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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Gravity of Living Systems: May the Force Be With You

Gravity, the force which shapes the architecture of organisms from single cells to dinosaurs, has been the most constant environmental factor during the evolution of species on Earth. With long-duration space flight, an understanding of how gravity affects living systems gains greater urgency in order to maintain the health and performance of crews who will explore the solar system. For example, the cardiovascular and musculoskeletal systems are normally exposed to gravitational gradients of blood pressure and weight on Earth. Such gradients increase blood pressure and tissue weight in dependent tissues of the body. Thus, from a physiologic standpoint, these systems are greatly affected by altered gravity. Exposure to actual and simulated microgravity causes blood and tissue fluid to shift from the legs to the head. Studies of humans in space have documented facial edema, space adaptation syndrome, decreased plasma volume, muscle atrophy, and loss of bone strength. Return of astronauts to Earth is accompanied by orthostatic intolerance, decreased neuromuscular coordination, and reduced exercise capacity. These factors decrease performance during descent from orbit and increase risk during emergency egress from the space craft. Models of simulated microgravity include 60 head-down tilt, immersion, and prolonged horizontal bedrest. Head-down tilt and dry immersion are the most accepted models and studies using these models of up to one year have been performed in Russia. Sensitive animal models which offer clear insights into the role of gravity on structure and function include the developing giraffe and snakes from various habitats. Finally, possible countermeasures to speed readaptation of astronauts to gravity after prolonged space flight include exercise, lower body negative pressure, and centrifugation.

Hargens, Alan R.↗
NASA NTRS2011

Assessment of NASA's Physiographic and Meteorological Datasets as Input to HSPF and SWAT Hydrological Models

This paper documents the use of simulated Moderate Resolution Imaging Spectroradiometer land use/land cover (MODIS-LULC), NASA-LIS generated precipitation and evapo-transpiration (ET), and Shuttle Radar Topography Mission (SRTM) datasets (in conjunction with standard land use, topographical and meteorological datasets) as input to hydrological models routinely used by the watershed hydrology modeling community. The study is focused in coastal watersheds in the Mississippi Gulf Coast although one of the test cases focuses in an inland watershed located in northeastern State of Mississippi, USA. The decision support tools (DSTs) into which the NASA datasets were assimilated were the Soil Water & Assessment Tool (SWAT) and the Hydrological Simulation Program FORTRAN (HSPF). These DSTs are endorsed by several US government agencies (EPA, FEMA, USGS) for water resources management strategies. These models use physiographic and meteorological data extensively. Precipitation gages and USGS gage stations in the region were used to calibrate several HSPF and SWAT model applications. Land use and topographical datasets were swapped to assess model output sensitivities. NASA-LIS meteorological data were introduced in the calibrated model applications for simulation of watershed hydrology for a time period in which no weather data were available (1997-2006). The performance of the NASA datasets in the context of hydrological modeling was assessed through comparison of measured and model-simulated hydrographs. Overall, NASA datasets were as useful as standard land use, topographical , and meteorological datasets. Moreover, NASA datasets were used for performing analyses that the standard datasets could not made possible, e.g., introduction of land use dynamics into hydrological simulations

Alacron, Vladimir J.↗
NASA NTRS1989

A Monte Carlo/response surface strategy for sensitivity analysis: application to a dynamic model of vegetative plant growth

We describe the application of a strategy for conducting a sensitivity analysis for a complex dynamic model. The procedure involves preliminary screening of parameter sensitivities by numerical estimation of linear sensitivity coefficients, followed by generation of a response surface based on Monte Carlo simulation. Application is to a physiological model of the vegetative growth of soybean plants. The analysis provides insights as to the relative importance of certain physiological processes in controlling plant growth. Advantages and disadvantages of the strategy are discussed.

Non-NASA Center↗
NASA NTRS1990

The theoretical relationship between foliage temperature and canopy resistance in sparse crops

One-dimensional, sparse-crop interaction theory is reformulated to allow calculation of the canopy resistance from measurements of foliage temperature. A submodel is introduced to describe eddy diffusion within the canopy which provides a simple, empirical simulation of the reported behavior obtained from a second-order closure model. The sensitivity of the calculated canopy resistance to the parameters and formulas assumed in the model is investigated. The calculation is shown to exhibit a significant but acceptable sensitivity to extreme changes in canopy aerodynamics, and to changes in the surface resistance of the substrate beneath the canopy at high and intermediate values of leaf area index. In very sparse crops changes in the surface resistance of the substrate are shown to contaminate the calculated canopy resistance, tending to amplify the apparent response to changes in water availability. The theory is developed to allow the use of a measurement of substrate temperature as an option to mitigate this contamination.

