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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO 2 , temperature, water, and nitrogen (version 1.0)

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

James A Franke↗
NASA NTRS2017

Global Gridded Crop Model Evaluation: Benchmarking, Skills, Deficiencies and Implications.

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.

wheat↗
NASA NTRS2000

Automated, Parametric Geometry Modeling and Grid Generation for Turbomachinery Applications

The objective of this Phase I project is to develop a highly automated software system for rapid geometry modeling and grid generation for turbomachinery applications. The proposed system features a graphical user interface for interactive control, a direct interface to commercial CAD/PDM systems, support for IGES geometry output, and a scripting capability for obtaining a high level of automation and end-user customization of the tool. The developed system is fully parametric and highly automated, and, therefore, significantly reduces the turnaround time for 3D geometry modeling, grid generation and model setup. This facilitates design environments in which a large number of cases need to be generated, such as for parametric analysis and design optimization of turbomachinery equipment. In Phase I we have successfully demonstrated the feasibility of the approach. The system has been tested on a wide variety of turbomachinery geometries, including several impellers and a multi stage rotor-stator combination. In Phase II, we plan to integrate the developed system with turbomachinery design software and with commercial CAD/PDM software.

Harrand, Vincent J.↗
DOE OSTI2013

Assessment of Industrial Load for Demand Response across U.S. Regions of the Western Interconnect

Demand response (DR) has the ability to both increase power grid reliability and potentially reduce operating system costs. Understanding the role of demand response in grid modeling has been difficult due to complex nature of the load characteristics compared to the modeled generation and the variation in load types. This is particularly true of industrial loads, where hundreds of different industries exist with varying availability for demand response. We present a framework considering industrial loads for the development of availability profiles that can provide more regional understanding and can be inserted into analysis software for further study. The developed framework utilizes a number of different informational resources, algorithms, and real-world measurements to perform a bottom-up approach in the development of a new database with representation of the potential demand response resource in the industrial sector across the U.S. This tool houses statistical values of energy and demand response (DR) potential by industrial plant and geospatially locates the information for aggregation for different territories without proprietary information. This report will discuss this framework and the analyzed quantities of demand response for Western Interconnect (WI) in support of evaluation of the cost production modeling with power grid modeling efforts of demand response.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Solid-State Transformer and Hybrid Transformer With Integrated Energy Storage in Active Distribution Grids: Technical and Economic Comparison, Dispatch, and Control

Solid-state transformer (SST) and hybrid transformer (HT) are promising alternatives to the line-frequency transformer (LFT) in smart grids. The SST features medium-frequency isolation, full controllability for voltage regulation, reactive power compensation, and the capability of battery energy storage system (BESS) integration with multiport configuration. The HT has a partially-rated converter for fractional controllability and can integrate a small BESS. Fast grid-edge voltage fluctuations from increased solar photovoltaic (PV) and electric vehicle (EV) penetration are difficult to manage for mechanical load tap changers. Hence, along with the trend towards more BESS in the grid, the controllability and the storage integration capability of the SST and HT are of strong interest. However, a review of literature shows existing SST and HT research is mostly at converter level, while system-level assessments are scarce. Assessing technical and economic impacts is critical to understanding the benefits and role of the SST and HT to guide future research, which is presented for the first time in this article. Experimental results from medium-voltage (MV) SST and MV HT prototypes are shown to confirm equipment-level feasibility, where the voltage controllability waveforms of a MV HT prototype are reported for the first time. Comparative simulations are performed on a modified IEEE 34-bus system. Here, a grid-model-less decentralized grid-edge voltage control method and a day-ahead BESS dispatch method are proposed for the SST and HT. The simulations show that the SST and HT with integrated storage can host more PV, achieve peak shaving, mitigate voltage fluctuation and reverse power flow, and support energy arbitrage for operational cost reduction, as compared to the LFT. Moreover, comprehensive analyses of net present value (NPV) and internal rate of return (IRR) are performed under different installed PV capacities, HT’s partial converter ratings, and BESS capacities. Sensitivities to future cost reductions of the PV and BESS are studied. Although the NPV and IRR are currently negative, 60% capital cost reduction or 150% revenue increase will make the SST and HT economically viable in the use case studied.

