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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 937 records · Page 52

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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↗

DOE OSTI2018

Theoretical quantification of shock-timing sensitivities for direct-drive inertial confinement fusion implosions on OMEGA

Using temporally shaped laser pulses, multiple shocks can be launched in direct-drive inertial confinement fusion implosion experiments to set the shell on a desired isentrope or adiabat. The velocity of the first shock and the times at which subsequent shocks catch up to it are measured through the VISAR diagnostic on OMEGA. Simulations reproduce these velocity and shock-merger time measurements when using laser pulses designed for setting mid-adiabat (α ~ 3) implosions, but agreement degrades for lower-adiabat (α ~ 1) designs. Several possibilities for this difference are studied: errors in placing the target at the center of irradiation (target offset), variations in energy between the different incident beams (power imbalance), and errors in modeling the laser energy coupled into the capsule. Simulation results indicate that shock timing is most sensitive to details of the density and temperature profiles in the coronal plasma, which influences the laser energy coupled into the target, and only marginally sensitive to target offset and beam power imbalance. A new technique under development to infer coronal profiles using x-ray self-emission imaging can be applied to the pulse shapes used in shock-timing experiments. In conclusion, this will help identify improved physics models to implement in codes and consequently enhance shock-timing predictive capability for low-adiabat pulses.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks

Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.

autoencoder neural networks↗

DOE OSTI2025

Marine and continental stratocumulus cloud microphysical properties obtained from routine ARM Cimel sunphotometer observations

This study investigates marine and continental stratocumulus (Sc) cloud properties obtained from an automated implementation of a multispectral photometer retrieval. Photometer methods simultaneously retrieve cloud optical depth (τ) and cloud droplet effective radius (r e ), with estimates for liquid water path (LWP) calculated on the availability of those quantities. These applied methods evaluate retrieved cloud properties for Sc identified during a recent 6 year period over the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program sites in Oklahoma, USA (SGP) and in the Azores, Portugal (ENA). Modest agreement in key quantity retrievals is found between the routine photometer products and multisensor collocated profiling references. Cumulative breakdowns contingent on cloud thickness indicate increases in all retrieved quantities in thicker clouds, with larger discrepancies in the relative performance between the retrievals collected in the presence of drizzle. Under continental cloud conditions, the clouds of a similar thickness and r e to those sampled under marine conditions report a factor of 1.5 larger τ and LWP. An r 2 ≅0.65 is found between photometer τ retrievals and shadowband radiometer measurements, with photometer retrievals reporting a high (relative) bias. The τ intercomparisons indicate that variability between retrievals is a factor of three larger than errors reported from individual retrieval input perturbation tests. Photometer r e retrievals suggest a low r 2 (< 0.1) having a standard deviation ≅ 3 µm when compared to ARM baseline multi-sensor radar/radiometer references (accounting for offsets in the cloud droplet number concentration assumptions of the latter). However, photometer LWP calculations remain relatively unbiased in non-drizzling conditions, with errors O (50 g m −2 ) and r 2 ≅0.5 to collocated radiometer and interferometer references. Additional sensitivity tests for island influences on marine Sc properties suggest that while island-influenced winds may promote larger cloud LWP or thickness, the influence could be within retrieval method uncertainty and/or collocated instrument variability.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1972

Calculation of photoionization cross section near auto-ionizing lines and magnesium photoionization cross section near threshold

The research performed is briefly reviewed. A simple method was developed for the calculation of continuum states of atoms when autoionization is present. The method was employed to give the first theoretical cross section for beryllium and magnesium; the results indicate that the values used previously at threshold were sometimes seriously in error. These threshold values have potential applications in astrophysical abundance estimates.

Moore, E. N.↗

NASA NTRS1973

Interior radiances in optically deep absorbing media. I - Exact solutions for one-dimensional model.

An exact analytic solution to the one-dimensional scattering problem with arbitrary single scattering albedo and arbitrary surface albedo is presented. Expressions are given for the emergent flux from a homogeneous layer, the internal flux within the layer, and the radiative heating. A comparison of these results with the values calculated from the matrix operator theory indicates an exceedingly high accuracy. A detailed study is made of the error in the matrix operator results and its dependence on the accuracy of the starting value.

