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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

COMPOFF: A Compiler Cost model using Machine Learning to predict the Cost of OpenMP Offloading

The HPC industry is inexorably moving towards an era of extremely heterogeneous architectures, with more devices configured on any given HPC platform and potentially more kinds of devices, some of them highly specialized. Writing a separate code suitable for each target system for a given HPC application is not practical. The better solution is to use directive-based parallel programming models such as OpenMP. OpenMP provides a number of options for offloading a piece of code to devices like GPUs. To select the best option from such options during compilation, most modern compilers use analytical models to estimate the cost of executing the original code and the different offloading code variants. Building such an analytical model for compilers is a difficult task that necessitates a lot of effort on the part of a compiler engineer. Recently, machine learning techniques have been successfully applied to build cost models for a variety of compiler optimization problems. In this paper, we present COMPOFF, a cost model which uses the multi-layer perceptrons to statically estimates the Cost of OpenMP OFFloading. We used six different transformations on a parallel code of Wilson Dslash Operator to support GPU offloading, and we predicted their cost of execution on different GPUs using COMPOFF during compile time. Our results show that this model can predict offloading costs with a root mean squared error in prediction of less than 0.5 seconds. Our preliminary findings indicate that this work will make it much easier and faster for scientists and compiler developers to port legacy HPC applications that use OpenMP to new heterogeneous computing environment.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Techniques for Estimating Emissions Factors from Forest Burning: ARCTAS and SEAC4RS Airborne Measurements Indicate which Fires Produce Ozone

Previous studies of emission factors from biomass burning are prone to large errors since they ignore the interplay of mixing and varying pre-fire background CO2 levels. Such complications severely affected our studies of 446 forest fire plume samples measured in the Western US by the science teams of NASA's SEAC4RS and ARCTAS airborne missions. Consequently we propose a Mixed Effects Regression Emission Technique (MERET) to check techniques like the Normalized Emission Ratio Method (NERM), where use of sequential observations cannot disentangle emissions and mixing. We also evaluate a simpler "consensus" technique. All techniques relate emissions to fuel burned using C(burn) = delta C(tot) added to the fire plume, where C(tot) approximately equals (CO2 = CO). Mixed-effects regression can estimate pre-fire background values of C(tot) (indexed by observation j) simultaneously with emissions factors indexed by individual species i, delta, epsilon lambda tau alpha-x(sub I)/C(sub burn))I,j. MERET and "consensus" require more than emissions indicators. Our studies excluded samples where exogenous CO or CH4 might have been fed into a fire plume, mimicking emission. We sought to let the data on 13 gases and particulate properties suggest clusters of variables and plume types, using non-negative matrix factorization (NMF). While samples were mixtures, the NMF unmixing suggested purer burn types. Particulate properties (b scant, b abs, SSA, AAE) and gas-phase emissions were interrelated. Finally, we sought a simple categorization useful for modeling ozone production in plumes. Two kinds of fires produced high ozone: those with large fuel nitrogen as evidenced by remnant CH3CN in the plumes, and also those from very intense large burns. Fire types with optimal ratios of delta-NOy/delta- HCHO associate with the highest additional ozone per unit Cburn, Perhaps these plumes exhibit limited NOx binding to reactive organics. Perhaps these plumes exhibit limited NOx binding to reactive organics

ozone↗
NASA NTRS2015

Techniques for Estimating Emissions Factors from Forest Burning: ARCTAS and SEAC4RS Airborne Measurements Indicate Which Fires Produce Ozone

