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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1954

Application of several methods for determining transfer functions and frequency response of aircraft from flight data

In the process of analyzing the longitudinal frequency-response characteristics of aircraft, information on some of the methods of analysis has been obtained by the Langley Aeronautical Laboratory of the National Advisory Committee for Aeronautics. In the investigation of these methods, the practical applications and limitations were stressed. In general, the methods considered may be classed as: (1) analysis of sinusoidal response, (2) analysis of transient response as to harmonic content through determination of the Fourier integral by manual or machine methods, and (3) analysis of the transient through the use of least-squares solutions of the coefficients of an assumed equation for either the transient time response or frequency response (sometimes referred to as curve-fitting methods). (author)

Eggleston, John M↗
NASA NTRS1979

Computation of atmospheric attenuation of sound for fractional-octave bands

Correct methods of accounting for atmospheric attenuation in band data requiring consideration of the integrated effect across the bands of the specific distance involved are discussed. Computer programs are provided that are understandable, efficient, and simple to use. It is hoped that this will facilitate more widespread use of correct computational methods, especially where routine computer processing of data is employed.

Montegani, F. J.↗
NASA NTRS2005

3DRT-MPASS

Data from all current JPL missions are stored in files called SPICE kernels. At present, animators who want to use data from these kernels have to either read through the kernels looking for the desired data, or write programs themselves to retrieve information about all the needed objects for their animations. In this project, methods of automating the process of importing the data from the SPICE kernels were researched. In particular, tools were developed for creating basic scenes in Maya, a 3D computer graphics software package, from SPICE kernels.

Lickly, Ben↗
NASA NTRS2022

Effective Uncertainty Quantification for Multi-Angle Polarimetric Aerosol Remote Sensing Over Ocean

Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

PACE↗
NASA NTRS1975

Synoptic analyses, 5-, 2-, and 0.4-millibar surfaces for January 1972 through June 1973

Data from meteorological rocketsonde and satellite radiance measurements were employed to analyze a series of high-altitude synoptic charts. The methods employed for processing the various types of data and the analysis procedure are described. Broad-scale analyses for the Northern Hemisphere 5-, 2-, and 0.4-mb surfaces are presented for each week from September through April and for each month from May through August during the period January 1972 through June 1973. A brief discussion of the height and temperature fields is also given. Circulation and temperature changes associated with a minor stratospheric warming in January and February 1972 and a major stratospheric warming in January and February 1973 are among the discussion items.

Source record↗
NASA NTRS2012

IT Data Mining Tool Uses in Aerospace

Data mining has a broad spectrum of uses throughout the realms of aerospace and information technology. Each of these areas has useful methods for processing, distributing, and storing its corresponding data. This paper focuses on ways to leverage the data mining tools and resources used in NASA's information technology area to meet the similar data mining needs of aviation and aerospace domains. This paper details the searching, alerting, reporting, and application functionalities of the Splunk system, used by NASA's Security Operations Center (SOC), and their potential shared solutions to address aircraft and spacecraft flight and ground systems data mining requirements. This paper also touches on capacity and security requirements when addressing sizeable amounts of data across a large data infrastructure.

Monroe, Gilena A.↗
NASA NTRS2016

Processing Satellite Data for Slant Total Electron Content Measurements

A method, system, and apparatus provide the ability to estimate ionospheric observables using space-borne observations. Space-borne global positioning system (GPS) data of ionospheric delay are obtained from a satellite. The space-borne GPS data are combined with ground-based GPS observations. The combination is utilized in a model to estimate a global three-dimensional (3D) electron density field.

Stephens, Philip John↗
NASA NTRSDate not supplied

Experimental Testing of Data Fusion in A Distributed Ground-Based Sensing Network for Advanced Air Mobility

Advanced Air Mobility (AAM) is an active area of development which foresees the integration of autonomous uncrewed aircraft into the civil airspace for air transportation of people and cargo. Safe integration requires significant technological developments and extensive testing phases of sensing and surveillance strategies in dense airspace. Compared to well-assessed manned aviation systems scenarios, surveillance strategies in the AAM and small Uncrewed Aircraft Vehicles(UAVs) context need to detect smaller platforms flying at lower altitude against cluttered backgrounds in dense airspace. Fusion of data provided by a network of distributed sensing nodes is a powerful tool to enable detection and tracking in such complex conditions. This paper contributes to this research direction by proposing a surveillance strategy for the AAM environment based on sensor fusion of data acquired by distributed ground-based radars. Specifically, experimental data collected with two independent radars, observing the flight of two small UAVs, are used. Data fusion at tracking level is based on a leader-helper strategy where the leader radar uses the helper’s measurements to increase the lifespan of its generated tracks. This solution shows promising results with a 10%increase in track coverage with respect to the standalone leader radar tracking solution. The paper also proposes an interference removal processing method which is applied on the data collected by one of the two radars.

