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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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Background (what): SI (International System of Units)-traceable Microwave Radiometer calibration; Motivation (why): NWP (Numerical Weather Prediction), FCDR (Fundamental Climate Data Record); Technology (how): NIST (National Inst. of Standards and Technology) blackbody target for ; Standards: Status & Future Plans. Development of a National Standard for Microwave Brightness Temperature (TB) at NIST.
Machine-oriented structural engineering firm TERA, Inc. is engaged in a project to evaluate the reliability of offshore pile driving prediction methods to eventually predict the best pile driving technique for each new offshore oil platform. Phase I Pile driving records of 48 offshore platforms including such information as blow counts, soil composition and pertinent construction details were digitized. In Phase II, pile driving records were statistically compared with current methods of prediction. Result was development of modular software, the CRIPS80 Software Design Analyzer System, that companies can use to evaluate other prediction procedures or other data bases.
The prediction of future greenhouse-gas-warming depends critically on the sensitivity of earth's climate to increasing atmospheric concentrations of these gases. Data from cores drilled in polar ice sheets show a remarkable correlation between past glacial-interglacial temperature changes and the inferred atmospheric concentration of gases such as carbon dioxide and methane. These and other palaeoclimate data are used to assess the role of greenhouse gases in explaining past global climate change, and the validity of models predicting the effect of increasing concentrations of such gases in the atmosphere.
There has been increasing effort in recent years to employ satellite remotely sensed data to identify and map vector habitat and malaria transmission risk in data sparse environments. In the current investigation, available satellite and other land surface climatology data products are employed in short-term forecasting of infection rates in the Mpumalanga Province of South Africa, using a multivariate autoregressive approach. The climatology variables include precipitation, air temperature and other land surface states computed by the Off-line Land-Surface Global Assimilation System (OLGA) including soil moisture and surface evaporation. Satellite data products include the Normalized Difference Vegetation Index (NDVI) and other forcing data used in the Goddard Earth Observing System (GEOS-1) model. Predictions are compared to long- term monthly records of clinical and microscopic diagnoses. The approach addresses the high degree of short-term autocorrelation in the disease and weather time series. The resulting model is able to predict 11 of the 13 months that were classified as high risk during the validation period, indicating the utility of applying antecedent climatic variables to the prediction of malaria incidence for the Mpumalanga Province.
A selected set of far ultra violet images of Earth have been analyzed quanitatively to establish their validity for studying thermospheric weather. The set of images chosen for the study was restricted to mostly geomagnetically quiet conditions in order to obtain a baseline understanding of the relationship between the observations and thermospheric phenomenology. The images included low to modrerate solar activity levels. A new model was developed to generate global dayglow images using first principles methods. The mass Spectrometer/incoherent scatter (MSIS-86) model was used to predict the thermospheric concentrations. The analyses of thermospheric images observed in the 123 to 160-nm nominal passband show that the spectral composition for observations on the projected earth disk is dominated by O I 130.4-nm radiation (85-90%), with concentrations from O I 135.6-nm and N2 Lyman-Birge-Hopefield (LBH) bands of about 5-8% each. The synthetic images reproduce the global features of the observed images rather well. Differences between the model and the data are attributed to real atmospheric effects, such as atomic oxygen depletions which are not well reproduced by the MSIS model when geomagnetic activity is elevated. The absoute values recorded were 38-54% higher than predicted. We attribute this discrepancy to low values of the solar extreme ultraviolet irradiances used in the model. Images obtained in the 136 to 165-nm nominal passband are a factor of 2.7 greater than the model. The excess signal observed is most likely due to a long wavelength tail in the instrument sensitivity which allowed Rayleigh scattered sunlight between 180and 250nm to be detected. The understanding of the Dynamics Explorer (DE 1) images gained by this study provides the basis for future work on the global response of the theremosphere to geomagnetic forcing.