Shuttleworth, W. James↗
DOE OSTI2024

Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions

Methane (CH 4 ) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH 4 fluxes is examined at 14 FLUXNET- CH 4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH 4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH 4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1980

Software reliability: A comparison of results obtained from established software reliability models

Two models of the software error detection process are compared, the Jelinski-Moranda model and a Bayes inference model. Simulation techniques are used to generate software related system failure data which is analyzed by both models. Point estimates and confidence limits are compared. It is demonstrated that uncertainty may be considerable for reasonable samples sizes and should be considered in any application of these techniques. The Jelinski-Moranda model is sensitive to the failure of data to follow internal assumptions of the model, often not providing any point estimates, a factor which may limit its usefulness in many real world situations. The Bayes model is shown to respond to the introduction of additional errors in the software correction process, a condition where error counting models such as the Jelinski-Moranda generally fail to converge.

Horn, M. H.↗
NASA NTRS1982

Predictabilty of time averages: The influence of the boundary forcing

The physical mechanisms through which changes in the boundary forcings of SST, soil moisture, albedo, sea ice, and snow influence the atmospheric circulation are discussed. Results of numerical experiments conducted with the GLAS climate model to determine the sensitivity of the model atmosphere to changes in boundary conditions of SST, soil moisture, and albedo over limited regions are dicussed. It is found that changes in SST and soil moisture in the tropic produce large changes in the atmospheric circulation and rainfall over the tropics as well as over mid-latitudes.

Shukla, J.↗
NASA NTRS1985

Sensitivity of a thermodynamic sea ice model with leads to time step size

The characteristics of sea ice models, developed to study the physics of the growth and melt of ice at the ocean surface and the variations in ice extent, depend on the size of the time step. Thus, to study longer-term variations within a reasonable computer budget, a model with a scheme allowing longer time steps has been constructed. However, the results produced by the model can definitely depend on the length of the time step. The sensitivity of a model to time-step size can be reduced by appropriate approaches. The present investigation is concerned with experiments which use a formulation of a lead parameterization that can be considered as a first step toward the development of a lead parameterization suitable for a use in long-term climate studies.

Ledley, T. S.↗
NASA NTRS1985

Mesoscale research activities with the LAMPS model

Researchers achieved full implementation of the LAMPS mesoscale model on the Atmospheric Sciences Division computer and derived balanced and real wind initial states for three case studies: March 6, April 24, April 26, 1982. Numerical simulations were performed for three separate studies: (1) a satellite moisture data impact study using Vertical Atmospheric Sounder (VAS) precipitable water as a constraint on model initial state moisture analyses; (2) an evaluation of mesoscale model precipitation simulation accuracy with and without convective parameterization; and (3) the sensitivity of model precipitation to mesoscale detail of moisture and vertical motion in an initial state.

Kalb, M. W.↗
NASA NTRS1986

Temporal sensitivity

Human visual temporal sensitivity is examined. The stimuli used to measure temporal sensitivity are described and the linear systems theory is reviewed in terms of temporal sensitivity. A working model which represents temporal sensitivity is proposed. The visibility of a number of temporal wave forms, sinusoids, rectangular pulses, and pulse pairs, is analyzed. The relation between spatial and temporal effects is studied. Temporal variations induced by image motion and the effects of light adaptation on temporal sensitivity are considered.

Watson, Andrew B.↗
NASA NTRS1997

Atmospheric Effects of Subsonic Aircraft: Interim Assessment Report of the Advanced Subsonic Technology Program

This first interim assessment of the subsonic assessment (SASS) project attempts to summarize concisely the status of our knowledge concerning the impacts of present and future subsonic aircraft fleets. It also highlights the major areas of scientific uncertainty, through review of existing data bases and model-based sensitivity studies. In view of the need for substantial improvements in both model formulations and experimental databases, this interim assessment cannot provide confident numerical predictions of aviation impacts. However, a number of quantitative estimates are presented, which provide some guidance to policy makers.