Federica Vitiello↗
NASA NTRS2025

Demonstration of Data Processing and Fusion from Distributed Radars for AAM Surveillance

Advanced Air Mobility (AAM) is an active area of development which foresees the integration of autonomous uncrewed aircraft into the civil airspace for air transportation of people and cargo. Safe integration requires significant technological developments and extensive testing phases of sensing and surveillance strategies in dense airspace. Compared to well-assessed manned aviation systems scenarios, surveillance strategies in the AAM and small Uncrewed Aircraft Vehicles (UAVs) context need to detect smaller platforms flying at lower altitude against cluttered backgrounds in dense airspace. Fusion of data provided by a network of distributed sensing nodes is a powerful tool to enable detection and tracking in such complex conditions. This paper contributes to this research direction by proposing a surveillance strategy for the AAM environment based on sensor fusion of data acquired by distributed ground-based radars. Specifically, experimental data collected with three independent radars, observing the flight of two small UAVs, are used. Data fusion at tracking level is based on a leader-helper strategy where the leader radar uses the helper’s measurements to increase the lifespan of its generated tracks. This solution shows promising results with a 10% increase in track coverage with respect to the standalone leader radar tracking solution. The paper also proposes an interference removal processing method which is applied on the data collected by two of the radars.

Federica Vitiello↗
NASA NTRS2007

Implementation of Leak Test Methods for the International Space Station (ISS) Elements, Systems and Components

The International Space Station (ISS has Qualification and Acceptance Environmental Test Requirements document, SSP 41172 that includes many environmental tests such as Thermal vacuum & Cycling, Depress/Repress, Sinusoidal, Random, and Acoustic Vibration, Pyro Shock, Acceleration, Humidity, Pressure, Electromatic Interference (EMI)/Electromagnetic Compatibility (EMCO), etc. This document also includes (13) leak test methods for Pressure Integrity Verification of the ISS Elements, Systems, and Components. These leak test methods are well known, however, the test procedure for specific leak test method shall be written and implemented paying attention to the important procedural steps/details that, if omitted or deviated, could impact the quality of the final product and affect the crew safety. Such procedural steps/details for different methods include, but not limited to: - Sequence of testing, f or example, pressurization and submersion steps for Method I (Immersion); - Stabilization of the mass spectrometer leak detector outputs fo r Method II (vacuum Chamber or Bell jar); - Proper data processing an d taking a conservative approach while making predictions for on-orbit leakage rate for Method III(Pressure Change); - Proper Calibration o f the mass spectrometer leak detector for all the tracer gas (mostly Helium) Methods such as Method V (Detector Probe), Method VI (Hood), Method VII (Tracer Probe), Method VIII(Accumulation); - Usage of visibl ility aides for Method I (Immersion), Method IV (Chemical Indicator), Method XII (Foam/Liquid Application), and Method XIII (Hydrostatic/Visual Inspection); While some methods could be used for the total leaka ge (either internal-to-external or external-to-internal) rate requirement verification (Vacuum Chamber, Pressure Decay, Hood, Accumulation), other methods shall be used only as a pass/fail test for individual joints (e.g., welds, fittings, and plugs) or for troubleshooting purposes (Chemical Indicator, Detector Probe, Tracer Probe, Local Vacuum Chamber, Foam/Liquid Application, and Hydrostatic/Visual Inspection). Any isolation of SSP 41172 requirements have led to either retesting of hardware or accepting a risk associated with the potential system or component pressure integrity problem during flight.

Underwood, Steve↗
NASA NTRS2016

Supercooled Liquid Water Content Instrument Analysis and Winter 2014 Data with Comparisons to the NASA Icing Remote Sensing System and Pilot Reports

The National Aeronautics and Space Administration (NASA) has developed a system for remotely detecting the hazardous conditions leading to aircraft icing in flight, the NASA Icing Remote Sensing System (NIRSS). Newly developed, weather balloon-borne instruments have been used to obtain in-situ measurements of supercooled liquid water during March 2014 to validate the algorithms used in the NIRSS. A mathematical model and a processing method were developed to analyze the data obtained from the weather balloon soundings. The data from soundings obtained in March 2014 were analyzed and compared to the output from the NIRSS and pilot reports.

aviation meterology↗
NASA NTRS2017

Examination of a Rotorcraft Noise Prediction Method and Comparison to Flight Test Data

With a view that rotorcraft noise should be included in the preliminary design process, a relatively fast noise prediction method is examined in this paper. A comprehensive rotorcraft analysis is combined with a noise prediction method to compute several noise metrics of interest. These predictions are compared to flight test data. Results show that inclusion of only the main rotor noise will produce results that severely underpredict integrated metrics of interest. Inclusion of the tail rotor frequency content is essential for accurately predicting these integrated noise metrics.