14 SOLAR ENERGY↗
DOE OSTI2018

Integrated Cyber/Physical Grid Resiliency Modeling

This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Efficient algorithm for locating and sizing series compensation devices in large power transmission grids: I. Model implementation

We explore optimization methods for planning the placement, sizing and operations of Flexible Alternating Current Transmission System (FACTS) devices installed to relieve transmission grid congestion. We limit our selection of FACTS devices to Series Compensation (SC) devices that can be represented by modification of the inductance of transmission lines. Our master optimization problem minimizes the l 1 norm of the inductance modification subject to the usual line thermal-limit constraints. We develop heuristics that reduce this non-convex optimization to a succession of Linear Programs (LP) which are accelerated further using cutting plane methods. The algorithm solves an instance of the MatPower Polish Grid model (3299 lines and 2746 nodes) in 40 seconds per iteration on a standard laptop—a speed up that allows the sizing and placement of a family of SC devices to correct a large set of anticipated congestions. We observe that our algorithm finds feasible solutions that are always sparse, i.e., SC devices are placed on only a few lines. In a companion manuscript, we demonstrate our approach on realistically-sized networks that suffer congestion from a range of causes including generator retirement. In this manuscript, we focus on the development of our approach, investigate its structure on a small test system subject to congestion from uniform load growth, and demonstrate computational efficiency on a realistically-sized network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

The Demand-Side Grid (dsgrid) Model Documentation

Electrical load is the backdrop for all power systems analysis. As a coequal partner in the supply-demand balance that must be maintained on electrical grids at all times and for all timescales, load is a major source of variability and uncertainty in grid operations. Despite this, utility load forecasting is typically done in a top-down manner that lacks the granularity in time, geography, end use, and technology that is needed to explore the potential impact of technological shifts; and many studies of future grid systems employ relatively simple scaling of historical loads, placing more emphasis on supply-side resources such as generation and transmission that are fewer in number and are better understood in terms of cost and performance. The primary purpose of the demand-side grid model (dsgrid) is to fill these gaps by creating comprehensive load data sets at a sufficient temporal, geographic, sectoral, and end-use resolution to enable detailed analyses of current patterns and future projections of end-use load. Furthermore, the dsgrid platform uses a bottom-up methodology that allows highly resolved analysis of 'what-if' scenarios. dsgrid leverages detailed sectoral energy models to provide hourly time series of load by subsector, end use, and county covering a full year. Although dsgrid currently emphasizes electricity load data, its component sector models for residential buildings, commercial buildings, and industry provide information on other fuel use, including natural gas. The data sets can thus be leveraged to support analysis of numerous demand-side technology-driven changes, such as energy efficiency, electrification, and operational flexibility (i.e., demand response). The electricity use data are time-synchronized with solar and wind data sets so as to be suitable for use in power systems analysis. This report documents dsgrid and its initial data set, which covers electricity demand in the contiguous United States for the historical year of 2012. We start with a historical year to enable model calibration and validation, as well as the development of a mathematical description of our model residuals that can be applied to future-year 'what-if' scenarios to remove known errors between our bottom-up modeling and historical load shapes. This work is part of the Electrification Futures Study (EFS), for which the dsgrid team will be developing future load snapshots that describe projected year 2050 under baseline and a range of electrification assumptions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

R-SEEDS Final Report

DER aggregation and grid digitalization imply a future grid that would benefit from more fluid interaction between the bulk power system and the distribution system. This scenario presents a challenge for grid modeling, which treats the two systems separately. Grid modeling also focuses on the engineering aspects of grids without incorporating modeling of the human and social dimensions of the grid as a cyber-physical-social system. The R-SEEDS project examines the integration of human and social models into grid modeling and its implications on decarbonization, energy justice, and distributional impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1992

