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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Employing Sensitivity Derivatives to Estimate Uncertainty Propagation in CFD

Two methods that exploit the availability of sensitivity derivatives are successfully employed to predict uncertainty propagation through Computational Fluid Dynamics (CFD) code for an inviscid airfoil problem. An approximate statistical second-moment method and a Sensitivity Derivative Enhanced Monte Carlo (SDEMC) method are successfully demonstrated on a two-dimensional problem. First- and second-order sensitivity derivatives of code output with respect to code input are obtained through an efficient incremental iterative approach. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables); these sensitivity derivatives enable one to formulate first- and second-order Taylor Series approximations for the mean and variance of CFD output quantities. Additionally, incorporation of the first-order sensitivity derivatives into the data reduction phase of a conventional Monte Carlo (MC) simulation allows for improved accuracy in determining the first moment of the CFD output. Both methods are compared to results generated using a conventional MC method. The methods that exploit the availability of sensitivity derivatives are found to be valid when considering small deviations from input mean values.

Putko, Michele M.↗
NASA NTRS2013

Computational Fluid Dynamics Uncertainty Analysis Applied to Heat Transfer over a Flat Plate

There have been few discussions on using Computational Fluid Dynamics (CFD) without experimental validation. Pairing experimental data, uncertainty analysis, and analytical predictions provides a comprehensive approach to verification and is the current state of the art. With pressed budgets, collecting experimental data is rare or non-existent. This paper investigates and proposes a method to perform CFD uncertainty analysis only from computational data. The method uses current CFD uncertainty techniques coupled with the Student-T distribution to predict the heat transfer coefficient over a at plate. The inputs to the CFD model are varied from a specified tolerance or bias error and the difference in the results are used to estimate the uncertainty. The variation in each input is ranked from least to greatest to determine the order of importance. The results are compared to heat transfer correlations and conclusions drawn about the feasibility of using CFD without experimental data. The results provide a tactic to analytically estimate the uncertainty in a CFD model when experimental data is unavailable

Computational↗
NASA NTRS2007

Blurring the Inputs: A Natural Language Approach to Sensitivity Analysis

To document model parameter uncertainties and to automate sensitivity analyses for numerical simulation codes, a natural-language-based method to specify tolerances has been developed. With this new method, uncertainties are expressed in a natural manner, i.e., as one would on an engineering drawing, namely, 5.25 +/- 0.01. This approach is robust and readily adapted to various application domains because it does not rely on parsing the particular structure of input file formats. Instead, tolerances of a standard format are added to existing fields within an input file. As a demonstration of the power of this simple, natural language approach, a Monte Carlo sensitivity analysis is performed for three disparate simulation codes: fluid dynamics (LAURA), radiation (HARA), and ablation (FIAT). Effort required to harness each code for sensitivity analysis was recorded to demonstrate the generality and flexibility of this new approach.

Kleb, William L.↗
NASA NTRS1993

Confidence bounds on structural reliability

Different approaches for quantifying physical, statistical, and model uncertainties associated with the distribution parameters which are aimed at determining structural reliability are described. Confidence intervals on the distribution parameters of the input random variables are estimated using four algorithms to evaluate uncertainty of the response. Design intervals are evaluated using either Monte Carlo simulation or an iterative approach. A first order approach can be used to compute a first approximation of the design interval, but its accuracy is not satisfactory. The regression approach which combines the iterative approach with Monte Carlo simulation is capable of providing good results if the performance function can be accurately represented using regression analysis. It is concluded that the design interval-based approach seems to be quite general and takes into account distribution and model uncertainties.

Mehta, S. R.↗
NASA NTRS2018

Comparisons of Two Spatial Implementations of a Crop Model Using Remotely Sensed Observations over Southeastern United States

