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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Modeling DNP3 Traffic Characteristics of Field Devices in SCADA Systems of the Smart Grid

In the generation, transmission, and distribution sectors of the smart grid, intelligence of field devices is realized by programmable logic controllers (PLCs). Many smart-grid subsystems are essentially cyber-physical energy systems (CPES): For instance, the power system process (i.e., the physical part) within a substation is monitored and controlled by a SCADA network with hosts running miscellaneous applications (i.e., the cyber part). To study the interactions between the cyber and physical components of a CPES, several co-simulation platforms have been proposed. However, the network simulators/emulators of these platforms do not include a detailed traffic model that takes into account the impacts of the execution model of PLCs on traffic characteristics. As a result, network traces generated by co-simulation only reveal the impacts of the physical process on the contents of the traffic generated by SCADA hosts, whereas the distinction between PLCs and computing nodes (e.g., a hardened computer running a process visualization application) has been overlooked. To generate realistic network traces using co-simulation for the design and evaluation of applications relying on accurate traffic profiles, it is necessary to establish a traffic model for PLCs. In this work, we propose a parameterized model for PLCs that can be incorporated into existing co-simulation platforms. We focus on the DNP3 subsystem of slave PLCs, which automates the processing of packets from the DNP3 master. To validate our approach, we extract model parameters from both the configuration and network traces of real PLCs. Simulated network traces are generated and compared against those from PLCs. Our evaluation shows that our proposed model captures the essential traffic characteristics of DNP3 slave PLCs, which can be used to extend existing co-simulation platforms and gain further insights into the behaviors of CPES.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1995

A simulation to study the feasibility of improving the temporal resolution of LAGEOS geodynamic solutions by using a sequential process noise filter

A key drawback to estimating geodetic and geodynamic parameters over time based on satellite laser ranging (SLR) observations is the inability to accurately model all the forces acting on the satellite. Errors associated with the observations and the measurement model can detract from the estimates as well. These 'model errors' corrupt the solutions obtained from the satellite orbit determination process. Dynamical models for satellite motion utilize known geophysical parameters to mathematically detail the forces acting on the satellite. However, these parameters, while estimated as constants, vary over time. These temporal variations must be accounted for in some fashion to maintain meaningful solutions. The primary goal of this study is to analyze the feasibility of using a sequential process noise filter for estimating geodynamic parameters over time from the Laser Geodynamics Satellite (LAGEOS) SLR data. This evaluation is achieved by first simulating a sequence of realistic LAGEOS laser ranging observations. These observations are generated using models with known temporal variations in several geodynamic parameters (along track drag and the J(sub 2), J(sub 3), J(sub 4), and J(sub 5) geopotential coefficients). A standard (non-stochastic) filter and a stochastic process noise filter are then utilized to estimate the model parameters from the simulated observations. The standard non-stochastic filter estimates these parameters as constants over consecutive fixed time intervals. Thus, the resulting solutions contain constant estimates of parameters that vary in time which limits the temporal resolution and accuracy of the solution. The stochastic process noise filter estimates these parameters as correlated process noise variables. As a result, the stochastic process noise filter has the potential to estimate the temporal variations more accurately since the constraint of estimating the parameters as constants is eliminated. A comparison of the temporal resolution of solutions obtained from standard sequential filtering methods and process noise sequential filtering methods shows that the accuracy is significantly improved using process noise. The results show that the positional accuracy of the orbit is improved as well. The temporal resolution of the resulting solutions are detailed, and conclusions drawn about the results. Benefits and drawbacks of using process noise filtering in this type of scenario are also identified.

