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NASA NTRS1974

Computer and display systems for large physiological models

This paper describes the computer and display systems which were developed for physiological models at NASA's Environmental Physiology Lab, Johnson Space Center. The systems involve hardware and software for remote communications, computation, and display. Also described is the development of a capability to proceed from a physiologist's hypothesis to meaningful presentations of model and laboratory data. Two large hardware and software computer configurations and their graphic display systems are discussed. One system is for dedicated use in the laboratory, and the other system is for remote use on a time-sharing basis. It is concluded that these techniques can add a new dimension to physiological data analysis tasks.

Hassell, R. F.↗

NASA NTRS1977

Determination of scattering functions and their effects on remote sensing of turbidity in natural waters

The development of quantitative analytical procedures for relating scattered signals, measured by a remote sensor, was considered. The applications of a Monte Carlo simulation model for radiative transfer in turbid water are discussed. The model is designed to calculate the characteristics of the backscattered signal from an illuminated body of water as a function of the turbidity level, and the spectral properties of the suspended particulates. The optical properties of the environmental waters, necessary for model applications, were derived from available experimental data and/or calculated from Mie formalism. Results of applications of the model are presented.

Ghovanlou, A. H.↗

NASA NTRS2009

Collaborative Approaches in Developing Environmental and Safety Management Systems for Commercial Space Transportation

The Federal Aviation Administration (FAA), Office of Commercial Space Transportation (AST) licenses and permits U.S. commercial space launch and reentry activities, and licenses the operation of non-federal launch and reentry sites. ASTs mission is to ensure the protection of the public, property, and the national security and foreign policy interests of the United States during commercial space transportation activities and to encourage, facilitate, and promote U.S. commercial space transportation. AST faces unique challenges of ensuring the protection of public health and safety while facilitating and promoting U.S. commercial space transportation. AST has developed an Environmental Management System (EMS) and a Safety Management System (SMS) to help meet its mission. Although the EMS and SMS were developed independently, the systems share similar elements. Both systems follow a Plan-Do-Act-Check model in identifying potential environmental aspects or public safety hazards, assessing significance in terms of severity and likelihood of occurrence, developing approaches to reduce risk, and verifying that the risk is reduced. This paper will describe the similarities between ASTs EMS and SMS elements and how AST is building a collaborative approach in environmental and safety management to reduce impacts to the environment and risks to the public.

Federal Aviation Administration (FAA)↗

NASA NTRS1985

Cloud bidirectional reflectance functions - A comparison of experimental and theoretical results

A comparison of bidirectional reflectance models developed by radiative transfer theory calculations with empirical models developed from Nimbus 7 Earth Radiation Budget (ERB) measurements indicates good overall comparison, with rms differences of about 10 percent for solar zenith angles lower than 60 deg. The results of these comparisons demonstrate that the theoretical model provides a good description of the reflected radiance fields of clouds as seen by satellite. A cloud model based on Geosynchronous Operational Environmental Satellite (GOES) data was also compared with the Nimbus 7 general cloud model, and it is found that bidirectional models which compare well with the Nimbus 7 ERB results can be reproduced using GOES data.

Stuhlmann, R.↗

NASA NTRSDate not supplied

Efficient and Accurate Terrain Integration & Application to Modeling and Simulation of Planetary Landing Systems

As planetary spacecraft landing system complexity increases, pre-flight performance assessments include correspondingly more detailed and complex models that represent the various environmental elements and vehicle subsystems. Specifically, the relevance and criticality of planetary terrain modeling in the context of precision landing and hazard avoidance (PL&HA) has become evident as both commercial and national agencies return to the Lunar surface and beyond.

Terrain↗

NASA NTRS1979

Development of an Accelerated Test Design for Predicting the Service Life of the Solar Array at Mead, Nebraska

An accelerated life test is described which was developed to predict the life of the 25 kW photovoltaic array installed near Mead, Nebraska. A quantitative model for accelerating testing using multiple environmental stresses was used to develop the test design. The model accounts for the effects of thermal stress by a relation of the Arrhenius form. This relation was then corrected for the effects of nonthermal environmental stresses, such as relative humidity, atmospheric pollutants, and ultraviolet radiation. The correction factors for the nonthermal stresses included temperature-dependent exponents to account for the effects of interactions between thermal and nonthermal stresses on the rate of degradation of power output. The test conditions, measurements, and data analyses for the accelerated tests are presented. Constant-temperature, cyclic-temperature, and UV types of tests are specified, incorporating selected levels of relative humidity and chemical contamination and an imposed forward-bias current and static electric field.

