Statistical analysis for thermometric sensors test program final report
Statistical models for regression analysis of thermometric sensors
SENSOR↗
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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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Evaluation of the Williams-type spring wheat model in North Dakota and Minnesota
The Williams type model, developed similarly to previous models of C.V.D. Williams, uses monthly temperature and precipitation data as well as soil and topological variables to predict the yield of the spring wheat crop. The models are statistically developed using the regression technique. Eight model characteristics are examined in the evaluation of the model. Evaluation is at the crop reporting district level, the state level and for the entire region. A ten year bootstrap test was the basis of the statistical evaluation. The accuracy and current indication of modeled yield reliability could show improvement. There is great variability in the bias measured over the districts, but there is a slight overall positive bias. The model estimates for the east central crop reporting district in Minnesota are not accurate. The estimate of yield for 1974 were inaccurate for all of the models.
Leduc, S.↗
A constitutive model for an overlay coating
Coatings are frequently applied to gas turbine blades and vanes to provide protection against oxidation and corrosion. The results of an experimental and analytical study to develop a constitutive model for an overlay coating is presented. Specimens were machined from a hot isostatically pressed billet of PWA 286. The tests consisted of isothermal stress relaxation cycles with monotonically increasing maximum strain and were conducted at various temperatures. The results were used to calculate the constants for various constitutive models, including the classical, the Walker isotropic, a simplified Walker, and Stowell models. A computerized regression analysis was used to calculate model constants from the data. The best fit was obtained for the Walker model, with the simplified Walker and classical models close behind.
Nissley, D. M.↗
Comparison of radar backscatter from Antarctic and Arctic sea ice
Two ship-based step-frequency radars, one at C-band (5.3 GHz) and one at Ku-band (13.9 GHz), measured backscatter from ice in the Weddell Sea. Most of the backscatter data were from first-year (FY) and second-year (SY) ice at the ice stations where the ship was stationary and detailed snow and ice characterizations were performed. The presence of a slush layer at the snow-ice interface masks the distinction between FY and SY ice in the Weddell Sea, whereas in the Arctic the separation is quite distinct. The effect of snow-covered ice on backscattering coefficients (sigma0) from the Weddell Sea region indicates that surface scattering is the dominant factor. Measured sigma0 values were compared with Kirchhoff and regression-analysis models. The Weibull power-density function was used to fit the measured backscattering coefficients at 45 deg.
Hosseinmostafa, R.↗
The European Southern Observatory-MIDAS table file system
The new and substantially upgraded version of the Table File System in MIDAS is presented as a scientific database system. MIDAS applications for performing database operations on tables are discussed, for instance, the exchange of the data to and from the TFS, the selection of objects, the uncertainty joins across tables, and the graphical representation of data. This upgraded version of the TFS is a full implementation of the binary table extension of the FITS format; in addition, it also supports arrays of strings. Different storage strategies for optimal access of very large data sets are implemented and are addressed in detail. As a simple relational database, the TFS may be used for the management of personal data files. This opens the way to intelligent pipeline processing of large amounts of data. One of the key features of the Table File System is to provide also an extensive set of tools for the analysis of the final results of a reduction process. Column operations using standard and special mathematical functions as well as statistical distributions can be carried out; commands for linear regression and model fitting using nonlinear least square methods and user-defined functions are available. Finally, statistical tests of hypothesis and multivariate methods can also operate on tables.
Peron, M.↗
Altitude dependence of stratospheric ozone trends based on Nimbus 7 SBUV data
A multiple regression statistical model is applied to estimate the altitude, latitude, and seasonal dependences of stratospheric ozone trends using 11.5 years of Nimbus 7 SBUV data for the period November 1978 to June 1990. In the upper stratosphere, the derived trends agree in both latitude dependence and approximate amplitude with published predictions from stratospheric models that consider gas-phase chemical processes together with the observed approx. 0.1 ppbV per year increase in tropospheric chlorine. The dominant contribution to column ozone trends occurs in the lower stratosphere where significant negative trends are present at latitudes greater than 20 deg in both hemispheres. The observed latitude dependence is qualitatively consistent with model predictions that include the effects of heterogeneous chemical ozone losses on lower stratospheric aerosols.
