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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Development of a residual acceleration data reduction and dissemination plan

A major obstacle in evaluating the residual acceleration environment in an orbiting space laboratory is the amount of data collected during a given mission: gigabytes of data will be available as SAMS units begin to fly regularly. Investigators taking advantage of the reduced gravity conditions of space should not be overwhelmed by the accelerometer data which describe these conditions. We are therefore developing a data reduction and analysis plan that will allow principal investigators of low-g experiments to create experiment-specific residual acceleration data bases for post-flight analysis. The basic aspects of the plan can also be used to characterize the acceleration environment of earth orbiting laboratories. Our development of the reduction plan is based on the following program of research: the identification of experiment sensitivities by order of magnitude estimates and numerical modelling; evaluation of various signal processing techniques appropriate for the reduction, supplementation, and dissemination of residual acceleration data; and testing and implementation of the plan on existing acceleration data bases. The orientation of the residual acceleration vector with respect to some set of coordinate axes is important for experiments with known directional sensitivity. Orientation information can be obtained from the evaluation of direction cosines. Fourier analysis is commonly used to transform time history data into the frequency domain. Common spectral representations are the amplitude spectrum which gives the average of the components of the time series at each frequency and the power spectral density which indicates the power or energy present in the series per unit frequency interval. The data reduction and analysis scheme developed involves a two tiered structure to: (1) identify experiment characteristics and mission events that can be used to limit the amount of accelerator data an investigator should be interested in; and (2) process the data in a way that will be meaningful to the experiment objectives. A general outline of the plan is given.

Rogers, Melissa J. B.↗
NASA NTRS1992

System identification from closed-loop data with known output feedback dynamics

This paper presents a procedure to identify the open loop systems when it is operating under closed loop conditions. First, closed loop excitation data are used to compute the system open loop and closed loop Markov parameters. The Markov parameters, which are the pulse response samples, are then used to compute a state space representation of the open loop system. Two closed loop configurations are considered in this paper. The closed loop system can have either a linear output feedback controller or a dynamic output feedback controller. Numerical examples are provided to illustrate the proposed closed loop identification method.

Phan, Minh↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗
DOE OSTI2024

Application of Partial Least Squares Approaches to Pyroprocessing ER Data

Multivariate approaches show promise for application to process monitoring for safeguards of pyroprocessing. Past MPACT work explored the application of Principal Component Analysis (PCA) to detect off-normal conditions in pyroprocessing electrorefiner (ER) data from in the Hot Fuel Examination Facility (HFEF) at Idaho National Laboratory (INL) known as the Scalable Pyrochemical Recycling testbed (SPyRe) ER. PCA, however, does not consider the output variables. In FY24, multivariate analysis was extended from PCA to Partial Least Squares (PLS) analysis. PLS maximizes the variance between both the input signals and output variables. In the case of this work, PLS was applied in two different manners: Predictive PLS and Discriminant PLS. Predictive PLS maximizes the covariance between the process variables of the ER and the measured U concentration from in-situ voltammetry. Discriminant PLS maximizes the covariance between the process variables and a set of training process “states” such as known off-normal conditions. By projecting into the latent variable space in PLS, the process variables can be regressed onto the outputs and predictions can be made for new data sets. In this work, by applying predictive PLS, a penalized non-linear PLS approach was able to make predictions of concentration based on test and training data and detect when operations were off-normal. However, the predictive PLS does not classify the signals to which off-normal operations are attributable. Discriminant PLS can be used to classify off-normal operations but is inadequate to properly classify specific off-normal classes like power supply faults when the Discriminant PLS model is only specifically trained to detect that off-normal class. When all faults are trained against the observation data, all three operational classes are accurately classified and distinguished. Thus, future application of latent variable techniques should not select any given method, but should use a mixture of PCA, Predictive PLS, and Discriminant PLS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1990

The effect of changing environmental conditions on microwave signatures of forest ecosystems - Preliminary results of the March 1988 Alaskan aircraft SAR experiment

