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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Mitigating Fading in Cislunar Communications: Application to the Human Landing System

NASA’s human exploration program is currently working towards landing astronauts on the surface of the Moon by 2024, close the lunar South Pole. To guarantee astronaut safety and maximize science data return, NASA is in the process of defining the communication architecture that will support all astronaut activities from launch to surface operations. Of particular interest to this paper are links from the lunar surface back to Earth without any intermediate relays. We show that the system geometry is such that antennas on the landing system will need to be pointed at low elevation angles, thus potentially causing multi-path fading effects not typically encountered in space communications. This paper is organized in three parts. First, we characterize the multi-path fading effects expected in links between the lunar South Pole and Earth and show that for moderate data rates (less than 1 Mbps) the links suffer from slow fading. We then show that for this operations regime the performance of forward error correction schemes is significantly worse for traditional Additive White Gaussian Noise channels. Finally, we investigate multi-copy mechanisms to mitigate the effects of fading, most notably repetition schemes and Automatic Repeat Request.

Sanchez Net, Marc↗
NASA NTRS1975

Shuttle propellant loading instrumenation development

A continuous capacitance sensor was developed and an analog signal conditioner was evaluated to demonstrate the acceptability of these items for use in the space shuttle propellant loading system. An existing basic sensor concept was redesigned to provide capability for cryogenic operation, to improve performance, and to minimize production costs. Sensor development verification consisted of evaluation of sensor linearity, cryogenic performance, and stability during vibration. The signal conditioner evaluation consisted mainly of establishing the effects of the variations in temperature and cable parameters and evaluating the stability. A sensor linearity of 0.04 in. was achieved over most of the sensor length. The sensor instability caused by vibration was 0.04 percent. The cryogenic performance data show a maximum instability of 0.19 percent at liquid hydrogen temperature; a theoretical calibration can be computed a within 1 percent. The signal conditioner evaluation showed that, with temperature compensation, all error sources typically contribute much less than 1 percent. An estimate of the accuracy achievable with the sensor and signal conditioner shows an rss estimate of 0.75 in. for liquid oxygen and 1.02 in. for liquid hydrogen. These are approximately four times better than the shuttle requirements. Comparison of continuous sensor and discrete sensor performance show the continuous sensor to be significantly better when there is surface activity due to sloshing, boiling, or other disturbances.

Hamlet, J.↗
NASA NTRS1996

Assessment of Error in Synoptic-Scale Diagnostics Derived from Wind Profiler and Radiosonde Network Data

A topic of current practical interest is the accurate characterization of the synoptic-scale atmospheric state from wind profiler and radiosonde network observations. We have examined several related and commonly applied objective analysis techniques for performing this characterization and considered their associated level of uncertainty both from a theoretical and a practical standpoint. A case study is presented where two wind profiler triangles with nearly identical centroids and no common vertices produced strikingly different results during a 43-h period. We conclude that the uncertainty in objectively analyzed quantities can easily be as large as the expected synoptic-scale signal. In order to quantify the statistical precision of the algorithms, we conducted a realistic observing system simulation experiment using output from a mesoscale model. A simple parameterization for estimating the uncertainty in horizontal gradient quantities in terms of known errors in the objectively analyzed wind components and temperature is developed from these results.

Mace, Gerald G.↗
NASA NTRS1982

Reassessment of net radiation measurements in the atmosphere of Venus

An analysis was performed of the possible error mechanisms which degraded the IR net flux measurements made by the three small atmospheric probes dispatched from the Pioneer Venus spacecraft. The larger errors began below 30 km, and caused the data to be inconsistent with previous estimates of the atmospheric opacity. Evaluations were made of the possible radiation field perturbations behind each probe, cloud particle deposition on the sensor windows, and thermal disturbances within the sensors because of gas flow through the window retainers. The gas flow through the retainers was identified as the most likely error source, and was demonstrated in laboratory tests. A strong Reynolds number dependence was also found. Radiative transfer calculations were performed to account for the errors, using the constraints defined by the tests. Upper and lower bounds were calculated for the true net flux for both day and night conditions.

