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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Predicting lettuce canopy photosynthesis with statistical and neural network models

An artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for 'Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 micromoles mol-1), photosynthetic photon flux (PPF) (600 to 1100 micromoles m-2 s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (> or = 6 days into the future).

Non-NASA Center↗
NASA NTRS2005

Entry Abort Determination Using Non-Adaptive Neural Networks for Mars Precision Landers

The 2009 Mars Science Laboratory (MSL) will attempt the first precision landing on Mars using a modified version of the Apollo Earth entry guidance program. The guidance routine, Entry Terminal Point Controller (ETPC), commands the deployment of a supersonic parachute after converging the range to the landing target. For very dispersed cases, ETPC may not converge the range to the target and safely command parachute deployment within Mach number and dynamic pressure constraints. A full-lift up abort can save 85% of these failed trajectories while abandoning the precision landing objective. Though current MSL requirements do not call for an abort capability, an autonomous abort capability may be desired, for this mission or future Mars precision landers, to make the vehicle more robust. The application of artificial neural networks (NNs) as an abort determination technique was evaluated by personnel at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC). In order to implement an abort, a failed trajectory needs to be recognized in real time. Abort determination is dependent upon several trajectory parameters whose relationships to vehicle survival are not well understood, and yet the lander must be trained to recognize unsafe situations. Artificial neural networks (NNs) provide a way to model these parameters and can provide MSL with the artificial intelligence necessary to independently declare an abort. Using the 2009 Mars Science Laboratory (MSL) mission as a case study, a non-adaptive NN was designed, trained and tested using Monte Carlo simulations of MSL descent and incorporated into ETPC. Neural network theory, the development history of the MSL NN, and initial testing with severe dust storm entry trajectory cases are discussed in Reference 1 and will not be repeated here. That analysis demonstrated that NNs are capable of recognizing failed descent trajectories and can significantly increase the survivability of MSL for very dispersed cases. NN testing was then broadened to evaluate fully dispersed entry trajectories. The NN correctly classified 99.7% of descent trajectories as abort or nonabort and reduced the probability of an unsafe parachute deployment by 83%. This second, broader testing phase is discussed in this paper.

Graybeal, Sarah R.↗
NASA NTRS2019

Sea Surface Salinity Retrievals from Aquarius Using Neural Networks

Even though the Sea Surface Salinity (SSS) retrieved from Aquarius are generally very close to in-situ measurements, the level of similarity varies with the region and with the circumstances of the observations (wind speed, sea surface temperature, etc.). SSS is currently retrieved from the brightness temperatures measured by Aquarius and applying the current theoretical model for the propagation and emission of the natural thermal radiation. In this contribution we consider an alternative retrieval approach based on a Neural Network (NN) with the goal of improving the subsets of Aquarius SSS data that are in poorer agreement within-situ measurements. The subset considered here are the SSS retrieved at latitudes higher than 30 ̊. The output of the NN approach are compared against in-situ measurements using four statistical metrics (correlation coefficient, bias, RMSD and 5% trimmed range). The output of the NN and the nominal Aquarius SSS are compared against SSS values from in-situ measurements and from ocean models. From these comparisons it appears that the output of the NN matches the in-situ measurements better than the nominal Aquarius SSS.

Soldo, Yan↗
NASA NTRSDate not supplied

An Online Reinforcement Learning Controller Design For Mars Ascent Vehicle

This paper presents a neural network (NN) approximator-based online reinforcement learning (ORL) controller design for Mars Ascent Vehicle (MAV) under parametric variation and significant external disturbances. The ORL controller, which does not require any offline training, involves two NNs where an action NN produces optimal short-term control performance while a critic NN evaluates the performance of the action NN using an approximated cost function. The simulation example with comparisons against baseline Proportional-Integral-Derivative (PID) and gain scheduled pole-placement PID (GS-PP-PID) controllers show the proposed controller’s effectiveness and robustness under parametric variation and high external disturbances.

Han Woong Bae↗
NASA NTRS2022

Instantaneous Photosynthetically Available Radiation (IPAR) prediction models based on Neural Network for ocean waters.