NASA Workshop on future directions in surface modeling and grid generation

Given here is a summary of the paper sessions and panel discussions of the NASA Workshop on Future Directions in Surface Modeling and Grid Generation held a NASA Ames Research Center, Moffett Field, California, December 5-7, 1989. The purpose was to assess U.S. capabilities in surface modeling and grid generation and take steps to improve the focus and pace of these disciplines within NASA. The organization of the workshop centered around overviews from NASA centers and expert presentations from U.S. corporations and universities. Small discussion groups were held and summarized by group leaders. Brief overviews and a panel discussion by representatives from the DoD were held, and a NASA-only session concluded the meeting. In the NASA Program Planning Session summary there are five recommended steps for NASA to take to improve the development and application of surface modeling and grid generation.

Vandalsem, W. R.↗
DOE OSTI2017

High Fidelity, “Faster than Real-Time” Simulator for Predicting Power System Dynamic Behavior - Final Technical Report

The “High Fidelity, Faster than Real­Time Simulator for Predicting Power System Dynamic Behavior” was designed and developed by Illinois Institute of Technology with critical contributions from Electrocon International, Argonne National Laboratory, Alstom Grid and McCoy Energy. Also essential to the project were our two utility partners: Commonwealth Edison and AltaLink. The project was a success due to several major breakthroughs in the area of large­scale power system dynamics simulation, including (1) a validated faster than real­ time simulation of both stable and unstable transient dynamics in a large­scale positive sequence transmission grid model, (2) a three­phase unbalanced simulation platform for modeling new grid devices, such as independently controlled single­phase static var compensators (SVCs), (3) the world’s first high fidelity three­phase unbalanced dynamics and protection simulator based on Electrocon’s CAPE program, and (4) a first­of­its­ kind implementation of a single­phase induction motor model with stall capability. The simulator results will aid power grid operators in their true time of need, when there is a significant risk of cascading outages. The simulator will accelerate performance and enhance accuracy of dynamics simulations, enabling operators to maintain reliability and steer clear of blackouts. In the long­term, the simulator will form the backbone of the newly conceived hybrid real­time protection and control architecture that will coordinate local controls, wide­area measurements, wide­area controls and advanced real­time prediction capabilities. The nation’s citizens will benefit in several ways, including (1) less down time from power outages due to the faster­than­real­time simulator’s predictive capability, (2) higher levels of reliability due to the detailed dynamics plus protection simulation capability, and (3) more resiliency due to the three­ phase unbalanced simulator’s ability to model three­phase and single­ phase networks and devices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2002

Geometry Modeling and Grid Generation for Computational Aerodynamic Simulations Around Iced Airfoils and Wings

Issues associated with analysis of 'icing effects' on airfoil and wing performances are discussed, along with accomplishments and efforts to overcome difficulties with ice. Because of infinite variations of ice shapes and their high degree of complexity, computational 'icing effects' studies using available software tools must address many difficulties in geometry acquisition and modeling, grid generation, and flow simulation. The value of each technology component needs to be weighed from the perspective of the entire analysis process, from geometry to flow simulation. Even though CFD codes are yet to be validated for flows over iced airfoils and wings, numerical simulation, when considered together with wind tunnel tests, can provide valuable insights into 'icing effects' and advance our understanding of the relationship between ice characteristics and their effects on performance degradation.

