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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Limitations on relating ocean surface chlorophyll to productivity

An important potential use of ocean color chlorophyll data is to determine other important properties of the marine biosphere, such as primary productivity, new production, and particulate fluxes at spatial scales larger and temporal scales longer than those possible with ground-based observations. Such determinations will likely progress from relatively simple empirical correlations to algorithms that are actually predictive models of ecosystem dynamics. As an example, this paper demonstrates how an empirical correlation between nitrate concentration and new production can be understood by a simple productivity model. Several models are then constructed to examine the functional relationship between total production and surface chlorophyll. The empirical correlation is substantially different than the analogous relation in the model. Understanding the relationship between surface chlorophyll and productivity on a global scale will probably require families of models for various marine ecosystems.

Volk, Tyler↗
NASA NTRS1989

Piloted simulation of a ground-based time-control concept for air traffic control

A concept for aiding air traffic controllers in efficiently spacing traffic and meeting scheduled arrival times at a metering fix was developed and tested in a real time simulation. The automation aid, referred to as the ground based 4-D descent advisor (DA), is based on accurate models of aircraft performance and weather conditions. The DA generates suggested clearances, including both top-of-descent-point and speed-profile data, for one or more aircraft in order to achieve specific time or distance separation objectives. The DA algorithm is used by the air traffic controller to resolve conflicts and issue advisories to arrival aircraft. A joint simulation was conducted using a piloted simulator and an advanced concept air traffic control simulation to study the acceptability and accuracy of the DA automation aid from both the pilot's and the air traffic controller's perspectives. The results of the piloted simulation are examined. In the piloted simulation, airline crews executed controller issued descent advisories along standard curved path arrival routes, and were able to achieve an arrival time precision of + or - 20 sec at the metering fix. An analysis of errors generated in turns resulted in further enhancements of the algorithm to improve the predictive accuracy. Evaluations by pilots indicate general support for the concept and provide specific recommendations for improvement.

Davis, Thomas J.↗
NASA NTRS1997

Recursive Deadbeat Controller Design

This paper presents a recursive algorithm for a deadbeat predictive controller design. The method combines together the concepts of system identification and deadbeat controller designs. It starts with the multi-step output prediction equation and derives the control force in terms of past input and output time histories. The formulation thus derived satisfies simultaneously system identification and deadbeat controller design requirements. As soon as the coefficient matrices are identified satisfying the output prediction equation, no further work is required to compute the deadbeat control gain matrices. The method can be implemented recursively just as any typical recursive system identification techniques.

Juang, Jer-Nan↗
NASA NTRS1998

Studies of Solar Flare and Interplanetary Particle Acceleration and Coordination of Ground-Based Solar Observations in Support of US and International Space Missions

A primary focus has been to conduct studies of particular types of hard X-ray evolution in solar flares and their associations with high energy interplanetary protons observed near Earth. Previously, two large investigations were conducted that revealed strong associations between episodes of progressive spectral hardening seen in solar events and interplanetary proton events (Kiplinger, 1995). An algorithm was developed for predicting interplanetary protons that is more accurate than those currently in use when hard X-ray spectra are available. The basic research on a third study of the remaining independent subset of Hard X-ray Burst Spectrometer (HXRBS) events randomly not selected by the original studies was completed. This third study involves independent analyses of the data by two analysts. The results echo the success of the earlier studies. Of 405 flares analyzed, 12 events were predicted to have associated interplanetary protons at the Space Environment Service Center (SESC) level. Of these, five events appear to be directly associated with SESC proton events, six other events had lower level associated proton events, and there was only one false alarm with no protons. Another study by Garcia and Kiplinger (1995) established that progressively hardening hard X-ray flares associated with interplanetary proton events are intrinsically cooler and not extremely intense in soft X-rays unless a "contaminating" large impulsive flare accompanies the hardening flare.

