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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 91 records · Page 5
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NASA NTRS2014

A Revised Calibration Function and Results for the Phoenix Mission TECP Relative Humidity Sensor

The original calibration function of the R(sub H) sensor on the Phoenix Thermal and Electrical Conductivity Sensor (TECP) has been revised in order to extend the range of the valid calibration, and to improve accuracy. The original function returned non-physical R(sub H) values at the lowest temperatures. To resolve this, and because the original calibration was performed against a pair of hygrometers that measured frost point (T(sub f)), the revised calibration equation is also cast in terms of frost point. Because of the complexity of maintaining very low temperatures and high R(sub H) in the laboratory, no calibration data exists at T is greater than 203K. However, sensor response during the mission was smooth and continuous down to 181 K. Therefore we have opted to include flight data in the calibration data set; selection was limited to data acquired during periods when the atmosphere is known to have been saturated. T(sub f) remained below 210 K throughout the mission(P is greater than 0.75 Pa). R(sub H), conversely, ranged from 1 to well under 0.01 diurnally, due to approximately 50 K temperature variations. To first order, both vapor pressure and its variance are greater during daylight hours. Variance in overnight humidity is almost entirely explained by temperature, while atmospheric turbulence contributes substantial variance to daytime humidity. Likewise, data gathered with the TECP aloft reflect higher H2O abundances than at the surface, as well as greater variance. There is evidence for saturation of the atmosphere overnight throughout much of the mission. In virtually every overnight observation, once the atmosphere cooled to T(sub f), water vapor begins to decrease, and tracks air temperature. There is no evidence for substantial decreases in water vapor prior to saturation, as expected for adsorptive exchange. Likewise, there is no evidence of local control of vapor by phases such as perchlorate hydrates hydrated minerals. The daytime average H2O pressure does not change substantially over the course of the mission, although the H2O column abundance varies by a factor of 2. Column abundances calculated from TECP data are lower than orbital measurements if one assumes that H2O is uniformly mixed through a single scale height. These results argue that the vertical distribution of H2O begins to change well in advance of surface concentrations as northern autumn approaches.

Phoenix Thermal and Electrical Conductivity Sensor↗
NASA NTRS2012

Calibration and Data Analysis of the MC-130 Air Balance

Design, calibration, calibration analysis, and intended use of the MC-130 air balance are discussed. The MC-130 balance is an 8.0 inch diameter force balance that has two separate internal air flow systems and one external bellows system. The manual calibration of the balance consisted of a total of 1854 data points with both unpressurized and pressurized air flowing through the balance. A subset of 1160 data points was chosen for the calibration data analysis. The regression analysis of the subset was performed using two fundamentally different analysis approaches. First, the data analysis was performed using a recently developed extension of the Iterative Method. This approach fits gage outputs as a function of both applied balance loads and bellows pressures while still allowing the application of the iteration scheme that is used with the Iterative Method. Then, for comparison, the axial force was also analyzed using the Non-Iterative Method. This alternate approach directly fits loads as a function of measured gage outputs and bellows pressures and does not require a load iteration. The regression models used by both the extended Iterative and Non-Iterative Method were constructed such that they met a set of widely accepted statistical quality requirements. These requirements lead to reliable regression models and prevent overfitting of data because they ensure that no hidden near-linear dependencies between regression model terms exist and that only statistically significant terms are included. Finally, a comparison of the axial force residuals was performed. Overall, axial force estimates obtained from both methods show excellent agreement as the differences of the standard deviation of the axial force residuals are on the order of 0.001 % of the axial force capacity.

Booth, Dennis↗
NASA NTRSDate not supplied

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

This work presents procedures to implement machine learning methods in the existing algorithms for multi-hole probe calibrations and data reduction. It is shown here, that utilizing artificial neural networks (ANNs) can reduce the amount of calibration data that needs to be acquired in order to obtain a specific calibration uncertainty, by more than 50% while simultaneously reducing data reduction times significantly. ANNs were used instead of the surface fitting methods, where first, the directional calibration coefficients related to the flow angles are calculated based on the pressure measurements, and then the flow angles are used as a set of the input parameters for the following ANNs to define Mach number and static and total pressure iteratively. In a second approach, novel calibration coefficients were used to directly relate the pressure measurements from five-hole probe to the quantities of interest thus, eliminating the need for iterative algorithms used in the conventional surface fitting methods. The advantageous features of this method are an average increase of less than 1% in the calibration uncertainty for flow angles and significant reduction of the data reduction times (few seconds). In addition, we confirmed the methodology to avoid over-fitting and under-fitting.