Hartman, Brian Davis↗
NASA NTRS2005

Business Intelligence Modeling in Launch Operations

This technology project is to advance an integrated Planning and Management Simulation Model for evaluation of risks, costs, and reliability of launch systems from Earth to Orbit for Space Exploration. The approach builds on research done in the NASA ARC/KSC developed Virtual Test Bed (VTB) to integrate architectural, operations process, and mission simulations for the purpose of evaluating enterprise level strategies to reduce cost, improve systems operability, and reduce mission risks. The objectives are to understand the interdependency of architecture and process on recurring launch cost of operations, provide management a tool for assessing systems safety and dependability versus cost, and leverage lessons learned and empirical models from Shuttle and International Space Station to validate models applied to Exploration. The systems-of-systems concept is built to balance the conflicting objectives of safety, reliability, and process strategy in order to achieve long term sustainability. A planning and analysis test bed is needed for evaluation of enterprise level options and strategies for transit and launch systems as well as surface and orbital systems. This environment can also support agency simulation .based acquisition process objectives. The technology development approach is based on the collaborative effort set forth in the VTB's integrating operations. process models, systems and environment models, and cost models as a comprehensive disciplined enterprise analysis environment. Significant emphasis is being placed on adapting root cause from existing Shuttle operations to exploration. Technical challenges include cost model validation, integration of parametric models with discrete event process and systems simulations. and large-scale simulation integration. The enterprise architecture is required for coherent integration of systems models. It will also require a plan for evolution over the life of the program. The proposed technology will produce long-term benefits in support of the NASA objectives for simulation based acquisition, will improve the ability to assess architectural options verses safety/risk for future exploration systems, and will facilitate incorporation of operability as a systems design consideration, reducing overall life cycle cost for future systems. The future of business intelligence of space exploration will focus on the intelligent system-of-systems real-time enterprise. In present business intelligence, a number of technologies that are most relevant to space exploration are experiencing the greatest change. Emerging patterns of set of processes rather than organizational units leading to end-to-end automation is becoming a major objective of enterprise information technology. The cost element is a leading factor of future exploration systems.

Bardina, Jorge E.↗
DOE OSTI2015

Atmosphere expansion and mass loss of close-orbit giant exoplanets heated by stellar XUV. II. Effects of planetary magnetic field; structuring of inner magnetosphere

This is the second paper in a series where we build a self-consistent model to simulate the mass-loss process of a close-orbit magnetized giant exoplanet, so-called hot Jupiter (HJ). In this paper we generalize the hydrodynamic (HD) model of an HJ's expanding hydrogen atmosphere, proposed in the first paper, to include the effects of intrinsic planetary magnetic field. The proposed self-consistent axisymmetric 2D magnetohydrodynamics model incorporates radiative heating and ionization of the atmospheric gas, basic hydrogen chemistry for the appropriate account of major species composing HJ's upper atmosphere and related radiative energy deposition, and H{sub 3}{sup +} and Lyα cooling processes. The model also takes into account a realistic solar-type X-ray/EUV spectrum for calculation of intensity and column density distribution of the radiative energy input, as well as gravitational and rotational forces acting in a tidally locked planet–star system. An interaction between the expanding atmospheric plasma and an intrinsic planetary magnetic dipole field leads to the formation of a current-carrying magnetodisk that plays an important role for topology and scaling of the planetary magnetosphere. A cyclic character of the magnetodisk behavior, composed of consequent phases of the disk formation followed by the magnetic reconnection with the ejection of a ring-type plasmoid, has been discovered and investigated. We found that the mass-loss rate of an HD 209458b analog planet is weakly affected by the equatorial surface field <0.3 G, but is suppressed by an order of magnitude at the field of 1 G.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Coupled Gas-Exchange Model for C4 Leaves Comparing Stomatal Conductance Models

Plant simulation models are abstractions of plant physiological processes that are useful for investigating the responses of plants to changes in the environment. Because photosynthesis and transpiration are fundamental processes that drive plant growth and water relations, a leaf gas-exchange model that couples their interdependent relationship through stomatal control is a prerequisite for explanatory plant simulation models. Here, we present a coupled gas-exchange model for C4 leaves incorporating two widely used stomatal conductance submodels: Ball–Berry and Medlyn models. The output variables of the model includes steady-state values of CO2 assimilation rate, transpiration rate, stomatal conductance, leaf temperature, internal CO2 concentrations, and other leaf gas-exchange attributes in response to light, temperature, CO2, humidity, leaf nitrogen, and leaf water status. We test the model behavior and sensitivity, and discuss its applications and limitations. The model was implemented in Julia programming language using a novel modeling framework. Our testing and analyses indicate that the model behavior is reasonably sensitive and reliable in a wide range of environmental conditions. The behavior of the two model variants differing in stomatal conductance submodels deviated substantially from each other in low humidity conditions. The model was capable of replicating the behavior of transgenic C4 leaves under moderate temperatures as found in the literature. The coupled model, however, underestimated stomatal conductance in very high temperatures. This is likely an inherent limitation of the coupling approaches using Ball–Berry type models in which photosynthesis and stomatal conductance are recursively linked as an input of the other.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Resolving Mesoscale Convective Systems: Grid Spacing Sensitivity in the Tropics and Midlatitudes