In preparation for the ESA ERS-1 mission, a series of multitemporal, multifrequency, multipolarization aircraft SAR data sets were acquired near Fairbanks in March 1988. P-, L-, and C-band data were acquired with the NASA/JPL Airborne SAR on five different days over a period of two weeks. The airborne data were augmented with intensive ground calibration data as well as detailed simultaneous in situ measurements of the geometric, dielectric, and moisture properties of the snow and forest canopy. During the time period over which the SAR data were collected, the environmental conditions changed significantly; temperatures ranged from unseasonably warm (1 to 9 C) to well below freezing (-8 to -15 C), and the moisture content of the snow and trees changed from a liquid to a frozen state. The SAR data clearly indicate the radar return is sensitive to these changing environmental factors, and preliminary analysis of the L-band SAR data shows a 0.4 to 5.8 dB increase (depending on polarization and canopy type) in the radar cross section of the forest stands under the warm conditions relative to the cold. These SAR observations are consistent with predictions from a theoretical scattering model.

Way, Jobea↗
DOE OSTI2023

Drivers of Cloud Condensation Nuclei in the Eastern North Atlantic as Observed at the ARM Site

Abstract Seven years of data collected at the Atmospheric Radiation Measurement's Eastern North Atlantic (ENA) site are analyzed to understand the controls of Cloud Condensation Nuclei (CCN) concentrations in the region. Day‐night differences in the aerosol data as segregated by wind direction demonstrate the aerosol observations to be impacted by local emissions when the wind direction (wdir) is between 90° and 310° (measured clockwise from the North where air is coming from). Data collected during marine conditions (wdir <90° or wdir >310°) show the CCN concentrations to be higher in the summer months as compared to the winter months. CCN budget analysis revealed advection and precipitation scavenging being primarily responsible for modulating the CCN concentrations at the site on monthly timescales, with rain rates driving the precipitation scavenging term. High (greater than 75th percentile) and low (lower than 25th percentile) CCN events were identified for each month to characterize the sub‐monthly variability of CCN concentrations. Low CCN events had thicker clouds, stronger rain rates, and lower reanalysis reported free‐tropospheric aerosol pseudo number concentration at the ENA site as compared to the high CCN events. Analysis of satellite data of air‐parcels 48 hr prior to their arrival at the ENA site demonstrated the air parcels during low CCN events to encounter higher cloudiness, stronger rain rates, and higher cloud top heights as compared to the high CCN events. The results presented herein provide key constraints for model evaluation studies and climatological studies conducted at the ENA site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Dynamic Behavior of Oval-Twisted Helical Tube Heat Exchanger: Numerical Study with RELAP5-3D

Convective heat transfer characteristics and theoretical thermal stress behaviors are numerically calculated using RELAP5-3D for the helical-coiled once-through steam generator (H-OTSG) and the novel heat exchanger design known as the oval-twisted helically coiled heat exchanger (OTHCHX) under (1) fluctuating wall temperature conditions, (2) square-wave pulsating flow conditions, and (3) the combined effects of fluctuating wall temperature and square-wave pulsating flow conditions. Heat transfer coefficient models for the H-OTSG and OTHCHX were developed based on existing data and implemented into RELAP5-3D, successfully capturing the N⁢uavg behavior within 8% to 10% of the reported data. Under fluctuating wall temperature conditions, the OTHCHX displayed higher N⁢u avg behavior than the H-OTSG. As 𝑓 increased, the $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ decreased. The $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ was higher for the OTHCHX than for the H-OTSG under fluctuating wall temperature conditions. Under pulsating flow conditions, the H-OTSG and OTHCHX displayed much higher 𝑁⁢𝑢 𝑎𝑣𝑔 than under constant flow conditions. The H-OTSG displayed a higher $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ over the OTHCHX. Under combined fluctuating wall temperature and pulsating flow conditions, the augmented heat transfer behavior from the pulsating flow was counteracted by the wall temperature fluctuations, producing slightly higher 𝑁⁢𝑢 𝑎𝑣𝑔 over constant wall temperature, constant flow conditions, but much lower than only constant pulsating flow under constant wall temperature conditions. The effects of simultaneous wall temperature fluctuations and square-wave pulsating flow caused higher $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ than that of only wall temperature fluctuations or pulsating flow. As the Reynolds number (Re) increased, $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ increased. However, when 𝑓=𝑓$_{\dot{m}}$, the $𝜎^{𝑚⁢𝑎⁢𝑥}_{𝑡⁢ℎ}$ showed decreasing values as Re increased. In conclusion, the results indicate that thermal-fluid resonance can help mitigate thermal stresses.