Machine Learning↗
NASA NTRS2008

Stable Calibration of Raman Lidar Water-Vapor Measurements

A method has been devised to ensure stable, long-term calibration of Raman lidar measurements that are used to determine the altitude-dependent mixing ratio of water vapor in the upper troposphere and lower stratosphere. Because the lidar measurements yield a quantity proportional to the mixing ratio, rather than the mixing ratio itself, calibration is necessary to obtain the factor of proportionality. The present method involves the use of calibration data from two sources: (1) absolute calibration data from in situ radiosonde measurements made during occasional campaigns and (2) partial calibration data obtained by use, on a regular schedule, of a lamp that emits in a known spectrum determined in laboratory calibration measurements. In this method, data from the first radiosonde campaign are used to calculate a campaign-averaged absolute lidar calibration factor (t(sub 1)) and the corresponding campaign-averaged ration (L(sub 1)) between lamp irradiances at the water-vapor and nitrogen wavelengths. Depending on the scenario considered, this ratio can be assumed to be either constant over a long time (L=L(sub 1)) or drifting slowly with time. The absolutely calibrated water-vapor mixing ratio (q) obtained from the ith routine off-campaign lidar measurement is given by q(sub 1)=P(sub 1)/t(sub 1)=LP(sub 1)/P(sup prime)(sub 1) where P(sub 1) is water-vapor/nitrogen measurement signal ration, t(sub 1) is the unknown and unneeded overall efficiency ratio of the lidar receiver during the ith routine off-campaign measurement run, and P(sup prime)(sub 1) is the water-vapor/nitrogen signal ratio obtained during the lamp run associated with the ith routine off-campaign measurement run. If L is assumed constant, then the lidar calibration is routinely obtained without the need for new radiosonde data. In this case, one uses L=L(sub 1) = P(sup prime)(sub 1)/t(sub 1), where P(sub 1)(sup prime) is the water-vapor/nitrogen signal ratio obtained during the lamp run associated with the first radiosonde campaign. If L is assumed to drift slowly, then it is necessary to postpone calculation of a(sub 1) until after a second radiosonde campaign. In this case, one obtains a new value, L(sub 2), from the second radiosonde campaign, and for the ith routine off-campaign measurement run, one uses an intermediate value of L obtained by simple linear time interpolation between L(sub 1) and L(sub 2).

Leblanc, Thierry↗
NASA NTRS1993

Comparison of methods for calibrating AVIRIS data to ground reflectance

We are comparing three basic methods of calibrating AVIRIS data to ground reflectance: (1) atmospheric radiative transfer models with the solar flux can be used to calibrate AVIRIS radiance data (Specific methods include the University of Colorado CSES ARP and ATREM algorithms); (2) Robert Green's modified MODTRAN and AVIRIS radiance model (This method is similar to 1 but differs in that the solar radiance is bypassed, so any errors in the solar flux are canceled, too); and (3) ground calibration using known sites in the AVIRIS scene. We are using 1992AVIRIS data over Cuprite, Nevada, and Blackhawk Island, Wisconsin, as our test scenes. Both these sites have extensive field measurements. The Cuprite site had a very clear atmosphere, thus path radiance was dominated by Rayleigh scattering with little or no flux beyond 1 micron. The Blackhawk site has more aerosols, with significant path radiance flux beyond 2 micron.

