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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 541 records · Page 30

All sources NASA NTRS DOE OSTI

DOE OSTI2014

Arbitrary-level hanging nodes for adaptive hphp-FEM approximations in 3D

In this paper we discuss constrained approximation with arbitrary-level hanging nodes in adaptive higher-order finite element methods (hphp-FEM) for three-dimensional problems. This technique enables using highly irregular meshes, and it greatly simplifies the design of adaptive algorithms as it prevents refinements from propagating recursively through the finite element mesh. The technique makes it possible to design efficient adaptive algorithms for purely hexahedral meshes. We present a detailed mathematical description of the method and illustrate it with numerical examples.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2014

Constructing a logical, regular axis topology from an irregular topology

Constructing a logical regular topology from an irregular topology including, for each axial dimension and recursively, for each compute node in a subcommunicator until returning to a first node: adding to a logical line of the axial dimension a neighbor specified in a nearest neighbor list; calling the added compute node; determining, by the called node, whether any neighbor in the node's nearest neighbor list is available to add to the logical line; if a neighbor in the called compute node's nearest neighbor list is available to add to the logical line, adding, by the called compute node to the logical line, any neighbor in the called compute node's nearest neighbor list for the axial dimension not already added to the logical line; and, if no neighbor in the called compute node's nearest neighbor list is available to add to the logical line, returning to the calling compute node.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

DIATOM (Data Initialization and Modification) Library Version 7.0

DIATOM is a library that provides numerical simulation software with a computational geometry front end that can be used to build up complex problem geometries from collections of simpler shapes. The library provides a parser which allows for application-independent geometry descriptions to be embedded in simulation software input decks. Descriptions take the form of collections of primitive shapes and/or CAD input files and material properties that can be used to describe complex spatial and temporal distributions of numerical quantities (often called “database variables” or “fields”) to help define starting conditions for numerical simulations. The capability is designed to be general purpose, robust and computationally efficient. By using a combination of computational geometry and recursive divide-and-conquer approximation techniques, a wide range of primitive shapes are supported to arbitrary degrees of fidelity, controllable through user input and limited only by machine resources. Through the use of call-back functions, numerical simulation software can request the value of a field at any time or location in the problem domain. Typically, this is used only for defining initial conditions, but the capability is not limited to just that use. The most recent version of DIATOM provides the ability to import the solution field from one numerical solution as input for another.

Crawford, David A.↗

DOE OSTI2016

Progress Version 1.0

Software library for parallel rapid order N and graph-based recursive electronic structure solvers with applications to material science, chemistry, and molecular biology.

Niklasson, Anders M.↗

DOE OSTI2016

SchemaOnRead: A Package for Schema-on-Read in R

Schema-on-read is an agile approach to data storage and retrieval that defers investments in data organization until production queries need to be run by working with data directly in native form. Schema-on-read functions have been implemented in a wide range of analytical systems, most notably Hadoop. SchemaOnRead is a CRAN package that uses R’s flexible data representations to provide transparent and convenient support for the schema-on-read paradigm in R. The schema-on- read tools within the package include a single function call that recursively reads folders with text, comma separated value, raster image, R data, HDF5, NetCDF, spreadsheet, Weka, Epi Info, Pajek network, R network, HTML, SPSS, Systat, and Stata files. The provided tools can be used as-is or easily adapted to implement customized schema-on-read tool chains in R. This paper’s contribution is that it introduces and describes SchemaOnRead, the first R package specifically focused on providing explicit schema-on-read support in R.

Data Science↗

DOE OSTI2016

SchemaOnRead: A Package for Schema-on-Read in R

Data Science↗

DOE OSTI2017

Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our lab.

Melin, Alexander M.↗

DOE OSTI2017

Control of Networked Traffic Flow Distribution - A Stochastic Distribution System Perspective

Networked traffic flow is a common scenario for urban transportation, where the distribution of vehicle queues either at controlled intersections or highway segments reflect the smoothness of the traffic flow in the network. At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic model for a one-way single-lane corridor traffic system based on theory of stochastic distribution control.. It has been shown that the developed stochastic model would provide the optimal probability density function (PDF) of the traffic queueing length as a dynamic function of the traffic signal setting parameters. Based upon such a stochastic distribution model, we have proposed a preliminary closed loop framework on stochastic distribution control for the traffic queueing system to make the traffic queueing length PDF follow a target PDF that potentially realizes the smooth traffic flow distribution in a concerned corridor.