Abstract Mesoscale convective systems (MCSs) are a critical global water cycle component and drive extreme precipitation events in tropical and midlatitude regions. However, simulating deep convection remains challenging for modern numerical weather and climate models due to the complex interactions of processes from microscales to synoptic scales. Recent models with kilometer‐scale horizontal grid spacings offer notable improvements in simulating deep convection compared to coarser‐resolution models. Still, deficiencies in representing key physical processes, such as entrainment, lead to systematic biases. Additionally, evaluating model outputs using process‐oriented observational data remain difficult. This study presents an ensemble of MCS simulations with spanning the deep convective gray zone ( from 12 km to 125 m) in the Southern Great Plains of the U.S. and the Amazon Basin. Comparing these simulations with Atmospheric Radiation Measurement (ARM) wind profiler observations, we find greater sensitivity in the Amazon Basin compared to the Great Plains. Convective drafts converge structurally at sub‐kilometer scales, but some deficiencies remain. In both regions, simulated up and downdrafts are too deep and extreme downdrafts are not strong enough. Furthermore, Amazonian updrafts are too strong. Overall, we observe higher sensitivity in the tropics, including an artificial buildup in vertical kinetic energy at scales of , suggesting a need for 250 m in this region. Nevertheless, bulk convergence—agreement of storm‐average statistics—is achievable with kilometer‐scale simulations within a 10% error margin with 1 km providing a good balance between accuracy and computational cost.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

Development of four-dimensional atmospheric models and application techniques for space mission simulation.

The major objective of the four-dimensional atmospheric model program is to provide global profiles of moisture, temperature, pressure, and density from the surface to 25 km altitude. These models may then be used as input to the attenuation models for the purpose of simulating earth resource missions. Aspects of data processing are discussed together with homogeneous moisture regions, details regarding four-dimensional atmospheric models, and mission simulation procedures.-

Spiegler, D. B.↗
NASA NTRS2000

Evolution of Plasmaspheric Refilling from Comparisons of Satellite Observations with Simulations by an Interhemispheric Plasmasphere Model

The evolution of the plasmasphere is closely coupled to processes governing flux tube refilling and transport. In this study we follow this evolution through a series of sequential ion observations made by the Retarding Ton Mass Spectrometer (RIMS) on the Dynamics Explorer 1 (DE1) satellite and with the intervening time intervals filled in by simulations made with the Field Line Interhemispheric Plasma (FLIP) model. The period of DE1 (approx. 7 hours) is such that nearly the same flux tubes are observed every second day, or every sixth orbit. The intervening observations provide additional information regarding longitudinal variations. Since the observations are necessarily restricted to two local time passes through the plasmasphere per orbit, the FLIP code will follow the observed flux tubes as they corotate and convect through the other local times. The simulations use the initial plasmasphere observations to define initial conditions of where the plasmapause is located and the extent of filling in the plasma trough. The composite of observations and simulations allows assessment of the degree to which the FLIP simulations accurately describe the processes which govern flux tube refilling over the two-day period between observations on the same flux tubes. To the extent that the convection electric field model in the FLIP code is accurate, this composite also provides a global picture of what is happening in and near the plasmasphere during the period. Of particular interest are changes in plasma composition as refilling proceeds.

Comfort, Richard H.↗
NASA NTRS2013

Model-Based Verification and Validation of the SMAP Uplink Processes

This case study stands as an example of how a project can validate a system-level design earlier in the project life cycle than traditional V&V processes by using simulation on a system model. Specifically, this paper describes how simulation was added to a system model of the Soil Moisture Active-Passive (SMAP) mission's uplink process.Also discussed are the advantages and disadvantages of the methods employed and the lessons learned; which are intended to benefit future model-based and simulation-based V&V development efforts.

verification and validation (V&V)↗
DOE OSTI2020

Conserving Land–Atmosphere Synthesis Suite (CLASS)