Clark, Roger N.↗
NASA NTRS2017

Data Reduction Pipeline for the CHARIS Integral-Field Spectrograph I: Detector Readout Calibration and Data Cube Extraction

We present the data reduction pipeline for CHARIS, a high-contrast integral-field spectrograph for the Subaru Telescope. The pipeline constructs a ramp from the raw reads using the measured nonlinear pixel response and reconstructs the data cube using one of three extraction algorithms: aperture photometry, optimal extraction, or chi-squared fitting. We measure and apply both a detector flatfield and a lenslet flatfield and reconstruct the wavelength- and position-dependent lenslet point-spread function (PSF) from images taken with a tunable laser. We use these measured PSFs to implement a chi-squared-based extraction of the data cube, with typical residuals of approximately 5 percent due to imperfect models of the under-sampled lenslet PSFs. The full two-dimensional residual of the chi-squared extraction allows us to model and remove correlated read noise, dramatically improving CHARIS's performance. The chi-squared extraction produces a data cube that has been deconvolved with the line-spread function and never performs any interpolations of either the data or the individual lenslet spectra. The extracted data cube also includes uncertainties for each spatial and spectral measurement. CHARIS's software is parallelized, written in Python and Cython, and freely available on github with a separate documentation page. Astrometric and spectrophotometric calibrations of the data cubes and PSF subtraction will be treated in a forthcoming paper.

Brandt, Timothy D.↗
NASA NTRS1994

Calibration of the COBE FIRAS instrument

The Far-Infrared Absolute Spectrophotometer (FIRAS) instrument on the Cosmic Background Explorer (COBE) satellite was designed to accurately measure the spectrum of the cosmic microwave background radiation (CMBR) in the frequency range 1-95/cm with an angular resolution of 7 deg. We describe the calibration of this instrument, including the method of obtaining calibration data, reduction of data, the instrument model, fitting the model to the calibration data, and application of the resulting model solution to sky observations. The instrument model fits well for calibration data that resemble sky condition. The method of propagating detector noise through the calibration process to yield a covariance matrix of the calibrated sky data is described. The final uncertainties are variable both in frequency and position, but for a typical calibrated sky 2.6 deg square pixel and 0.7/cm spectral element the random detector noise limit is of order of a few times 10(exp -7) ergs/sq cm/s/sr cm for 2-20/cm, and the difference between the sky and the best-fit cosmic blackbody can be measured with a gain uncertainty of less than 3%.

Fixsen, D. J.↗
NASA NTRS1981

IECM calibration and data reduction requirements

The induced environment contamination monitor (IECM) tape recorder format, as it relates to the ouput of meaningful data from the IECM instrument, is explained. Eight-bit words (or bytes) generate numbers that represent voltage levels of electronic detection probes for each experiment. This information is amalgamated by the IECM Data Acquisition and Control System (DACS). In some cases bits represent certain status situations concerning an experiment, such as whether a valve is opened or closed. Voltages are transformed into meaningful physical phenomena through equations of calibration. Data formats and plots are generated as requested for each IECM experimenter.

Wills, F. D.↗
NASA NTRS2011

High Gain Antenna Calibration on Three Spacecraft

This paper describes the alignment calibration of spacecraft High Gain Antennas (HGAs) for three missions. For two of the missions (the Lunar Reconnaissance Orbiter and the Solar Dynamics Observatory) the calibration was performed on orbit. For the third mission (the Global Precipitation Measurement core satellite) ground simulation of the calibration was performed in a calibration feasibility study. These three satellites provide a range of calibration situations-Lunar orbit transmitting to a ground antenna for LRO, geosynchronous orbit transmitting to a ground antenna fer SDO, and low Earth orbit transmitting to TDRS satellites for GPM The calibration results depend strongly on the quality and quantity of calibration data. With insufficient data the calibration Junction may give erroneous solutions. Manual intervention in the calibration allowed reliable parameters to be generated for all three missions.

