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At least 19 records

Langley Wind Tunnel Data Quality Assurance-Check Standard Results

A framework for statistical evaluation, control and improvement of wind funnel measurement processes is presented The methodology is adapted from elements of the Measurement Assurance Plans developed by the National Bureau of Standards (now the National Institute of Standards and Technology) for standards and calibration laboratories. The present methodology is based on the notions of statistical quality control (SQC) together with check standard testing and a small number of customer repeat-run sets. The results of check standard and customer repeat-run -sets are analyzed using the statistical control chart-methods of Walter A. Shewhart long familiar to the SQC community. Control chart results are presented for. various measurement processes in five facilities at Langley Research Center. The processes include test section calibration, force and moment measurements with a balance, and instrument calibration.

Hemsch, Michael J.↗

Use of the Ames Check Standard Model for the Validation of Wall Interference Corrections

The new check standard model of the NASA Ames 11-ft Transonic Wind Tunnel was chosen for a future validation of the facility's wall interference correction system. The chosen validation approach takes advantage of the fact that test conditions experienced by a large model in the slotted part of the tunnel's test section will change significantly if a subset of the slots is temporarily sealed. Therefore, the model's aerodynamic coefficients have to be recorded, corrected, and compared for two different test section configurations in order to perform the validation. Test section configurations with highly accurate Mach number and dynamic pressure calibrations were selected for the validation. First, the model is tested with all test section slots in open configuration while keeping the model's center of rotation on the tunnel centerline. In the next step, slots on the test section floor are sealed and the model is moved to a new center of rotation that is 33 inches below the tunnel centerline. Then, the original angle of attack sweeps are repeated. Afterwards, wall interference corrections are applied to both test data sets and response surface models of the resulting aerodynamic coefficients in interference-free flow are generated. Finally, the response surface models are used to predict the aerodynamic coefficients for a family of angles of attack while keeping dynamic pressure, Mach number, and Reynolds number constant. The validation is considered successful if the corrected aerodynamic coefficients obtained from the related response surface model pair show good agreement. Residual differences between the corrected coefficient sets will be analyzed as well because they are an indicator of the overall accuracy of the facility's wall interference correction process.

wind tunnel wall interferenc↗

LAVA CFD Analysis of the Check Standard Model within the Langley Unitary Plan Wind Tunnel

This paper is part of an e ort to assess the current capability of Computational Fluid Dynamics (CFD) to accurately predict the experimental forces and base pressure on the Check Standard Model (CSM). The wind tunnel data for this model is gathered during annual monitoring of test-to-test repeatability for the Langley Unitary Plan Wind Tunnel (LUPWT). A blind database designed to help quantify the uncertainty of simulating LUPWT for the CSM repeatability tests was run using the Launch, Ascent and Vehicle Aerodynamics (LAVA) CFD code. The Reynolds-averaged Navier-Stokes equations were used for the predictions presented in the paper. Prior to running the database, best practices were established both for simulating and characterizing the ow of the high-speed legof the LUPWT where the test took place, as well the CSM when inserted within its test section.

ARMD↗

Check-Standard Testing Across Multiple Transonic Wind Tunnels with the Modern Design of Experiments

This paper reports the result of an analysis of wind tunnel data acquired in support of the Facility Analysis Verification & Operational Reliability (FAVOR) project. The analysis uses methods referred to collectively at Langley Research Center as the Modern Design of Experiments (MDOE). These methods quantify the total variance in a sample of wind tunnel data and partition it into explained and unexplained components. The unexplained component is further partitioned in random and systematic components. This analysis was performed on data acquired in similar wind tunnel tests executed in four different U.S. transonic facilities. The measurement environment of each facility was quantified and compared.

Deloach, Richard↗

NTF Test 201 - FAVOR - F-111 Check Standard

Presentation of test process at the National Transonic Facility, Nasa Langley Research Center. The test showcased was part of a cooperative test effort between ARC, LaRC, GRC, and AEDC collecting aerodynamic and process data on the modified F-111 FAVOR model.