Kiplinger, Alan L.↗
NASA NTRS1999

Solving Upwind-Biased Discretizations: Multigrid Solver Using Semicoarsening - 2

This paper studies a novel multigrid approach to the solution for a second order upwind biased discretization of the convection equation in two dimensions. This approach is based on semi-coarsening and well balanced explicit correction terms added to coarse-grid operators to maintain on coarse-grid the same cross-characteristic interaction as on the target (fine) grid. Colored relaxation schemes are used on all the levels allowing a very efficient parallel implementation. The results of the numerical tests can be summarized as follows: 1) The residual asymptotic convergence rate of the proposed V(0, 2) multigrid cycle is about 3 per cycle. This convergence rate far surpasses the theoretical limit (4/3) predicted for standard multigrid algorithms using full coarsening. The reported efficiency does not deteriorate with increasing the cycle, depth (number of levels) and/or refining the target-grid mesh spacing. 2) The full multi-grid algorithm (FMG) with two V(0, 2) cycles on the target grid and just one V(0, 2) cycle on all the coarse grids always provides an approximate solution with the algebraic error less than the discretization error. Estimates of the total work in the FMG algorithm are ranged between 18 and 30 minimal work units (depending on the target (discretizatioin). Thus, the overall efficiency of the FMG solver closely approaches (if does not achieve) the goal of the textbook multigrid efficiency. 3) A novel approach to deriving a discrete solution approximating the true continuous solution with a relative accuracy given in advance is developed. An adaptive multigrid algorithm (AMA) using comparison of the solutions on two successive target grids to estimate the accuracy of the current target-grid solution is defined. A desired relative accuracy is accepted as an input parameter. The final target grid on which this accuracy can be achieved is chosen automatically in the solution process. the actual relative accuracy of the discrete solution approximation obtained by AMA is always better than the required accuracy; the computational complexity of the AMA algorithm is (nearly) optimal (comparable with the complexity of the FMG algorithm applied to solve the problem on the optimally spaced target grid).

Diskin, Boris↗
NASA NTRS1998

Observation and Modeling of Single Wall Carbon Nanotube Bend Junctions

Single wall carbon nanotube (SWNT) bends, with diameters from approx. 1.0 to 2.5 nm and bend angles from 18 deg. to 34 deg., are observed in catalytic decomposition of hydrocarbons at 600 - 1200 C. An algorithm using molecular dynamics simulation (MD) techniques is developed to model these structures that are considered to be SWNT junctions formed by topological defects (i.e. pentagon-heptagon pairs). The algorithm is used to predict the tube helicities and defect configurations for bend junctions using the observed tube diameters and bend angles. The number and arrangement of the defects at the junction interfaces are found to depend on the tube helicities and bend angle. The structural and energetic calculations using the Brenner potential show a number of stable junction configurations for each bend angle with the 34 deg. bends being more stable than the others. Tight binding calculations for local density of state (LDOS) and transmission coefficients are carried out to investigate electrical properties of the bend junctions.

Han, Jie↗
NASA NTRS2005

An Investigation of the Combined Effect of Stress, Fatigue and Workload on Human Performance: Position Paper

Stress, fatigue, and workload affect worker performance. NSF reported that 61% of respondents state losing concentration at work while 79% occasionally or frequently made errors as a result of being fatigued. Shift work, altered work schedules, long hours of continuous wakefulness, and sleep loss can create sleep and circadian disruptions that degrade waking fundions causing stress and fatigue. Review of the literature has proven void of information that links the combined effects of fatigue, stress, and workload to human performance. This paper will address which occupational factors within stress, fatigue, and workload were identified as occupational contributors to performance changes. The results of this research will be apglied to underlying models and algorithms that will help predict performance changes in control room operators.

Mock, Jessica↗
NASA NTRS2014

Embedded Data Acquisition Tools for Rotorcraft Diagnostic Sensors

Rotorcraft drive trains must withstand enormous pressure while operating continuously in extreme temperature and vibration environments. Captive components, such as planetary and spiral bevel gears, see enormous strain but are not accessible to fixed instrumentation, such as a piezoelectric transducer. Thus, it is difficult to directly monitor components that are most susceptible to damage. This innovation is a self-contained data processing unit within a specialized fixture that installs directly inside the rotating pinion gear in the gearbox. From this location, it detects and transmits high-resolution prognostic data to a fixed transceiver. The sensor is based on microelectromechanical systems (MEMS) technology and uses innovative circuit designs to capture high-bandwidth data and transmit it wirelessly from inside an operational helicopter transmission. With Ridgetop's advanced MEMS-based sensor, researchers have, for the first time, been able to extract high-resolution acoustic signatures wirelessly from sensors within the transmission that would otherwise be muffled by background gear noises. Ridgetop's innovative instrument will help researchers perform dynamic analysis of gear interaction and develop improved designs for gear components. In addition, data from this instrument can be used to validate new algorithms that detect and predict faults based on external acoustic signatures, for prognostic purposes. The result of this work will be an improvement in safety, performance, and cost for future generations of rotating components.