Previous studies of emission factors from biomass burning are prone to large errors since they ignore the interplay of mixing and varying pre-fire background CO2 levels. Such complications severely affected our studies of 446 forest fire plume samples measured in the Western US by the science teams of NASA's SEAC4RS and ARCTAS airborne missions. Consequently we propose a Mixed Effects Regression Emission Technique (MERET) to check techniques like the Normalized Emission Ratio Method (NERM), where use of sequential observations cannot disentangle emissions and mixing. We also evaluate a simpler "consensus" technique. All techniques relate emissions to fuel burned using C(sub burn) = delta C(sub tot) added to the fire plume, where C(sub tot) approximately equals (CO2 + CO). Mixed-effects regression can estimate pre-fire background values of Ctot (indexed by observation j) simultaneously with emissions factors indexed by individual species i, delta epsilon lambda tau alpha−x(sub i)/(C(sub burn))i,j., MERET and "consensus" require more than two emissions indicators. Our studies excluded samples where exogenous CO or CH4 might have been fed into a fire plume, mimicking emission. We sought to let the data on 13 gases and particulate properties suggest clusters of variables and plume types, using non-negative matrix factorization (NMF). While samples were mixtures, the NMF unmixing suggested purer burn types. Particulate properties (bscat, babs, SSA, AAE) and gas-phase emissions were interrelated. Finally, we sought a simple categorization useful for modeling ozone production in plumes. Two kinds of fires produced high ozone: those with large fuel nitrogen as evidenced by remnant CH3CN in the plumes, and also those from very intense large burns. Fire types with optimal ratios of delta-NOy/delta- HCHO associate with the highest additional ozone per unit Cburn, Perhaps these plumes exhibit limited NOx binding to reactive organics. Perhaps these plumes exhibit limited NOx binding to reactive organics.

fires↗
NASA NTRS1981

Applications systems verification and transfer project. Volume 1: Operational applications of satellite snow cover observations: Executive summary

Both LANDSAT and NOAA satellite data were used in improving snowmelt runoff forecasts. When the satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. A cost benefit analysis run in conjunction with the snow mapping indicated a $36.5 million annual benefit accruing from a one percent improvement in forecast accuracy using the snow cover data for the western United States. The annual cost of employing the system would be $505,000. The snow mapping has proven that satellite snow cover data can be used to reduce snowmelt runoff forecast error in a cost effective manner once all operational satellite data are available within 72 hours after acquisition. Executive summaries of the individual snow mapping projects are presented.

Rango, A.↗
NASA NTRS1989

Report on the multicolor pyrometry session at the Second NASA Non-Contact Temperature-Measurement Workshop

The session met jointly with the other special sessions. Due to time constraints, the concerns of multicolor pyrometry were not addressed in depth. Multicolor pyrometry attempts to determine an object's temperature and emittance as a function of wavelength from simultaneous measurement of the spectral radiance of the object at multiple wavelengths. The main issues are those of the most appropriate model for the wavelength dependence of the emittance, the number of undetermined parameters, and the error in the measured temperature. It has long been known that it is inappropriate to attempt to model the emittance with a functional form that contains as many unknowns as wavelengths. Such attempts will lead to gross errors in the measured temperature. However, more recent work by Hunter and Khan has indicated that emittance can be modeled by a function with many fewer degrees of freedom than sampled wavelengths (e.g., it may be appropriate to use a second order polynomial to fit data from a forty-color pyrometer). In a platform lecture, Khan described algorithms for determination of the parameters by minimization of the errors between the model and the data in a least-squares sense. The first was a linear method and the second was a non-linear method where the emittance could assume any functional form. Both methods were shown to be very sensitive to random noise and non-linearity in the measurement system.

Spjut, R. Erik↗
DOE OSTI2018

Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm

Magnetic resonance imaging (MRI) protocoling can be time- and resource-intensive, and protocols can often be suboptimal dependent upon the expertise or preferences of the protocoling radiologist. Providing a best-practice recommendation for an MRI protocol has the potential to improve efficiency and decrease the likelihood of a suboptimal or erroneous study. The goal of this study was to develop and validate a machine learning-based natural language classifier that can automatically assign the use of intravenous contrast for musculoskeletal MRI protocols based upon the free-text clinical indication of the study, thereby improving efficiency of the protocoling radiologist and potentially decreasing errors. We utilized a deep learning-based natural language classification system from IBM Watson, a question-answering supercomputer that gained fame after challenging the best human players on Jeopardy! in 2011. We compared this solution to a series of traditional machine learning-based natural language processing techniques that utilize a term-document frequency matrix. Each classifier was trained with 1240 MRI protocols plus their respective clinical indications and validated with a test set of 280. Ground truth of contrast assignment was obtained from the clinical record. For evaluation of inter-reader agreement, a blinded second reader radiologist analyzed all cases and determined contrast assignment based on only the free-text clinical indication. In the test set, Watson demonstrated overall accuracy of 83.2% when compared to the original protocol. This was similar to the overall accuracy of 80.2% achieved by an ensemble of eight traditional machine learning algorithms based on a term-document matrix. When compared to the second reader’s contrast assignment, Watson achieved 88.6% agreement. When evaluating only the subset of cases where the original protocol and second reader were concordant (n = 251), agreement climbed further to 90.0%. The classifier was relatively robust to spelling and grammatical errors, which were frequent. Implementation of this automated MR contrast determination system as a clinical decision support tool may save considerable time and effort of the radiologist while potentially decreasing error rates, and require no change in order entry or workflow.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1992