Global food security is one of the most pressing issues of the current century, particularly for developing nations. Agricultural simulation models can be a key component in testing new technologies, seeds and cultivars etc. However, inaccurate input information, model related errors and the mode of implementation can also add to model uncertainties. In this study, the crop model is implemented in two separate fashions: a)gridded (GriDSSAT model) and b) using random spatial ensembles (RHEAS model). This is done in the Southeastern US to evaluate and understand the modelperformance over a region data availabilities. Once the model performance is assessed, multiple satellite based earth observation parameters such as soil moisture, vegetation index etc. can be assimilated into crop models to reduce input and model related uncertainties particularly in data limited regions. In this study, the National Agricultural Statistical Services (NASS) reported yield data at county levels are used for comparison andvalidation purposes. The GriDSSAT model estimation of corn yields in comparison with the reported NASS yields showed an overall RMSD of nearly 3720 (kg/ha) whereas RMSD for the RHEAS model implementation was 3550 (kg/ha). Overall the GriDSSAT model had negative bias of nearly 2400 kg/ha (except for 2013) while RHEAS had a slight positive bias of 400 kg/ha (approx.).

SERVIR↗
NASA NTRS1986

Assessment of measurement error due to sampling perspective in the space-based Doppler lidar wind profiler

A Multipair Algorithm (MPA) has been developed to minimize the contribution of the sampling error in the simulated Doppler lidar wind profiler measurements (due to angular and spatial separation between shots in a shot pair) to the total measurement uncertainty. Idealized wind fields are used as input to the profiling model, and radial wind estimates are passed through the MPA to yield a wind measurement for 300 x 300 sq km areas. The derived divergence fields illustrate the gradient patterns that are particular to the Doppler lidar sampling strategy and perspective.

Houston, S. H.↗
NASA NTRS1992

The role of global cloud climatologies in validating numerical models

Global maps of the monthly mean net upward longwave radiation flux at the ocean surface were obtained for April, July, October 1985 and January 1986. These maps were produced by blending information obtained from a combination of general circulation model cloud radiative forcing fields, the top of the atmosphere cloud radiative forcing from ERBE and TOVS profiles and sea surface temperature on ISCCP C1 tapes. The fields are compatible with known meteorological regimes of atmospheric water vapor content and cloudiness. There is a vast area of high net upward longwave radiation flux (greater than 80/sq Wm) in the eastern Pacific Ocean throughout most of the year. Areas of low net upward longwave radiation flux ((less than 40/sq Wm) are the tropical convective regions and extra tropical regions that tend to have persistent low cloud cover.The technique used relies on General Circulation Model simulations and so is subject to some of the uncertainties associated with the model. However, all input information regarding temperature, moisture, and cloud cover is from satellite data having near global coverage. This feature of the procedure alone warrants its consideration for further use in compiling global maps of longwave radiation.

HARSHVARDHAN↗
NASA NTRS1992

Fuzzy simulation in concurrent engineering

Concurrent engineering is becoming a very important practice in manufacturing. A problem in concurrent engineering is the uncertainty associated with the values of the input variables and operating conditions. The problem discussed in this paper concerns the simulation of processes where the raw materials and the operational parameters possess fuzzy characteristics. The processing of fuzzy input information is performed by the vertex method and the commercial simulation packages POLYMATH and GEMS. The examples are presented to illustrate the usefulness of the method in the simulation of chemical engineering processes.

Kraslawski, A.↗
NASA NTRS2002

Improving the Accuracy of Satellite Sea Surface Temperature Measurements by Explicitly Accounting for the Bulk-Skin Temperature Difference

The focus of this research was to determine whether the accuracy of satellite measurements of sea surface temperature (SST) could be improved by explicitly accounting for the complex temperature gradients at the surface of the ocean associated with the cool skin and diurnal warm layers. To achieve this goal, work was performed in two different major areas. The first centered on the development and deployment of low-cost infrared radiometers to enable the direct validation of satellite measurements of skin temperature. The second involved a modeling and data analysis effort whereby modeled near-surface temperature profiles were integrated into the retrieval of bulk SST estimates from existing satellite data. Under the first work area, two different seagoing infrared radiometers were designed and fabricated and the first of these was deployed on research ships during two major experiments. Analyses of these data contributed significantly to the Ph.D. thesis of one graduate student and these results are currently being converted into a journal publication. The results of the second portion of work demonstrated that, with presently available models and heat flux estimates, accuracy improvements in SST retrievals associated with better physical treatment of the near-surface layer were partially balanced by uncertainties in the models and extra required input data. While no significant accuracy improvement was observed in this experiment, the results are very encouraging for future applications where improved models and coincident environmental data will be available. These results are included in a manuscript undergoing final review with the Journal of Atmospheric and Oceanic Technology.