Wagoner, Robert↗
NASA NTRS2019

Radiometric Calibration of A Non-Imaging Airborne Spectrometer To Measure the Greenland Ice Sheet Surface

Methods to radiometrically calibrate a non-imaging airborne visible-to-shortwave infrared (VSWIR) spectrometer to measure the Greenland ice sheet surface are presented. Airborne VSWIR measurement performance for bright Greenland ice and dark bare rock/soil targets is compared against the MODerate resolution atmospheric TRANsmission (MODTRAN ® ) radiative transfer code (version 6.0), and a coincident Landsat 8 Operational Land Imager (OLI) acquisition on 29 July 2015 during an in-flight radiometric calibration experiment. Airborne remote sensing flights were carried out in northwestern Greenland in preparation for the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) laser altimeter mission. A total of nine science flights were conducted over the Greenland ice sheet, sea ice, and open-ocean water. The campaign's primary purpose was to correlate green laser pulse penetration into snow and ice with spectroscopic-derived surface properties. An experimental airborne instrument configuration that included a nadir-viewing (looking downward at the surface) non-imaging Analytical Spectral Devices (ASD) Inc. spectrometer that measured upwelling VSWIR (0.35 to 2.5 µm) spectral radiance (Wm −2 sr −1 µm −1 ) in the two-color Slope Imaging Multi-polarization Photon-Counting Lidar's (SIMPL) ground instantaneous field of view, and a zenith-viewing (looking upward at the sky) ASD spectrometer that measured VSWIR spectral irradiance (W m −2 nm −1 ) was flown. National Institute of Standards and Technology (NIST) traceable radiometric calibration procedures for laboratory, in-flight, and field environments are described in detail to achieve a targeted VSWIR measurement requirement of within 5 % to support calibration/validation efforts and remote sensing algorithm development. Our MODTRAN predictions for the 29 July flight line over dark and bright targets indicate that the airborne nadir-viewing spectrometer spectral radiance measurement uncertainty was between 0.6 % and 4.7 % for VSWIR wavelengths (0.4 to 2.0 µm) with atmospheric transmittance greater than 80 %. MODTRAN predictions for Landsat 8 OLI relative spectral response functions suggest that OLI is measuring 6 % to 16 % more top-of-atmosphere (TOA) spectral radiance from the Greenland ice sheet surface than was predicted using apparent reflectance spectra from the nadir-viewing spectrometer. While more investigation is required to convert airborne VSWIR spectral radiance into atmospherically corrected airborne surface reflectance, it is expected that airborne science flight data products will contribute to spectroscopic determination of Greenland ice sheet surface optical properties to improve understanding of their potential influence on ICESat-2 measurements.

Christopher J. Crawford↗
NASA NTRS2021

ICESat-2 Constraint Analysis and Monitoring System (CAMS)

The Ice, Cloud and land Elevation Satellite-2 was launched on 15 September 2018 with mission goals of collecting surface elevation data that can precisely measure ice sheet topography, cloud and aerosol heights, land topography, as well as vegetation height for ecosystems studies. The Constraint Analysis and Monitoring System (CAMS) was implemented as an ICESat-2 ground system element to perform Mission Planning and Spacecraft Safety Monitoring. Mission Planning requires CAMS to serve as an interface between the Project Science Office, the Instrument Support Facility and the Mission Operation Control Center. By ingesting inputs received from all three groups, the CAMS builds an optimized and deconflicted timeline of science and instrument activities. Based upon the timeline of activities, the CAMS utilizes a sophisticated set of algorithms to model and predict the location and pointing of the spacecraft instrument relative to the Earth and Sun. From the predicted position and pointing, the CAMS provides precise monitoring of instrument health, and performs space asset laser conjunction detection. Furthermore, the CAMS determines alternative plans to prevent detected health constraint violations or mitigate potential laser conjunctions. This paper provides an overview of the CAMS and presents the operational performance of planning science and instrument activities, the monitoring of instrument constraints for health and safety, and the space asset laser conjunction screening and mitigation process.