Accurate estimates of terrestrial water and energy cycle components are needed to better understand climate processes and improve models’ ability to simulate future change. Various observational estimates are available for the individual budget terms; however, these typically show inconsistencies when combined in a budget. In this work, a Conserving Land–Atmosphere Synthesis Suite (CLASS) of estimates of simultaneously balanced surface water and energy budget components is developed. Individual CLASS variable datasets, where possible, 1) combine a range of existing variable product estimates, and hence overcome the limitations of estimates from a single source; 2) are observationally constrained with in situ measurements; 3) have uncertainty estimates that are consistent with their agreement with in situ observations; and 4) are consistent with each other by being able to solve the water and energy budgets simultaneously. First, available datasets of a budget variable are merged by implementing a weighting method that accounts both for the ability of datasets to match in situ measurements and the error covariance between datasets. Then, the budget terms are adjusted by applying an objective variational data assimilation technique (DAT) that enforces the simultaneous closure of the surface water and energy budgets linked through the equivalence of evapotranspiration and latent heat. Comparing component estimates before and after applying the DAT against in situ measurements of energy fluxes and streamflow showed that modified estimates agree better with in situ observations across various metrics, but also revealed some inconsistencies between water budget terms in June over the higher latitudes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Convective Systems Over the Japan Sea: Cloud-Resolving Model Simulations

Wintertime observations of MCSs (Mesoscale Convective Systems) over the Sea of Japan - 2001 (WMO-01) were collected from January 12 to February 1, 2001. One of the major objectives is to better understand and forecast snow systems and accompanying disturbances and the associated key physical processes involved in the formation and development of these disturbances. Multiple observation platforms (e.g., upper-air soundings, Doppler radar, wind profilers, radiometers, etc.) during WMO-01 provided a first attempt at investigating the detailed characteristics of convective storms and air pattern changes associated with winter storms over the Sea of Japan region. WMO-01 also provided estimates of the apparent heat source (Q1) and apparent moisture sink (Q2). The vertical integrals of Q1 and Q2 are equal to the surface precipitation rates. The horizontal and vertical adjective components of Q1 and Q2 can be used as large-scale forcing for the Cloud Resolving Models (CRMs). The Goddard Cumulus Ensemble (GCE) model is a CRM (typically run with a 1-km grid size). The GCE model has sophisticated microphysics and allows explicit interactions between clouds, radiation, and surface processes. It will be used to understand and quantify precipitation processes associated with wintertime convective systems over the Sea of Japan (using data collected during the WMO-01). This is the first cloud-resolving model used to simulate precipitation processes in this particular region. The GCE model-simulated WMO-01 results will also be compared to other GCE model-simulated weather systems that developed during other field campaigns (i.e., South China Sea, west Pacific warm pool region, eastern Atlantic region and central USA).

Tao, Wei-Kuo↗
DOE OSTI2023

Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest

Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. SOA formation involves chemistry in gas phase, aqueous aerosols, and clouds. Simulating these chemical processes involve solving a stiff set of differential equations, which are computationally expensive steps for three-dimensional chemical transport models. Deep neural networks (DNNs) are universal function approximators that could be used to represent the complex nonlinear changes in aerosol physical and chemical processes; however, key challenges such as generalizability to extended time periods, preservation of mass balance, simulating sparse model outputs, and maintaining physical constraints have limited their use in atmospheric chemistry. Here, we develop an approach of using a physics-informed DNN that overcomes previous such challenges and demonstrates its applicability for the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest. The DNN is trained with data generated by simulating IEPOX-SOA over the entire atmospheric column, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The trained DNN is then embedded within WRF-Chem to replace the computationally expensive default solver of IEPOX-SOA formation. The trained DNN predictions generalizes well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution (20 size bins) over several days of simulations in both dry and wet seasons. The embedded DNN reduces the computational expense of WRF-Chem by a factor of 2. Our approach shows promise in terms of application to other computationally expensive chemistry solvers in climate models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

A mesoscale numerical forecast of an intense convective snowburst along the East Coast

Mesoscale numerical forecasts utilizing the Mesoscale Atmospheric Simulation System (MASS) are documented for a convective snowburst in the Washington, D.C.-Baltimore, Maryland, region on March 8, 1984 that was associated with secondary cyclogenesis along the East Coast. The forecasts are presented to demonstrate the ability of a mesoscale model to simulate dynamical interactions and diabatic process for a wintertime convective event that was inadequately predicted by local forecasters and to note some of the possible benefits of using mesoscale models for day-to-day forecasting. The results from this and other recent mesoscale modeling studies indicate that three-hourly output of key model fields, when combined with other data sources, can be a valuable aid to forecasters concerned with predicting weather events that are mesoscale in character.

