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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Progressive Failure Analysis Correlation with Notched Composite Laminate Test Data

Testing of small and intermediate laminate panels with notches was performed and Progressive Failure Analyses (PFA) models were developed for aid in test planning and for correlation with the test data. Two progressive damage failure models were included in the study: the commercially available Abaqus built-in damage model and COmplete STress Reduction (COSTR) damage model developed at NASA Langley Research Center (LaRC). The finite elements models used for the analysis were developed using shell elements. The pre-test PFA results obtained from the two damage models were compared to test data. Then a post-test PFA with updated material properties based on additional available material property data was executed with the COSTR damage model and compared to the test data. The panels tested in compression all exhibited less scatter in the failure load with self-similar failure behavior. The panels tested in tension exhibited more scatter in the failure load with a failure mode that included delamination of plies. However, a self-similar crack path was also observed as the overall failure mode for all panels tested. When comparing test data to the pre-test analysis results, both damage models with shell element models were considered adequate for predicting the behavior and failure load of both the small and intermediate panels when tested in compression. However, for the tension load cases, larger differences were observed between the test and analysis results. When comparing test data to the post-test analysis performed using the COSTR damage model, better correlation was observed. However, large discrepancies were still observed for the tension load cases. Consequently, a higher fidelity finite element model including solid elements for sub-laminates and the modeling of cohesive layers between sub-laminate layers is recommended for tension loading of notched composite laminates.

Damage Model,Progressive Failure Analysis,Open Hol↗
DOE OSTI2018

Azura WEC 1/15th Wave Tank Test Data

Test data from the 1/15th Wave Tank Tests of the Azura performed in 2017/2018 to validate the power performance and survivability of the Azura Design planned for deployment at the US Navy's Wave Energy Test Site. Raw and processed data included, along with test plan, test report, and summary data in the content model: "WEC Lab Testing content Model".

16 TIDAL AND WAVE POWER↗
NASA NTRS2015

Comparison of the NASA Common Research Model European Transonic Wind Tunnel Test Data to NASA Test Data

Experimental aerodynamic investigations of the NASA Common Research Model have been conducted in the NASA Langley National Transonic Facility, the NASA Ames 11-ft wind tunnel, and the European Transonic Wind Tunnel. In the NASA Ames 11-ft wind tunnel, data have been obtained at only a chord Reynolds number of 5 million for a wing/body/tail = 0 degree incidence configuration. Data have been obtained at chord Reynolds numbers of 5, 19.8 and 30 million for the same configuration in the National Transonic Facility and in the European Transonic Facility. Force and moment, surface pressure, wing bending and twist, and surface flow visualization data were obtained in all three facilities but only the force and moment, surface pressure and wing bending and twist data are presented herein.

Rivers, Melissa B.↗
NASA NTRS2015

Comparison of the NASA Common Research Model European Transonic Wind Tunnel Test Data to NASA Test Data

Experimental aerodynamic investigations of the NASA Common Research Model have been conducted in the NASA Langley National Transonic Facility, the NASA Ames 11-ft wind tunnel, and the European Transonic Wind Tunnel. In the NASA Ames 11-ft wind tunnel, data have been obtained at only a chord Reynolds number of 5 million for a wing/body/tail = 0 degree incidence configuration. Data have been obtained at chord Reynolds numbers of 5, 19.8 and 30 million for the same configuration in the National Transonic Facility and in the European Transonic Facility. Force and moment, surface pressure, wing bending and twist, and surface flow visualization data were obtained in all three facilities but only the force and moment and surface pressure data are presented herein.

Rivers, Melissa↗
NASA NTRS2010

Results and Interpretation of the WFRD ELS Distillation Down-Select Test Data

Testing of the Wiped-film Rotating-disk (WFRD) evaporator was conducted in support of the Exploration Life Support Distillation Down-Select Test. The WFRD was constructed at NASA Ames Research Center (ARC) and tested at NASA Marshall Space Flight Center (MSFC). The WFRD was delivered to MSFC in September 2009, and testing of solution #1 and solution #2 immediately following. Solution #1 was composed of humidity condensate and urine, including flush water and pretreatment chemicals. Solution #2 was composed of hygiene water, humidity condensate, and urine, including flush water and pretreatment chemicals. During the testing, the operational parameters of the WFRD were recorded and samples of the feed, brine, and product were collected and analyzed. The steady-state results of processing 414L of feed solution #1 and 1283L of feed solution #2 demonstrated that running the WFRD at a brine temperature of 50 C gave an average production rate of 16.7 L/hr. The specific energy consumption was 80.5W-hr/L. Data Analysis shows that the water recovery rates were 94% and 91%, respectively. The total mass of the WFRD as delivered to MSFC was 300 Kg. The volume of the tests stand rack was 1m width x 0.7m depth x 1.9m height or 1.5 cu m of which about half of the total volume is occupied by equipment. Chemical analysis of the distillate showed an average TOC of 20ppm, a pH of 3.5, and a conductivity of 98 mho/cm. The conductivity of the distillate, compared to the feed, decreased by 98.9%., the total ion concentration decreased by 99.6%, the total organics decreased 98.6%, and the metals were at or below detection limits