Revercomb, H. E.↗
DOE OSTI2025

Data-Driven State of Health Estimation for Second-Life Batteries Using Interpolated Synthetic Data and Feature Selection

Accurate estimation of the State of Health (SOH) for second-life batteries (SLBs) is crucial given their increasing use in energy storage applications. Precise SOH prediction is essential for safe operation and robust battery management systems. A major challenge is the limited availability of datasets for building reliable degradation models. To address this, synthetic data generation through linear interpolation is performed to extend the available data, making it more representative of real-world battery operating conditions. By analyzing feature correlation with SOH, the most relevant features are selected for the model. The proposed approach employs a convolutional neural network (CNN) model trained on this interpolated, feature-selected dataset, using time series data of voltage, temperature, and current over a cycle. By focusing on highly correlated features, the model achieves over 95% accuracy, with mean absolute error and root mean squared error up to 2.27% and 2.64%, respectively, in SOH estimation for two battery datasets tested. These results highlight the potential of combining synthetic data generation and feature selection to enhance SOH predictions, showcasing the superior performance of the proposed CNN model for both new batteries and SLBs.

feature selection↗
NASA NTRS1982

Performance of the Rotor Systems Research Aircraft calibrated rotor loads measurement system

The compound Rotor Systems Research Aircraft (RSRA) is designed to make high accuracy, simultaneous measurements of all rotor forces and moments in flight. Physical calibration of the rotor force- and moment-measurement system when installed in the aircraft is required to account for any errors and to ensure that measurement system accuracy is traceable to the National Bureau of Standards. The first static calibration and associated analysis has been completed with good results. Hysteresis was a potential cause of static calibration errors, but was found to be negligible in flight, and analytical methods have been devised to eliminate its effects on calibration data. Flight tests confirmed that the calibrated rotor loads measurement system performs as expected in flight, and that it can dependably make direct measurements of fuselage vertical drag in hover.

Acree, C. W., Jr.↗
DOE OSTI2025

Temporal Forecasting of Distributed Temperature Sensing in a Thermal Hydraulic System With Machine Learning and Statistical Models

We benchmark performance of long-short term memory (LSTM) network machine learning model and autoregressive integrated moving average (ARIMA) statistical model in temporal forecasting of distributed temperature sensing (DTS). Data in this study consists of fluid temperature transient measured with two co-located Rayleigh scattering fiber optic sensors (FOS) in a forced convection mixing zone of a thermal tee. We treat each gauge of a FOS as an independent temperature sensor. We first study prediction of DTS time series using Vanilla LSTM and ARIMA models trained on prior history of the same FOS that is used for testing. The results yield maximum absolute percentage error (MaxAPE) and root mean squared percentage error (RMSPE) of 1.58% and 0.06% for ARIMA, and 3.14% and 0.44% for LSTM, respectively. Next, we investigate zero-shot forecasting (ZSF) with LSTM and ARIMA trained on history of the co-located FOS only, which is advantageous when limited training data is available. The ZSF MaxAPE and RMSPE values for ARIMA are comparable to those of the Vanilla use case, while the error values for LSTM increase. We show that in ZSF, performance of LSTM network can be improved by training on most correlated gauges between the two FOS, which are identified by calculating the Pearson correlation coefficient. The improved ZSF MaxAPE and RMSPE for LSTM are 4.4% and 0.33%, respectively. Performance of ZSF LSTM can be further enhanced through transfer learning (TL), where LSTM is re-trained on a subset of the FOS that is the target of forecasting. We show that LSTM pre-trained on correlated dataset and re-trained on 30% of testing target dataset achieves MaxAPE and RMSPE values of 2.32% and 0.28%, respectively.

ARIMA↗
NASA NTRS1984

Analysis of the quality of image data acquired by the LANDSAT-4 Thematic Mapper and Multispectral Scanners

The geometric quality of TM film and digital products is evaluated by making selective photomeasurements and by measuring the coordinates of known features on both the TM products and map products. These paired observations are related using a standard linear least squares regression approach. Using regression equations and coefficients developed from 225 (TM film product) and 20 (TM digital product) control points, map coordinates of test points are predicted. The residual error vectors and analysis of variance (ANOVA) were performed on the east and north residual using nine image segments (blocks) as treatments. Based on the root mean square error of the 223 (TM film product) and 22 (TM digital product) test points, users of TM data expect the planimetric accuracy of mapped points to be within 91 meters and within 117 meters for the film products, and to be within 12 meters and within 14 meters for the digital products.

