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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A State-Space Model of an Inverter-Based Microgrid for Multivariable Feedback Control Analysis and Design

In this work, a synchronous model for grid-connected and islanded microgrids is presented. The grid-connected model is based on the premise that the reference frame is synchronized with the AC bus. The quadrature component of the AC bus voltage can be cancelled, which allows to express output power as a linear equation for nominal values in the AC bus amplitude voltage. The model for the islanded microgrid is developed by integrating all the inverter dynamics using a state-space model for the load currents. This model is presented in a comprehensive way such that it could be scalable to any number of inverter-based generators using inductor–capacitor–inductor (LCL) output filters. The use of these models allows designers to assess microgrid stability and robustness using modern control methods such as eigenvalue analysis and singular value diagrams. Both models were tested and validated in an experimental setup to demonstrate their accuracy in describing microgrid dynamics. In addition, three scenarios are presented: non-controlled model, Linear-Quadratic Integrator (LQI) power control, and Power-Voltage (PQ/Vdq) droop–boost controller. Experimental results demonstrate the effectiveness of the control strategies and the accuracy of the models to describe microgrid dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

FEOTS v0.0.0: a new offline code for the fast equilibration of tracers in the ocean

Abstract. In this paper we introduce a new software framework for the offline calculation of tracer transport in the ocean. The Fast Equilibration of Ocean Tracers Software (FEOTS) is an end-to-end set of tools to efficiently calculate tracer distributions on a global or regional sub-domain using transport operators diagnosed from a comprehensive ocean model. To the best of our knowledge, this is the first application of a transport matrix model to an eddying ocean state. While a Newton–Krylov-based equilibration capability is still under development and not presented here, we demonstrate in this paper the transient modeling capabilities of FEOTS in an application focused on the Argentine Basin, where intense eddy activity and the Zapiola Anticyclone lead to strong mixing of water masses. The demonstration illustrates progress in developing offline passive tracer simulation capabilities, while highlighting the challenges of the impulse response functions approach in capturing tracer transports by a non-linear advection scheme. Our future work will focus on improving the computational efficiency of the code to reduce time-to-solution, using different basis functions to better represent non-linear advection operators, applying FEOTS to a parent model with unstructured grids (Ocean Model for Prediction Across Scales, MPAS-Ocean), and fully implementing a Newton–Krylov steady-state solver.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Triangle Geometry Processing for Surface Modeling and Cartesian Grid Generation

Cartesian mesh generation is accomplished for component based geometries, by intersecting components subject to mesh generation to extract wetted surfaces with a geometry engine using adaptive precision arithmetic in a system which automatically breaks ties with respect to geometric degeneracies. During volume mesh generation, intersected surface triangulations are received to enable mesh generation with cell division of an initially coarse grid. The hexagonal cells are resolved, preserving the ability to directionally divide cells which are locally well aligned.

Aftosmis, Michael J.↗
NASA NTRS2019

PyDDA: A New Pythonic Wind Retrieval Package

PyDDA (Pythonic Direct Data Assimilation) is a new community framework aimed at wind retrievals that depends only upon utilities in the SciPy ecosystem such as scipy, numpy, and dask. It can support retrievals of winds using information from weather radar networks constrained by high resolution forecast models over grids that cover thousands of kilometers at kilometer-scale resolution. Unlike past wind retrieval packages, this package can be installed using anaconda for easy installation and, with a focus on ease of use can retrieve winds from gridded radar and model data with just a few lines of code. The package is currently available for download at https://github.com/openradar/PyDDA.

Radar↗
NASA NTRS2021

Strong Regional Influence of Climatic Forcing Datasets on Global Crop Model Ensembles

We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.