Wick, Gary A.↗
NASA NTRS2007

Statistical Analyses of Satellite Cloud Object Data from CERES: Comparison with Cloud-Resolving Model Simulations of Tropical Convective Clouds - Part III

The present study evaluates the ability of a cloud-resolving model (CRM) to simulate the physical properties of tropical deep convective cloud objects identified from a Clouds and the Earth s Radiant Energy System (CERES) data product. The emphasis of this study is the comparisons among the small-, medium- and large-size categories of cloud objects observed during March 1998 and between the large-size categories of cloud objects observed during March 1998 (strong El Ni o) and March 2000 (weak La Ni a). Results from the CRM simulations are analyzed in a way that is consistent with the CERES retrieval algorithm and they are averaged to match the scale of the CERES satellite footprints. Cloud physical properties are analyzed in terms of their summary histograms for each category. It is found that there is a general agreement in the overall shapes of all cloud physical properties between the simulated and observed distributions. Each cloud physical property produced by the CRM also exhibits different degrees of disagreement with observations over different ranges of the property. The simulated cloud tops are generally too high and cloud top temperatures are too low except for the large-size category of March 1998. The probability densities of the simulated top-of-the-atmosphere (TOA) albedos for all four categories are underestimated for high albedos, while those of cloud optical depth are overestimated at its lowest bin. These disagreements are mainly related to uncertainties in the cloud microphysics parameterization and inputs such as cloud ice effective size to the radiation calculation. Summary histograms of cloud optical depth and TOA albedo from the CRM simulations of the large-size category of cloud objects do not differ significantly between the March 1998 and 2000 periods, consistent with the CERES observations. However, the CRM is unable to reproduce the significant differences in the observed cloud top height while it overestimates the differences in the observed outgoing longwave radiation and cloud top temperature between the two periods. Comparisons between the CRM results and the observations for most parameters in March 1998 consistently show that both the simulations and observations have larger differences between the large- and small-size categories than between the large- and medium-size, or between the medium- and small-size categories. However, the simulated cloud properties do not change as much with size as observed. These disagreements are likely related to the spatial averaging of the forcing data and the mismatch in time and in space between the numerical weather prediction model from which the forcing data are produced and the CERES observed cloud systems.

Luo, Yali↗
NASA NTRS2011

Reliability Impacts in Life Support Architecture and Technology Selection

Equivalent System Mass (ESM) and reliability estimates were performed for different life support architectures based primarily on International Space Station (ISS) technologies. The analysis was applied to a hypothetical 1-year deep-space mission. High-level fault trees were initially developed relating loss of life support functionality to the Loss of Crew (LOC) top event. System reliability was then expressed as the complement (nonoccurrence) this event and was increased through the addition of redundancy and spares, which added to the ESM. The reliability analysis assumed constant failure rates and used current projected values of the Mean Time Between Failures (MTBF) from an ISS database where available. Results were obtained showing the dependence of ESM on system reliability for each architecture. Although the analysis employed numerous simplifications and many of the input parameters are considered to have high uncertainty, the results strongly suggest that achieving necessary reliabilities for deep-space missions will add substantially to the life support system mass. As a point of reference, the reliability for a single-string architecture using the most regenerative combination of ISS technologies without unscheduled replacement spares was estimated to be less than 1%. The results also demonstrate how adding technologies in a serial manner to increase system closure forces the reliability of other life support technologies to increase in order to meet the system reliability requirement. This increase in reliability results in increased mass for multiple technologies through the need for additional spares. Alternative parallel architecture approaches and approaches with the potential to do more with less are discussed. The tall poles in life support ESM are also reexamined in light of estimated reliability impacts.

Lange, Kevin E.↗
NASA NTRS2017

Assessing Inter-Sectoral Climate Change Risks: The Role of ISIMIP

The aims of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) are to provide a framework for the intercomparison of global and regional-scale risk models within and across multiple sectors and to enable coordinated multi-sectoral assessments of different risks and their aggregated effects. The overarching goal is to use the knowledge gained to support adaptation and mitigation decisions that require regional or global perspectives within the context of facilitating transformations to enable sustainable development, despite inevitable climate shifts and disruptions. ISIMIP uses community-agreed sets of scenarios with standardized climate variables and socioeconomic projections as inputs for projecting future risks and associated uncertainties, within and across sectors. The results are consistent multi-model assessments of sectoral risks and opportunities that enable studies that integrate across sectors, providing support for implementation of the Paris Agreement under the United Nations Framework Convention on Climate Change.