In an effort to develop occupant protection standards for future multipurpose crew vehicles, the National Aeronautics and Space Administration (NASA) has looked to evaluate the test device for human occupant restraint with the modification kit (THOR‐K) anthropomorphic test device (ATD) in relevant impact test scenarios. With the allowance and support of the National Highway Traffic Safety Administration, NASA has performed a series of sled impact tests on the latest developed THOR‐K ATD. These tests were performed to match test conditions from human volunteer data previously collected by the U.S. Air Force. The objective of this study was to evaluate the THOR‐K finite element (FE) model and the Total HUman Model for Safety (THUMS) FE model with respect to the tests performed. These models were evaluated in spinal and frontal impacts against kinematic and kinetic data recorded in ATD and human testing. Methods: The FE simulations were developed based on recorded pretest ATD/human position and sled acceleration pulses measured during testing. Predicted responses by both human and ATD models were compared to test data recorded under the same impact conditions. The kinematic responses of the models were quantitatively evaluated using the ISO‐metric curve rating system. In addition, ATD injury criteria and human stress/strain data were calculated to evaluate the risk of injury predicted by the ATD and human model, respectively. Results: Preliminary results show well‐correlated response between both FE models and their physical counterparts. In addition, predicted ATD injury criteria and human model stress/strain values are shown to positively relate. Kinematic comparison between human and ATD models indicates promising biofidelic response, although a slightly stiffer response is observed within the ATD. Conclusion: As a compliment to ATD testing, numerical simulation provides efficient means to assess vehicle safety throughout the design process and further improve the design of physical ATDs. The assessment of the THOR‐K and THUMS FE models in a spaceflight testing condition is an essential first step to implementing these models in the computational evaluation of spacecraft occupant safety. Promising results suggest future use of these models in the aerospace field.
The Long Duration Exposure Facility (LDEF) postflight thermal model predicted temperatures were matched to flight temperature data recorded by the Thermal Measurement System (THERM), LDEF experiment P0003. Flight temperatures, recorded at intervals of approximately 112 minutes for the first 390 days of LDEF's 2105 day mission were compared with predictions using the thermal mathematical model (TMM). This model was unverified prior to flight. The postflight analysis has reduced the thermal model uncertainty at the temperature sensor locations from +/- 40 F to +/- 18 F. The improved temperature predictions will be used by the LDEF's principal investigators to calculate improved flight temperatures experienced by 57 experiments located on 86 trays of the facility.
Accuracy and reliability of the load prediction equations of a low-load calibration data set of a force balance were investigated. First, independent load prediction equations were generated from the data of a full-load and low-load machine calibration. Then, the low- load data set was processed as a set of precision check loads for the load prediction equations that were obtained from the full-load and low-load calibrations. Finally, the load prediction equations of the two calibrations were applied to four manual check load data sets that were recorded between 2009 and 2022. The residuals of the predicted calibration and check loads were compared in all cases. No systematic improvement of the load prediction accuracy was observed when the load prediction equations of the low-load calibration were applied to the check load data sets. Therefore, it is recommended to apply a full-load calibration to a six-component balance even if the balance is not used across its entire design envelope. This approach has the advantage that the resulting load prediction equations are less likely to be applied outside of the calibration load ranges. In addition, it is expected that the numerical estimates of the gage sensitivities are more reliable assuming that the uncertainties of the applied loads and measured outputs of the calibration data are more or less constant across the load range of the balance.
Accuracy and reliability of the load prediction equations of a low-load calibration data set of a force balance were investigated. First, independent load prediction equations were generated from the data of a full-load and low-load machine calibration. Then, the low-load data set was processed as a set of precision check loads for the load prediction equations that were obtained from the full-load and low-load calibrations. Finally, the load prediction equations of the two calibrations were applied to four manual check load data sets that were recorded between 2009 and 2022. The residuals of the predicted calibration and check loads were compared in all cases. No systematic improvement of the load prediction accuracy was observed when the load prediction equations of the low-load calibration were applied to the check load data sets. Therefore, it is recommended to apply a full-load calibration to a six-component balance even if the balance is not used across its entire design envelope. This approach has the advantage that the resulting load prediction equations are less likely to be applied outside of the calibration load ranges. In addition, it is expected that the numerical estimates of the bridge sensitivities are more reliable assuming that the uncertainties of the applied loads and measured outputs of the calibration data are more or less constant across the load range of the balance.
A numerical analysis was developed to determine the airloads on helicopter rotors operating under near-hovering flight conditions capable of producing impulsive noise. A computer program was written in which the solutions for the rotor tip vortex geometry, inflow, aeroelastic response, and airloads are solved in a coupled manner at sequential time steps, with or without the influence of an imposed steady ambient wind or transient gust. The program was developed for future applications in which predicted airloads would be incorporated in an acoustics analysis to attempt to predict and analyze impulsive noise (blade slap). The analysis was applied to a hovering full-scale rotor for which impulsive noise was recorded in the presence of ambient wind. The predicted tip vortex coordinates are in reasonable agreement with the test data, and the blade airload solutions converged to a periodic behavior for an imposed steady ambient wind conditions.
Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.
This report describes the deployment of eleven laser sensors to measure the clearance between blades and tower in a 1.5-MW wind turbine whose rotor was mounted first in upwind and then in downwind configurations. The experimental recordings are compared to the numerical predictions generated by an aeroservoelastic model of the turbine. Good agreement is found between the two datasets, although discrepancies up to 30~cm are observed. The sources of this error are discussed. This methodology is found to be a valuable resource for the validation of the numerical predictions of the flapwise deflections of wind turbine blades. The accurate prediction of these deflections is increasingly important as wind turbines grow in size and become increasingly flexible.
Report 1 of these studies described a basic method for predicting lunar surface temperatures, using readings recorded by Surveyor I and extrapolating to obtain surface temperatures in other selected areas. The areas selected were those considered as potential Apollo landing sites. This second report indicates that the temperature isotherms are directly related to the topographical profile of the surface areas they cover. If, after further observations by Surveyor 11, it becomes evident that the "Successive Transformation Method" provides accurate temperature data, this technique would become a valuable tool for charting the profile of any remote surface where manned spacecraft could be landed.
The kinetics of the hydrolysis of polyester urethane binders of magnetic recording tape is described. Kinetic data were generated from measurements of acetone-extractable hydrolyzed binder products versus time for various humidity-temperature environments. These data can be described by a linear, single product, reversible rate equation. This equation, coupled with measurements on the effect of hydrolysis on recorded tape performance, is used to predict proper environmental storage conditions for magnetic tape.
The dynamic prediction model along with its macro-processor capability and data flow system from the Drexel Limited-Area and Mesoscale Prediction System (LAMPS) were converted and recorded for the Perkin-Elmer 3220. The previous version of this model was written for Control Data Corporation 7600 and CRAY-1a computer environment which existed until recently at the National Center for Atmospheric Research. The purpose of this conversion was to prepare LAMPS for porting to computer environments other than that encountered at NCAR. The emphasis was shifted from programming tasks to model simulation and evaluation tests.
A model for the prediction and analysis of sensor information recorded during robotic performance of telemanipulation tasks is presented. The model uses the hidden Markov model to describe the task structure, the operator's or intelligent controller's goal structure, and the sensor signals. A methodology for constructing the model parameters based on engineering knowledge of the task is described. It is concluded that the model and its optimal state estimation algorithm, the Viterbi algorithm, are very succesful at the task of segmenting the data record into phases corresponding to subgoals of the task. The model provides a rich modeling structure within a statistical framework, which enables it to represent complex systems and be robust to real-world sensory signals.
Experimental and numerical results are presented here for a separate flow nozzle employing chevrons arranged in an alternating pattern on the core nozzle. Comparisons of these results demonstrate that the combination of the WIND/MGBK suite of codes can predict the noise reduction trends measured between separate flow jets with and without chevrons on the core nozzle. Mean flow predictions were validated against Particle Image Velocimetry (PIV), pressure, and temperature data, and noise predictions were validated against acoustic measurements recorded in the NASA Glenn Aeroacoustic Propulsion Lab. Comparisons are also made to results from the CRAFT code. The work presented here is part of an on-going assessment of the WIND/MGBK suite for use in designing the next generation of quiet nozzles for turbofan engines.
Driven by the need for low production costs, electronics cooling fans have evolved differently than the bladed components of gas turbine engines which incorporate multiple technologies to enhance performance and durability while reducing noise emissions. Drawing upon NASA Glenn's experience in the measurement and prediction of gas turbine engine aeroacoustic performance, tests have been conducted to determine if these tools and techniques can be extended for application to the aerodynamics and acoustics of electronics cooling fans. An automated fan plenum installed in NASA Glenn's Acoustical Testing Laboratory was used to map the overall aerodynamic and acoustic performance of a spaceflight qualified 80 mm diameter axial cooling fan. In order to more accurately identify noise sources, diagnose performance limiting aerodynamic deficiencies, and validate noise prediction codes, additional aerodynamic measurements were recorded for two operating points: free delivery and a mild stall condition. Non-uniformities in the fan s inlet and exhaust regions captured by Particle Image Velocimetry measurements, and rotor blade wakes characterized by hot wire anemometry measurements provide some assessment of the fan aerodynamic performance. The data can be used to identify fan installation/design changes which could enlarge the stable operating region for the fan and improve its aerodynamic performance and reduce noise emissions.