Michael D Treece↗

Calibration of the 11x11-Foot Transonic Wind Tunnel at NASA Ames Research Center

A static pipe calibration characterizing the streamwise static pressure distribution was conducted at the 11-By 11-Foot Transonic Wind Tunnel at NASA Ames Research Center. This data is used to determine the local Mach number within the test section and evaluate buoyancy corrections to axial force measurements. The 60 foot long, 6 inch diameter pipe contained 444 static pressure taps spanning the test section and nozzle regions of the tunnel. The forward end of the pipe extends into the settling chamber and is held by four cables mounting to the tunnel shell, and the aft end is fixed on the institutional model support system. A hydraulic cylinder at the aft end of the pipe provides tension on the system to reduce vibration and to keep the pipe as level as possible throughout the test section. The previous calibration was improved upon by using pressure scanners with greater accuracy, ensuring a uniform pressure tube length for each tap to control pneumatic lag, optically tracking any streamwise movement of the pipe, and more tightly controlling the tunnel condition set points. Typically this calibration is conducted with the pipe on tunnel centerline and 33 inches below centerline for sting-mounted models and semi-span (i.e. floor-mounted) models respectively, however schedule demands permitted only the centerline calibration. The semi-span calibration is planned to be completed in the summer of 2020. Immediately following the static pipe calibration, a shorter, 9 foot static pipe used as the calibration check standard was installed to obtain its first post-calibration pressure dataset. This short static pipe consists of 148 static pressure taps distributed along the pipe section and one total pressure tap at the end of an ogive nose. Performing the calibration test and its check standard back-to-back allows this dataset to establish a reliable baseline for future calibration check standard testing. Over time the use of a calibration check standard offers the ability to assess the stability of the calibration through statistical process control in an efficient and costeffective manner, thereby potentially increasing the time required between full tunnel calibrations.

White, Bethany↗

GC/MS Method Development for Separating Lunar Volatile Ice Simulant Headspace Gases