Kattawar, G. W.↗

NASA NTRS1974

Microwave landing system requirements for STOL operations

The operational/functional requirements for the new Microwave Landing System (MLS) are examined for STOL operations. The study utilizes a nonlinear six-degree-of-freedom simulation of a De Havilland Buffalo C-8A aircraft and automatic flight control system to assess the MLS/STOL accuracy, coverage, and data rate requirements for the azimuth, DME, primary elevation, and flare elevation functions. The aircraft performance is statistically determined for representative curved flight paths through touchdown. A range of MLS errors and coverages, environmental disturbances, and navigation filtering are investigated. The study indicates that STOL applications do not place any unique requirements on the MLS.

Burrous, C. N.↗

NASA NTRS1974

Internal Ultraviolet Explorer (IUE) study program. Volume 1: Technology discussion

The results of a simulation study to predict the IUE imaging chain performance are presented, including simulation photographs and transparencies of ultraviolet echelle spectra. Errors in wavelength determination and radiometric measurements due to system degradation are indicated to be minor. Some order overlap is predicted at the bottom of the echelle format.

Turner, T. S., Jr.↗

NASA NTRS1975

Tests and comparisons of satellite derived geoids with Skylab altimeter data

The SKYLAB-193 radar altimeter was operated nearly continuously around the world on January 31, 1974. This direct measurement of the sea surface topography provided an independent basis for the evaluation of global geoids computed from satellite derived gravity models. The differences between the altimeter geoid and the satellite geoids were as large as 25 meters with rms values ranging from 8 to 10 meters. These differences also indicated a systematic long wavelength variation (approximately 100 deg) not related to error in the SKYLAB orbits. Truncation of the models to degree and order eight did not eliminate the long wavelength variation, but in every case the rms agreement between satellite and altimeter geoids was improved. Orbits computed with the truncated models were in contrast found to be inferior to those computed using the complete models.

Marsh, J. G.↗

NASA NTRS1978

Bit-synchronizer lock detector

Circuit measures phase error that exists in phase-locked loop between clock signal recorded in data on magnetic tape and reconstructed clock signal. Circuit presents error as digital word that can be compared with predetermined threshold to indicate lock status. With simple alterations, circuit can also be used as phase detector.

Huey, D. C.↗

NASA NTRS1978

26.3 MHz radio source survey. III - Correlation with extragalactic X-ray sources

The correlation between extragalactic X-ray sources and radio sources in a 26.3-MHz catalog is studied. A list of reliably identified sources is developed by examining X-ray, optical, and radio data for those candidate objects that are in or near the X-ray error boxes. The source 3UR 0432+05 is identified with 3C 120, 3UR 1555+27 (identified with A2142) is shown to be a steep-spectrum radio source, and it is found that 86% of the high-galactic-latitude sources can be reliably identified when the X-ray source error areas are no more than 1 sq deg. The results also indicate that: (1) X-ray sources identified with clusters of galaxies as a whole, with individual galaxies in clusters, and with separate isolated galaxies have similar decametric properties in that their spectral indices and radio luminosities fall in the same range; (2) there is a Bautz-Morgan dependence of both the X-ray and the decametric luminosities of clusters and of individual objects in clusters; and (3) the X-ray luminosity of all except compact sources appears to be approximately proportional to the decametric luminosity.

Erickson, W. C.↗

NASA NTRS1979

A maximum likelihood convolutional decoder model vs experimental data comparison

This article describes the comparison of a maximum likelihood convolutional decoder (MCD) prediction model and the actual performance of the MCD at the Madrid Deep Space Station. The MCD prediction model is used to develop a subroutine that has been utilized by the Telemetry Analysis Program (TAP) to compute the MCD bit error rate for a given signal-to-noise ratio. The results indicate that that the TAP can predict quite well compared to the experimental measurements. An optimal modulation index also can be found through TAP.

Chen, R. Y.↗