Daily estimates of the earth's pole position with the Global Positioning System

Daily estimates of the earth's pole position have been obtained with measurements from a worldwide network of GPS receivers, obtained during the three week GIG '91 experiment in January-February 1991. For this short-term study, the GPS based polar motion series agrees with the other space based geodetic techniques (Very Long Baseline Interferometry and Satellite Laser Ranging) to about 0.4 mas rms, after the removal of mean biases of order 1-3 mas. The small error in day-to-day variability is not sensitive to the fiducial strategy used, nor are fiducial sites even necessary for monitoring high frequency pole position variability. The small biases indicate that the applied reference frames of the three geodetic techniques are nearly aligned, that the GPS fiducial errors are small, and that systematic errors in GPS are also small (of order 5 ppb). A well determined reference frame is necessary for monitoring the long-term stability of polar motion and for separating it from other long-term signals such as tectonic motion and internal systematic errors.

Lindqwister, Ulf J.↗
NASA NTRS1960

Satellite Attitude Control Using a Combination of Inertia Wheels and a Bar Magnet

The use of inertia wheels to control the attitude of a satellite has currently aroused much interest. The stability of such a system has been studied in this investigation. A single-degree-of-freedom analysis indicates that a response with suitable dynamic characteristics and precise control can be achieved by commanding the angular velocity of the inertia wheel with an error signal that is the sum of the attitude error, the attitude rate, and the integral of the attitude error. A digital computer was used to study the three-degree-of-freedom response to step displacements, and the results indicate that the cross-coupling effects of inertia coupling and precession coupling had no effect on system stability. A study was also made of the use of a bar magnet to supplement the inertia wheels by providing a means of removing any momentum introduced into the system by disturbances such as aerodynamic torques. A study of a case with large aerodynamic torques, with a typical orbit, indicated that the magnet was a suitable device for supplying the essential trimming force. Single-degree-of-freedom bench tests generally verified the dynamic response predicted by the analytical study. the test table to within plus or minus 9 arc-seconds of the reference direction, even though the hardware components that were used in these tests were not specifically designed for the control system.

Adams, James J.↗
NASA NTRS1960

A Homing Missile Control System to Reduce the Effects of Radome Diffraction

The problem of radome diffraction in radar-controlled homing missiles at high speeds and high altitudes is considered from the point of view of developing a control system configuration which will alleviate the deleterious effects of the diffraction. It is shown that radome diffraction is in essence a kinematic feedback of body angular velocities which causes the radar to sense large apparent line-of-sight angular velocities. The normal control system cannot distinguish between the erroneous and actual line-of-sight rates, and entirely wrong maneuvers are produced which result in large miss distances. The problem is resolved by adding to the control system a special-purpose computer which utilizes measured body angular velocity to extract from the radar output true line-of-sight information for use in steering the missile. The computer operates on the principle of sampling and storing the radar output at instants when the body angular velocity is low and using this stored information for maneuvering commands. In addition, when the angular velocity is not low the computer determines a radome diffraction compensation which is subtracted from the radar output to reduce the error in the sampled information. Analog simulation results for the proposed control system operating in a coplanar (vertical plane) attack indicate a potential decrease in miss distance to an order of magnitude below that for a conventional system. Effects of glint noise, random target maneuvers, initial heading errors, and missile maneuverability are considered in the investigation.

Smith, Gerald L.↗
NASA NTRS1972

An occultation satellite system for determining pressure levels in the atmosphere

An operational two-satellite microwave occultation system will establish a pressure reference level to be used in fixing the temperature-pressure profile generated by the SIRS infrared sensor as a function of altitude. In the final error analysis, simulated data for the SIRS sensor were used to test the performance of the occultation system. The results of this analysis indicate that the occultation system is capable of measuring the altitude of the 300-mb level to within 24 mrms, given a maximum error of 2 K in the input temperature profile. The effects of water vapor can be corrected by suitable climatological profiles, and improvements in the accuracy of the SIRS instrument should yield additional improvements in the performance of the occultation system.