Hashmall, Joseph A.↗
NASA NTRS2019

NASA’s Orbital Debris JAO/ES-MCAT Optical Telescope Facility on Ascension Island

The NASA Orbital Debris Program Office has a long-standing optical program begun over three and a half decades ago in 1984, designed to observe the Earth-orbiting environment with optical telescopes. Photometrically calibrated optical data provides a statistical sample for input to NASA models of the debris population for understanding the current and future debris environment around the Earth. Tracked objects and orbits allow for analysis of break-up events. Both known (correlated target in the SSN catalogue, or CT) and unknown (uncorrelated target, or UCT) objects are of interest to better understand how to protect current spacecraft and design more robust future operational satellites, and advise on how policies and practices can lead to protecting the environment itself for future generations. In 2015, a joint NASA JSC – Air Force Research Labs (AFRL) project culminated in the installation of the 1.3-meter Eugene Stansbery Meter Class Autonomous Telescope, ES-MCAT (a.k.a. MCAT) on Ascension Island. This DFM Engineering designed telescope provides nearly five-times greater light-collecting power than its predecessor, the 0.6-m MODEST telescope, and faster tracking capabilities by both the telescope and the 7-m ObservaDome. This allows for all orbital regimes to be easily within reach, ranging from low Earth to geosynchronous orbits. Extensive testing and commissioning activities of this custom system led to successfully reaching Initial Operational Capability in 2018, and the facility is currently on track to reach Full Operational Capability. The John Africano Observatory (JAO) comprises the primary 1.3-m ES-MCAT facility, the adjacent tower platform with a 0.4-m telescope, a sophisticated suite of weather instruments, and custom software by Euclid Research for autonomously running the entire system, including monitoring the weather and hardware, tasking all components, and collecting, processing, and analyzing the data. The mission of JAO and MCAT will be discussed, including survey and tracking tasking, a full discussion of data calibration, and both optics and weather-dependent performance.

Lederer, S. M.↗
NASA NTRSDate not supplied

Invited: uPSP Launch Vehicle Demonstration Test at NASA Ames Research Center

The Unsteady Pressure-Sensitive Paint (uPSP) Development Team outof NASA Ames Research Center (ARC) has spent the past five yearsimproving the systems and processes to advance the uPSP technology for production-level wind tunnel testing. Already considered turnkey for small-scale and research applications, development in acquisition, calibration, data transfer, and data processing were needed to be useful to customers testing at NASA wind tunnels. This development focused at ARC at the Unitary Plan Wind Tunnel (UPWT) 11-by 11-ft Transonic Wind Tunnel due to the large optical access of the test section and the NASA Advanced Supercomputer(NAS), also located at ARC. A Launch Vehicle Demonstration Test (LVDT) at the UPWT represents a milestone of this initial phase of development where several new improvements were demonstrated in a production wind tunnel environment for the first time. LVDT was conducted in April 2024 and used a 4% forebody Space Launch System (SLS) Block 1B model as the test article. Both a crew and cargo configuration were tested, with varying Mach numbers, pressures, model positions, and camera magnifications. This paper summarizes the details of the test and is part of a collection with four additional papers that provide greater detail on: high-speed lifetime methodology, spectral proper orthogonal decomposition analysis, quality of high-resolution data compared to Corcos model, and data quality, calibration, and uncertainty.

SLS↗
NASA NTRSDate not supplied

Analysis and Evaluation of NASA's MK40B Six-Component TASK Balance

The paper discusses the physical characteristics NASA's MK40B TASK balance. It is NASA's newest TASK balance that was optimized for the testing of wind tunnel models in the NASA Ames 11-ft Transonic Wind Tunnel. The calibration data of the MK40B balance was obtained in Calspan's Automatic Balance Calibration System. A machine load schedule design was used for the calibration that was developed at the NASA Ames Balance Calibration Laboratory. The calibration data was analyzed using both the Non-Iterative Method and the Iterative Method. Then, the chosen regression models of the calibration data were assessed and compared using the percent contribution, the variance inflation factor, and the calibration load residuals. Interactions of single-component loads were also quantified. In addition, the bi-directional characteristics of the six gage outputs of the balance were assessed by applying a semi-empirical test that was developed at the Ames Balance Calibration Laboratory. It was concluded that four of the six gages of the balance have bi-directional characteristics. Finally, the accuracy of the load prediction equations of the balance was tested by applying the equations to independent check load data that was obtained at the NASA Ames Balance Calibration Laboratory. The standard deviation of the check load residuals met expectations. The check load data also confirmed that the interactions of the forward and aft normal forces on the axial force gage output are very small. - This paper is to be presented in the invited special session titled: "65 Years of TASK Wind Tunnel Balances - Past, Present, and Future".

wind tunnel balance↗
NASA NTRS1998

OARE STS-87 (USMP-4)

The report is organized into sections representing the phases of work performed in analyzing the STS-87 (USMP-4) results. Section 1 briefly outlines the OARE system features, coordinates, and measurement parameters. Section 2 describes the results from STS-87. The mission description, data calibration, and representative data obtained on STS-87 are presented. Finally, Section 3 presents a discussion of accuracy achieved and achievable with OARE. Appendix A discusses the calibration and data processing methodology in detail.

