Search NASA⌕ Search

SEARCH · Search NASA

Results for “grid scenarios”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Development of a Flexible Framework for Hypersonic Navier-Stoke Space Shuttle Orbiter Meshes

A flexible framework constructing block structured volume grids for hypersonic Navier-Strokes flow simulations was developed for the analysis of the Shuttle Orbiter Columbia. The development of the framework, which was partially basedon the requirements of the primary flow solvers used resulted in an ability to directly correlate solutions contributed by participating groups on a common surface mesh. A foundation was built through the assessment of differences between differnt solvers, which provided confidence for independent assessment of other damage scenarios by team members. The framework draws on the experience of NASA Langley and NASA Ames Research Centers in structured grid generation, and consists of a grid generation, and consist of a grid generation process implemented through a division of responsibilities. The nominal division of labor consisted of NASA Johnson Space Center coordinating the damage scenarios to be analyzed by the Aerothermodynamics Columbia Accident Investigation (ACAI) team, Ames developing the surface grids that described the computational volume about the Orbiter, and Langley improving grid quality of Ames generated data and constructing the final computational volume grids. Distributing the work among the participant in th ACAI team resulted in significantl less time required to construct complete meshes than possible by any individual participant. The approach demonstrated that the One-NASA grid generation team could sustain the demand of for five new meshes to explore new damage scenarios within an aggressive time-line.

Alter, Stephen J.↗
NASA NTRS2015

The AgMIP GRIDded Crop Modeling Initiative (AgGRID) and the Global Gridded Crop Model Intercomparison (GGCMI)

Climate change is a significant risk for agricultural production. Even under optimistic scenarios for climate mitigation action, present-day agricultural areas are likely to face significant increases in temperatures in the coming decades, in addition to changes in precipitation, cloud cover, and the frequency and duration of extreme heat, drought, and flood events (IPCC, 2013). These factors will affect the agricultural system at the global scale by impacting cultivation regimes, prices, trade, and food security (Nelson et al., 2014a). Global-scale evaluation of crop productivity is a major challenge for climate impact and adaptation assessment. Rigorous global assessments that are able to inform planning and policy will benefit from consistent use of models, input data, and assumptions across regions and time that use mutually agreed protocols designed by the modeling community. To ensure this consistency, large-scale assessments are typically performed on uniform spatial grids, with spatial resolution of typically 10 to 50 km, over specified time-periods. Many distinct crop models and model types have been applied on the global scale to assess productivity and climate impacts, often with very different results (Rosenzweig et al., 2014). These models are based to a large extent on field-scale crop process or ecosystems models and they typically require resolved data on weather, environmental, and farm management conditions that are lacking in many regions (Bondeau et al., 2007; Drewniak et al., 2013; Elliott et al., 2014b; Gueneau et al., 2012; Jones et al., 2003; Liu et al., 2007; M¨uller and Robertson, 2014; Van den Hoof et al., 2011;Waha et al., 2012; Xiong et al., 2014). Due to data limitations, the requirements of consistency, and the computational and practical limitations of running models on a large scale, a variety of simplifying assumptions must generally be made regarding prevailing management strategies on the grid scale in both the baseline and future periods. Implementation differences in these and other modeling choices contribute to significant variation among global-scale crop model assessments in addition to differences in crop model implementations that also cause large differences in site-specific crop modeling (Asseng et al., 2013; Bassu et al., 2014).

climate↗
NASA NTRS1989

A tesselated probabilistic representation for spatial robot perception and navigation

The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations.

Elfes, Alberto↗
NASA NTRS2001

NAS Grid Benchmarks: A Tool for Grid Space Exploration

We present an approach for benchmarking services provided by computational Grids. It is based on the NAS Parallel Benchmarks (NPB) and is called NAS Grid Benchmark (NGB) in this paper. We present NGB as a data flow graph encapsulating an instance of an NPB code in each graph node, which communicates with other nodes by sending/receiving initialization data. These nodes may be mapped to the same or different Grid machines. Like NPB, NGB will specify several different classes (problem sizes). NGB also specifies the generic Grid services sufficient for running the bench-mark. The implementor has the freedom to choose any specific Grid environment. However, we describe a reference implementation in Java, and present some scenarios for using NGB.