Thermal stress↗
NASA NTRS1995

Inertial acceleration as a measure of linear vection: An alternative to magnitude estimation

The present study focused on the development of a procedure to assess perceived self-motion induced by visual surround motion - vection. Using an apparatus that permitted independent control of visual and inertial stimuli, prone observers were translated along their head x-axis (fore/aft). The observers' task was to report the direction of self-motion during passive forward and backward translations of their bodies coupled with exposure to various visual surround conditions. The proportion of 'forward' responses was used to calculate each observer's point of subjective equality (PSE) for each surround condition. The results showed that the moving visual stimulus produced a significant shift in the PSE when data from the moving surround condition were compared with the stationary surround and no-vision condition. Further, the results indicated that vection increased monotonically with surround velocities between 4 and 40/s. It was concluded that linear vection can be measured in terms of changes in the amplitude of whole-body inertial acceleration required to elicit equivalent numbers of 'forward' and 'backward' self-motion reports.

Carpenter-Smith, Theodore R.↗
NASA NTRS1996

Dynamic Inlet Distortion Prediction with a Combined Computational Fluid Dynamics and Distortion Synthesis Approach

A procedure has been developed for predicting peak dynamic inlet distortion. This procedure combines Computational Fluid Dynamics (CFD) and distortion synthesis analysis to obtain a prediction of peak dynamic distortion intensity and the associated instantaneous total pressure pattern. A prediction of the steady state total pressure pattern at the Aerodynamic Interface Plane is first obtained using an appropriate CFD flow solver. A corresponding inlet turbulence pattern is obtained from the CFD solution via a correlation linking root mean square (RMS) inlet turbulence to a formulation of several CFD parameters representative of flow turbulence intensity. This correlation was derived using flight data obtained from the NASA High Alpha Research Vehicle flight test program and several CFD solutions at conditions matching the flight test data. A distortion synthesis analysis is then performed on the predicted steady state total pressure and RMS turbulence patterns to yield a predicted value of dynamic distortion intensity and the associated instantaneous total pressure pattern.

Norby, W. P.↗
NASA NTRS2010

Validation of the Integrated Medical Model Using Historical Space Flight Data

The Integrated Medical Model (IMM) utilizes Monte Carlo methodologies to predict the occurrence of medical events, utilization of resources, and clinical outcomes during space flight. Real-world data may be used to demonstrate the accuracy of the model. For this analysis, IMM predictions were compared to data from historical shuttle missions, not yet included as model source input. Initial goodness of fit test-ing on International Space Station data suggests that the IMM may overestimate the number of occurrences for three of the 83 medical conditions in the model. The IMM did not underestimate the occurrence of any medical condition. Initial comparisons with shuttle data demonstrate the importance of understanding crew preference (i.e., preferred analgesic) for accurately predicting the utilization of re-sources. The initial analysis demonstrates the validity of the IMM for its intended use and highlights areas for improvement.

Kerstman, Eric L.↗
NASA NTRS2011

Performance of Airborne Precision Spacing Under Realistic Wind Conditions and Limited Surveillance Range

With the expected worldwide increase of air traffic during the coming decade, both the Federal Aviation Administration's (FAA's) Next Generation Air Transportation System (NextGen), as well as Eurocontrol's Single European Sky ATM Research (SESAR) program have, as part of their plans, air traffic management (ATM) solutions that can increase performance without requiring time-consuming and expensive infrastructure changes. One such solution involves the ability of both controllers and flight crews to deliver aircraft to the runway with greater accuracy than they can today. Previous research has shown that time-based spacing techniques, wherein the controller assigns a time spacing to each pair of arriving aircraft, can achieve this goal by providing greater runway delivery accuracy and producing a concomitant increase in system-wide performance. The research described herein focuses on one specific application of time-based spacing, called Airborne Precision Spacing (APS), which has evolved over the past ten years. This research furthers APS understanding by studying its performance with realistic wind conditions obtained from atmospheric sounding data and with realistic wind forecasts obtained from the Rapid Update Cycle (RUC) short-range weather forecast. In addition, this study investigates APS performance with limited surveillance range, as provided by the Automatic Dependent Surveillance-Broadcast (ADS-B) system, and with an algorithm designed to improve APS performance when ADS-B surveillance data is unavailable. The results presented herein quantify the runway threshold delivery accuracy of APS under these conditions, and also quantify resulting workload metrics such as the number of speed changes required to maintain spacing.