Colwell, R. N.↗
NASA NTRS2001

Sources of Variability in Chlorophyll Analysis by Fluorometry and by High Performance Liquid Chromatography

The need for accurate determination of chlorophyll a (chl a) is of interest for numerous reasons. From the need for ground-truth data for remote sensing to pigment detection for laboratory experimentation, it is essential to know the accuracy of the analyses and the factors potentially contributing to variability and error. Numerous methods and instrument techniques are currently employed in the analyses of chl a. These methods range from spectrophotometric quantification, to fluorometric analysis and determination by high performance liquid chromatography. Even within the application of HPLC techniques, methods vary. Here we provide the results of a comparison among methods and provide some guidance for improving the accuracy of these analyses. These results are based on a round-robin conducted among numerous investigators, including several in the Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) and HyCODE Programs. Our purpose here is not to present the full results of the laboratory intercalibration; those results will be presented elsewhere. Rather, here we highlight some of the major factors that may contribute to the variability observed. Specifically, we aim to assess the comparability of chl a analyses performed by fluorometry and HPLC, and we identify several factors in the analyses which may contribute disproportionately to this variability.

VanHeukelem, Laurie↗
DOE OSTI2026

Comprehensive assessment of metrology techniques for heliostat efficiency and performance evaluation

Concentrating solar power plants, specifically central receiver type systems and their heliostat field, are struggling with negative reputation in the USA, due to perceived underperformance and reliability issues. This is in part due to a lack of standards for performance assessment as well as overly simplified techno-economical models. A better understanding of influences and losses along the solar radiation path from the sun, across the solar collector to the receiver, increases the fidelity of heliostat efficiency assessment as well as solar field performance predictions. Such data are currently scarce and require a complete set of metrology capabilities to evaluate direct solar irradiance, sun shape, atmospheric attenuation, reflectance, collector shape, slope errors and total beam dispersion. In preparation for establishing a 3rd party metrology platform in collaboration with Sandia National Labs, NLR conducted a scoping study on available metrology. We present an extensive overview of techniques and commercial systems for each category. Our work includes an analysis to increase understanding of strengths and limitations of the many techniques used for surface shape and slope measurement. This applies to a controlled, indoor or outdoor laboratory environment assessing a single heliostat.

14 SOLAR ENERGY↗
DOE OSTI2025

Data efficiency assessment of generative adversarial networks in energy applications

This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. Generative AI, though seen as a solution to data limitations, requires substantial data to learn meaningful distributions—a challenge often overlooked. This study addresses the challenge through synthetic data generation for critical heat flux (CHF) and power grid demand, focusing on renewable and nuclear energy. Two variants of GAN employed are conditional GAN (cGAN) and Wasserstein GAN (wGAN). Our findings include the strong dependency of GAN on data size, with performance declining on smaller datasets and varying performance when generalizing to unseen experiments. Mass flux and heated length significantly influence CHF predictions. wGAN is more robust to feature exclusion, making it suitable for constrained synthetic data generation. In energy demand forecasting, wGAN performed well for solar, wind, and load predictions. Longer lookback hours and larger datasets improved predictions, especially for load power. Seasonal variations posed challenges, with wGAN achieving a relatively high error of Root Mean Squared Error (RMSE) of 0.32 for load power prediction, compared to RMSE of 0.07 under same-season conditions. Feature exclusions impacted cGAN the most, while wGAN showed greater robustness. This study concludes that, while generative AI is effective for data augmentation, it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Correlating and Simulating Socio-Demographically Driven Residential End-Use Activity Schedules

