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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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NASA NTRS1984

An Eulerian transport/transformation/removal model for SO2 and sulfate. I - Model development. II - Model calculation of SO(x) transport in the eastern United States

A three-dimensional, time dependent Eulerian atmospheric SO2 and sulfate transport/transformation/removal model is described and applied to the eastern U.S. The model was developed in anticipation of increased input to the atmospheric sulfur content by coal-burning power plants in the near future and is intended as an aid in identifying sources of SO2. The Eulerian transport model incorporates functions for chemical transformations, dry deposition, spatial topographical variations, spatial and temporal variations of mixing layer heights, the wind field, eddy diffusivities, deposition velocities, and temperature and water concentration profiles. Attention is also given to the SO2 photochemical oxidation mechanism and rates. Results from a 72-hr simulation of SO(x) transport over the eastern U.S., using actual 1974 meteorological data, illustrate the model's capability to depict interactions between emissions, transport, chemistry and removal. Concentration distributions are demonstrated to have significant spatial and temporal variations.

Carmichael, G. R.↗

NASA NTRS2010

Improved Hypoxia Modeling for Nutrient Control Decisions in the Gulf of Mexico

As required by the Harmful Algal Bloom and Hypoxia Research Control Act of 1998, the Mississippi River/Gulf of Mexico Watershed Nutrient Task Force issued the 2001 Gulf Hypoxia Action Plan (updated in 2008). In response to the Gulf Hypoxia Action Plan of 2001 (updated in 2008), the EPA Gulf of Mexico Hypoxia Modeling and Monitoring Project has established a detailed model for the Mississippi-Attchafalaya River Basin which provides a capability to forecast the multi-source nutrient loading to the Gulf and the subsequent bio-geochemical processes leading to hypoxic conditions and subsequent effects on Gulf habitats and fisheries. The primary purpose of the EPA model is to characterize the impacts of nutrient management actions, or proposed actions on the spatial and temporal characteristics of the Gulf hypoxic zone. The model is expected to play a significant role in determining best practices and improved strategies for incentivizing nutrient reduction strategies, including installation of on-farm structures to reduce sediment and nutrient runoff, use of cover crops and other agricultural practices, restoration of wetlands and riparian buffers, improved waste water treatment and decreased industrial nitrogen emissions. These decisions are currently made in a fragmented way by federal, state, and local agencies, using a variety of small scale models and limited data. During the past three years, EPA has collected an enormous amount of in-situ data to be used in the model. We believe that the use of NASA satellite data products in the model and for long term validation of the model has the potential to significantly increase the accuracy and therefore the utility of the model for the decision making described above. This proposal addresses the Gulf of Mexico Alliance (GOMA) priority issue of reductions in nutrient inputs to coastal ecosystem. It further directly relates to water quality for healthy beaches and shellfish beds and wetland and coastal conservation restoration.

Habib, Shaid↗

NASA NTRS2005

Towards an A-train Aerosol Assimilation System: Assimilation of MODIS Aerosol Optical Thickness Retrievals into a Global Aerosol Transport and Radiation Model

To understand the role of atmospheric aerosols in Earth's climate system we ideally require a global, three-dimensional, and temporally continuous system of measurements. The view of aerosols provided by a single polar-orbiting satellite instrument (e.g., MODIS), while providing the greatest spatial coverage of any measurement system available, can offer only a single relevant observation per day of a particular location. Furthermore, the quantitative interpretation of those observations is subject to considerable uncertainty because of we do not know the aerosol optical properties or vertical profiles well enough. Ground-based and in situ measurements can provide missing detail about aerosol vertical and optical properties, but lack the spatial (and sometimes temporal) coverage desired. Chemical transport models treating the aerosol lifecycle complement this picture by providing three-dimensional, global, and continuous aerosol fields, but suffer from poor constraints on their initial conditions (e.g., the aerosol source, sink, and transformation mechanisms). Here we present a global, three- dimensional aerosol transport model and assimilation system. The model assimilates the MODIS aerosol optical thickness product to constrain its aerosol distributions and burdens. We provide an analysis of the model aerosol distributions as constrained by ground-based sun photometer measurements and aerosol products from other satellite measurements.