Delzeit, Lance Dean↗
NASA NTRS2021

Determining Aircraft Moments of Inertia from Flight Test Data

Flight test maneuvers and dynamic modeling techniques were developed for determining aircraft moments of inertia from flight test data. Full nonlinear rigid-body rotational equations of motion were used in the analysis, with aerodynamic moment dependencies modeled by linear expansions in the aircraft states and controls. Aerodynamic parameters were estimated simultaneously with inertia parameters using equation-error modeling applied to flight test data from maneuvers designed specifically for this problem. The approach was demonstrated using a nonlinear F 16 simulation, then applied to a remotely-piloted subscale aircraft flight test. Errors in the aircraft moment of inertia parameters determined from simulated F-16 flight test data were less than 6 percent compared to the true values in the simulation. Flight test results for the subscale aircraft were within 6 percent of ground-test values obtained using the same aircraft.

Parameter estimation↗
NASA NTRSDate not supplied

Determining Aircraft Moments of Inertia from Flight Test Data

Flight test maneuver design and dynamic modeling techniques were developed for determining aircraft moments of inertia from flight test data. Full nonlinear rigid-body rotational equations of motion were used in the analysis, with aerodynamic moment dependencies modeled by linear expansions in the aircraft states and controls. Aerodynamic parameters were estimated simultaneously with inertia parameters using equation-error modeling applied to flight test data from maneuvers designed specifically for this problem. The approach was demonstrated using a nonlinear F-16 simulation, then applied to a remotely-piloted subscale aircraft flight test. Errors in the aircraft moment of inertia parameters determined from simulated F-16 flight test maneuver data were less than 6 percent compared to the true values in the simulation. Flight test results for the subscale aircraft were within 6 percent of ground-test values obtained using the same aircraft.

moments of inertia↗
NASA NTRS2003

Reference Material Kydex(registered trademark)-100 Test Data Message for Flammability Testing

The Marshall Space Flight Center (MSFC) Materials and Processes Technical Information System (MAPTIS) database contains, as an engineering resource, a large amount of material test data carefully obtained and recorded over a number of years. Flammability test data obtained using Test 1 of NASA-STD-6001 is a significant component of this database. NASA-STD-6001 recommends that Kydex 100 be used as a reference material for testing certification and for comparison between test facilities in the round-robin certification testing that occurs every 2 years. As a result of these regular activities, a large volume of test data is recorded within the MAPTIS database. The activity described in this technical report was undertaken to mine the database, recover flammability (Test 1) Kydex 100 data, and review the lessons learned from analysis of these data.

Engel, Carl D.↗
NASA NTRSDate not supplied

Determining Aircraft Moments of Inertia from Flight Test Data

Flight test maneuver design and dynamic modeling techniques were developed for determining aircraft moments of inertia from flight test data. Full nonlinear rigid-body rotational equations of motion were used in the analysis, with aerodynamic moment dependencies modeled by linear expansions in the aircraft states and controls. Aerodynamic parameters were estimated simultaneously with inertia parameters using equation-error modeling applied to flight test data from maneuvers designed specifically for this problem. The approach was demonstrated using a nonlinear F 16 simulation, then applied to a remotely piloted subscale aircraft flight test. Errors in the aircraft moment of inertia parameters determined from simulated F-16 flight test maneuver data were less than 6 percent compared to the true values in the simulation. Flight test results for the subscale aircraft were within 6 percent of ground-test values obtained using the same aircraft.

Aircraft Moments of Inertia↗
DOE OSTI2020

Laboratory Drilling Test Data of PDC bits with Sierra White Granite Rock

This file contains unprocessed drilling data tests on Sierra White Granite (SWG) using two new PDC bits. The tests were conducted at Sandia National Laboratories (SNL) in the Hard Rock Drilling Facility (HRDF). The collected data includes ROP data at rotational speeds of 80, 120, and 160 RPMs, with incremental weight on bit (WOB) up to 5100 lbs. The diameter of the 4-bladed and 5-bladed PDC bits was 3 3/4" and supplied by National Oilwell Varco (NOV). Five tests modes were conducted for both bits, which are as follow: (1) Rigid configuration, with no vibration compliance (2) Flywheel configuration (3) Torsional compliance configuration (4) Axial Compliance configuration (5) Combined Axial and Torsional compliance configuration Note: the WOB and torque in the drilling data should be calibrated by zeroing the WOB and torque when the drill bit tags the rock sample.

15 GEOTHERMAL ENERGY↗
NASA NTRS1993

A function approximation approach to anomaly detection in propulsion system test data

Ground test data from propulsion systems such as the Space Shuttle Main Engine (SSME) can be automatically screened for anomalies by a neural network. The neural network screens data after being trained with nominal data only. Given the values of 14 measurements reflecting external influences on the SSME at a given time, the neural network predicts the expected nominal value of a desired engine parameter at that time. We compared the ability of three different function-approximation techniques to perform this nominal value prediction: a novel neural network architecture based on Gaussian bar basis functions, a conventional back propagation neural network, and linear regression. These three techniques were tested with real data from six SSME ground tests containing two anomalies. The basis function network trained more rapidly than back propagation. It yielded nominal predictions with, a tight enough confidence interval to distinguish anomalous deviations from the nominal fluctuations in an engine parameter. Since the function-approximation approach requires nominal training data only, it is capable of detecting unknown classes of anomalies for which training data is not available.