Instantaneous photosynthetically available radiation (IPAR) at the ocean surface and its vertical profile below the surface play a critical role in models to calculate net primary productivity of marine phytoplankton. In this work, we report two IPAR prediction models based on neural network (NN) approach, one for open ocean and the other for coastal waters. These models are trained, validated, and tested using a large volume of synthetic datasets for open ocean and coastal waters simulated by a radiative transfer model. Our NN models are designed to predict IPAR under a large range of atmospheric and oceanic conditions. The NN models can compute subsurface IPAR profile very accurately up to euphotic zone depth. The root mean square errors associated with the diffuse attenuation coefficient of IPAR are less than 0.011 𝑚−1 and 0.036 𝑚−1 for open ocean and coastal waters respectively. The performance of the NN models is better than presently available semi analytical models, with significant superiority in coastal waters.

PACE↗
NASA NTRS2017

Data Assimilation to Extract Soil Moisture Information From SMAP Observations

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, they can be used to reduce the need for localized bias correction techniques typically implemented in data assimilation (DA) systems that tend to remove some of the independent information provided by satellite observations. Here, we use a statistical neural network (NN) algorithm to retrieve SMAP (Soil Moisture Active Passive) surface soil moisture estimates in the climatology of the NASA Catchment land surface model. Assimilating these estimates without additional bias correction is found to significantly reduce the model error and increase the temporal correlation against SMAP CalVal in situ observations over the contiguous United States. A comparison with assimilation experiments using traditional bias correction techniques shows that the NN approach better retains the independent information provided by the SMAP observations and thus leads to larger model skill improvements during the assimilation. A comparison with the SMAP Level 4 product shows that the NN approach is able to provide comparable skill improvements and thus represents a viable assimilation approach.

D↗
NASA NTRS1989

Genetic algorithms applied to the scheduling of the Hubble Space Telescope

A prototype system employing a genetic algorithm (GA) has been developed to support the scheduling of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network (NN) based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better.

Sponsler, Jeffrey L.↗
NASA NTRS2001

Proton-Nucleus Elastic Cross Sections Using Two-Body In-Medium Scattering Amplitudes

Recently, a method was developed of extracting nucleon-nucleon (NN) cross sections in the medium directly from experiment. The in-medium NN cross sections form the basic ingredients of several heavy-ion scattering approaches including the coupled-channel approach developed at the Langley Research Center. The ratio of the real to the imaginary part of the two-body scattering amplitude in the medium was investigated. These ratios are used in combination with the in-medium NN cross sections to calculate elastic proton-nucleus cross sections. The agreement is excellent with the available experimental data. These cross sections are needed for the radiation risk assessment of space missions.

Tripathi, R. K.↗
NASA NTRS2004

A Tool for Verification and Validation of Neural Network Based Adaptive Controllers for High Assurance Systems

High reliability of mission- and safety-critical software systems has been identified by NASA as a high-priority technology challenge. We present an approach for the performance analysis of a neural network (NN) in an advanced adaptive control system. This problem is important in the context of safety-critical applications that require certification, such as flight software in aircraft. We have developed a tool to measure the performance of the NN during operation by calculating a confidence interval (error bar) around the NN's output. Our tool can be used during pre-deployment verification as well as monitoring the network performance during operation. The tool has been implemented in Simulink and simulation results on a F-15 aircraft are presented.

Gupta, Pramod↗
NASA NTRS2007

Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

Jafri, Madiha J.↗
NASA NTRS2010

Comparison of Node-Centered and Cell-Centered Unstructured Finite-Volume Discretizations: Inviscid Fluxes