Gaines, G. B.↗

NASA NTRS2018

Comparing Free-Free and Shaker Table Model Correlation Methods Using Jim Beam

Finite element model correlation as part of a spacecraft program has always been a challenge. For any NASA mission, the coupled system response of the spacecraft and launch vehicle can be determined analytically through a Coupled Loads Analysis (CLA), as it is not possible to test the spacecraft and launch vehicle coupled system before launch. The value of the CLA is highly dependent on the accuracy of the frequencies and mode shapes extracted from the spacecraft model. NASA standards require the spacecraft model used in the final Verification Loads Cycle to be correlated by either a modal test or by comparison of the model with Frequency Response Functions (FRFs) obtained during the environmental qualification test. Due to budgetary and time constraints, most programs opt to correlate the spacecraft dynamic model during the environmental qualification test, conducted on a large shaker table. For any model correlation effort, the key has always been finding a proper definition of the boundary conditions. This paper is a correlation case study to investigate the difference in responses of a simple structure using a free-free boundary, a fixed boundary on the shaker table, and a base-drive vibration test, all using identical instrumentation. The NAVCON Jim Beam test structure, featured in the IMAC round robin modal test of 2009, was selected as a simple, well recognized and well characterized structure to conduct this investigation. First, a free-free impact modal test of the Jim Beam was done as an experimental control. Second, the Jim Beam was mounted to a large 20,000 lbf shaker, and an impact modal test in this fixed configuration was conducted. Lastly, a vibration test of the Jim Beam was conducted on the shaker table. The free-free impact test, the fixed impact test, and the base-drive test were used to assess the effect of the shaker modes, evaluate the validity of fixed-base modeling assumptions, and compare final model correlation results between these boundary conditions.

Modal Test↗

NASA NTRS2018

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗

NASA NTRS2023

An Ensemble Neural Network Model for Predicting Rare-Earth Oxide and Silicate Heat Capacities at High Temperature

In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.

environmental barrier coatings↗

NASA NTRS2003

Molecular modeling of polymer composite-analyte interactions in electronic nose sensors

We report a molecular modeling study to investigate the polymer-carbon black (CB) composite-analyte interactions in resistive sensors. These sensors comprise the JPL electronic nose (ENose) sensing array developed for monitoring breathing air in human habitats. The polymer in the composite is modeled based on its stereoisomerism and sequence isomerism, while the CB is modeled as uncharged naphthalene rings with no hydrogens. The Dreiding 2.21 force field is used for the polymer, solvent molecules and graphite parameters are assigned to the carbon black atoms. A combination of molecular mechanics (MM) and molecular dynamics (NPT-MD and NVT-MD) techniques are used to obtain the equilibrium composite structure by inserting naphthalene rings in the polymer matrix. Polymers considered for this work include poly(4-vinylphenol), polyethylene oxide, and ethyl cellulose. Analytes studied are representative of both inorganic and organic compounds. The results are analyzed for the composite microstructure by calculating the radial distribution profiles as well as for the sensor response by predicting the interaction energies of the analytes with the composites. c2003 Elsevier Science B.V. All rights reserved.

NASA Center JPL↗

NASA NTRS1983

Modeling of bottom-related surface patterns imaged by synthetic aperture radar

A hydrodynamic electromagnetic model is developed in order to provide a qualitative and quantitative description of the relationship between Seasat synthetic aperture radar (SAR) signatures and the bottom topography of the ocean in the English Channel region of the North Sea. The model is based on environmental data for winds, currents, and depth changes, and the SAR parameters of frequency polarization, incidence angle, and resolution cell size. The data are used as inputs and SAR backscatter changes are predicted for individual topographic changes on the ocean floor. It is found that the model estimates of backscatter values are in good agreement with actual Seasat SAR-observed backscatter values. A comparison of the model and actual data shows agreement to be within 1.5 dB. The model is considered to be valid for only shallow water areas (less than 50 meters in depth). It is suggested that for bottom features to be visible on SAR imagery at greater depths, a moderate-to-high velocity current of at least 0.4 m/s and a moderate wind no more than 7.5 m/sec must be present.