Ungar, S. G.↗
NASA NTRS1987

The effect of atmospheric drag on position and velocity errors of orbiting spacecraft

A model, in closed form, of position and velocity changes due to atmospheric drag of a spacecraft in orbit about a planetary body is presented, considering primarily the effects of a two-body force field. Calculations for both elliptical and circular geometries indicate that the simplifying assumptions in the analysis make the theory more accurate (to less than 1 percent error) for lengthier mappings, when the drag effect becomes quite significant. The downtrack error demonstrates a quadratic growth, while the radial error demonstrates a linear growth.

Russell, R. Kent↗
NASA NTRS1983

Sensitivity of ozone retrievals in limb-viewing experiments to errors in line-width parameters

The variance of atmospheric ozone concentration profiles retrieved in i.r. limb-viewing experiments is examined over the range of published ozone halfwidth values. Simulated retrievals are performed for a typical satellite-based high-resolution absorption experiment and a broad-band emission experiment. The results indicate that, for limb-viewing experiments, inaccurate collision-broadened linewidth values can lead to significant errors in retrieved profiles for broad-band radiometric data or for isolated strong lines in high-resolution spectra.

Smith, M. A. H.↗
NASA NTRS1994

Advanced technology for a satellite multichannel demultiplexer/demodulator

Satellite on-board processing is needed to efficiently service multiple users while at the same time minimizing earth station complexity. The processing satellite receives a wideband uplink at 30 GHz and down-converts it to a suitable intermediate frequency. A multichannel demultiplexer then separates the composite signal into discrete channels. Each channel is then demodulated by bulk demodulators, with the baseband signals routed to the downlink processor for retransmission to the receiving earth stations. This type of processing circumvents many of the difficulties associated with traditional bent-pipe repeater satellites. Uplink signal distortion and interference are not retransmitted on the downlink. Downlink power can be allocated in accordance with user needs, independent of uplink transmissions. This allows the uplink users to employ different data rates as well as different modulation and coding schemes. In addition, all downlink users have a common frequency standard and symbol clock on the satellite, which is useful for network synchronization in time division multiple access schemes. The purpose of this program is to demonstrate the concept of an optically implemented multichannel demultiplexer (MCD). A proof-of-concept (POC) model has been developed which has the ability to receive a 40 MHz wide composite signal consisting of up to 1000 40 kHz QPSK modulated channels and perform the demultiplexing process. In addition a set of special test equipment (STE) has been configured to evaluate the performance of the POC model. The optical MCD is realized as an acousto-optic spectrum analyzer utilizing the capability of Bragg cells to perform the required channelization. These Bragg cells receive an optical input from a laser source and an RF input (the signal). The Bragg interaction causes optical output diffractions at angles proportional to the RF input frequency. These discrete diffractions are optically detected and output to individual demodulators for baseband conversion. Optimization of the MCD design was conducted in order to achieve a compromise between two opposing sources of signal degradation: adjacent channel interference and intersymbol interference. The system was also optimized to allow simple, inexpensive ground stations communications with the MCD. These design goals led to the realization of a POC MCD which demonstrates the demultiplexing function with minimal signal degradation. Performance evaluation results using the STE equipment indicate that the dynamic range of the demultiplexer in the presence of adjacent and multiple channel loading is 40 - 50 dB. Measured bit error rate (BER) probabilities varied from the predicted theoretical results by one dB or less. The performance of the proof-of-concept model indicate that the development of a space qualified optically implemented MCD are feasible. The advantages to such an implementation include reduced size, weight and power and increased reliability when compared with electronic approaches. All of these factors are critical to on-board satellite processors. Further optimization can be conducted which trade ground station complexity and MCD performance to achieve desired system results.

