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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

Impact of Reordering on the LU Factorization Performance of Bordered Block-Diagonal Sparse Matrix

Power engineers rely on computer-based simulation tools to assess grid performance and ensure security. At the core of these tools are solvers for sparse linear equations. When transformed into a bordered block-diagonal (BBD) structure, part of the sparse linear equation solving can be parallelized. This work focuses on using the Schur-complement-based method for LU factorization on BBD matrices, specifically, Jacobian matrices from large-scale systems. Our findings show that the natural ordering method outperforms the default ordering method in computational performance for each block of the BBD matrix. This observation is validated using synthetic 25k-bus and 70k-bus cases, showing a speedup of up to 38% when using natural ordering without permutation. Additionally, the impact of the number of partitions is studied, and the result shows that computational performance improves with more, smaller partitions in the BBD matrices.

BBD matrix↗

Exploring New Methods of Displaying Bit-Level Quality and Other Flags for MODIS Data

The NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC) archives and distributes snow and sea ice products derived from the MODerate resolution Imaging Spectroradiometer (MODIS) on board NASA's Terra and Aqua satellites. All MODIS standard products are in the Earth Observing System version of the Hierarchal Data Format (HDF-EOS). The MODIS science team has packed a wealth of information into each HDF-EOS file. In addition to the science data arrays containing the geophysical product, there are often pixel-level Quality Assurance arrays which are important for understanding and interpreting the science data. Currently, researchers are limited in their ability to access and decode information stored as individual bits in many of the MODIS science products. Commercial and public domain utilities give users access, in varying degrees, to the elements inside MODIS HDF-EOS files. However, when attempting to visualize the data, users are confronted with the fact that many of the elements actually represent eight different 1-bit arrays packed into a single byte array. This project addressed the need for researchers to access bit-level information inside MODIS data files. In an previous NASA-funded project (ESDIS Prototype ID 50.0) we developed a visualization tool tailored to polar gridded HDF-EOS data set. This tool,called the Polar researchers to access, geolocate, visualize, and subset data that originate from different sources and have different spatial resolutions but which are placed on a common polar grid. The bit-level visualization function developed under this project was added to PHDIS, resulting in a versatile tool that serves a variety of needs. We call this the EOS Imaging Tool.

Khalsa, Siri Jodha Singh↗

Grid Event Signature Library's Signature Matching Tool

The Signature Matching Tool (SMT) is a tool that assists users in labeling any unlabeled signatures, according to the hierarchical, event tags taxonomy developed for the Department of Energy's (DOE) Grid Event Signature Library (GESL).

Joo, Jhi Young [Lawrence Livermore National Labora↗

Characterization of Slosh Damping for Ortho-Grid and Iso-Grid Internal Tank Structures

Grid stiffened tank structures such as Ortho-Grid and Iso-Grid are widely used in cryogenic tanks for providing stiffening to the tank while reducing mass, compared to tank walls of constant cross-section. If the structure is internal to the tank, it will positively affect the fluid dynamic behavior of the liquid propellant, in regard to fluid slosh damping. As NASA and commercial companies endeavor to explore the solar system, vehicles will by necessity become more mass efficient, and design margin will be reduced where possible. Therefore, if the damping characteristics of the Ortho-Grid and Iso-Grid structure is understood, their positive damping effect can be taken into account in the systems design process. Historically, damping by internal structures has been characterized by rules of thumb and for Ortho-Grid, empirical design tools intended for slosh baffles of much larger cross-section have been used. There is little or no information available to characterize the slosh behavior of Iso-Grid internal structure. Therefore, to take advantage of these structures for their positive damping effects, there is much need for obtaining additional data and tools to characterize them. Recently, the NASA Marshall Space Flight Center conducted both sub-scale testing and computational fluid dynamics (CFD) simulations of slosh damping for Ortho-Grid and Iso-Grid tanks for cylindrical tanks containing water. Enhanced grid meshing techniques were applied to the geometrically detailed and complex Ortho-Grid and Iso-Grid structures. The Loci-STREAM CFD program with the Volume of Fluid Method module for tracking and locating the water-air fluid interface was used to conduct the simulations. The CFD simulations were validated with the test data and new empirical models for predicting damping and frequency of Ortho-Grid and Iso-Grid structures were generated.

