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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Analysis of Skylab MSS data, and calibration of thermal data over Lake Monroe, Indiana

The author has identified the following significant results. The SL/4-S192 data over northern Indiana looks good. The thermal band looks very encouraging. The preliminary analysis indicates so far that it will be difficult to distinguish some land use classes because of the lack of vegetation. Preliminary analysis of the thermal band in particular indicates that there appears to be a correlation of the hot spots in the data over the Ft. Wayne, Indiana area and its industrial centers.

Silva, L. F.↗
NASA NTRS1991

Interpretation of the ion mass spectra in the mass per charge range 25-35 amu/e obtained in the inner coma of Halley's comet by the HIS-sensor of the Giotto IMS experiment

The IMS-HIS double-focusing mass spectrometer that flew on the Giotto spacecraft covered the mass per charge range from 12 to 56 (amu/e). By comparing flight data, calibration data and results of model calculations of the ion population in the inner coma, the absolute mass scale is established, and ions in the mass range 25 to 35 are identified. Ions resulting from protonation of molecules with high proton affinity are relatively abundant, enabling us to estimate relative source strengths for H2CO, CH3OH, HCN, and H2S, providing for the first time a positive in situ measurement of methanol. Also, upper limits for NO and some hydrocarbons are derived.

Geiss, J.↗
NASA NTRS1991

Interpretation of the ion mass spectra in the mass range 25-35 obtained in the inner coma of Halley's comet by the HIS-sensor of the Giotto IMS Experiment

The IMS-HIS double-focussing mass spectrometer that flew on the Giotto spacecraft covered the mass per charge range from 12 to 56 (AMU/e). By comparing flight data, calibration data, and results of model calculations of the ion population in the inner coma, the absolute mass scale is established, and ions in the mass range 25 to 35 are identified. Ions resulting from protonation of molecules with high proton affinity are relatively abundant, enabling us to estimate relative source strengths for H2CO, CH3OH, HCN, and H2S, providing for the first time a positive in situ measurement of methanol. Also, upper limits for NO and some hydrocarbons are derived.

Geiss, J.↗
NASA NTRS1998

AXAF: Current Developments and Future Plans for the Scientific Data Analysis

AXAF, the Advanced X-ray Astrophysics Facility, is one of NASA's great observatories scheduled for launch in January of 1999 by a Space Shuttle. This facility will be available to scientists in the United States and to the international astronomical community over an anticipated mission lifetime of at least 5 years. The objective of AXAF is to make astrophysical observations in the 0.09 to 10.0 keV energy range with two imaging detectors, and two sets of transmission gratings giving high spatial (0.5 arcsec) and spectral (E/(Delta)E=100-2000) resolution. The AXAF Science Center (ASC) in Cambridge, MA, USA is responsible for obtaining and reviewing observation proposals submitted by users, providing long range science planning and performing all science data calibration, data analysis, science instrument trend analysis, and science data archiving. The ASC will distribute science data products to the users. We will review here the current developments and future plans for the AXAF scientific data analysis system, its infrastructure and new key features, including the global modelling and fitting environment, integrated data analysis GUIs and new analysis tools.

Fruscione, Antonella↗
NASA NTRS2001

Cryogenic Pressure Calibrator for Wide Temperature Electronically Scanned (ESP) Pressure Modules

Electronically scanned pressure (ESP) modules have been developed that can operate in ambient and in cryogenic environments, particularly Langley's National Transonic Facility (NTF). Because they can operate directly in a cryogenic environment, their use eliminates many of the operational problems associated with using conventional modules at low temperatures. To ensure the accuracy of these new instruments, calibration was conducted in a laboratory simulating the environmental conditions of NTF. This paper discusses the calibration process by means of the simulation laboratory, the system inputs and outputs and the analysis of the calibration data. Calibration results of module M4, a wide temperature ESP module with 16 ports and a pressure range of +/- 4 psid are given.