Friedl, Randall R.↗
NASA NTRS2019

Pilot Sensitivity to Simulator Flight Dynamics Model Formulation for Stall Training

A piloted simulation study was performed in the Cockpit Motion Facility at the National Aeronautics and Space Administration Langley Research Center. The research was motivated by the desire to reduce the commercial transport airplane fatal accident rate due to in-flight loss of control. The purpose of this study, which focused on a generic T-tail transport airplane, was to assess pilot sensitivity to flight dynamics model formulation used during a simulator stall recognition and recovery training/demonstration profile. To accomplish this, the flight dynamics model was designed with many configuration options. The model options were based on recently acquired static and dynamic stability and control data from sources that included wind tunnel, water tunnel, and computational fluid dynamics. The results, which are specific to a transport airplane stall recognition and recovery guided demonstration scenario, showed the two most important aerodynamic effects (other than stick pusher) to model were stall roll- off and the longitudinal static stability characteristic associated with the pitch break.

Cunningham, Kevin↗
NASA NTRS1986

The evaluation of the OSGLR algorithm for restructurable controls

The detection and isolation of commercial aircraft control surface and actuator failures using the orthogonal series generalized likelihood ratio (OSGLR) test was evaluated. The OSGLR algorithm was chosen as the most promising algorithm based on a preliminary evaluation of three failure detection and isolation (FDI) algorithms (the detection filter, the generalized likelihood ratio test, and the OSGLR test) and a survey of the literature. One difficulty of analytic FDI techniques and the OSGLR algorithm in particular is their sensitivity to modeling errors. Therefore, methods of improving the robustness of the algorithm were examined with the incorporation of age-weighting into the algorithm being the most effective approach, significantly reducing the sensitivity of the algorithm to modeling errors. The steady-state implementation of the algorithm based on a single cruise linear model was evaluated using a nonlinear simulation of a C-130 aircraft. A number of off-nominal no-failure flight conditions including maneuvers, nonzero flap deflections, different turbulence levels and steady winds were tested. Based on the no-failure decision functions produced by off-nominal flight conditions, the failure detection performance at the nominal flight condition was determined. The extension of the algorithm to a wider flight envelope by scheduling the linear models used by the algorithm on dynamic pressure and flap deflection was also considered. Since simply scheduling the linear models over the entire flight envelope is unlikely to be adequate, scheduling of the steady-state implentation of the algorithm was briefly investigated.

Bonnice, W. F.↗
NASA NTRS2019

Observation of Absorbing Aerosols Above Clouds over the South-East Atlantic Ocean from the Geostationary Satellite SEVIRI – Part 1: Method Description and Sensitivity

High temporal resolution observations from satellites have a great potential for studying the impact of biomass burning aerosols and clouds over the South East Atlantic Ocean (SEAO). This paper presents a method developed to retrieve simultaneously aerosol and cloud properties in aerosol above cloud conditions from the geostationary instrument Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). The above-cloud Aerosol Optical Thickness (AOT), the Cloud Optical Thickness (COT) and the Cloud droplet Effective Radius (CER) are derived from the spectral contrast and the magnitude of the signal measured in three channels in the visible to shortwave infrared region. The impact of the absorption from atmospheric gases on the satellite signal is corrected by applying transmittances calculated using the water vapour profiles from a Met Office forecast model. The sensitivity analysis shows that a 10% error on the humidity profile leads to an 18.5% bias on the above-cloud AOT, which highlights the importance of an accurate atmospheric correction scheme. In situ measurements from the CLARIFY-2017 airborne field campaign are used to constrain the aerosol size distribution and refractive index that is assumed for the aforementioned retrieval algorithm. The sensitivities in the retrieved AOT, COT and CER to the aerosol model assumptions are assessed. Between 09:00-15:00 UTC, an uncertainty of 40% is estimated on the above-cloud AOT, which is dominated by the sensitivity of the retrieval to the single scattering albedo. The absorption AOT is less sensitive to the aerosol assumptions with an uncertainty generally lower than 17% between 09:00-15:00 UTC. Outside of that time range, as the scattering angle decreases, the sensitivity of the AOT and the absorption AOT to the aerosol model increases. The retrieved cloud properties are only weakly sensitive to the aerosol model assumptions throughout the day, with biases lower than 6% on the COT and 3% on the CER. The stability of the retrieval over time is analysed. For observations outside of the backscattering glory region, the time-series of the aerosol and cloud properties are physically consistent, which confirms the ability of the retrieval to monitor the temporal evolution of aerosol above cloud events over the SEAO.