Colarco, P.↗

NASA NTRS2010

Utilization of Hydrologic Remote Sensing Data in Land Surface Modeling and Data Assimilation: Current Status and Challenges

Recent advances in remote sensing technologies have enabled the monitoring and measurement of the Earth's land surface at an unprecedented scale and frequency. The myriad of these land surface observations must be integrated with the state-of-the-art land surface model forecasts using data assimilation to generate spatially and temporally coherent estimates of environmental conditions. These analyses are of critical importance to real-world applications such as agricultural production, water resources management and flood, drought, weather and climate prediction. This need motivated the development of NASA Land Information System (LIS), which is an expert system encapsulating a suite of modeling, computational and data assimilation tools required to address challenging hydrological problems. LIS integrates the use of several community land surface models, use of ground and satellite based observations, data assimilation and uncertainty estimation techniques and high performance computing and data management tools to enable the assessment and prediction of hydrologic conditions at various spatial and temporal scales of interest. This presentation will focus on describing the results, challenges and lessons learned from the use of remote sensing data for improving land surface modeling, within LIS. More specifically, studies related to the improved estimation of soil moisture, snow and land surface temperature conditions through data assimilation will be discussed. The presentation will also address the characterization of uncertainty in the modeling process through Bayesian remote sensing and computational methods.

Kumar, Sujay V.↗

NASA NTRS2013

Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast

Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1∕8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.

extreme climate index↗

NASA NTRS1995

The lognormal distribution as a model for bio-optical variability in the sea

The lognormal distribution is presented as a useful model for bio-optical variability at a variety of spatial and temporal scales. A parametric statistical framework is presented for using the lognormal model to assess the effects of heterogeneity and scale on closure. Variability at small scales affects but is unresolved by large-scale measurements. An assumed lognormal distribution allows one to integrate over small-scale variability to predict large-scale measurements. Examples are presented to demonstrate how knowledge of the variance can be incorporated into models to relate measurements made at different scales.

Campbell, Janet W.↗

NASA NTRS2008

3D Air Quality and the Clean Air Interstate Rule: Lagrangian Sampling of CMAQ Model Results to Aid Regional Accountability Metrics

The Clean Air Interstate Rule (CAIR) is expected to reduce transport of air pollutants (e.g. fine sulfate particles) in nonattainment areas in the Eastern United States. CAIR highlights the need for an integrated air quality observational and modeling system to understand sulfate as it moves in multiple dimensions, both spatially and temporally. Here, we demonstrate how results from an air quality model can be combined with a 3d monitoring network to provide decision makers with a tool to help quantify the impact of CAIR reductions in SO2 emissions on regional transport contributions to sulfate concentrations at surface monitors in the Baltimore, MD area, and help improve decision making for strategic implementation plans (SIPs). We sample results from the Community Multiscale Air Quality (CMAQ) model using ensemble back trajectories computed with the NASA Langley Research Center trajectory model to provide Lagrangian time series and vertical profile information, that can be compared with NASA satellite (MODIS), EPA surface, and lidar measurements. Results are used to assess the regional transport contribution to surface SO4 measurements in the Baltimore MSA, and to characterize the dominant source regions for low, medium, and high SO4 episodes.

Fairlie, T. D.↗

NASA NTRSDate not supplied

WMLES of Boundary-Layer Transition

The potential for the accurate prediction of laminar-turbulent transition by Wall-Modeled Large-Eddy Simulation (WMLES) is investigated through analysis of Direct Numerical Simulation (DNS) and WMLES results. The analysis indicates satisfactory prediction of transition by WMLES is possible, provided a) a sufficiently accurate wall model for the initial stage of transition is employed, b) a satisfactory transition is made between this initial-stage wall model and the wall model for the downstream fully-developed turbulence, and c) limitations on temporal and spatial resolution, and on the thickness of the wall-modeled region, imposed by the need to accurately resolve the disturbance waves and the transition region are observed. This is illustrated with a WMLES computation that yields reasonable agreement with DNS results; however, the wall-modeled region thickness, and thus the cost savings, is less than is typical for WMLES of fully developed turbulence.

wall-modeled large-eddy simulation, boundary-layer↗

NASA NTRSDate not supplied

WMLES of Boundary-Layer Transition

An analysis of the potential for the accurate prediction of laminar-turbulent transition by Wall-Modeled Large-Eddy Simulation (WMLES) indicates that satisfactory prediction of transition by WMLES is possible, provided a) a sufficiently accurate wall model for the initial stage of transition is employed, b) a satisfactory transition is made between this initial-stage wall model and the wall model for the downstream fully-developed turbulence, and c) limitations on temporal and spatial resolution, and on the thickness of the wall-modeled region, imposed by the need to accurately resolve the disturbance waves and the transition region are observed. This is illustrated with WMLES computations of subharmonic transition in a flat-plate boundary layer that yield reasonable agreement with DNS results. However, the wall-modeled region thickness, and thus the cost savings, is less than is typical for WMLES of fully developed turbulence.