Cell-centered and node-centered approaches have been compared for unstructured finite-volume discretization of inviscid fluxes. The grids range from regular grids to irregular grids, including mixed-element grids and grids with random perturbations of nodes. Accuracy, complexity, and convergence rates of defect-correction iterations are studied for eight nominally second-order accurate schemes: two node-centered schemes with weighted and unweighted least-squares (LSQ) methods for gradient reconstruction and six cell-centered schemes two node-averaging with and without clipping and four schemes that employ different stencils for LSQ gradient reconstruction. The cell-centered nearest-neighbor (CC-NN) scheme has the lowest complexity; a version of the scheme that involves smart augmentation of the LSQ stencil (CC-SA) has only marginal complexity increase. All other schemes have larger complexity; complexity of node-centered (NC) schemes are somewhat lower than complexity of cell-centered node-averaging (CC-NA) and full-augmentation (CC-FA) schemes. On highly anisotropic grids typical of those encountered in grid adaptation, discretization errors of five of the six cell-centered schemes converge with second order on all tested grids; the CC-NA scheme with clipping degrades solution accuracy to first order. The NC schemes converge with second order on regular and/or triangular grids and with first order on perturbed quadrilaterals and mixed-element grids. All schemes may produce large relative errors in gradient reconstruction on grids with perturbed nodes. Defect-correction iterations for schemes employing weighted least-square gradient reconstruction diverge on perturbed stretched grids. Overall, the CC-NN and CC-SA schemes offer the best options of the lowest complexity and secondorder discretization errors. On anisotropic grids over a curved body typical of turbulent flow simulations, the discretization errors converge with second order and are small for the CC-NN, CC-SA, and CC-FA schemes on all grids and for NC schemes on triangular grids; the discretization errors of the CC-NA scheme without clipping do not converge on irregular grids. Accurate gradient reconstruction can be achieved by introducing a local approximate mapping; without approximate mapping, only the NC scheme with weighted LSQ method provides accurate gradients. Defect correction iterations for the CC-NA scheme without clipping diverge; for the NC scheme with weighted LSQ method, the iterations either diverge or converge very slowly. The best option in curved geometries is the CC-SA scheme that offers low complexity, second-order discretization errors, and fast convergence.

Diskin, Boris↗
NASA NTRS2017

Satellite-Based Drought Reporting on the Navajo Nation

The Navajo Nation (NN) is the largest reservation in the US, and faces challenges related to water management during long-term and widespread drought episodes. The Navajo Nation is a federally recognized tribe, which has boundaries within Arizona, New Mexico, and Utah. The Navajo Nation has a land area of over 70,000 square kilometers. The Navajo Nation Department of Water Resources (NNDWR) reports on drought and climatic conditions through the use of regional Standardized Precipitation Index (SPI) values and a network of in-situ rainfall, streamflow, and climate data. However, these data sources lack the spatial detail and consistent measurements needed to provide a coherent understanding of the drought regime within the Nation's regional boundaries. This project, as part of NASA's Western Water Applications Office (WWAO), improves upon the recently developed Drought Severity Assessment Tool (DSAT) to ingest satellite-based precipitation data to generate SPI values for specific administrative boundaries within the reservation. The tool aims to: (1) generate SPI values and summary statistics for regions of interest on various timescales, (2) to visualize SPI values within a web-map application, and (3) produce maps and comparative statistical outputs in the format required for annual drought reporting. The co-development of the DSAT with NN partners is integral to increasing the sustained use of Earth Observations for water management applications. This tool will provide data to support the NN in allocation of drought contingency dollars to the regions most adversely impacted by declines in water availability.

Satellite-based↗
NASA NTRS2024

Noise Reduction for Solar-Induced Fluorescence Retrievals Using Machine Learning and Principal Component Analysis: Simulations and Applications to GOME-2 Satellite Retrievals

We use a spectral-based approach that employs principal component analysis along with a relatively shallow artificial neural network (NN) to substantially reduce noise and other artifacts in terrestrial chlorophyll solar-induced fluorescence (SIF) retrievals. SIF is a very small emission at red and far-red wavelengths that is difficult to measure and is highly sensitive to random errors and systematic artifacts. Our approach relies upon an assumption that a trained NN can effectively reconstruct the total SIF signal from a relatively small number of leading principal components of the satellite-observed far-red radiance spectra without using information from the trailing modes that contain most of the random errors. We test the approach with simulated reflectance spectra produced with a full atmospheric and surface radiative transfer model using different observing and geophysical parameters and various noise levels. Resulting noisy and noise-reduced retrieved SIF values are compared with true values to assess performance. We then apply our noise reduction approach to SIF derived from two different satellite spectrometers. For evaluation, since the truth in this case is unknown, we compare SIF retrievals from two independent sensors with each other. We also compare the noise-reduced SIF temporal variations with those from an independent gross primary product (GPP) product that should display similar variations. Results show that our noise reduction approach improves capture of SIF seasonal and inter-annual variability. Our approach should be applicable to many noisy data products derived from spectral measurements. Our methodology does not replace the original retrieval algorithms; rather the original noisy retrievals are needed as the target for the NN training process.