Lyzenga, D. R.↗

NASA NTRS2014

Development and Implementation of Dynamic Scripts to Execute Cycled WRF/GSI Forecasts

Automating the coupling of data assimilation (DA) and modeling systems is a unique challenge in the numerical weather prediction (NWP) research community. In recent years, the Development Testbed Center (DTC) has released well-documented tools such as the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolation (GSI) DA system that can be easily downloaded, installed, and run by researchers on their local systems. However, developing a coupled system in which the various preprocessing, DA, model, and postprocessing capabilities are all integrated can be labor-intensive if one has little experience with any of these individual systems. Additionally, operational modeling entities generally have specific coupling methodologies that can take time to understand and develop code to implement properly. To better enable collaborating researchers to perform modeling and DA experiments with GSI, the Short-term Prediction Research and Transition (SPoRT) Center has developed a set of Perl scripts that couple GSI and WRF in a cycling methodology consistent with the use of real-time, regional observation data from the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC). Because Perl is open source, the code can be easily downloaded and executed regardless of the user's native shell environment. This paper will provide a description of this open-source code and descriptions of a number of the use cases that have been performed by SPoRT collaborators using the scripts on different computing systems.

Zavodsky, Bradley↗

NASA NTRS2021

Carolina Coastal Plain Ecological Forecasting: Utilizing NASA Earth Observations to Map Suitable Venus Flytrap Habitat in an Effort to Inform Conservation, Seed Banking, and Reintroduction in the Carolina Coastal Plain and Sandhills Regions

Although the carnivorous plant Venus flytrap (Dionaea muscipula) is recognized globally, its native range is restricted to a small portion of the North and South Carolina Coastal Plain and Sandhills. Within this limited range, Venus flytrap populations are threatened by habitat loss, fire suppression, and poaching. NASA DEVELOP partnered with the North Carolina Botanical Garden (NCBG), the University of North Carolina Herbarium (NCU), and the North Carolina Natural Heritage Program (NCNHP) to support Venus flytrap conservation. The team developed models based on species presence data and environmental variables using the Software for Assisted Habitat Modeling (SAHM) to create a 2021 habitat suitability map for Venus flytrap. These models incorporated Earth observations collected by Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), and Sentinel-2 Multispectral Instrument (MSI). To predict areas at high risk of development, the team produced a 2050 land-use change map using TerrSet Land Change Modeler. The team found potential areas of conflict between predicted habitat and forecasted future development. Many areas of suitable habitat were concentrated along the coast where development was likely to occur, placing populations there at risk of extirpation. Overlaying suitable habitat with forecasted land change also identified suitable habitats with minimal risk of development, which may serve as lasting Venus flytrap habitat. These results can inform the NCBG and NCNHP’s conservation decision-making, including targeted seed banking, reintroduction, and prioritization of enduring habitats for protection and management.

Monika Rock↗

NASA NTRS2022

Carolina Coastal Plain Ecological Forecasting

Monika Rock↗

NASA NTRS2001

Entropy generation method to quantify thermal comfort

The present paper presents a thermodynamic approach to assess the quality of human-thermal environment interaction and quantify thermal comfort. The approach involves development of entropy generation term by applying second law of thermodynamics to the combined human-environment system. The entropy generation term combines both human thermal physiological responses and thermal environmental variables to provide an objective measure of thermal comfort. The original concepts and definitions form the basis for establishing the mathematical relationship between thermal comfort and entropy generation term. As a result of logic and deterministic approach, an Objective Thermal Comfort Index (OTCI) is defined and established as a function of entropy generation. In order to verify the entropy-based thermal comfort model, human thermal physiological responses due to changes in ambient conditions are simulated using a well established and validated human thermal model developed at the Institute of Environmental Research of Kansas State University (KSU). The finite element based KSU human thermal computer model is being utilized as a "Computational Environmental Chamber" to conduct series of simulations to examine the human thermal responses to different environmental conditions. The output from the simulation, which include human thermal responses and input data consisting of environmental conditions are fed into the thermal comfort model. Continuous monitoring of thermal comfort in comfortable and extreme environmental conditions is demonstrated. The Objective Thermal Comfort values obtained from the entropy-based model are validated against regression based Predicted Mean Vote (PMV) values. Using the corresponding air temperatures and vapor pressures that were used in the computer simulation in the regression equation generates the PMV values. The preliminary results indicate that the OTCI and PMV values correlate well under ideal conditions. However, an experimental study is needed in the future to fully establish the validity of the OTCI formula and the model. One of the practical applications of this index is that could it be integrated in thermal control systems to develop human-centered environmental control systems for potential use in aircraft, mass transit vehicles, intelligent building systems, and space vehicles.