Various investigators propose the lunar surface contains widely distributed volatiles, especially water- like species, i.e. OH and H2O. Surface volatiles are theorized to exist as a hydrated regolith layer, concentrated in extremely cold polar permanently shadowed regions (PSR), and/or solar wind implantation reservoirs in lunar glasses. The proposed sources of lunar surface volatiles range from cometary impacts, solar wind, or a supply present during moon formation. Future Artemis missions aim to collect and return the samples containing volatiles collected near lunar polar craters or PSRs. We, as advanced curation scientists, are responsible for developing techniques and methodologies for preserving returned sample integrity as much as possible. Pristine volatile-bearing samples are invaluable to the scientific community seeking to unravel the history of the solar system. Realistically, a sample will experience alteration during collection, transportation back to earth, and storage. The Planetary Exploration and Astromaterials Research Lab (PEARL) seeks to understand temperature and pressure effects on high-fidelity volatile-containing regolith simulants, the foundation for the future of cold curation. This abstract outlines the separation, identification, and quantification of headspace gases over volatile ice feed stock material using gas chromatography/mass spectrometry (GC/MS). Preliminary objectives concentrated on sample handling, reproducibility, and understanding the elution characteristics for each analyte. Initial GC/MS method development experiments utilized diluted static headspace sample preparation. Diluted samples were used because sampling headspace gases directly from a vial containing liquid analyte resulted in overloading of the column and detector. Overloading is evident based on chromatogram peak shapes and instrument contamination, or carry over, between experiments. A mixture of three alcohols were used for a majority of the sample handling and reproducibility studies. Reproducibility was tested via multiple users, calibration curves, and check standards. Stock solutions of condensed lunar volatile analytes included methanol, ammonia in methanol, hydrogen sulfide in water, and an equal volume mixture of methanol, ethanol, and isopropanol. Current samples use room air as the headspace sample matrix, however future experiments will incorporate an inert purge gas, such as argon or nitrogen. Three mL of each analyte solution were capped in separate 20 mL crimp top GC vials. Dilutions were carried out by removing an aliquot of headspace gases with a calibrated 1 mL gastight syringe and immediately transferring to a 20 mL capped crimp top vial. The GC/MS is a Thermo Fisher Trace 1310/ISQ 7000 with a TriPlus RSH autosampler and split/splitless injector module. The experiments outlined in this abstract use the following hardware: a 2.5 mL gastight headspace syringe tool, 1 mm ID x 78.5 mm length ultra-inert straight injection liner, and a TG-BondQ 30 m × 0.32 mm × 10 μm column. Various parameters, such as hardware selection and the temperature, pressure, and split ratio set points, continue to evolve as the overall experiment is refined. Diluted headspace chromatograms were collected for the individual stock solutions. Retention times, peak shapes, and mass spectra were evaluated and added to the data processing method for each molecule of interest. Figure 1 shows the total ion chromatograms for the three major lunar volatile simulant stock solutions: methanol, 7 N ammonia in methanol, and 0.4% hydrogen sulfide in water. Tailing peak shapes for ammonia (2.98 min rt) and water (4.06 min rt) indicate the molecules are not properly eluting from the selected column with the current separation method. Additionally, hydrogen sulfide and ammonia have overlapping peak windows, which could impact quantification. Ongoing experiments aim to address the peak shape and overlapping via the separation method and hardware selection. Sample preparation reproducibility experiments used stock solution containing equal volumes of a non- interactive mixture of methanol, ethanol, and isopropanol. Mass spectrum ion traces were used to identify and quantify all three alcohols. Peaks were automatically detected, identified, and integrated through the mass spectra detection and processing parameters. Calibration response curves and check standards were used to evaluate the validity of the sample preparation procedure. Figure 2 shows the methanol chromatogram peak area versus total headspace dilution volume transferred from the alcohol mixture vial. The calibration response curves and check standards validate sample preparation procedure. Continuing data analysis efforts are working towards correlating the peak area and instrument response factor to the headspace analyte concentration and condensed phase composition. Static headspace gas chromatography theory relies on Dalton’s law, Raoult’s law, Henry’s Law, and the Kolb and Ettre equation to associate peak area to the analyte composition in a non-ideal solution. Equation 1 is a simplified expression derived from the aforementioned theories. Future experiments involve liquid injections of the individual stock solutions, liquid and headspace analysis of various stock solution combinations, and the addition of regolith simulants to the mixtures. Temperature is another variable expected to affect reaction rates and will be explored.

Cecilia L. Amick↗

Development of an Uncertainty Model for the National Transonic Facility

This paper introduces an uncertainty model being developed for the National Transonic Facility (NTF). The model uses a Monte Carlo technique to propagate standard uncertainties of measured values through the NTF data reduction equations to calculate the combined uncertainties of the key aerodynamic force and moment coefficients and freestream properties. The uncertainty propagation approach to assessing data variability is compared with ongoing data quality assessment activities at the NTF, notably check standard testing using statistical process control (SPC) techniques. It is shown that the two approaches are complementary and both are necessary tools for data quality assessment and improvement activities. The SPC approach is the final arbiter of variability in a facility. Its result encompasses variation due to people, processes, test equipment, and test article. The uncertainty propagation approach is limited mainly to the data reduction process. However, it is useful because it helps to assess the causes of variability seen in the data and consequently provides a basis for improvement. For example, it is shown that Mach number random uncertainty is dominated by static pressure variation over most of the dynamic pressure range tested. However, the random uncertainty in the drag coefficient is generally dominated by axial and normal force uncertainty with much less contribution from freestream conditions.

Walter, Joel A.↗

A real-time digital computer program for the simulation of a single rotor helicopter

A computer program was developed for the study of a single-rotor helicopter on the Langley Research Center real-time digital simulation system. Descriptions of helicopter equations and data, program subroutines (including flow charts and listings), real-time simulation system routines, and program operation are included. Program usage is illustrated by standard check cases and a representative flight case.

Houck, J. A.↗

Measurement of Force Balance Repeatability and Reproducibility in the NTF

A recently published statistical approach for measuring and evaluating wind tunnel force balance repeatability and reproducibility is applied to three check standard tests in the National Transonic Facility at NASA Langley Research Center. Two different airframe models and force balances were used. The short-term repeatability and within-test reproducibility are separately estimated and correlations with tunnel parameters are carried out. Conjectures are presented for the development of scaling laws for predicting the repeatability and reproducibility of other force balance tests in the tunnel.