Frumkin, Michael↗
NASA NTRS2012

Prognostics for Microgrid Components

Prognostics is the science of predicting future performance and potential failures based on targeted condition monitoring. Moving away from the traditional reliability centric view, prognostics aims at detecting and quantifying the time to impending failures. This advance warning provides the opportunity to take actions that can preserve uptime, reduce cost of damage, or extend the life of the component. The talk will focus on the concepts and basics of prognostics from the viewpoint of condition-based systems health management. Differences with other techniques used in systems health management and philosophies of prognostics used in other domains will be shown. Examples relevant to micro grid systems and subsystems will be used to illustrate various types of prediction scenarios and the resources it take to set up a desired prognostic system. Specifically, the implementation results for power storage and power semiconductor components will demonstrate specific solution approaches of prognostics. The role of constituent elements of prognostics, such as model, prediction algorithms, failure threshold, run-to-failure data, requirements and specifications, and post-prognostic reasoning will be explained. A discussion on performance evaluation and performance metrics will conclude the technical discussion followed by general comments on open research problems and challenges in prognostics.

Saxena, Abhinav↗
NASA NTRS2002

The Computing and Data Grid Approach: Infrastructure for Distributed Science Applications

With the advent of Grids - infrastructure for using and managing widely distributed computing and data resources in the science environment - there is now an opportunity to provide a standard, large-scale, computing, data, instrument, and collaboration environment for science that spans many different projects and provides the required infrastructure and services in a relatively uniform and supportable way. Grid technology has evolved over the past several years to provide the services and infrastructure needed for building 'virtual' systems and organizations. We argue that Grid technology provides an excellent basis for the creation of the integrated environments that can combine the resources needed to support the large- scale science projects located at multiple laboratories and universities. We present some science case studies that indicate that a paradigm shift in the process of science will come about as a result of Grids providing transparent and secure access to advanced and integrated information and technologies infrastructure: powerful computing systems, large-scale data archives, scientific instruments, and collaboration tools. These changes will be in the form of services that can be integrated with the user's work environment, and that enable uniform and highly capable access to these computers, data, and instruments, regardless of the location or exact nature of these resources. These services will integrate transient-use resources like computing systems, scientific instruments, and data caches (e.g., as they are needed to perform a simulation or analyze data from a single experiment); persistent-use resources. such as databases, data catalogues, and archives, and; collaborators, whose involvement will continue for the lifetime of a project or longer. While we largely address large-scale science in this paper, Grids, particularly when combined with Web Services, will address a broad spectrum of science scenarios. both large and small scale.

Johnston, William E.↗
NASA NTRS1987

Experimental observation of ion-acoustic double layers in laboratory plasma

Experimental results on the observation of ion-acoustic double layers in laboratory plasma are presented. In a double-plasma device, modified suitably to inject an electron beam into the target plasma, modulation of the beam through a step potential leads to excitation of ion-acoustic fluctuations. The fluctuations, growing away from the separating grids, develop into weak asymmetric ion-acoustic double layers. The observations are compared with the scenario emerging out of the computer simulations and analytical results on ion-acoustic double layers.

Saxena, Y. C.↗
NASA NTRS2017

Global and Regional Sea Level Rise Scenarios for the United States

The Sea Level Rise and Coastal Flood Hazard Scenarios and Tools Interagency Task Force, jointly convened by the U.S. Global Change Research Program (USGCRP) and the National Ocean Council (NOC), began its work in August 2015. The Task Force has focused its efforts on three primary tasks: 1) updating scenarios of global mean sea level (GMSL) rise, 2) integrating the global scenarios with regional factors contributing to sea level change for the entire U.S. coastline, and 3) incorporating these regionally appropriate scenarios within coastal risk management tools and capabilities deployed by individual agencies in support of the needs of specific stakeholder groups and user communities. This technical report focuses on the first two of these tasks and reports on the production of gridded relative sea level (RSL, which includes both ocean-level change and vertical land motion) projections for the United States associated with an updated set of GMSL scenarios. In addition to supporting the longer-term Task Force effort, this new product will be an important input into the USGCRP Sustained Assessment process and upcoming Fourth National Climate Assessment (NCA4) due in 2018. This report also serves as a key technical input into the in-progress USGCRP Climate Science Special Report (CSSR).