Models, Biological↗

NASA NTRS2001

Quasi-Equilibrium States in the Tropics Simulated by a Cloud-Resolving Model: Specific Features and Budget Analysis - Part 1

A series of long-term integrations using the two-dimensional Goddard Cumulus Ensemble (GCE) model were performed by altering imposed environmental components to produce various quasi-equilibrium thermodynamic states. Model results show that the genesis of a warm/wet quasi-equilibrium state is mainly due to either strong vertical wind shear (from nudging) or large surface fluxes (from strong surface winds), while a cold/dry quasi-equilibrium state is attributed to a remarkably weakened mixed-wind shear (from vertical mixing due to deep convection) along with weak surface winds. In general, latent heat flux and net large-scale temperature forcing, the two dominant physical processes, dominate in the beginning stage of the simulated convective systems, then considerably weaken in the final stage, which leads to quasi-equilibrium states. A higher thermodynamic regime is found to produce a larger rainfall amount, as convective clouds are the leading source of rainfall over stratiform clouds even though the former occupy much less area. Moreover, convective clouds are more likely to occur in the presence of strong surface winds (latent heat flux), while stratiform clouds (especially the well-organized type) are favored in conditions with strong wind shear (large-scale forcing). The convective systems, which consist of distinct cloud types due to the variation in horizontal winds, are also found to propagate differently. Accordingly, convective systems with mixed-wind shear generally propagate in the direction of shear, while the system with strong (multidirectional) wind shear propagates in a more complex way. Based on the results from the temperature (Q1) and moisture (Q2) budgets, cloud-scale eddies are found to act as a hydrodynamic 'vehicle' that cascades the heat and moisture vertically. Several other specific features such as atmospheric stability, CAPE, and mass fluxes are also investigated and found to be significantly different between diverse quasi-equilibrium states. Detailed comparisons between the various states are presented.

Shie, C.-L.↗

NASA NTRS2007

Probabilistic Thermal Analysis During Mars Reconnaissance Orbiter Aerobraking

A method for performing a probabilistic thermal analysis during aerobraking has been developed. The analysis is performed on the Mars Reconnaissance Orbiter solar array during aerobraking. The methodology makes use of a response surface model derived from a more complex finite element thermal model of the solar array. The response surface is a quadratic equation which calculates the peak temperature for a given orbit drag pass at a specific location on the solar panel. Five different response surface equations are used, one of which predicts the overall maximum solar panel temperature, and the remaining four predict the temperatures of the solar panel thermal sensors. The variables used to define the response surface can be characterized as either environmental, material property, or modeling variables. Response surface variables are statistically varied in a Monte Carlo simulation. The Monte Carlo simulation produces mean temperatures and 3 sigma bounds as well as the probability of exceeding the designated flight allowable temperature for a given orbit. Response surface temperature predictions are compared with the Mars Reconnaissance Orbiter flight temperature data.

Dec, John A.↗

NASA NTRS1999

Global Precipitation during the 1997-98 El Nino and Initiation of the 1998-99 La Nina

The 1997-99 ENSO (El nino Southern Oscillation) cycle was very powerful, but also well observed. The best satellite rainfall estimates combined with gauge observations allow for a global analysis of precipitation anomalies accompanying the 1997-98 El Nino and initiation of the 1998-99 La Nina. For the period April 1997 to March 1998 the central to eastern Pacific, southeastern and western U.S., Argentina, eastern Africa, South China, eastern Russia, and North Atlantic were all more than two standard deviations wetter than normal. During the same year the Maritime Continent, eastern Indian Ocean, subtropical North Pacific, northeastern South America, and much of the mid- latitude southern oceans were more than two standard deviations drier than normal. An analysis of the evolution of the El Nino and accompanying precipitation anomalies revealed that a dry Maritime Continent led the formation of the El Nino SST (Sea Surface Temperature), while in the central Pacific, precipitation anomalies lagged the El Nino SST by a season. A rapid transition from El Nino to La Nina occurred in May 1998, but as early as October-November 1997 precipitation indices captured precursor changes in Pacific rainfall anomalies. Differences were found between observed and modeled [NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis] precipitation anomalies for 1997 and 98. In particular, the model had a bias towards positive precipitation anomalies and the magnitudes of the anomalies in the equatorial Pacific were small compared to the observations. Also, the evolution of the precipitation field, including the drying of the Maritime Continent and eastward progression of rainfall in the equatorial Pacific, was less pronounced for the model compared to the observations. One degree daily estimates of rainfall show clearly the MaddenJulian Oscillation and related westerly wind burst events over the Maritime Continent, which are key indicators for the onset of El Nino.

Curtis, Scott↗