Hemsch, M. J.↗

Analysis of Flow Angularity Repeatability Tests in the NTF

An extensive data base of flow angularity repeatability measurements from four NTF check standard model tests is analyzed for statistical consistency and to characterize the results for prediction of angle-of-attack uncertainty for customer tests. A procedure for quality assurance for flow angularity measurements during customer tests is also presented. The efficacy of the procedure is tested using results from a customer test.

Hemsch, Michael J.↗

A Practical Methodology for Quantifying Random and Systematic Components of Unexplained Variance in a Wind Tunnel

This paper documents a check standard wind tunnel test conducted in the Langley 0.3-Meter Transonic Cryogenic Tunnel (0.3M TCT) that was designed and analyzed using the Modern Design of Experiments (MDOE). The test designed to partition the unexplained variance of typical wind tunnel data samples into two constituent components, one attributable to ordinary random error, and one attributable to systematic error induced by covariate effects. Covariate effects in wind tunnel testing are discussed, with examples. The impact of systematic (non-random) unexplained variance on the statistical independence of sequential measurements is reviewed. The corresponding correlation among experimental errors is discussed, as is the impact of such correlation on experimental results generally. The specific experiment documented herein was organized as a formal test for the presence of unexplained variance in representative samples of wind tunnel data, in order to quantify the frequency with which such systematic error was detected, and its magnitude relative to ordinary random error. Levels of systematic and random error reported here are representative of those quantified in other facilities, as cited in the references.

Deloach, Richard↗

Repeatability Modeling for Wind-Tunnel Measurements: Results for Three Langley Facilities

Data from extensive check standard tests of seven measurement processes in three NASA Langley Research Center wind tunnels are statistically analyzed to test a simple model previously presented in 2000 for characterizing short-term, within-test and across-test repeatability. The analysis is intended to support process improvement and development of uncertainty models for the measurements. The analysis suggests that the repeatability can be estimated adequately as a function of only the test section dynamic pressure over a two-orders- of-magnitude dynamic pressure range. As expected for low instrument loading, short-term coefficient repeatability is determined by the resolution of the instrument alone (air off). However, as previously pointed out, for the highest dynamic pressure range the coefficient repeatability appears to be independent of dynamic pressure, thus presenting a lower floor for the standard deviation for all three time frames. The simple repeatability model is shown to be adequate for all of the cases presented and for all three time frames.

Hemsch, Michael J.↗

Influence of Strain-Gage Balance Characteristics on the Precision Error of the Drag Coefficient

Improvements and results of a new method are presented that computes a pre-test estimate of the precision error of the drag coefficient of a wind tunnel model. The error estimate is defined as the part of the drag coefficient's precision error that is primarily associated with the precision error of the angle of attack measurement and physical characteristics of the chosen strain-gage balance. The method indirectly describes the precision error of the angle of attack measurement by using an assumed balance gage output variation of one microV/V. The physical characteristics of the balance, on the other hand, are described by partial derivatives of the axial and normal forces with respect to the strain-gage outputs. These derivatives can directly be obtained from the data reduction matrix of the balance. The precision error estimate itself is calculated by applying a simple explicit equation that uses the model reference area, the dynamic pressure, the angle of attack, the coefficients of the linear terms of the data reduction matrix, and the electrical output variation of one microvolt per volt as input. Precision errors at constant angle of attack may be visualized as contour plots by plotting them, for example, versus the Mach number and the total pressure. Characteristics of NASA's MC60E balance are used in combination with the reference area of a generic wind tunnel model in order to demonstrate that error estimates are independent of both the balance load format and the units chosen for the description of balance loads, model reference area, and the dynamic pressure. Finally, experimental data from a wind tunnel test of the Ames Check Standard Model in the NASA Ames 11-foot Transonic Wind Tunnel illustrates the application of the method to real-world test data.

precision error↗