Wieland, Frederick↗
DOE OSTI2025

Implementation of disruptive designs for gas turbine components using direct energy deposition additive manufacturing

This research aims to develop a framework for establishing the correlation between in-situ monitoring data, process parameters, and microstructure evolution in blown-powder laser-directed energy deposition (DED) additive manufacturing (AM). To achieve this, a comprehensive manufacturing framework has been developed, spanning from in-situ data acquisition, melt-pool simulation, microstructure modeling, and statistical microstructure quantification. A machine learning-based surrogate model is constructed to predict melt pool geometry directly from in-situ coaxial camera data. The surrogate model is trained using outputs from a high-fidelity melt pool simulation, which provides accurate melt pool dimension data under varying process conditions. The predicted melt pool geometry is then used as input to a microstructure model to predict microstructural features. To rigorously compare and analyze microstructures, the project introduces statistical metrics that quantify differences based on key features such as morphology and texture. Microstructures are represented using advanced statistical descriptors including angular chord length distribution, two-point spatial statistics, orientation distribution function, and global spherical harmonic. These representations are used to compute four distinct “dissimilarity scores” that quantitatively capture differences in texture and morphology. This framework is demonstrated to enable automated calibration of simulation parameters by minimizing discrepancies between simulated and target microstructures. The technology developed in this project enables direct correlation between in-situ monitoring data and resulting microstructure, paving the way for adaptive microstructure control in metal AM. This capability strengthens the connection between process parameters and final material properties, facilitating more precise and reliable material design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures

Classification machine learning models require high-quality labeled datasets for training. Among the most useful datasets for photovoltaic array fault detection and diagnosis are module or string current-voltage (IV) curves. Unfortunately, such datasets are rarely collected due to the cost of high fidelity monitoring, and the data that is available is generally not ideal, often consisting of unbalanced classes, noisy data due to environmental conditions, and few samples. In this paper, we propose an alternate approach that utilizes physics-based simulations of string-level IV curves as a fully synthetic training corpus that is independent of the test dataset. In our example, the training corpus consists of baseline (no fault), partial soiling, and cell crack system modes. The training corpus is used to train a 1D convolutional neural network (CNN) for failure classification. The approach is validated by comparing the model’s ability to classify failures detected on a real, measured IV curve testing corpus obtained from laboratory and field experiments. Results obtained using a fully synthetic training dataset achieve identical accuracy to those obtained with use of a measured training dataset. When evaluating the measured data’s test split, a 100% accuracy was found both when using simulations or measured data as the training corpus. When evaluating all of the measured data, a 96% accuracy was found when using a fully synthetic training dataset. The use of physics-based modeling results as a training corpus for failure detection and classification has many advantages for implementation as each PV system is configured differently, and it would be nearly impossible to train using labeled measured data.

Hopwood, Michael W. (ORCID:0000000161901767)↗
NASA NTRS1978

Variation of solar cell sensitivity and solar radiation on tilted surfaces

An empirical study was performed (1) to evaluate the validity of various insolation models used to compute solar radiation incident on tilted surfaces from global data measured on horizontal surfaces and (2) to determine the variation of solar cell sensitivity to solar radiation over a wide range of atmospheric condition. Evaluation of the insolation data indicates that the isotropic sky model of Liu and Jordan underestimates the amount of solar radiation falling on tilted surfaces by as much as 10%. An anisotropic-clear-sky model proposed by Temps and Coulson was also evaluated and found to be deficient under cloudy conditions. A new model, formulated herein, reduced the deviations between measured and predicted insolation to less than 3%. Evaluation of solar cell sensitivity data indicates small change (2-3%) in sensitivity from winter to summer for tilted cells. The feasibility of using such global data as a means for calibrating terrestrial solar cells as done by Treble is discussed.