Faulcon, Nettie D.↗
NASA NTRS2001

Use of 3000 Bragg Grating Strain Sensors Distributed on Four Eight-meter Optical Fibers During Static Load Tests of a Composite Structure

This paper describes the use of a fiber optic system to measure strain at thousands of locations along optical fibers where weakly reflecting Bragg gratings have been photoetched. The optical fibers were applied to an advanced composite transport wing along with conventional foil strain gages. A comparison of the fiber optic and foil gage systems used for this test will be presented including: a brief description of both strain data systems; a discussion of the process used for installation of the optical fiber; comparative data from the composite wing test; the processes used for the location and display of the high density fiber optic data. Calibration data demonstrating the potential accuracy of the fiber optic system will also be presented. The opportunities for industrial and commercial applications will be discussed. The fiber optic technique is shown to be a valuable augmentation to foil strain gages providing insight to structural behavior previously requiring reliance on modeling.

Childers, Brooks A.↗
NASA NTRS1986

The Multispectral Atmospheric Mapping Sensor (MAMS): Instrument description, calibration and data quality

A new instrument has been developed to produce high resolution imagery in eight visible and three infared spectral bands from an aircraft platform. An analysis of the data and calibration procedures has shown that useful data can be obtained at up to 50 m resolution with a 2.5 milliradian aperture. Single sample standard errors for the measurements are 0.5, 0.2, and 0.9 K for the 6.5, 11.1, and 12.3 micron spectral bands, respectively. These errors are halved when a 5.0 milliradian aperture is used to obtain 100 m resolution data. Intercomparisons with VAS and AVHRR measurements show good relative calibration. MAMS development is part of a larger program to develop multispectral Earth imaging capabilities from space platforms during the 1990s.

Jedlovec, G. J.↗
NASA NTRS2010

Comparison of Iterative and Non-Iterative Strain-Gage Balance Load Calculation Methods

The accuracy of iterative and non-iterative strain-gage balance load calculation methods was compared using data from the calibration of a force balance. Two iterative and one non-iterative method were investigated. In addition, transformations were applied to balance loads in order to process the calibration data in both direct read and force balance format. NASA's regression model optimization tool BALFIT was used to generate optimized regression models of the calibration data for each of the three load calculation methods. This approach made sure that the selected regression models met strict statistical quality requirements. The comparison of the standard deviation of the load residuals showed that the first iterative method may be applied to data in both the direct read and force balance format. The second iterative method, on the other hand, implicitly assumes that the primary gage sensitivities of all balance gages exist. Therefore, the second iterative method only works if the given balance data is processed in force balance format. The calibration data set was also processed using the non-iterative method. Standard deviations of the load residuals for the three load calculation methods were compared. Overall, the standard deviations show very good agreement. The load prediction accuracies of the three methods appear to be compatible as long as regression models used to analyze the calibration data meet strict statistical quality requirements. Recent improvements of the regression model optimization tool BALFIT are also discussed in the paper.

Ulbrich, N.↗
NASA NTRS1991

MAESTRO1 data set calibration: The JRC approach

Calibration is a fundamental task for analysis. Therefore, the Microwave Team of the JRC Ispra has put considerable effort investigating and checking the different calibration techniques, using the data collected during the MAESTRO1 campaign. We report on this activity. First, we give an overview of the algorithms used for phase and radiometric calibration. Then, we describe the software tools that were developed to support the calibration data processing. Finally, we present and discuss the results obtained from the calibration in the test sites of Freiburg and Flevoland.

Lavalle, C.↗
NASA NTRS1992

Using calibrated SAR data

A number of approaches for calibrating SAR imaging data including data from multipolarization and multifrequency systems developed at the Jet Propulsion Laboratory are discussed. It is shown how calibrated SAR data can be used to improve the understanding of the physical properties of the earth's surface layer and how calibration uncertainties and system noise should be handled by the SAR data user. Examples of calibrated SAR data from the NASA/JPL DC-8 SAR are used to demonstrate use of calibrated data to monitor temporal change and to improve the classification of land cover type.

Freeman, Anthony↗
NASA NTRS1984

Calibration or inverse regression: Which is appropriate for crop surveys using LANDSAT data?

Calibration and inverse regression estimators of crop proportions are investigated where the auxiliary variable is obtained from binary classification of multivariate LANDSAT data. The appropriate model relating classifier proportions and ground observed proportions for a given crop type is the calibration model. Under this model the inverse regression estimator is superior to the calibration estimator in estimating the crop acreage or proportion for a region of interest.