Regional-scale risk models↗
NASA NTRS2019

Anthropogenic Nitrogen Inputs and Impacts on Oceanic N2O Fluxes in the Northern Indian Ocean: the Need for an Integrated Observation and Modelling Approach

Anthropogenically-derived nitrogen input to the northern Indian Ocean has increased significantly in recent decades, based on both observational and model derived estimates This external nutrient source is supplied by atmospheric deposition and riverine fluxes, and has the potential to affect the vulnerable biogeochemical systems of the Arabian Sea and Bay of Bengal, influencing productivity and oceanic production of the greenhouse-gas nitrous-oxide (N2O). We summarize current estimates of this external nitrogen source to the northern Indian Ocean from observations and models, highlight implications for regional marine N2O emissions using model-based analyses, and make recommendations for measurement and model needs to improve current estimates and future predictions of this impact. Current observationally-derived estimates of deposition and riverine nitrogen inputs are limited by sparse measurements and uncertainties on accurate characterization of nitrogen species composition. Ocean model assessments of the impact of external nitrogen sources on regional marine N2O production in the northern Indian Ocean estimate potentially significant changes but also have large associated uncertainties. We recommend an integrated program of basin-wide measurements combined with high-resolution modeling and more detailed characterization of nitrogen-cycle process to address these uncertainties and improve current estimates and predictions.

Parvadha Suntharalingam↗
NASA NTRSDate not supplied

Medium-Range River Flood Forecasts Using a Long Short-Term Memory Network

River flooding and the impacts are a concern for decision makers throughout the United States. Accurate medium-range forecasts (~3-7 days) are critical for providing advanced outlooks to emergency management officials. Unfortunately, accurately forecasting rainfall-runoff and the subsequent rise and fall within rivers remain a challenge in hydrological modeling. While complex physical modeling systems are the standard for representing the hydrological processes, they are computationally demanding and can require extensive calibration. Further, uncertainties remain in the model parameters and input data. The use of machine learning can reduce some of the computational demand while maintaining high accuracy. Therefore, this project makes use of a Long Short-Term Memory (LSTM) network which explicitly accounts for the time-dependent nature of rainfall-runoff modeling. The developed LSTM was trained to predict river gauge height, or stage height, based on time-lagged input features which include: gauge height to initialize the model, the NASA Short-term Prediction Research and Transition Center’s instance of the Land Information System (SPoRT-LIS) relative soil moisture to describe the rainfall infiltration rate, and 6-hr Multi-Radar Multi-Sensor quantitative precipitation estimate (MRMS QPE). The developed LSTM based system is then used to produce 7-day forecasts with a 6-hr temporal resolution using three different quantitative precipitation forecasts (QPF) from the NWS’s Weather Prediction Center (WPC), the NCEP Global Forecast System (GFS) model and the National Blend of Models (NBM). This trained modeling system has been implemented as an experimental product at over 100 different rivers in collaboration with at multiple National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC) across the eastern half of the United States. The developed LSTM model achieved average Nash-Sutcliffe efficiency (NSE) 0.89 higher than the equivalent medium-range National Water Model ensemble member forecast over a 7-day forecast. In addition to the initial development and evaluation, this project has continued to expand. While the initial model was developed for precipitation dominated basins, expansion of the project has taken it to basins effected by snow melt. This presentation will provide an overview of the project with focus on recent developments on incorporating snow melt processes into the model.