Klucher, T. M.↗
NASA NTRS2003

In-Flight Capability for Evaluating Skin-Friction Gages and Other Near-Wall Flow Sensors

An 8-in.-square boundary-layer sensor panel has been developed for in-flight evaluation of skin-friction gages and other near-wall flow sensors on the NASA Dryden Flight Research Center F-15B/Flight Test Fixture (FTF). Instrumentation on the sensor panel includes a boundary-layer rake, temperature sensors, static pressure taps, and a Preston tube. Space is also available for skin-friction gages or other near-wall flow sensors. Pretest analysis of previous F-15B/FTF flight data has identified flight conditions suitable for evaluating skin-friction gages. At subsonic Mach numbers, the boundary layer over the sensor panel closely approximates the two-dimensional (2D), law-of-the-wall turbulent boundary layer, and skin-friction estimates from the Preston tube and the rake (using the Clauser plot method) can be used to evaluate skin-friction gages. At supersonic Mach numbers, the boundary layer over the sensor panel becomes complex, and other means of measuring skin friction are needed to evaluate the accuracy of new skin-friction gages. Results from the flight test of a new rubber-damped skin-friction gage confirm that at subsonic Mach numbers, nearly 2D, law-of-the-wall turbulent boundary layers exist over the sensor panel. Sensor panel data also show that this new skin-friction gage prototype does not work in flight.

Bui, Trong T.↗
NASA NTRS1970

Saturn S-2 base environment for flight evaluation

Computer program reduces base region flight data to isothermal and cold wall conditions and predicts instrument readings before and after flight. The flight data is made appropriate for nominal J-2 engine combustion. This program is used for any five-engined stage with center engine fixed.

Georgatsos, F. D.↗
NASA NTRS2003

Jet Noise Modeling for Coannular Nozzles Including the Effects of Chevrons

Development of good predictive models for jet noise has always been plagued by the difficulty in obtaining good quality data over a wide range of conditions in different facilities.We consider such issues very carefully in selecting data to be used in developing our model. Flight effects are of critical importance, and none of the means of determining them are without significant problems. Free-jet flight simulation facilities are very useful, and can provide meaningful data so long as they can be analytically transformed to the flight frame of reference. In this report we show that different methodologies used by NASA and industry to perform this transformation produce very different results, especially in the rear quadrant; this compels us to rely largely on static data to develop our model, but we show reasonable agreement with simulated flight data when these transformation issues are considered. A persistent problem in obtaining good quality data is noise generated in the experimental facility upstream of the test nozzle: valves, elbows, obstructions, and especially the combustor can contribute significant noise, and much of this noise is of a broadband nature, easily confused with jet noise. Muffling of these sources is costly in terms of size as well as expense, and it is particularly difficult in flight simulation facilities, where compactness of hardware is very important, as discussed by Viswanathan (Ref. 13). We feel that the effects of jet density on jet mixing noise may have been somewhat obscured by these problems, leading to the variable density exponent used in most jet noise prediction procedures including our own. We investigate this issue, applying Occam s razor, (e.g., Ref. 14), in a search for the simplest physically meaningful model that adequately describes the observed phenomena. In a similar vein, we see no reason to reject the Lighthill approach; it provides a very solid basis upon which to build a predictive procedure, as we believe we demonstrate in this report. Another feature of our approach is that the analyses are all conducted with lossless spectra, rather than Standard Day spectra, as is often done in industry. We feel that it is important to isolate the effects of as many physical processes as practical. Atmospheric attenuation can then be included using the relations developed for NASA by Shields and Bass (Ref. 15), which are available in both FOOTPR and ANOPP. The current approach to coannular jet noise prediction used in FOOTPR is reported in Reference 16, which updates the earlier conventional-velocity-profile (CVP, Ref. 17) and inverted-velocity-profile (IVP, Ref. 18) models.

Stone, James R.↗