Hood, Lon L.↗
Ozone trends deduced from combined Nimbus 7 SBUV and NOAA 11 SBUV/2 data
The long-term time series of global ozone from the Nimbus-7 Solar Backscatter Ultraviolet instrument (SBUV) (Nov. 1978-June 1990) are extended through June 1994 by using measurements from the NOAA-11 SBUV/2. The data sets are merged based upon comparisons during the 18-month overlap period in which both instruments were operational. During this period, the average offset between the two time series is less than 2% in total ozone, and less than 6% in Umkehr layers 1-10. A linear-regression trend model is applied to the extended time series to calculate updated trends as a function of latitude and altitude. Trends through June 1994 are 1.5-2% per decade less negative than through June 1990 in the tropical middle stratosphere (35-40 km) and in the upper stratosphere (45-50 km) at mid-latitudes. In the lower stratosphere, the trends are nearly 1.5% per decade more negative in the southern hemisphere tropical regions to 25 deg S, but remain relatively unchanged elsewhere. The seasonal structure of the total ozone trends is similar to past trend study results, but the magnitude of the seasonal trend can vary by 2% per decade depending on the length of the time series. Both Total Ozone Mapping Spectrometer (TOMS) (through April 1993) and SBUV total ozone time series show small negative trends in the equatorial region, though they are not statistically at the 2-sigma level.
Hollandsworth, S. M.↗
Initial Flight Test Evaluation of the F-15 ACTIVE Axisymmetric Vectoring Nozzle Performance
A full envelope database of a thrust-vectoring axisymmetric nozzle performance for the Pratt & Whitney Pitch/Yaw Balance Beam Nozzle (P/YBBN) is being developed using the F-15 Advanced Control Technology for Integrated Vehicles (ACTIVE) aircraft. At this time, flight research has been completed for steady-state pitch vector angles up to 20' at an altitude of 30,000 ft from low power settings to maximum afterburner power. The nozzle performance database includes vector forces, internal nozzle pressures, and temperatures all of which can be used for regression analysis modeling. The database was used to substantiate a set of nozzle performance data from wind tunnel testing and computational fluid dynamic analyses. Findings from initial flight research at Mach 0.9 and 1.2 are presented in this paper. The results show that vector efficiency is strongly influenced by power setting. A significant discrepancy in nozzle performance has been discovered between predicted and measured results during vectoring.
Orme, John S.↗
Rheoencephalographic and electroencephalographic measures of cognitive workload: analytical procedures
This investigation demonstrates the feasibility of mental workload assessment by rheoencephalographic (REG) and multichannel electroencephalographic (EEG) monitoring. During the performance of this research, unique testing, analytical and display procedures were developed for REG and EEG monitoring that extend the current state of the art and provide valuable tools for the study of cerebral circulatory and neural activity during cognition. REG records are analyzed to provide indices of the right and left hemisphere hemodynamic changes that take place during each test sequence. The EEG data are modeled using regression techniques and mathematically transformed to provide energy-density distributions of the scalp electrostatic field. These procedures permit concurrent REG/EEG cognitive testing not possible with current techniques. The introduction of a system for recording and analysis of cognitive REG/EEG test sequences facilitates the study of learning and memory disorders, dementia and other encephalopathies.
Arousal/physiology↗
Upper-Ocean Heat Balance Processes and the Walker Circulation in CMIP5 Model Projections
Considerable uncertainty remains as to the importance of mechanisms governing decadal and longer variability of the Walker Circulation, its connection to the tropical climate system, and prospects for tropical climate change in the face of anthropogenic forcing. Most contemporary climate models suggest that in response to elevated CO2 and a warmer but more stratified atmosphere, the required upward mass flux in tropical convection will diminish along with the Walker component of the tropical mean circulation as well. Alternatively, there is also evidence to suggest that the shoaling and increased vertical stratification of the thermocline in the eastern Pacific will enable a muted SST increase there-- preserving or even enhancing some of the dynamical forcing for the Walker cell flow. Over the past decade there have been observational indications of an acceleration in near-surface easterlies, a strengthened Pacific zonal SST gradient, and globally-teleconnected dislocations in precipitation. But is this evidence in support of an ocean dynamical thermostat process posited to accompany anthropogenic forcing, or just residual decadal fluctuations associated with variations in warm and cold ENSO events and other stochastic forcing? From a modeling perspective we try to make headway on this question by examining zonal variations in surface energy fluxes and dynamics governing tropical upper ocean heat content evolution in the WCRP CMIP5 model projections. There is some diversity among model simulations; for example, the CCSM4 indicates net ocean warming over the IndoPacific region while the CSIRO model concentrates separate warming responses over the central Pacific and Indian Ocean regions. The models, as with observations, demonstrate strong local coupling between variations in column water vapor, downward surface longwave radiation and SST; but the spatial patterns of changes in the sign of this relationship differ among models and, for models as a whole, with observations. Our analysis focuses initially on probing the inter-model differences in energy fluxes / transports and Walker Circulation response to forcing. We then attempt to identify statistically the El Nino- / La Nina-related ocean heat content variability unique to each model and regress out the associated energy flux, ocean heat transport and Walker response on these shorter time scales for comparison to that of the anthropogenic signals.