Sweet, William V.↗
NASA NTRS2024

Quantifying Food Security and Mitigation Risks Consequential to Climate Change Impacts on Crop Yields

Climate change is expected to impact crop yields globally, with some regions benefiting from favorable conditions and CO2 fertilization, while others face adverse effects from altered precipitation and higher temperatures. Changes in crop yields can destabilize the global food system and pose challenges to food security. Moreover, crop production is crucial, as biofuels are becoming increasingly important contributors to climate change mitigation measures aimed at limiting global warming. This study uses the Integrated Model to Assess the Global Environment integrated assessment model framework to analyze different indicators related to food security and climate change mitigation under varying climate change impacts on crop yields. Twelve spatially explicit crop productivity projections were taken from the full archive of the Global Gridded Crop Model Intercomparison of 120 climate-crop model combinations, forced by CMIP6-based climate scenarios. The selection includes two average-performing climate-crop model combinations, two pessimistic combinations that perform one standard deviation below the mean, and two optimistic model combinations that perform one standard deviation above the mean. To single out the effect of climate change on productivity changes, we drew samples from two representative concentration pathways (RCP2.6 and RCP8.5). These productivity projections were applied within an otherwise uniform scenario (SSP2) and analyzed for their effect on total calorie demand, crop prices, and number of people at risk of undernourishment to quantify food security. Risks to climate change mitigation targets were explored by modeling the total bioenergy supply, emissions, and global mean temperature. The results revealed significant differences in the risk of food security and mitigation potential between different regions and climate change scenarios. Across scenarios, the crop area extent can vary up to 2 million km2 due to changing crop yields. The projected change in global hunger ranges from 60 to 160 million undernourished people, indicating uncertainty between climate and crop model combinations. Low-income regions are especially impacted because of their high sensitivity to changes in food prices. Global climate change mitigation ambitions can also deviate by the latter part of the 21st century, as changes in yields will impact biofuel production as well as agriculture, forestry and other land use emissions. The quantitative insights generated by this study highlight the need for global policy efforts to make the agricultural system more adaptive to climate change to handle potential negative impacts.

crop yields↗
NASA NTRS2001

A Simple Case Study of a Grid Performance System

This document presents a simple case study of a Grid performance system based on the Grid Monitoring Architecture (GMA) being developed by the Grid Forum Performance Working Group. It describes how the various system components would interact for a very basic monitoring scenario, and is intended to introduce people to the terminology and concepts presented in greater detail in other Working Group documents. We believe that by focusing on the simple case first, working group members can familiarize themselves with terminology and concepts, and productively join in the ongoing discussions of the group. In addition, prototype implementations of this basic scenario can be built to explore the feasibility of the proposed architecture and to expose possible shortcomings. Once the simple case is understood and agreed upon, complexities can be added incrementally as warranted by cases not addressed in the most basic implementation described here. Following the basic performance monitoring scenario discussion, unresolved issues are introduced for future discussion.

Aydt, Ruth↗
NASA NTRS2019

Global Crop Production: Adaptation to Temperature Increase Is Possible by Maintaining the Current Growing Periods

Increasing temperature trends are expected to impact yields of major field crops by affecting various plant processes, such as phenology and growth. However, future projections, especially at the global scale, do not consider any agronomic adaptation in farming practices. We use an ensemble of seven Global Gridded Crop Models (GGCMs) contributing to the AgMIP-Gridded project to quantify the impacts and adaptation potential of field crops under increasing temperature. We study how uniform warming scenarios up to 6 K affect the productivity and growing period duration of five major crops. The design of the experiment aims at understanding adaptation measures targeted to temperature-driven impacts and therefore leaves out changes in any other climate variable. We find that, without adaptation the dominant effect of temperature increase is to shorten the growing period and to reduce yields and production, consistently across crops and regions. We then test the potential of two agronomic measures to contrast warming-induced yield reduction, assuming that (i) cultivars with adjusted phenology would be used to regain the reference growing period duration; (ii) rainfed systems would be converted to irrigated. Despite substantial uncertainties in model parametrization and little agreement in spatial patterns, at the global aggregation, model results are robust. We find that up to 3 K of temperature increase, production losses could be fully compensated by maintaining the original crop growing period. Irrigation would also compensate production losses, but would not reduce the temperature impacts. Across regions, the model ensemble estimates larger adaptation potentials from unchanged growing periods in continental and temperate regions, than in tropical and arid regions, where also irrigation has respectively little effects and availability.

