of ~40ps for 200 μm pitch devices, in line with the best performances of LGAD sensors at the same gain.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1979

Aerodynamic heating to representative SRB and ET protuberances

Heating data and data scaling methods which can be used on representative solid rocket booster and external tank (ET) protuberances are described. Topics covered include (1) ET geometry and heating points; (2) interference heating test data (51A); (3) heat transfer data from tests FH-15 and FH-16; (4) individual protuberance data; and (5) interference heating of paint data from test IH-42. A set of drawings of the ET moldline and protuberances is included.

Engel, C. D.↗