Wang, Hong↗

DOE OSTI2018

System for detecting objects in streaming 3D images formed from data acquired with a medium penetrating sensor

A detection system that detects subsurface objects within a medium and estimates various features of the objects is provided. The detection system receives a streaming sequence of image frames of the medium at various along-medium locations. An image frame contains voxel values (intensities) representing characteristics of the medium across the medium and in the depth (range) direction. The detection system depth-compensates the intensities for determining which voxels are part of an object using an unsupervised binary classifier. The detection system then connects object voxels into distinct objects and recursively estimates the features of those objects as the image frames stream based on the locations and intensities of the object voxels.

Paglieroni, David W.↗

DOE OSTI2018

Methods for generating barcoded combinatorial libraries

Provided herein are methods and composition for trackable genetic variant libraries. Further provided herein are methods and compositions for recursive engineering. Further provided herein are methods and compositions for multiplex engineering. Further provided herein are methods and compositions for enriching for editing and trackable engineered sequences and cells using nucleic acid-guided nucleases.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Development and Calibration of an Online Energy Model for AHU Fan

The model development is necessary for the study of the energy consumption of Heating, Ventilation, and Air Conditioning (HVAC) systems. To predict the HVAC energy consumption accurately, one needs to model the individual HVAC components either from the measured data or based on the knowledge of the underlying physical phenomenon. Online model characterization is critical for improving the performance of real-time model-based fault detection and diagnosis (FDD) strategies. For HVAC control, models can be used to optimize the supervisory and local feedback control strategies to improve the energy consumption efficiency, or for providing ancillary services to the grid. It has been reported that, fans in HVAC systems of commercial buildings alone can provide substantial frequency regulation service, with little change in the indoor environment. In this paper, a real-time data-driven Air Handling Unit (AHU) fan model was developed based on recursive multi regression model. A generic nonlinear polynomial model has been studied to cover scenarios with different combinations of measurement variables, variable orders as well as different training and prediction horizons. Typical measurements including static pressure, mass flow rate, and damper positions are utilized as inputs to model the power consumption of the fan. The developed models have been validated both with simulation data from EnergyPlus-Dymola co-simulation model and with field measurement data for small to medium commercial buildings. The validation results show that the online model proposed can provide an effective prediction of the AHU fan power consumption.

Dong, Jin↗

DOE OSTI2019

Methods for generating barcoded combinatorial libraries

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Methods for generating barcoded combinatorial libraries

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Use of a State-Level Integrated Assessment Model for Policy Analysis: An Uncertainty Analysis of Energy and Water

This presentation discusses some recent uncertainty assessment work being done on climate policy issues at the state level. The work is being done with the Platform for Regional Integrated Modeling and Analysis (PRIMA), a new climate change policy assessment framework developed at Pacific Northwest National Laboratory in Richland and Seattle, Washington and College Park, Maryland. The central computaional engine of PRIMA is the Global Change Assessment Model (GCAM), a dynamic-recursive integrated assessment model of the world economy, climate policy, and climate change that that has been regionalized to provide policy analysis down to the level of U.S. states. The PRIMA framework also includes detailed sectoral models that address issues in the energy-land-water interface geospatially at the sub-national level as the climate change and socioeconomic change occur. This presentation illustrates two uncertainty analyses conducted with GCAM and with water demand and supply components of PRIMA. The energy uncertainty analysis was conducted with GCAM alone and examines the impact of aggressive residential and commercial building equipment standards across the United States in the context of socioeconomic and climate uncertainty. The analysis focuses on the impact on future net energy service costs under aggressive standards in the residential and commercial building sectors from 2005 to 2050. The analysis uses a combination of discrete scenarios and Monte Carlo sampling approaches to address uncertainties. Results for Arizona and Florida are used to illustrate whether aggressive building equipment standards save energy and reduce net energy service costs when compared with current standards. Analysis of the output indicates a robust result under a broad range of future socioeconomic, policy, and climate scenarios. The water uncertainty analysis demonstrates a multi-model approach used to bound the prospects for future water shortages in Florida, given socioeconomic uncertainty and future water supply under a future climate. In this analysis, water demands from GCAM are coupled with a water management model that simulates regulated streamflows and water supply deficits given naturalized streamflows, water demands, and reservoir operating rules. The naturalized streamflows are simulated with a regional earth system model coupled to a river routing model. The climate is obtained from a regional climate projection of the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway 8.5 (RCP 8.5) for all of the river basins in the United States. GCAM is used in Monte Carlo mode to estimate the outer bounds of water demand given socioeconomic uncertainty and RCP 8.5 climate. Future water demand is downscaled to a river basin level for Florida in high, low, and middle cases and is compared with available water supply for the same region for RCP 8.5 climate to identify the potential location and severity of water shortages.