wall-modeled large-eddy simulation↗

NASA NTRSDate not supplied

WMLES of Boundary-Layer Transition

Wall-modeled large-eddy simulation↗

NASA NTRS1979

Program of research in severe storms

Two modeling areas, the development of a mesoscale chemistry-meteorology interaction model, and the development of a combined urban chemical kinetics-transport model are examined. The problems associated with developing a three dimensional combined meteorological-chemical kinetics computer program package are defined. A similar three dimensional hydrostatic real time model which solves the fundamental Navier-Stokes equations for nonviscous flow is described. An urban air quality simulation model, developed to predict the temporal and spatial distribution of reactive and nonreactive gases in and around an urban area and to support a remote sensor evaluation program is reported.

Source record↗

NASA NTRS1983

Numerical Simulation for Droplet Combustion Using Lagrangian Hydrodynamics

A predictive model of spray combustion must incorporate models for the wide variety of physical environments in a practical combustor. In regions where droplets are closely spaced, combustion resembles a diffusion flame; where they are well separated, an envelope or wake flame results. The relative velocity field between the fuel droplets and oxidizer in influences boundary layer development about the droplet, recirculating flow patterns, and droplet shape and stability. A model must encompass these interacting temporal and spatial effects as well as complicated combustor boundaries. The objective of the current work is to develop the triangular gridding method for describing the individual and collective properties of vaporizing and burning fuel droplets.

Fritts, M. J.↗

NASA NTRS1985

Contribution to the CIRA Model from Ground-based Lidar

A new technique, the Rayleigh-Lidar Sounding, has been developed recently to probe the Middle Atmosphere and has demonstrated its ability to provide density and temperature profiles in the height range 30 to 90 km. It has been limited until now to ground-based observations. The set of data, as it exists to-day, has been obtained from a unique midlatitude site. Even with this limiting factor, it is thought to be a useful complement to a global model, mainly because of its spatial and temporal resolutions, usually not available from global data. Such resolutions give an insight into the atmospheric variability as a function of height and time, for time-scale ranging from minutes to years. The process is briefly described. A description of the analysis and its results is given.

Chanin, M. I.↗

NASA NTRS1996

LES on unstructured deforming meshes: Towards reciprocating IC engines

A variable explicit/implicit characteristics-based advection scheme that is second-order accurate in space and time has been developed recently for unstructured deforming meshes (O'Rourke & Sahota 1996a). To explore the suitability of this methodology for Large-Eddy Simulation (LES), three subgrid-scale turbulence models have been implemented in the CHAD CFD code (O'Rourke & Sahota 1996b): a constant-coefficient Smagorinsky model, a dynamic Smagorinsky model for flows having one or more directions of statistical homogeneity, and a Lagrangian dynamic Smagorinsky model for flows having no spatial or temporal homogeneity (Meneveau et al. 1996). Computations have been made for three canonical flows, progressing towards the intended application of in-cylinder flow in a reciprocating engine. Grid sizes were selected to be comparable to the coarsest meshes used in earlier spectral LES studies. Quantitative results are reported for decaying homogeneous isotropic turbulence, and for a planar channel flow. Computations are compared to experimental measurements, to Direct-Numerical Simulation (DNS) data, and to Rapid-Distortion Theory (RDT) where appropriate. Generally satisfactory evolution of first and second moments is found on these coarse meshes; deviations are attributed to insufficient mesh resolution. Issues include mesh resolution and computational requirements for a specified level of accuracy, analytic characterization of the filtering implied by the numerical method, wall treatment, and inflow boundary conditions. To resolve these issues, finer-mesh simulations and computations of a simplified axisymmetric reciprocating piston-cylinder assembly are in progress.