Chhikara, R. S.↗
NASA NTRS1999

Strain Gauge Balance Calibration and Data Reduction at NASA Langley Research Center

This paper will cover the standard force balance calibration and data reduction techniques used at Langley Research Center. It will cover balance axes definition, balance type, calibration instrumentation, traceability of standards to NIST, calibration loading procedures, balance calibration mathematical model, calibration data reduction techniques, balance accuracy reporting, and calibration frequency.

Ferris, A. T. Judy↗
NASA NTRS2019

Calibration and Data Analysis Recommendations for Three-Component Moment Balances

Fundamental characteristics of design, calibration, and application of three-component moment balances are investigated in great detail. These balances are typically used to determine loads on control surfaces, canards, or other parts that are attached to a wind tunnel model. First, three different descriptions of the load state of a moment balance are reviewed. Then, load transformations between different load formats and the combined load diagram for two of the three load components are discussed. An error analysis showed that it is critical to maximize the product of the distance between the bending moment gages and their sensitivities in order to minimize the overall error in the normal force prediction. In addition, it is important to apply a sufficient number of calibration loadings near the first bending moment gage. Then, unwanted near-linear dependencies between the two bending moment gage outputs can be avoided. The error in the bending moment prediction is also investigated that results from the elastic deformation of the metric part of the balance under load. Finally, the application of the Non-Iterative Method to three-component moment balance calibration data is described in order to obtain regression models that can be used to predict loads from measured outputs during a wind tunnel test.

Strain-Gage Balance↗
NASA NTRS2007

A Comparison of Two Balance Calibration Model Building Methods

Simulated strain-gage balance calibration data is used to compare the accuracy of two balance calibration model building methods for different noise environments and calibration experiment designs. The first building method obtains a math model for the analysis of balance calibration data after applying a candidate math model search algorithm to the calibration data set. The second building method uses stepwise regression analysis in order to construct a model for the analysis. Four balance calibration data sets were simulated in order to compare the accuracy of the two math model building methods. The simulated data sets were prepared using the traditional One Factor At a Time (OFAT) technique and the Modern Design of Experiments (MDOE) approach. Random and systematic errors were introduced in the simulated calibration data sets in order to study their influence on the math model building methods. Residuals of the fitted calibration responses and other statistical metrics were compared in order to evaluate the calibration models developed with different combinations of noise environment, experiment design, and model building method. Overall, predicted math models and residuals of both math model building methods show very good agreement. Significant differences in model quality were attributable to noise environment, experiment design, and their interaction. Generally, the addition of systematic error significantly degraded the quality of calibration models developed from OFAT data by either method, but MDOE experiment designs were more robust with respect to the introduction of a systematic component of the unexplained variance.

DeLoach, Richard↗
NASA NTRS1973

Apollo experience report: Flight instrumentation calibration

Three types of instrumentation-calibration data were used in the Apollo Program to provide the correct engineering data for tests and mission support. The command and service module instrumentation-component procurement specifications required individual-component calibration, and calibration data for these individual components (conventional-calibration data) were always used for mission data support. A mean standard type of calibration data derived from a statistical sampling of conventional-calibration data was used for test and checkout during the latter part of the Apollo Program. The lunar module instrumentation procurement specification permitted the use of standard-calibration data. These data were applicable to similarly instrumented measurements. The definition, merit, and application of each type of data are discussed.

Demoss, J. F.↗
NASA NTRSDate not supplied

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures for implementing machine learning methods into existing algorithms for multi-hole probe calibration and data reduction. It demonstrates that using artificial neural networks (ANNs) can decrease the amount of calibration data needed to achieve a specific calibration uncertainty by over 50%, while also significantly reducing data reduction times. Instead of surface fitting methods, ANNs are employed. Initially, directional calibration coefficients related to flow angles are computed based on pressure measurements, and then these flow angles serve as input parameters for subsequent ANNs to iteratively define Mach number, static pressure, and total pressure. In an alternative approach, new calibration coefficients directly relate pressure measurements from the five-hole probe to the quantities of interest, thereby eliminating the need for iterative algorithms used in conventional surface fitting methods. This method offers several advantages: an average increase of less than 1%in calibration uncertainty for flow angles and a significant reduction in data reduction times to a few seconds on average. Additionally, the methodology is confirmed to avoid both over- and under-fitting.

Machine Learning↗