Agricultural Model Intercomparison and Improvement↗
DOE OSTI2019

Egret v. 0.1 (beta)

Egret is a package that provides tools for building electric grid optimization models. This software includes objects and functions for parsing, storing, and managing electric grid data. Egret also includes the following electric grid models: - ACOPF (alternating current optimal power flow) - DCOPF – a linearized transmission model - ACOPF relaxations - Unit commitment - Economic dispatch

Knueven, Bernard↗
DOE OSTI2025

A Model Predictive Control to Improve Grid Resilience

The following article details a model predictive control (MPC) to improve grid resilience when faced with variable generation resources. This topic is of significant interest to utility power systems where distributed intermittent energy sources will increase significantly and be relied on for electric grid ancillary services. Previous work on MPCs has focused on narrowly targeted control applications such as improving electric vehicle (EV) charging infrastructure or reducing the cost of integrating Energy Storage Systems (ESSs) into the grid. In contrast, this article develops a comprehensive treatment of the construction of an MPC tailored to electric grids and then applies it integration of intermittent energy resources. To accomplish this, the following article includes a description of a reduced order model (ROM) of an electric power grid based on a circuit model, an optimization formulation that describes the MPC, a collocation method for solving linear time-dependent differential algebraic equations (DAEs) that result from the ROM, and an overall strategy for iteratively refining the behavior of the MPC. Next, the algorithm is validated using two separate numerical experiments. First, the algorithm is compared to an existing MPC code and the results are verified by a numerically precise simulation. It is shown that this algorithm produces a control comparable to existing algorithms and the behavior of the control carefully respects the bounds specified. Second, the MPC is applied to a small nine bus system that contains a mix of turbine-spinning-machine-based and intermittent generation in order to demonstrate the algorithm’s utility for resource planning and control of intermittent resources. This study demonstrates how the MPC can be tuned to change the behavior of the control, which can then assist with the integration of intermittent resources into the grid. The emphasis throughout the paper is to provide systematic treatment of the topic and produce a novel nonlinear control compatible design framework applicable to electric grids and the control of variable resources. This differs from the more targeted application-based focus in most presentations.

microgrid↗
NASA NTRS1990

Space-time modeling using environmental constraints in a mobile robot system

Grid-based models of a robot's local environment have been used by many researchers building mobile robot control systems. The attraction of grid-based models is their clear parallel between the internal model and the external world. However, the discrete nature of such representations does not match well with the continuous nature of actions and usually serves to limit the abilities of the robot. This work describes a spatial modeling system that extracts information from a grid-based representation to form a symbolic representation of the robot's local environment. The approach makes a separation between the representation provided by the sensing system and the representation used by the action system. Separation allows asynchronous operation between sensing and action in a mobile robot, as well as the generation of a more continuous representation upon which to base actions.

Slack, Marc G.↗
DOE OSTI2013

Carbon Data Assimilation with a Coupled Ensemble Kalman Filter (Final Technical Report)

We proposed (and accomplished) the development of an Ensemble Kalman Filter (EnKF) approach for the estimation of surface carbon fluxes as if they were parameters, augmenting the model with them. Our system is quite different from previous approaches, such as carbon flux inversions, 4D-­Var, and EnKF with approximate background error covariance (Peters et al., 2008). We showed (using observing system simulation experiments, OSSEs) that these differences lead to a more accurate estimation of the evolving surface carbon fluxes at model grid-scale resolution. The main properties of the LETKF-­C are: a) The carbon cycle LETKF is coupled with the simultaneous assimilation of the standard atmospheric variables, so that the ensemble wind transport of the CO2 provides an estimation of the carbon transport uncertainty. b) The use of an assimilation window (6hr) much shorter than the months-long windows used in other methods. This avoids the inevitable “blurring” of the signal that takes place in long windows due to turbulent mixing since the CO2 does not have time to mix before the next window. In this development we introduced new, advanced techniques that have since been adopted by the EnKF community (Kang, 2009, Kang et al., 2011, Kang et al. 2012). These advances include “variable localization” that reduces sampling errors in the estimation of the forecast error covariance, more advanced adaptive multiplicative and additive inflations, and vertical localization based on the time scale of the processes. The main result has been obtained using the LETKF-­C with all these advances, and assimilating simulated atmospheric CO2 observations from different observing systems (surface flask observations of CO2 but no surface carbon fluxes observations, total column CO2 from GoSAT/OCO-­2, and upper troposphere AIRS retrievals). After a spin-­up of about one month, the LETKF-­C succeeded in reconstructing the true evolving surface fluxes of carbon at a model grid resolution. When applied to the CAM3.5 model, the LETKF gave very promising results as well, although only one month is available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter"