Kocin, P. J.↗
NASA NTRS2016

The Impact of ENSO on Trace Gas Composition in the Upper Troposphere to Lower Stratosphere

The El Nino-Southern Oscillation (ENSO) is the dominant mode of interannual variability in the tropical troposphere and its effects extend well into the stratosphere. Its impact on atmospheric dynamics and chemistry cause important changes to trace gas constituent distributions. A comprehensive suite of satellite observations, reanalyses, and chemistry climate model simulations are illuminating our understanding of processes like ENSO. Analyses of more than a decade of observations from NASAs Aura and Aqua satellites, combined with simulations from the Goddard Earth Observing System Chemistry-Climate Model (GEOSCCM) and other Chemistry Climate Modeling Initiative (CCMI) models, and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis have provided key insights into the response of atmospheric composition to ENSO. While we will primarily focus on ozone and water vapor responses in the upper troposphere to lower stratosphere, the effects of ENSO ripple through many important trace gas species throughout the atmosphere. The very large 2015-2016 El Nino event provides an opportunity to closely examine these impacts with unprecedented observational breadth. An improved quantification of natural climate variations, like those from ENSO, is needed to detect and quantify anthropogenic climate changes.

ENSO↗
DOE OSTI2020

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in-situ and remote sensing datasets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Design and Modeling of an 80 bar Oxy-Combustor for Direct Fired Supercritical CO2 Applications

As supercritical CO2 power cycles for fossil energy power generation continue to generate interest, there are significant issues and unanswered questions regarding injector design, flame stabilization, wall heat transfer, CO emissions, combustion dynamics and other combustion phenomenon. For natural gas, direct-fired cycles with carbon capture it is believed that Computational Fluid Dynamics (CFD) modeling will play an essential role in the combustor design process. To accurately model turbulent reacting flows at these unique conditions, experimental data is needed to validate CFD codes and sub-models and is currently lacking at conditions relevant for these cycles. This paper presents the conceptual design and CFD simulations of an experimental 80 bar oxy-combustion facility and test article currently under construction at NETL. The facility is targeted towards the testing of a single injector, direct-fired sCO2 combustor at the 100 kW thermal output level. While these conditions do not reflect the actual Allam cycle operating conditions (300 bar) they are viewed as a stepping stone in the model validation process at supercritical conditions. Reynolds Averaged Navier-Stokes as well as Large Eddy Simulation are used to model the turbulent combustion process and aid in the design of the combustor and injector. Process parameters including oxidizer preheat temperature and combustor flowrate are investigated.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Impact of Urban Pollution on Organic-Mediated New-Particle Formation and Particle Number Concentration in the Amazon Rainforest

A major challenge in assessing the impact of aerosols on climate change is to understand how human activities change aerosol loading and properties relative to the pristine-preindustrial baseline. Here we combine chemical transport simulation and field measurements to investigate the effect of anthropogenic pollution from an isolated metropolis on particle number concentration over the preindustrial-like Amazon rainforest through various new-particle formation (NPF) mechanisms and primary particle emissions. To represent organic-mediated NPF, we employ a state-of-the-art model that systematically simulates the formation chemistry and thermodynamics of extremely low volatility organic compounds as well as their roles in NPF processes, and further update the model to improve organic NPF simulations under human-influenced conditions. Results show that urban pollution from the metropolis increases particle number concentration by a factor of 5–25 over the downwind region (within 200 km from city center) compared to background conditions. Overall, our model indicates that NPF contributes over 70% of the total particle number in the downwind region except immediately adjacent to the sources. Among different NPF mechanisms, the ternary NPF involving organics and sulfuric acid overwhelmingly dominates. The improved understanding of particle formation mechanisms will help better quantify anthropogenic aerosol forcing from pristine-preindustrial times to present day.

54 ENVIRONMENTAL SCIENCES↗