Whitehead, Bruce A.↗
NASA NTRS1976

Correlation of AH-1G helicopter flight vibration data and tailboom static test data with NASTRAN analytical results

Level flight airframe vibration at main rotor excitation frequencies was calculated. A NASTRAN tailboom analysis was compared with test data for evaluation of methods used to determine effective skin in a semimonocoque sheet-stringer structure. The flight vibration correlation involved comparison of level flight vibration for two helicopter configurations: clean wing, at light gross weight and wing stores at heavy gross weight. In the tailboom correlation, deflections and internal loads were compared using static test data and a NASTRAN analysis. An iterative procedure was used to determine the amount of effective skin of buckled panels under compression load.

Cronkhite, J. D.↗
DOE OSTI2013

Uncertainty of Personnel Dosimetry Systems Using Performance Data Testing

While accreditation programs require a study of uncertainty for each dosimeter model for which accreditation is sought (Torres 2005, DOE 1986), no one method is specifically recommended by accrediting bodies. Typically, the methods used to determine uncertainty have included the partial differentiation method described in the U.S. Guide to Uncertainty in Measurements (ANSI 1997) or the use of Monte Carlo techniques and probability distribution functions to generate simulated dose results. Each of these techniques has particular strengths and should be employed when the areas of uncertainty are required to be understood in detail. However, the uncertainty of dosimeter results can also be determined using a Model II One-Way Analysis of Variance technique and accreditation testing data (Cummings and Flood 2010). The strengths of this method are (1) the method is based in robust and well-accepted statistical techniques; (2) the method is straightforward and easily applied to dose algorithms, which are so complex as to render the use of other methods unwieldy; and (3) the method uses dosimeter performance data obtained during accreditation. The general method and criteria of inclusion of radiation types and fields are discussed.

Cummings, Frederick (Rick) M.↗
NASA NTRS2011

Payload and Components Real-Time Automated Test System (PACRATS), Data Acquisition of Leak Rate and Pressure Data Test Procedure

The purpose of this activity is to provide the Mechanical Components Test Facility (MCTF) with the capability to obtain electronic leak test and proof pressure data, Payload and Components Real-time Automated Test System (PACRATS) data acquisition software will be utilized to display real-time data. It will record leak rates and pressure/vacuum level(s) simultaneously. This added functionality will provide electronic leak test and pressure data at specified sampling frequencies. Electronically stored data will provide ES61 with increased data security, analysis, and accuracy. The tasks performed in this procedure are to verify PACRATS only, and are not intended to provide verifications for MCTF equipment.

Rinehart, Maegan L.↗
DOE OSTI2012

Three-Dimensional Bayesian Geostatistical Aquifer Characterization at the Hanford 300 Area using Tracer Test Data

Tracer testing under natural or forced gradient flow holds the potential to provide useful information for characterizing subsurface properties, through monitoring, modeling and interpretation of the tracer plume migration in an aquifer. Non-reactive tracer experiments were conducted at the Hanford 300 Area, along with constant-rate injection tests and electromagnetic borehole flowmeter (EBF) profiling. A Bayesian data assimilation technique, the method of anchored distributions (MAD) [Rubin et al., 2010], was applied to assimilate the experimental tracer test data with the other types of data and to infer the three-dimensional heterogeneous structure of the hydraulic conductivity in the saturated zone of the Hanford formation. In this study, the Bayesian prior information on the underlying random hydraulic conductivity field was obtained from previous field characterization efforts using the constant-rate injection tests and the EBF data. The posterior distribution of the conductivity field was obtained by further conditioning the field on the temporal moments of tracer breakthrough curves at various observation wells. MAD was implemented with the massively-parallel three-dimensional flow and transport code PFLOTRAN to cope with the highly transient flow boundary conditions at the site and to meet the computational demands of MAD. A synthetic study proved that the proposed method could effectively invert tracer test data to capture the essential spatial heterogeneity of the three-dimensional hydraulic conductivity field. Application of MAD to actual field data shows that the hydrogeological model, when conditioned on the tracer test data, can reproduce the tracer transport behavior better than the field characterized without the tracer test data. This study successfully demonstrates that MAD can sequentially assimilate multi-scale multi-type field data through a consistent Bayesian framework.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

Modeling methodology for MLS range navigation system errors using flight test data

Flight test data was used to develop a methodology for modeling MLS range navigation system errors. The data used corresponded to the constant velocity and glideslope approach segment of a helicopter landing trajectory. The MLS range measurement was assumed to consist of low frequency and random high frequency components. The random high frequency component was extracted from the MLS range measurements. This was done by appropriate filtering of the range residual generated from a linearization of the range profile for the final approach segment. This range navigation system error was then modeled as an autoregressive moving average (ARMA) process. Maximum likelihood techniques were used to identify the parameters of the ARMA process.

Karmali, M. S.↗