We proposed (and accomplished) the development of an Ensemble Kalman Filter (EnKF) approach for the estimation of surface carbon fluxes as if they were parameters, augmenting the model with them. Our system is quite different from previous approaches, such as carbon flux inversions, 4D-Var, and EnKF with approximate background error covariance (Peters et al., 2008). We showed (using observing system simulation experiments, OSSEs) that these differences lead to a more accurate estimation of the evolving surface carbon fluxes at model grid-scale resolution. The main properties of the LETKF-C are: a) The carbon cycle LETKF is coupled with the simultaneous assimilation of the standard atmospheric variables, so that the ensemble wind transport of the CO2 provides an estimation of the carbon transport uncertainty. b) The use of an assimilation window (6hr) much shorter than the months-long windows used in other methods. This avoids the inevitable “blurring” of the signal that takes place in long windows due to turbulent mixing since the CO2 does not have time to mix before the next window. In this development we introduced new, advanced techniques that have since been adopted by the EnKF community (Kang, 2009, Kang et al., 2011, Kang et al. 2012). These advances include “variable localization” that reduces sampling errors in the estimation of the forecast error covariance, more advanced adaptive multiplicative and additive inflations, and vertical localization based on the time scale of the processes. The main result has been obtained using the LETKF-C with all these advances, and assimilating simulated atmospheric CO2 observations from different observing systems (surface flask observations of CO2 but no surface carbon fluxes observations, total column CO2 from GoSAT/OCO-2, and upper troposphere AIRS retrievals). After a spin-up of about one month, the LETKF-C succeeded in reconstructing the true evolving surface fluxes of carbon at a model grid resolution. When applied to the CAM3.5 model, the LETKF gave very promising results as well, although only one month is available.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Representing the Sub-Grid Heterogeneity of Surface Precipitation in A General Circulation Model

Precipitation variability on spatial scales smaller than a typical general circulation model (GCM) grid box is often neglected, with the grid-mean precipitation rate being applied uniformly to underlying surface tiles. This reduces the extrema seen by the surface, with corresponding reductions in surface runoff and altered land-atmosphere fluxes. Here we present a novel approach to stochastically distribute precipitation across sub-grid surface tiles within a GCM. Based on 4 km Stage IV precipitation data, the scheme parameterizes the dry area fraction as a function of grid mean precipitation rate, and defines the relative distribution of intensities across non-dry surface tiles. To incorporate memory and mimic the persistence of precipitating storms, the relative intensity assigned to each sub-grid tile is determined by an autoregressive process. Using single column experiments, the scheme is shown to reproduce observed precipitation statistics at the scale of model surface tiles. We also document impacts on surface hydrology and energy partitioning, with notable increases in precipitation runoff, surface temperature variance, and the Bowen ratio.

GCM↗
DOE OSTI2013

THE RELATIONSHIP BETWEEN {nu}{sub max} AND AGE t FROM ZAMS TO RGB-TIP FOR LOW-MASS STARS

Stellar age is an important quantity in astrophysics, which is useful for many fields both in the universe and galaxies. It cannot be determined by direct measurements, but can only be estimated or inferred. We attempt to find a useful indicator of stellar age, which is accurate from the zero-age main sequence to the tip of red giant branch for low-mass stars. Using the Yale Rotation and Evolution Code (YREC), a grid of stellar models has been constructed. Meanwhile, the frequency of maximum oscillations' power {nu}{sub max} and the large frequency separation {Delta}{nu} are calculated using the scaling relations. For the stars, the masses of which are from 0.8 M{sub Sun} to 2.8 M{sub Sun }, we can obtain the {nu}{sub max} and stellar age by combing the scaling relations with the four sets of grid models (YREC, Dotter et al., Marigo et al., and YY isochrones). We find that {nu}{sub max} is tightly correlated and decreases monotonically with the age of the star from the main sequence to the red giant evolutionary stages. Moreover, we find that the line shapes of the curves in the Age versus {nu}{sub max} diagram, which is plotted by the four sets of grid models, are consistent for red giants with masses from 1.1 M{sub Sun} to 2.8 M{sub Sun }. For red giants, the differences of correlation coefficients between Age and {nu}{sub max} for different grid models are minor and can be ignored. Interestingly, we find two peaks that correspond to the subgiants and bump of red giants in the Age versus {nu}{sub max} diagram. By general linear least-squares, we make the polynomial fitting and deduce the relationship between log(Age) and log({nu}{sub max}) in red giants' evolutionary state.

79 ASTRONOMY AND ASTROPHYSICS↗