Douglas G. Westra↗

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Geophysical data analysis and visualization using the Grid Analysis and Display System

Several problems posed by the rapidly growing volume of geophysical data are described, and a selected set of existing solutions to these problems is outlined. A recently developed desktop software tool called the Grid Analysis and Display System (GrADS) is presented. The GrADS' user interface is a natural extension of the standard procedures scientists apply to their geophysical data analysis problems. The basic GrADS operations have defaults that naturally map to data analysis actions, and there is a programmable interface for customizing data access and manipulation. The fundamental concept of the GrADS' dimension environment, which defines both the space in which the geophysical data reside and the 'slice' of data which is being analyzed at a given time, is expressed The GrADS' data storage and access model is described. An argument is made in favor of describable data formats rather than standard data formats. The manner in which GrADS users may perform operations on their data and display the results is also described. It is argued that two-dimensional graphics provides a powerful quantitative data analysis tool whose value is underestimated in the current development environment which emphasizes three dimensional structure modeling.

Doty, Brian E.↗

Parallel implementation of an adaptive scheme for 3D unstructured grids on the SP2

Dynamic mesh adaption on unstructured grids is a powerful tool for computing unsteady flows that require local grid modifications to efficiently resolve solution features. For this work, we consider an edge-based adaption scheme that has shown good single-processor performance on the C90. We report on our experience parallelizing this code for the SP2. Results show a 47.0X speedup on 64 processors when 10 percent of the mesh is randomly refined. Performance deteriorates to 7.7X when the same number of edges are refined in a highly-localized region. This is because almost all the mesh adaption is confined to a single processor. However, this problem can be remedied by repartitioning the mesh immediately after targeting edges for refinement but before the actual adaption takes place. With this change, the speedup improves dramatically to 43.6X.

Strawn, Roger C.↗

Parallel Implementation of an Adaptive Scheme for 3D Unstructured Grids on the SP2

Dynamic mesh adaption on unstructured grids is a powerful tool for computing unsteady flows that require local grid modifications to efficiently resolve solution features. For this work, we consider an edge-based adaption scheme that has shown good single-processor performance on the C90. We report on our experience parallelizing this code for the SP2. Results show a 47.OX speedup on 64 processors when 10% of the mesh is randomly refined. Performance deteriorates to 7.7X when the same number of edges are refined in a highly-localized region. This is because almost all mesh adaption is confined to a single processor. However, this problem can be remedied by repartitioning the mesh immediately after targeting edges for refinement but before the actual adaption takes place. With this change, the speedup improves dramatically to 43.6X.

Oliker, Leonid↗

TetrUSS Capabilities for S and C Applications

TetrUSS is a suite of loosely coupled computational fluid dynamics software that is packaged into a complete flow analysis system. The system components consist of tools for geometry setup, grid generation, flow solution, visualization, and various utilities tools. Development began in 1990 and it has evolved into a proven and stable system for Euler and Navier-Stokes analysis and design of unconventional configurations. It is 1) well developed and validated, 2) has a broad base of support, and 3) is presently is a workhorse code because of the level of confidence that has been established through wide use. The entire system can now run on linux or mac architectures. In the following slides, I will highlight more of the features of the VGRID and USM3D codes.

Frink, Neal T.↗

3D Feature Extraction for Unstructured Grids

Visualization techniques provide tools that help scientists identify observed phenomena in scientific simulation. To be useful, these tools must allow the user to extract regions, classify and visualize them, abstract them for simplified representations, and track their evolution. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This article explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and those from Finite Element Analysis.

Silver, Deborah↗

Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. Here, this paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In general, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types. With the advent of foundational models in ML and the exponential increase in GPU computing clusters, generalizable ML models will significantly enhance how utilities manage power systems and make decisions in real-time or near-real-time.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

PLUM: Parallel Load Balancing for Unstructured Adaptive Meshes

Dynamic mesh adaption on unstructured grids is a powerful tool for computing large-scale problems that require grid modifications to efficiently resolve solution features. By locally refining and coarsening the mesh to capture physical phenomena of interest, such procedures make standard computational methods more cost effective. Unfortunately, an efficient parallel implementation of these adaptive methods is rather difficult to achieve, primarily due to the load imbalance created by the dynamically-changing nonuniform grid. This requires significant communication at runtime, leading to idle processors and adversely affecting the total execution time. Nonetheless, it is generally thought that unstructured adaptive- grid techniques will constitute a significant fraction of future high-performance supercomputing. Various dynamic load balancing methods have been reported to date; however, most of them either lack a global view of loads across processors or do not apply their techniques to realistic large-scale applications.