Robertson, F. R.↗
Projections of Temperature-Attributable Premature Deaths in 209 U.S. Cities Using a Cluster-Based Poisson Approach
Background: A warming climate will affect future temperature-attributable premature deaths. This analysis is the first to project these deaths at a near national scale for the United States using city and month-specific temperature-mortality relationships. Methods: We used Poisson regressions to model temperature-attributable premature mortality as a function of daily average temperature in 209 U.S. cities by month. We used climate data to group cities into clusters and applied an Empirical Bayes adjustment to improve model stability and calculate cluster-based month-specific temperature-mortality functions. Using data from two climate models, we calculated future daily average temperatures in each city under Representative Concentration Pathway 6.0. Holding population constant at 2010 levels, we combined the temperature data and cluster-based temperature-mortality functions to project city-specific temperature-attributable premature deaths for multiple future years which correspond to a single reporting year. Results within the reporting periods are then averaged to account for potential climate variability and reported as a change from a 1990 baseline in the future reporting years of 2030, 2050 and 2100. Results: We found temperature-mortality relationships that vary by location and time of year. In general, the largest mortality response during hotter months (April - September) was in July in cities with cooler average conditions. The largest mortality response during colder months (October-March) was at the beginning (October) and end (March) of the period. Using data from two global climate models, we projected a net increase in premature deaths, aggregated across all 209 cities, in all future periods compared to 1990. However, the magnitude and sign of the change varied by cluster and city. Conclusions: We found increasing future premature deaths across the 209 modeled U.S. cities using two climate model projections, based on constant temperature-mortality relationships from 1997 to 2006 without any future adaptation. However, results varied by location, with some locations showing net reductions in premature temperature-attributable deaths with climate change.
Schwartz, Joel D.↗
Mapping 2000 2010 Impervious Surface Change in India Using Global Land Survey Landsat Data
Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 200-02010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approach An independent validation dataset was also developed using Google Earth imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 +/- 7.84 sq km.
Wang, Panshi↗
Temperature Increase Reduces Global Yields of Major Crops in Four Independent Estimates
Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multi-method analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.
Zhao, Chuang↗
Thermal Testing and Analysis Techniques for Wires and Wire Bundles
Determining wire and wire bundle amperage capacity (i.e., “ampacity”) currently relies on the use of standards to derate wire ampacity when in a bundle configuration. The feasibility of developing physics-based and regression thermal models of single wires and wire bundles to determine ampacity using a customized test apparatus was investigated during a pathfinder study. A wire bundle test apparatus was developed for measuring the temperatures of single wires and wire bundles and collected wire conductor temperature data under high vacuum and atmospheric conditions and varying environmental temperatures and currents for the two most common wire types used on spacecraft.
Thermal↗
Comparison and Regression Analysis of Measured and Modeled Sonic Booms Across a Lateral Array
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sonic boom↗
MINLP for regularized symbolic regression with applications to data-driven modeling of critical minerals processes
The poster summarizes recent advances in symbolic regression developed as part of the PrOMMiS project over the past year. In particular, it describes the comparison of surrogates for critical minerals (CM) & rare earth element (REE) recovery flowsheets obtained via symbolic regression and ALAMO. It also compares the predictive ability and solvability of optimization models that incorporate these surrogates.
36 MATERIALS SCIENCE↗
SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models
The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Using ridge regression in systematic pointing error corrections
A pointing error model is used in the antenna calibration process. Data from spacecraft or radio star observations are used to determine the parameters in the model. However, the regression variables are not truly independent, displaying a condition known as multicollinearity. Ridge regression, a biased estimation technique, is used to combat the multicollinearity problem. Two data sets pertaining to Voyager 1 spacecraft tracking (days 105 and 106 of 1987) were analyzed using both linear least squares and ridge regression methods. The advantages and limitations of employing the technique are presented. The problem is not yet fully resolved.
Guiar, C. N.↗