Abramovitz, Irwin J.↗
NASA NTRS1992

Review of performance, medical, and operational data on pilot aging issues

An extensive review of the literature and studies relating to performance, medical, operational, and legal data regarding pilot aging issues was performed in order to determine what evidence there is, if any, to support mandatory pilot retirement. Popular misconceptions about aging, including the failure to distinguish between the normal aging process and disease processes that occur more frequently in older individuals, continue to contribute to much of the misunderstanding and controversy that surround this issue. Results: Review of medical data related to the pilot aging issue indicate that recent improvement in medical diagnostics and treatment technology have made it possible to identify to a high degree individuals who are at risk for developing sudden incapacitating illness and for treating those with disqualifying medical conditions. Performance studies revealed that after controlling for the presence of disease states, older pilots are able to perform as well as younger pilots on many performance tasks. Review of accident data showed that older, healthy pilots do not have higher accident rates than younger pilots, and indeeed, evidence suggests that older pilots have an advantage in the cockpit due to higher experience levels. The Man-Machine-Mission-Environment interface of factors can be managed through structured, supervised, and enhanced operations, maintenance, flight reviews, and safety procedures in order to ensure safe and productive operations by reducing the margin of error and by increasing the margin of safety. Conclusions: There is no evidence indicating any specific age as an arbitrary cut-off point for pilots to perform their fight duties. A combination of regular medical screening, performance evaluation, enhanced operational maintenance, and safety procedures can most effectively ensure a safe pilot population than can a mandatory retirement policy based on arbitrary age restrictions.

Stoklosa, J. H.↗
DOE OSTI2019

Capturing Maize Stand Heterogeneity Across Yield-Stability Zones Using Unmanned Aerial Vehicles (UAV)

Despite the new equipment capabilities, uneven crop stands are still common occurrences in crop fields, mainly due to spatial heterogeneity in soil conditions, seedling mortality due to herbivore predation and disease, or human error. Non-uniform plant stands may reduce grain yield in crops like maize. Thus, detecting signs of variability in crop stand density early in the season provides critical information for management decisions and crop yield forecasts. Processing techniques applied on images captured by unmanned aerial vehicles (UAVs) has been used successfully to identify crop rows and estimate stand density and, most recently, to estimate plant-to-plant interval distance. Here, we further test and apply an image processing algorithm on UAV images collected from yield-stability zones in a commercial crop field. Our objective was to implement the algorithm to compare variation of plant-spacing intervals to test whether yield differences within these zones are related to differences in crop stand characteristics. Our analysis indicates that the algorithm can be reliably used to estimate plant counts (precision >95% and recall >97%) and plant distance interval (R2 ~0.9 and relative error <10%). Analysis of the collected data indicated that plant spacing variability differences were small among plots with large yield differences, suggesting that it was not a major cause of yield variability across zones with distinct yield history. This analysis provides an example of how plant-detection algorithms can be applied to improve the understanding of patterns of spatial and temporal yield variability.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

The electric vehicles-solar photovoltaics Nexus: Driving cross-sectoral adoption of sustainable technologies

Residential and transportation energy consumption account for more than one-half of the overall energy consumption in the United States. Adoption of electric vehicles (EVs) can play a key role in decarbonizing the transportation sector, while the adoption of renewable energy sources (e.g., solar photovoltaics [PVs]) could bring similar benefits to the residential energy sector and in turn support transport electrification. Although the market shares for both EVs and PVs continue to grow, both of these emerging technologies are deployed rather disjointly, without considering the existence of potential similarities among users who own (or aspire to own) these technologies. This might be due to lack of understanding of the behavioral interdependence in consumer preferences toward these technologies. To fill this gap in knowledge, this study utilizes data from the 2018 WholeTraveler Transportation Behavior Study to develop an integrated model system that explores interactive EV and PV adoption behaviors. A structural equation model is employed that incorporates direct effects as well as error correlations among the adoption behaviors for EVs and PVs. Model results indicate that the adoption behavior for both these technologies is indeed interconnected and significantly influenced by attitudes, values, and personality traits. Findings from this research suggest that incentives (e.g., subsidies) that drive bundled adoption of EV-PV systems could accelerate the adoption of both of these sustainable technologies. In conclusion, this study highlights the need to consider transport and building energy-efficient technology adoption behavior in a single integrated structure.

14 SOLAR ENERGY↗
DOE OSTI2024

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15–385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Further, quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R 2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. We report our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