Irrigation↗
NASA NTRS1994

Three dimensional inventories of aircraft emissions

Three dimensional inventories of fuel burned and emissions (NO(x), CO, and hydrocarbons) have been developed on a 1 deg latitude by 1 deg longitude by 1 km altitude grid for scheduled commercial air traffic (passenger jet, cargo, and turboprop aircraft) for 1990 and projected to the year 2015. Emission scenarios have also been calculated for projected fleets of Mach 2 and Mach 2.4 high speed civil transports. The methodology and results are discussed. These emission inventories are now in use in the assessment of aircraft emissions impact by NASA.

Baughcum, Steven L.↗
NASA NTRSDate not supplied

An Approach for Practical Grid-Resolved Roughness Aerothermodynamic Simulations for Woven Heatshield Surfaces

An approach for simulating the aerothermal environment over a patterned roughness surface within an otherwise axisymmetric flow is developed. The patterned roughness is simplified into a sinusoidal shape that captures the basic features of a charred 3MDCP ablator. Two orientations of this pattern, which are equally flight relevant due to the forming process, enable periodic boundary conditions along the edges of a grid spanning a single roughness element. This simplified three-dimensional grid represents the minimum problem size for a patterned roughness simulation, which enables computationally efficient grid-resolved roughness simulations. This approach is validated by simulating the Langley Mach 6 measurements made on a similar sinusoidal surface, which results in convective heating within 5% of the experimental data. Applying this approach to Mars Sample Return (MSR) Earth Entry System (EES) flight cases, considering simulations with both 11 species air and 30 species air with ablation products, results in heating augmentation that is slightly lower than the widely-used Dahm correlation approach, with the difference being dependent on pattern orientation. This result provides the only insight into the behavior of the roughness augmentation at the high temperature reacting flow conditions present for EES flight cases, which are not captured by available ground test measurements. For shear augmentation, the simulations revealed that the surface-parallel pressure component is dominant. For arc-jet tests targeted to flight values for shear, the higher arc-jet pressures required to match flight values for shear result in up to a 400% increase in the rough wall shear value, which is not captured by the Dahm correlation. This indicates that, to avoid significant over-testing, grid-resolved roughness simulations are required to determine arc-jet conditions that produce flight shear levels in rough-wall scenarios.

Christopher O Johnston↗
NASA NTRS2010

Preprocessor that Enables the Use of GridProTM Grids for Unsteady Reynolds-Averaged Navier-Stokes Code TURBO

A preprocessor for the Computational Fluid Dynamics (CFD) code TURBO has been developed and tested. The preprocessor converts grids produced by GridPro (Program Development Company (PDC)) into a format readable by TURBO and generates the necessary input files associated with the grid. The preprocessor also generates information that enables the user to decide how to allocate the computational load in a multiple block per processor scenario.

Shyam, Vikram↗
NASA NTRS2019

Importance of Tree- and Species-Level Interactions With Wildfire, Climate, and Soils in Interior Alaska: Implications for Forest Change Under A Warming Climate

The boreal zone of Alaska is dominated by interactions between disturbances, vegetation, and soils. These interactions are likely to change in the future through increasing permafrost thaw, more frequent and intense wildfires, and vegetation change from drought and competition. We utilize an individual tree-based vegetation model, the University of Virginia Forest Model Enhanced (UVAFME), to estimate current and future forest conditions across sites within interior Alaska. We updated UVAFME for application within interior Alaska, including improved simulation of permafrost dynamics, litter decay, nutrient dynamics, fire mortality, and post-fire regrowth. Following these updates, UVAFME output on species-specific biomass and stem density was comparable to inventory measurements at various forest types within interior Alaska. We then simulated forest response to climate change at specific inventory locations and across the Tanana Valley River Basin on a 2 × 2 km2 grid. We derived projected temperature and precipitation from a five-model average taken from the CMIP5 archive under the RCP 4.5 and 8.5 scenarios. Results suggest that climate change and the concomitant impacts on wildfire and permafrost dynamics will result in overall decreases in biomass (particularly for spruce (Picea spp.)) within the interior Tanana Valley, despite increases in quaking aspen (Populus tremuloides) biomass, and a resulting shift towards higher deciduous fraction. Simulation results also predict increases in biomass at cold, wet locations and at high elevations, and decreases in biomass in dry locations, under both moderate (RCP 4.5) and extreme (RCP 8.5) climate change scenarios. These simulations demonstrate that a highly detailed, species interactive model can be used across a large region within Alaska to investigate interactions between vegetation, climate, wildfire, and permafrost. The vegetation changes predicted here have the capacity to feed back to broader scale climate-forest interactions in the North American boreal forest, a region which contributes significantly to the global carbon and energy budgets.