Rice, James E.↗
NASA NTRS2012

Influence of Primary Gage Sensitivities on the Convergence of Balance Load Iterations

The connection between the convergence of wind tunnel balance load iterations and the existence of the primary gage sensitivities of a balance is discussed. First, basic elements of two load iteration equations that the iterative method uses in combination with results of a calibration data analysis for the prediction of balance loads are reviewed. Then, the connection between the primary gage sensitivities, the load format, the gage output format, and the convergence characteristics of the load iteration equation choices is investigated. A new criterion is also introduced that may be used to objectively determine if the primary gage sensitivity of a balance gage exists. Then, it is shown that both load iteration equations will converge as long as a suitable regression model is used for the analysis of the balance calibration data, the combined influence of non linear terms of the regression model is very small, and the primary gage sensitivities of all balance gages exist. The last requirement is fulfilled, e.g., if force balance calibration data is analyzed in force balance format. Finally, it is demonstrated that only one of the two load iteration equation choices, i.e., the iteration equation used by the primary load iteration method, converges if one or more primary gage sensitivities are missing. This situation may occur, e.g., if force balance calibration data is analyzed in direct read format using the original gage outputs. Data from the calibration of a six component force balance is used to illustrate the connection between the convergence of the load iteration equation choices and the existence of the primary gage sensitivities.

Ulbrich, Norbert Manfred↗
NASA NTRS1998

OARE STS-94 (MSL-1R)

The report is organized into sections representing the phases of work performed in analyzing the STS-94 (MSL-IR) results. STS-94 (MSL1R) is a reflight of the STS-83 (MSL-1) mission which was terminated early because of a fuel cell problem. Section I briefly outlines the OARE system features, coordinates, and measurement parameters. Section 2 describes the results from STS-94. The mission description, data calibration, and representative data obtained on STS-94 are presented. Also, the anomalous performance of OARE on STS-94 is discussed. Finally, Section 3 presents a discussion of accuracy achieved and achievable with OARE. Appendix A discuss the calibration and data processing methodology in detail.

Rice, James E.↗
NASA NTRS2018

Regression Analysis and Calibration Recommendations for the Characterization of Balance Temperature Effects

Analysis and use of temperature-dependent wind tunnel strain-gage balance calibration data are discussed in the paper. First, three different methods are presented and compared that may be used to process temperature-dependent strain-gage balance data. The first method uses an extended set of independent variables in order to process the data and predict balance loads. The second method applies an extended load iteration equation during the analysis of balance calibration data. The third method uses temperature-dependent sensitivities for the data analysis. Physical interpretations of the most important temperature-dependent regression model terms are provided that relate temperature compensation imperfections and the temperature-dependent nature of the gage factor to sets of regression model terms. Finally, balance calibration recommendations are listed so that temperature-dependent calibration data can be obtained and successfully processed using the reviewed analysis methods.

wind tunnel test↗
NASA NTRS2014

Detection of Unexpected High Correlations between Balance Calibration Loads and Load Residuals

An algorithm was developed for the assessment of strain-gage balance calibration data that makes it possible to systematically investigate potential sources of unexpected high correlations between calibration load residuals and applied calibration loads. The algorithm investigates correlations on a load series by load series basis. The linear correlation coefficient is used to quantify the correlations. It is computed for all possible pairs of calibration load residuals and applied calibration loads that can be constructed for the given balance calibration data set. An unexpected high correlation between a load residual and a load is detected if three conditions are met: (i) the absolute value of the correlation coefficient of a residual/load pair exceeds 0.95; (ii) the maximum of the absolute values of the residuals of a load series exceeds 0.25 % of the load capacity; (iii) the load component of the load series is intentionally applied. Data from a baseline calibration of a six-component force balance is used to illustrate the application of the detection algorithm to a real-world data set. This analysis also showed that the detection algorithm can identify load alignment errors as long as repeat load series are contained in the balance calibration data set that do not suffer from load alignment problems.

strain-gage balance↗