ICESat-2↗
DOE OSTI2021

Tutorial: Machine Learning and Artificial Intelligence in Batteries

Machine learning (ML) promises to compress the time needed to characterize battery performance, lifetime and safety. By coupling ML with physical models and metrics, that learning can bridge across materials, chemistries and cell designs. This tutorial will discuss the most popular ML techniques and resources and review recent work in the electrochemical literature. Applications include materials discovery, image recognition for quantitative microscopy analysis, fast charge algorithm development and life prediction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Twenty Years and Counting—Where are they? Practical Recommendations for Commercializing AI/ML for Intrusion Detection in the Nuclear Industry

Research and development into applications for improving equipment condition monitoring programs at nuclear facilities has been around since the 1990s. However, while the field has moved from using data-driven machine learning (ML) algorithms for detection and prediction of equipment degradation and failure to prognostic capabilities, these applications are still not widely used in the U.S. nuclear industry. Additionally, there has been significant effort in designing both data-driven and physics-based artificial intelligence (AI) and ML models for many other potential applications in the nuclear industry, including cyber intrusion detection systems (IDS). However, as the last twenty years in condition-based maintenance research has shown us, there are significant hurdles that must be overcome for deployment of IDS on plant systems. This paper provides a discussion on the practical recommendations that researchers should consider for successful adoption of AI/ML IDS in the nuclear industry.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

Presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

This is the conference paper accompanying an oral presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
NASA NTRS1984

Phase 2 development of Great Lakes algorithms for Nimbus-7 coastal zone color scanner

A series of experiments have been conducted in the Great Lakes designed to evaluate the application of the NIMBUS-7 Coastal Zone Color Scanner (CZCS). Atmospheric and water optical models were used to relate surface and subsurface measurements to satellite measured radiances. Absorption and scattering measurements were reduced to obtain a preliminary optical model for the Great Lakes. Algorithms were developed for geometric correction, correction for Rayleigh and aerosol path radiance, and prediction of chlorophyll-a pigment and suspended mineral concentrations. The atmospheric algorithm developed compared favorably with existing algorithms and was the only algorithm found to adequately predict the radiance variations in the 670 nm band. The atmospheric correction algorithm developed was designed to extract needed algorithm parameters from the CZCS radiance values. The Gordon/NOAA ocean algorithms could not be demonstrated to work for Great Lakes waters. Predicted values of chlorophyll-a concentration compared favorably with expected and measured data for several areas of the Great Lakes.

Tanis, Fred J.↗
NASA NTRS2013

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.

multivariate experimental data↗
DOE OSTI2023

Neural Networks-Based Inverter Control: Modeling and Adaptive Optimization for Smart Distribution Networks

The optimal voltage control of inverter-based resources, especially under the high penetration of solar photovoltaics, is critical to the stability of the distribution power system. However, the computational complexity as well as the coordinated operation performance of the voltage control optimization in the distribution power system limits the real-time applications. To mitigate this issue, a model-free based adaptive optimal control scheme for the smart inverter is proposed to maximize the active power generation, minimize the power loss, and maintain the bus voltages in smart distribution networks. An inverter-based optimization model for coordinated operation is first established, considering the uncertainties of renewable power generation. Subsequently, by collecting the data and control strategies, the neural networks (NNs) based algorithm is proposed to efficiently predict the best possible control strategy. The main objective of this scheme is to accurately predict candidate optimal solutions with near-negligible feasibility and optimization gaps, with the advantage of avoiding complicated iteration-based numerical algorithms. Thereafter, the co-simulation among OpenDSS, MATLAB, and Python is set up to fully take advantage of the three individual software. Experiments are conducted based on different control parameter characteristics and structures of NNs. Finally, the results reveal that an average mean squared error of 0.013 and 1 ms response time are achieved, which is lower than some state-of-the-art methods.

42 ENGINEERING↗
NASA NTRS1995

Linear and nonlinear trending and prediction for AVHRR time series data

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Smid, J.↗