forest modeling↗
NASA NTRS2022

Lightning Over Central Canada: Skill Assessment for Various Land-Atmosphere Model Configurations and Lightning Indices Over a Boreal Study Area

Current lightning predictions are uncertain because they rely on empirical diagnostic relationships and often use coarse-scale climate scenario simulations in which deep convection is parameterized. Previous studies demonstrated that simulations with convection permitting resolutions improve lightning predictions compared to coarser-grid simulations using convection parameterizations for different geographical locations but not over the boreal zone. In this study, lightning simulations with the NASA Unified-Weather Research and Forecasting (NU-WRF) model are evaluated over a domain including the Great Slave Lake in Canada, for six lightning seasons. The simulations are performed at convection parameterized (9 km) and convection-permitting (3 km) resolution using the Goddard 4ICE and the Thompson microphysics schemes. Four lightning indices are evaluated against observations from the Canadian Lightning Detection Network, in terms of spatiotemporal frequency distribution, spatial pattern, daily climatology, and an event-based overall skill assessment. The Thompson scheme is, regardless of the spatial resolution, superior to the Goddard 4ICE scheme in predicting daily climatology but worse in predicting the spatial patterns of lightning occurrence. Results indicate that lightning estimation benefits from modeling at convection-permitting resolution, in particular for the ice based lightning indices. In contrast, the product of convective available potential energy and precipitation rate proved to be the most robust index that was largely invariant to varying spatial resolution. Finally, this study reveals issues of the models to reproduce the observed spatial pattern of lightning well, which might be related to an insufficient representation of land surface heterogeneity, including peatlands, in the study area.

Jonas Mortelmans↗
NASA NTRS2008

Interpolating Fields of Carbon Monoxide Data Using a Hybrid Statistical-Physical Model

Atmospheric Carbon Monoxide (CO) is a pollutant gas of which the US congress has mandated regular monitoring, and satellite sensors can be used to retrieve regional concentrations of CO over several vertical layers. However, CO at cloudy locations cannot be observed and have to be estimated from the observed data set, resulting in an interpolation problem. The current state-of-the-art solution is to combine prior information, computed by a deterministic physical model, with observations. However, the deterministic model may introduce uncertainties that do not derive from the data. While sharing certain features with the physical model, this paper presents a Bayesian hierarchical model for interpolating CO on a 3-dimensional spatial grid, across time. To our knowledge such a model has not been considered before. The model is applied to a hypothetical air-quality monitoring scenario, and is compared to existing interpolation methods. The results provide motivation for the use of the statistical model for regional to local applications.

Arellano, A. A.↗
NASA NTRS2019

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO Seasonal Forecast System

El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite sea surface salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (North American Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF (Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive satellite) V4.1 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by satellite at approximately 1-centimeter depth) and in situ measurements (typically measured by Argo floats at 3-meters depth). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from GMAO S2S spring reanalyses that assimilate/withhold along-track (L2) SSS. In particular, we contrast forecasts of the big 2015 El Nino, the 2017 La Nina and the 2018 weak El Nino. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrade the mixed layer depth leading to more accurate coupled air/sea interaction. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP, allows a unique opportunity to compare and contrast forecasts initialized with the benefit of these two satellite SSS observation types. We assess the impact of gridded satellite sea surface salinity observations on dynamical ENSO forecasts for the big 2015 El Nino.

Hackert, E.↗