Climate change, policy, mitigation, impacts, uncer↗

DOE OSTI2019

Efficient track-before detect algorithm with minimal prior knowledge

Apparatus, system and method for tracking an image target in a system, wherein a system receives an image comprising a plurality of pixels. The received image is processed via a plurality of different recursive motion model kernels in parallel to provide a plurality of kernel outputs, wherein each of the motion model kernels may include a respective pixel mask. Per-pixel energy is estimated of at least some of the plurality of kernel outputs. Velocity of at least one of the image pixels may also be estimated by generating a directional energy vector for each motion model kernel. The per-pixel energy and velocity estimates are fused to produce a fused estimate representing at least some of the motion model kernels for the image.

42 ENGINEERING↗

DOE OSTI2022

Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks

We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser representations of a given graph. The neural network is implemented using SAGE graph convolution layers, and trained using an advantage actor critic (A2C) agent. We present two variants, one for finding an edge separator that minimizes the normalized cut or quotient cut, and one that finds a small vertex separator. The vertex separators are then used to construct a nested dissection ordering to permute a sparse matrix so that its triangular factorization will incur less fill-in. The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU. Our results show that the proposed method achieves similar partitioning quality as METIS and SCOTCH. Furthermore, the method generalizes across different classes of graphs, and works well on a variety of graphs from the SuiteSparse sparse matrix collection.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning.

This paper describes an efficient reverse-mode differentiation algorithm for contraction operations of tensor networks that may have arbitrary and unconventional network topologies. The approach leverages the tensor contraction tree of Evenbly and Pfeifer (2014), which provides an instruction set for the contraction sequence of a network. We show that this tree can be efficiently leveraged for differentiation of a full tensor network contraction using a recursive scheme that exploits (1) the bilinear property of contraction and (2) the property that trees have single path from root to leaves. While differentiation of tensor-tensor contraction is already possible in most automatic differentiation packages, we show that exploiting these two additional properties in the specific context of contraction sequences can improve efficiency. Following a description of the algorithm and computational complexity analysis, we investigate its utility for gradient-based supervised learning for low-rank function recovery and for fitting real-world unstructured datasets. We demonstrate improved performance over alternating least-squares optimization approaches and the capability to handle heterogeneous and arbitrary tensor network formats. When compared to alternating minimization algorithms, we find that the gradient-based approach requires a smaller oversampling ratio (number of samples compared to number model parameters) for recovery. This increased efficiency extends to fitting unstructured data of varying dimensionality and when employing a variety of tensor network formats. Here, we show improved learning using the hierarchical Tucker method over the tensor-train in high-dimensional settings on a number of benchmark problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

The HUNTER Dynamic Human Reliability Analysis Tool: Coupling an External Plant Code

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a framework to support dynamic human reliability analysis (HRA) with the aim to develop standalone software to perform the dynamic HRA calculations. Within the HRA, human actions in nuclear power plants (NPPs) are predicated by plant states, and human actions influence the plant. In other words, plant operations are necessarily recursive, and it becomes challenging to model complex human-plant interactions. Consequently, we have linked two software simulations that complement those shortcomings. RELAP5-3D—the Reactor Excursion and Leak Analysis Program (RELAP; Aumiller, Tomlinson, and Bauer 2001) is the foundational thermal-hydraulic software used to model nuclear systems. Using RELAP5-3D, we have simulated the plant operations proceeding according to procedures developed to address emergent situations in NPPs. Plant operations include various actions such as the operator checking plant parameters, as well as actions that are continuously performed over time until a specific parameter reaches certain criteria. That means that HUNTER and RELAP5-3D exchange information with each other and should be carried out simultaneously over time. To simulate plant operations, which represent the actual operator checks of plant parameters and corresponding manual control actions, changes in plant status are identified through simulation and performed according to the criteria and order of the procedure. Thus, the goal of coupling HUNTER with RELAP5-3D is to facilitate synchronous coupling, where human and plant models provide iterative feedback loops that drive the course of actions. The advantage of coupling with RELAP5-3D to serve as the external environment module in HUNTER is the ability to customize the plant model and streamline for particular model applications. In this paper, we will address the key features of the coupling and the coupling structure built to perform the feedback loops.

99 GENERAL AND MISCELLANEOUS↗