Incorporating socio-demographic and behavioral considerations into decision-support tools is crucial for identifying gaps and addressing consumer needs to ensure reliable and affordable energy solutions. In energy simulation models, the correlation between socio-demographics and time-use behavior is not well-captured. Thus, we developed a large-scale simulation workflow to generate schedules for 10 residential activities across 24 population segments defined by age, income, and employment status. Using pre-pandemic 2015-2019 American Time Use Survey (ATUS) data, we used ANOVA to confirm the correlation between demographic factors and time use. We explored three k-modes clustering methods-backward, forward, and a new hybrid approach-to delineate the occupancy patterns based on demographics. Using the probability of cluster membership for each population segment and a time inhomogeneous Markov chain to generate activity transition probabilities for each cluster, we simulated 50,000 schedules per segment and validated them against the ATUS data. The hybrid method produced the most socio-demographically differentiated clusters while demonstrating comparable performance to other approaches, with an overall root mean square error of 0.12 for both weekday and weekend schedules. Thus, the hybrid method, where each cluster is dominated by certain demographic segments and occupancy patterns, offers more modeling versatility in terms of scenario analysis. The new workflow improves the socio demographic differentiation of energy consumption by considering differences in time use. This approach enables future research on demographically segmented time of use (TOU) energy consumption, including impacts of TOU utility bills and rate analysis, long-run marginal emissions, and energy retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments

Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.

Saranathan, Gayathri [Hewlett-Packard]↗
NASA NTRS1979

A rocket ozonesonde for geophysical research and satellite intercomparison

The in-situ rocketsonde for ozone profile measurements developed and flown for geophysical research and satellite comparison is reviewed. The measurement principle involves the chemiluminescence caused by ambient ozone striking a detector and passive pumping as a means of sampling the atmosphere as the sonde descends through the atmosphere on a parachute. The sonde is flown on a meteorological sounding rocket, and flight data are telemetered via the standard meteorological GMD ground receiving system. The payload operation, sensor performance, and calibration procedures simulating flight conditions are described. An error analysis indicated an absolute accuracy of about 12 percent and a precision of about 8 percent. These are combined to give a measurement error of 14 percent.

Hilsenrath, E.↗
NASA NTRS1979

Multivariate approximation methods and applications to geophysics and geodesy

The first report in a series is presented which is intended to be written by the author with the purpose of treating a class of approximation methods of functions in one and several variables and ways of applying them to geophysics and geodesy. The first report is divided in three parts and is devoted to the presentation of the mathematical theory and formulas. Various optimal ways of representing functions in one and several variables and the associated error when information is had about the function such as satellite data of different kinds are discussed. The framework chosen is Hilbert spaces. Experiments were performed on satellite altimeter data and on satellite to satellite tracking data.

Munteanu, M. J.↗
NASA NTRS1980

Determination of efficiencies, loss mechanisms, and performance degradation factors in chopper controlled dc vehical motors. Section 1: Test program results and recommendations

The conventional series motor model is discussed as well as procedures for obtaining, by test, the parameters necessary for calculating performance and losses. The calculated results for operation from ripple free DC are compared with observed test results, indicating approximately 5% or less error. Experimental data indicating the influence of brush shift and chopper frequency are also presented. Both factors have a significant effect on the speed and torque relationships. The losses and loss mechanisms present in a DC series motor are examined and an attempt is made to evaluate the added losses due to harmonic currents and fluxes. Findings with respect to these losses is summarized.

Hamilton, H. B.↗
NASA NTRS1981

The effects of tropical wind data on the prediction of ultralong waves

The influence of tropical wind data on the prediction of planetary waves were studied. Two assimilation experiments were performed, one with and one without FGGE tropical winds. The planetary wave error was then analyzed in 72 h forecasts from the initial conditions provided by the two assimilations.

Baker, W. E.↗
NASA NTRS1986

Differential GPS approaches to orbit determination of high-altitude earth satellites

Orbit determination of earth satellites using conventional (up-looking) differential GPS is limited to lower altitudes due to the finite antenna beamwidth and altitude of GPS satellites. Higher user satellites suffer from having short GPS view period; at sufficiently high altitudes, up-looking GPS visibility becomes so poor that no differential measurements are possible. For such higher user satellites, two alternative differential GPS approaches are suggested. A covariance analysis is performed investigating the capabilities of these approaches at various user satellite altitudes. Best differential GPS data types are identified for satellites within different altitude ranges. Error sources and other factors affecting the orbit determination accuracy are examined. Relative merits of these approaches are compared. When applied to QUASAT, the proposed earth orbiting radio telescope with an elliptical orbit, a sub-meter orbit determination accuracy is expected.

Wu, S.-C.↗