Andrew T. White↗
NASA NTRSDate not supplied

Towards an Optimal Estimation Retrieval of Cirrus Cloud Optical and Microphysical Properties Using Hyperspectral Shortwave Instruments and A Fast Radiative Transfer Algorithm

Cirrus cloud retrieval products (here, cloud optical depth, effective particle size, and cloud top height) are important inputs into numerical weather and climate models. Uncertainties in such retrieval products, as a matter of course, propagate downstream, impacting model calculations. Improvements in high-quality global cirrus cloud optical and microphysical data products from satellite observations are needed to understand and reduce retrieval uncertainties. Hyperspectral instruments produce high-resolution and information-dense radiance spectra, thus offering the opportunity to reduce uncertainties in retrieval products. An optimal estimation-based cirrus cloud optical and microphysical product retrieval is under development for the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Earth Surface Mineral Dust Source Investigation (EMIT) instruments, and the forthcoming Climate Absolute Radiance and Refractivity Observatory Pathfinder (CLARREO-Pathfinder). The spectral coverage of all three instruments includes the ultraviolet, visible, and near-infrared. In this study a very fast radiative transfer model, the Principal Component-based Radiative Transfer Model in the solar spectral region (PCRTM-Solar), is used for forward modeling computations. This will reduce the computational burden in the forward radiance and Jacobian calculations as the cost function is minimized, enabling the entire AVIRIS, EMIT, and CLARREO-Pathfinder spectral range to be used in the optimal estimation framework. Using the entire spectrum will maximize the information content, resulting in a more robust and more accurate retrieval. As a first step, the retrieval is being designed for single layer ice clouds over open ocean water. Preliminary results will be shown.

Jeffrey Mast↗
NASA NTRSDate not supplied

Towards an Optimal Estimation Retrieval of Cirrus Cloud Optical and Microphysical Properties Using Hyperspectral Shortwave Instruments and A Fast Radiative Transfer Algorithm

Cirrus cloud retrieval products (here, cloud optical depth, effective particle size, and cloud top height) are important inputs into numerical weather and climate models. Uncertainties in such retrieval products, as a matter of course, propagate downstream, impacting model calculations. Improvements in high-quality global cirrus cloud optical and microphysical data products from satellite observations are needed to understand and reduce retrieval uncertainties. Hyperspectral shortwave instruments produce high-resolution and information-dense spectra, thus offering the opportunity to reduce uncertainties in retrieval products. We are in the process of developing a retrieval that uses the very fast Principal Component Radiative Transfer Model in the solar spectral region (PCRTM-Solar) in the forward model calculations. This retrieval will use measured reflectances from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) and the forthcoming Climate Absolute Radiance and Refractivity Observatory Pathfinder (CLARREO-Pathfinder) instruments. In this manuscript we present progress towards a reference retrieval employing a widely used, verified, accurate, yet computationally slower radiative transfer modeling technique. The reference retrieval, while too slow for using the complete hyperspectral measurement, will allow us to study the behavior of retrieval products and help us verify results from our in-development fast retrieval. Both retrievals will use the optimal estimation retrieval framework. In this manuscript we present results from an uncertainty analysis considering three uncertainty sources for a cirrus cloud retrieval in the form of error covariance matrices: reflectance uncertainty due to water vapor, the reflectance uncertainty due to ice crystal scattering assumptions, and the instrument measurement uncertainty. Results show that the uncertainty due to habit selection is the largest, while that due to water vapor is at most 0.6% relative to channel reflectance. As a first step, the retrieval is being designed for single layer ice clouds over open ocean water.

Cirrus cloud↗
NASA NTRS1982

Solar irradiance modulation by active regions during 1980

The solar irradiance modulation due to active regions during 1980 has been investigated in detail. Specifically, the uncertainties caused by ground-based data used as input in the modeling effort, and by currently incomplete knowledge of the proper parameters that describe the angular variation of sunspot and facular contrasts are estimated. It is concluded that the most significant uncertainties are due to errors in area measurements and, possibly, varying spot and facular brightness. A 'standard model' for later use is derived to a best-fit technique of the currently available ACRIM irradiance data and the predictions of models with appropriately varied parameters. Finally, the expected irradiance for the entire year of 1980 is computed.

Sofia, S.↗
NASA NTRS1991

Robustified time-optimal control of uncertain structural dynamic systems

A new approach for computing open-loop time-optimal control inputs for uncertain linear dynamical systems is developed. In particular, the single-axis, rest-to-rest maneuvering problem of flexible spacecraft in the presence of uncertainty in model parameters is considered. Robustified time-optimal control inputs are obtained by solving a parameter optimization problem subject to robustness constraints. A simple dynamical system with a rigid-body mode and one flexible mode is used to illustrate the concept.

Liu, Qiang↗