Peers, Fanny↗
NASA NTRS2019

Observation of absorbing aerosols above clouds over the south-east Atlantic Ocean from the geostationary satellite SEVIRI – Part 1: Method description and sensitivity

High-temporal-resolution observations from satellites have a great potential for studying the impact of biomass burning aerosols and clouds over the south-east Atlantic Ocean (SEAO). This paper presents a method developed to simultaneously retrieve aerosol and cloud properties in aerosol above-cloud conditions from the geostationary instrument Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). The above-cloud aerosol optical thickness (AOT), the cloud optical thickness (COT) and the cloud droplet effective radius (CER) are derived from the spectral contrast and the magnitude of the signal measured in three channels in the visible to shortwave infrared region. The impact of the absorption from atmospheric gases on the satellite signal is corrected by applying transmittances calculated using the water vapour profiles from a Met Office forecast model. The sensitivity analysis shows that a 10 % error on the humidity profile leads to an 18.5 % bias on the above-cloud AOT, which highlights the importance of an accurate atmospheric correction scheme. In situ measurements from the CLARIFY-2017 airborne field campaign are used to constrain the aerosol size distribution and refractive index that is assumed for the aforementioned retrieval algorithm. The sensitivities in the retrieved AOT, COT and CER to the aerosol model assumptions are assessed. Between 09:00 and 15:00 UTC, an uncertainty of 40 % is estimated on the above-cloud AOT, which is dominated by the sensitivity of the retrieval to the single-scattering albedo. The absorption AOT is less sensitive to the aerosol assumptions with an uncertainty generally lower than 17 % between 09:00 and 15:00 UTC. Outside of that time range, as the scattering angle decreases, the sensitivity of the AOT and the absorption AOT to the aerosol model increases. The retrieved cloud properties are only weakly sensitive to the aerosol model assumptions throughout the day, with biases lower than 6 % on the COT and 3 % on the CER. The stability of the retrieval over time is analysed. For observations outside of the backscattering glory region, the time series of the aerosol and cloud properties are physically consistent, which confirms the ability of the retrieval to monitor the temporal evolution of aerosol above-cloud events over the SEAO.

cloud properties↗
NASA NTRSDate not supplied

Low-cost Quantification of Fluid Flow Parameter Sensitivity using Reduced-order Modeling

Uncertainty quantification of computational fluid dynamics (CFD) simulations is a complicated procedure which still relies in many cases on engineering judgment and factors of safety. This is in part because the computational cost of measuring the simulation's sensitivity to all meaningful parameters (e.g., body surface roughness) and hyperparameters (e.g., subiteration convergence criterion) is intractable for even a single simulation. Reduced-order modeling dramatically lowers this computational cost of simulating fluid flows, but usually only where similar data is already available. In this work, fluid reduced-order models are utilized to quantify a flow's sensitivity to certain physical parameters for the purposes of improved uncertainty quantification. Characteristic observability and sensitivity are both explored. The resulting sensitivity quantifications enable more informed CFD frameworks and more rigorous uncertainty bounds on the resulting data.

uncertainty quantification↗
NASA NTRSDate not supplied

Low-cost Quantification of Fluid Flow Parameter Sensitivity using Reduced-order Modeling

Uncertainty quantification of computational fluid dynamics (CFD) simulations is a complicated procedure which still relies in many cases on engineering judgment and factors of safety. This is in part because the computational cost of measuring the simulation's sensitivity to all meaningful parameters (e.g., body surface roughness) and hyperparameters (e.g., subiteration convergence criterion) is intractable for even a single simulation. Reduced-order modeling dramatically lowers this computational cost of simulating fluid flows, but usually only where similar data is already available. In this work, fluid reduced-order models are utilized to quantify a flow's sensitivity to certain physical parameters for the purposes of improved uncertainty quantification. Characteristic observability and sensitivity are both explored. The resulting sensitivity quantifications enable more informed CFD frameworks and more rigorous uncertainty bounds on the resulting data.

uncertainty quantification↗
NASA NTRS2017

Classifying Multi-Model Wheat Yield Impact Response Surfaces Showing Sensitivity to Temperature and Precipitation Change

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (minus 2 to plus 9 degrees Centigrade) and precipitation (minus 50 to plus 50 percent). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

Classification; Climate change; Crop model; Ensemb↗