Haworth, D. C.↗

NASA NTRS1998

Globalized Newton-Krylov-Schwarz Algorithms and Software for Parallel Implicit CFD

Implicit solution methods are important in applications modeled by PDEs with disparate temporal and spatial scales. Because such applications require high resolution with reasonable turnaround, "routine" parallelization is essential. The pseudo-transient matrix-free Newton-Krylov-Schwarz (Psi-NKS) algorithmic framework is presented as an answer. We show that, for the classical problem of three-dimensional transonic Euler flow about an M6 wing, Psi-NKS can simultaneously deliver: globalized, asymptotically rapid convergence through adaptive pseudo- transient continuation and Newton's method-, reasonable parallelizability for an implicit method through deferred synchronization and favorable communication-to-computation scaling in the Krylov linear solver; and high per- processor performance through attention to distributed memory and cache locality, especially through the Schwarz preconditioner. Two discouraging features of Psi-NKS methods are their sensitivity to the coding of the underlying PDE discretization and the large number of parameters that must be selected to govern convergence. We therefore distill several recommendations from our experience and from our reading of the literature on various algorithmic components of Psi-NKS, and we describe a freely available, MPI-based portable parallel software implementation of the solver employed here.

Gropp, W. D.↗

NASA NTRS1999

Impact of Assimilating GOES-Derived Land Surface Variables into the PSU/NCAR MM5

Land surface processes are known to have a profound impact on the overlying atmosphere over a wide range of spatial and temporal scales. Many atmospheric numerical models include special parameterizations to improve the specification and partitioning of surface fluxes which are critical to the accurate prediction of warm season boundary layer behavior, organized mesoscale circulations, and convective precipitation. However, the added degrees of freedom resulting from the inclusion of vegetation and soil schemes require the specification of additional surface parameters such as vegetative resistances, green vegetation fraction, leaf area index, soil physical and hydraulic characteristics, and the vertical distribution of soil moisture. As satellite data have become more readily available in recent years, many investigations have attempted to use these new measurements to infer missing components of the surface energy budget. Sensitivity studies have shown land-skin temperature (LST) tendencies during the mid-morning hours are strongly sensitive to the surface moisture availability (a function of soil wetness and vegetation) and less sensitive to other parameters such as surface roughness. Based upon results from these and other studies, developed a simple technique that dynamically assimilates Geostationary Operational Environmental Satellite (GOES) derived land-surface products into the surface energy budget of a mesoscale model. The purpose of this paper is to demonstrate that assimilating the GOES satellite data has the potential to improve the representation of land surface characteristics within the model without prior knowledge of the land surface characteristics. The assimilation technique is presented in Section 2 and the numerical experiments are detailed in Section 3. Preliminary results and conclusions are presented in Sections 4 and 5, respectively.

Lapenta, William M.↗

NASA NTRS2003

Crustal Deformation in Southcentral Alaska: The 1964 Prince William Sound Earthquake Subduction Zone

This article, for Advances in Geophysics, is a summary of crustal deformation studies in southcentral Alaska. In 1964, southcentral Alaska was struck by the largest earthquake (moment magnitude 9.2) occurring in historical times in North America and the second largest earthquake occurring in the world during the past century. Conventional and space-based geodetic measurements have revealed a complex temporal-spatial pattern of crustal movement. Numerical models suggest that ongoing convergence between the North America and Pacific Plates, viscoelastic rebound, aseismic creep along the tectonic plate interface, and variable plate coupling all play important roles in controlling both the surface and subsurface movements. The geodetic data sets include tide-gauge observations that in some cases provide records back to the decades preceding the earthquake, leveling data that span a few decades around the earthquake, VLBI data from the late 1980s, and GPS data since the mid-1990s. Geologic data provide additional estimates of vertical movements and a chronology of large seismic events. Some of the important features that are revealed by the ensemble of studies that are reviewed in this paper include: (1) Crustal uplift in the region that subsided by up 2 m at the time of the earthquake is as much as 1 m since the earthquake. In the Turnagain Arm and Kenai Peninsula regions of southcentral Alaska, uplift rates in the immediate aftermath of the earthquake reached 150 mm/yr , but this rapid uplift decayed rapidly after the first few years following the earthquake. (2) At some other locales, notably those away the middle of the coseismic rupture zone, postseismic uplift rates were initially slower but the rates decay over a longer time interval. At Kodiak Island, for example, the uplift rates have been decreasing at a rate of about 7mm/yr per decade. At yet other locations, the uplift rates have shown little time dependence so far, but are thought not to be sustainable throughout the several hundred year recurrence time for great earthquake. The nearly 10 mm/yr uplift rate at Seldovia on the Kenai Peninsula is an example.

Cohen, Steven C.↗

NASA NTRS2014

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

Data Analytics↗