Oliker, Leonid↗

PLUM: Parallel Load Balancing for Unstructured Adaptive Meshes

Dynamic mesh adaption on unstructured grids is a powerful tool for computing large-scale problems that require grid modifications to efficiently resolve solution features. Unfortunately, an efficient parallel implementation is difficult to achieve, primarily due to the load imbalance created by the dynamically-changing nonuniform grid. To address this problem, we have developed PLUM, an automatic portable framework for performing adaptive large-scale numerical computations in a message-passing environment. First, we present an efficient parallel implementation of a tetrahedral mesh adaption scheme. Extremely promising parallel performance is achieved for various refinement and coarsening strategies on a realistic-sized domain. Next we describe PLUM, a novel method for dynamically balancing the processor workloads in adaptive grid computations. This research includes interfacing the parallel mesh adaption procedure based on actual flow solutions to a data remapping module, and incorporating an efficient parallel mesh repartitioner. A significant runtime improvement is achieved by observing that data movement for a refinement step should be performed after the edge-marking phase but before the actual subdivision. We also present optimal and heuristic remapping cost metrics that can accurately predict the total overhead for data redistribution. Several experiments are performed to verify the effectiveness of PLUM on sequences of dynamically adapted unstructured grids. Portability is demonstrated by presenting results on the two vastly different architectures of the SP2 and the Origin2OOO. Additionally, we evaluate the performance of five state-of-the-art partitioning algorithms that can be used within PLUM. It is shown that for certain classes of unsteady adaption, globally repartitioning the computational mesh produces higher quality results than diffusive repartitioning schemes. We also demonstrate that a coarse starting mesh produces high quality load balancing, at a fraction of the cost required a fine initial mesh. Results indicate that our parallel load balancing strategy will remain viable on large numbers of processors.

Oliker, Leonid↗

Load Balancing Unstructured Adaptive Grids for CFD Problems

Mesh adaption is a powerful tool for efficient unstructured-grid computations but causes load imbalance among processors on a parallel machine. A dynamic load balancing method is presented that balances the workload across all processors with a global view. After each parallel tetrahedral mesh adaption, the method first determines if the new mesh is sufficiently unbalanced to warrant a repartitioning. If so, the adapted mesh is repartitioned, with new partitions assigned to processors so that the redistribution cost is minimized. The new partitions are accepted only if the remapping cost is compensated by the improved load balance. Results indicate that this strategy is effective for large-scale scientific computations on distributed-memory multiprocessors.

Biswas, Rupak↗

Tools and Techniques for Measuring and Improving Grid Performance

This viewgraph presentation provides information on NASA's geographically dispersed computing resources, and the various methods by which the disparate technologies are integrated within a nationwide computational grid. Many large-scale science and engineering projects are accomplished through the interaction of people, heterogeneous computing resources, information systems and instruments at different locations. The overall goal is to facilitate the routine interactions of these resources to reduce the time spent in design cycles, particularly for NASA's mission critical projects. The IPG (Information Power Grid) seeks to implement NASA's diverse computing resources in a fashion similar to the way in which electric power is made available.

Biswas, Rupak↗

The National Transmission Planning Study: Executive Summary

The National Transmission Planning Study (NTP Study) was led by the U.S. Department of Energy's Grid Deployment Office, in partnership with the National Renewable Energy Laboratory and Pacific Northwest National Laboratory. The study sought to develop new national grid-scale planning tools and methods that can be used by industry, especially when planning for interregional transmission capacity needs; identify potential transmission solutions that will provide broad-scale benefits to electric customers under a wide range of potential futures; inform planning processes for regional and interregional transmission; and identify interregional and national strategies to maintain grid reliability as the grid transitions, including to a reliance on low- and zero-carbon energy resources. The NTP Study is presented as a collection of six chapters.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