Boyd, D. Douglas, Jr.↗
NASA NTRS2017

An Optimized Photoelectron Track Reconstruction Method for Photoelectric X-Ray Polarimeters

We present a data processing algorithm for angular reconstruction and event selection applied to 2-D photoelectron track images from X-ray polarimeters. The method reconstructs the initial emission angle of a photoelectron from the initial portion of the track, which is obtained by continuously cutting a track until the image moments or number of pixels fall below tunable thresholds. In addition, event selection which rejects round tracks quantified with eccentricity and circularity is performed so that polarimetry sensitivity considering a trade-off between the modulation factor and signal acceptance is maximized. The modulation factors with applying track selection are 26.6 ± 0.4, 46.1 ± 0.4, 62.3 ± 0.4, and 61.8 ± 0.3% at 2.7, 4.5, 6.4, and 8.0 keV, respectively, using the same data previously analyzed by Iwakiri et al. (2016), where the corresponding numbers are 26.9±0.4, 43.4±0.4, 54.4±0.3, and 59.1 ± 0.3%. The method improves polarimeter sensitivity by 5%–10% at the high energy end of the band previously presented (Iwakiri et al. 2016).

Kitaguchi, Takao↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
NASA NTRS2015

Phase-Based Adaptive Estimation of Magnitude-Squared Coherence Between Turbofan Internal Sensors and Far-Field Microphone Signals

A cross-power spectrum phase based adaptive technique is discussed which iteratively determines the time delay between two digitized signals that are coherent. The adaptive delay algorithm belongs to a class of algorithms that identifies a minimum of a pattern matching function. The algorithm uses a gradient technique to find the value of the adaptive delay that minimizes a cost function based in part on the slope of a linear function that fits the measured cross power spectrum phase and in part on the standard error of the curve fit. This procedure is applied to data from a Honeywell TECH977 static-engine test. Data was obtained using a combustor probe, two turbine exit probes, and far-field microphones. Signals from this instrumentation are used estimate the post-combustion residence time in the combustor. Comparison with previous studies of the post-combustion residence time validates this approach. In addition, the procedure removes the bias due to misalignment of signals in the calculation of coherence which is a first step in applying array processing methods to the magnitude squared coherence data. The procedure also provides an estimate of the cross-spectrum phase-offset.

Combustion↗
DOE OSTI2026

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗
NASA NTRS1983

Organizing Space Shuttle parametric data for maintainability

A model of organization and management of Space Shuttle data is proposed. Shuttle avionics software is parametrically altered by a reconfiguration process for each flight. As the flight rate approaches an operational level, current methods of data management would become increasingly complex. An alternative method is introduced, using modularized standard data, and its implications for data collection, integration, validation, and reconfiguration processes are explored. Information modules are cataloged for later use, and may be combined in several levels for maintenance. For each flight, information modules can then be selected from the catalog at a high level. These concepts take advantage of the reusability of Space Shuttle information to reduce the cost of reconfiguration as flight experience increases.

Angier, R. C.↗
NASA NTRSDate not supplied

Fostering Better Collaboration in Software Development Cycles Between Scientists and Programmers to Ensure the Integrity of and Promote the Development of New Scientific Data Products.

Misaligned incentives lead to reduced interaction between scientists and programmers on modern NASA science data-product development teams. Typically, situations arise where the scientist is not incentivized to learn modern coding practices and the programmer does not understand the science algorithms in the code. A programmer is responsible for the deliverable code thus setting a tradeoff between the desire for code improvement versus fear of compromising the integrity of data-product while the scientist continues to rely on their legacy codebases owing to the complexity of using the delivered code outside the processing environment and lack of validation modules. The NASA/CERES-TISA project has adopted a collaborative approach, with scientists and programmers both utilizing the same software repository with multiple branches, some optimized for delivery to a processing datacenter and others for scientific product development and validation. A team of scientists and programmers jointly review any new science code updates for integration into the codebase and strive to improve practices through promoting algorithm understanding, better institutional knowledge exchange and documentation, modularization, and developing data processing flow-dictated validation and debugging methods. This leads to a reduction in the personnel single point failures and reduced development time for creation of new science data-products.

CERES↗