Choo, Yung K.↗
DOE OSTI2019

Adaptive Grid Lens Modeling of the Cosmic Horseshoe Using Hubble Space Telescope Imaging

The Cosmic Horseshoe gravitational lensing system is an extraordinary example of strong gravitational lensing both due to the nearly complete Einstein ring formed in this system and due to the star-forming nature of the lensed $z$ = 2.38 Lyman-break galaxy. In this paper, we describe the development of a new lens modeling package and the lens models produced using the Hubble Space Telescope imaging. Our new lens modeling package uses adaptive grid methods, based especially on the algorithm described by Vegetti & Koopmans. The new lens modeling package introduces the K-means method to deal with multiple background sources. We utilize two parameterized models, the singular isothermal ellipsoid and the Navarro–Frenk–White model in order to optimize the Bayesian penalty function. Color–color diagrams show two distinct colors exist in the Einstein ring, which suggests that the Cosmic Horseshoe is formed from two background galaxies or from a highly irregular galaxy. We find that the best lens model includes two components and the total mass within the Einstein ring is $(5.43\pm 0.08)\times {10}^{12}\,{M}_{\odot }$. Furthermore, the background source reconstruction shows two peaks in the source plane and the most probable lens profile is consistent with the light profile of the foreground galaxy when comparing axis ratios and position angles.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1999

Smagglce: Surface Modeling and Grid Generation for Iced Airfoils: Phase 1 Results

SmaggIce (Surface Modeling and Grid Generation for Iced Airfoils) is a software toolkit used in the process of aerodynamic performance prediction of iced airfoils with grid-based Computational Fluid Dynamics (CFD). It includes tools for data probing, boundary smoothing, domain decomposition, and structured grid generation and refinement. SmaggIce provides the underlying computations to perform these functions, a GUI (Graphical User Interface) to control and interact with those functions, and graphical displays of results, it is being developed at NASA Glenn Research Center. This paper discusses the overall design of SmaggIce as well as what has been implemented in Phase 1. Phase 1 results provide two types of software tools: interactive ice shape probing and interactive ice shape control. The ice shape probing tools will provide aircraft icing engineers and scientists with an interactive means to measure the physical characteristics of ice shapes. On the other hand, the ice shape control features of SmaggIce will allow engineers to examine input geometry data, correct or modify any deficiencies in the geometry, and perform controlled systematic smoothing to a level that will make the CFD process manageable.

Vickerman, Mary B.↗
NASA NTRS1995

Future requirements in surface modeling and grid generation

The past ten years have seen steady progress in surface modeling procedures, and wholesale changes in grid generation technology. Today, it seems fair to state that a satisfactory grid can be developed to model nearly any configuration of interest. The issues at present focus on operational concerns such as cost and quality. Continuing evolution of the engineering process is placing new demands on the technologies of surface modeling and grid generation. In the evolution toward a multidisciplinary analysis-bascd design environment, methods developed for Computational Fluid Dynamics are finding acceptance in many additional applications. These two trends, the normal evolution of the process and a watershed shift toward concurrent and multidisciplinary analysis, will be considered in assessing current capabilities and needed technological improvements.

Cosner, Raymond R.↗
NASA NTRS1992

Software Surface Modeling and Grid Generation Steering Committee

It is a NASA objective to promote improvements in the capability and efficiency of computational fluid dynamics. Grid generation, the creation of a discrete representation of the solution domain, is an essential part of computational fluid dynamics. However, grid generation about complex boundaries requires sophisticated surface-model descriptions of the boundaries. The surface modeling and the associated computation of surface grids consume an extremely large percentage of the total time required for volume grid generation. Efficient and user friendly software systems for surface modeling and grid generation are critical for computational fluid dynamics to reach its potential. The papers presented here represent the state-of-the-art in software systems for surface modeling and grid generation. Several papers describe improved techniques for grid generation.

Smith, Robert E.↗
NASA NTRS1981

On the effect of high latitude filtering in global grid point models

The application of various Fourier filtering techniques in a coarse resolution grid point model and the poor simulation of the nonlinear effects responsible for change in the mean zonal flow is discussed. The poor simulation results in spurious energy and momentum transfers. An operational filter, a second order filter on a staggered grid, a fourth order filter on a nonstaggered grid, and a biharmonic diffusion filter were considered.

Takacs, L. L.↗