noise reduction, solar-induced fluorescence, SIF, ↗
NASA NTRS1984

Flat-plate solar array project process development area, process research of non-CZ silicon material

The program is designed to investigate the fabrication of solar cells on N-type base material by a simultaneous diffusion of N-type and P-type dopants to form an P(+)NN(+) structure. The results of simultaneous diffusion experiments are being compared to cells fabricated using sequential diffusion of dopants into N-base material in the same resistivity range. The process used for the fabrication of the simultaneously diffused P(+)NN(+) cells follows the standard Westinghouse baseline sequence for P-base material except that the two diffusion processes (boron and phosphorus) are replaced by a single diffusion step. All experiments are carried out on N-type dendritic web grown in the Westinghouse pre-pilot facility. The resistivities vary from 0.5 (UC OMEGA)cm to 5 (UC OMEGA)cm. The dopant sources used for both the simultaneous and sequential diffusion experiments are commercial metallorganic solutions with phosphorus or boron components. After these liquids are applied to the web surface, they are baked to form a hard glass which acts as a diffusion source at elevated temperatures. In experiments performed thus far, cells produced in sequential diffusion tests have properties essentially equal to the baseline N(+)PP(+) cells. However, the simultaneous diffusions have produced cells with much lower IV characteristics mainly due to cross-doping of the sources at the diffusion temperature. This cross-doping is due to the high vapor pressure phosphorus (applied as a metallorganic to the back surface) diffusion through the SiO2 mask and then acting as a diffusant source for the front surface.

Campbell, R. B.↗
NASA NTRS1993

Relativistic proton-nucleus scattering and one-boson-exchange models

Relativistic p-(Ca-40) elastic scattering observables are calculated using four sets of relativistic NN amplitudes obtained from different one-boson-exchange (OBE) models. The first two sets are based upon a relativistic equation in which one particle is on mass shell and the other two sets are obtained from a quasipotential reduction of the Bethe-Salpeter equation. Results at 200, 300, and 500 MeV are presented for these amplitudes. Differences between the predictions of these models provide a study of the uncertainty in constructing Dirac optical potentials from OBE-based NN amplitudes.

Maung, Khin Maung↗
NASA NTRS1992

Cohesive Energy-Lattice Constant and Bulk Modulus-Lattice Constant Relationships: Alkali Halides, Ag Halides, Tl Halides

In this note we present two expressions relating the cohesive energy, E(sub coh), and the zero pressure isothermal bulk modulus, B(sub 0), of the alkali halides. Ag halides and TI halides, with the nearest neighbor distances, d(sub nn). First, we show that the product E(sub coh)d(sub 0) within families of halide crystals with common crystal structure is to a good approximation constant, with maximum rms deviation of plus or minus 2%. Secondly, we demonstrate that within families of halide crystals with a common cation and common crystal structure the product B(sub 0)d(sup 3.5)(sub nn) is a good approximation constant, with maximum rms deviation of plus or minus 1.36%.

Schlosser, Herbert↗
NASA NTRS1998

Parameterizations of Pion Energy Spectrum in Nucleon-Nucleon Collisions

The effects of pion (PI) production are expected to play an important role in radiation exposures in the upper atmosphere or on the Martian surface. Nuclear databases for describing pion production are developed for radiation transport codes to support these studies. We analyze the secondary energy spectrum of pions produced in nucleon-nucleon (NN) collisions in the relativistic one-pion exchange model. Parametric formulas of the isospin cross sections for one-pion production channels are discussed and are used to renormalize the model spectrum. Energy spectra for the deuteron related channels (NN yields dPi) are also described.

Cucinotta, Franics A.↗