pnnl/Temwizard
TEMWizard is used to visualize results from Atomap, an open-source software tool for determining the position and other features of atomic columns in transmission electron microscopy images.
Oostrom, Marjolein↗
Engineering topics
Central, PNNL Developer
Publications and source records attributed to Central, PNNL Developer.
pnnl/mcl-runtime
The Minos Computing Library (MCL) is a task-based programming language and runtime for extremely heterogeneous systems. MCL facilitates writing program for heterogeneous devices and porting applications across different systems and devices. MCL supports asynchronous execution of computing tasks on all available heterogeneous devices, including GPUs, FGPAs, fixed-point accelerators, and AI accelerators. MCL provides a high-level programming interface and automatically and autonomously performs resource management, load balancing, and locality-aware scheduling.
Central, PNNL Developer↗
TEMWizard pyCHIP.gui
TEMWizard pyCHIP.gui is used to visualize intermediate and final results from Atomap, an open-source software tool for determining the position and other features of atomic columns in transmission electron microscopy images. Also known as pyCHIP.gui.
Spurgeon, Steven R.↗
pnnl/blueprint-styler
Make custom blueprint.css styles A framework for creating custom visual themes and styles for the Blueprintjs React component library: https://blueprintjs.com/docs/ Features create custom css themes and styles for Blueprintjs components visualize the effect of the custom styles during development publish an example application that includes all available themes: https://stash.pnnl.gov/pages/UXRSRC/blueprint-styler/master/browse/build/index.html publish the compiled theme to npm (or artifactory)
Bradford, James↗
pnnl/CPACE
C-PACE Market Assessment Tool This tool was developed for the U.S. Department of Energy’s Commercial Property Assessed Clean Energy (C-PACE) Working Group to support state and local governments creating, joining, and implementing C-PACE programs. This tool allows users to input a jurisdiction (city, county, or state) and automate a report with building characteristics, energy use, and energy efficiency savings opportunities for commercial buildings in that jurisdiction. The report generated by the tool identifies jurisdiction-specific energy end uses, building use types, and building technologies that a local C-PACE program should consider focusing on to more effectively allocate limited resources and maximize energy savings and investment.
Keene, Kevin↗
pnnl/DPVPROT
Distributed photovoltaics protection software toolkit The code package includes several important functions for design and analysis of new grid protection schemes, for scenarios of distributed photovoltaic (DPV) generation approaching 100% of the peak feeder load, i.e., high-penetration DPV. Convert distribution feeder models, used by planning engineers, to electromagnetic transient (EMT) models that produce waveforms for advanced relay algorithms. Visualize the results of protection scheme operation. Tools to determine distance relay settings on distribution systems. Tools to predict the performance of time-domain, incremental distance relay algorithms
McDermott, Tom↗
pnnl/UUDEX
UUDEX describes a communications architecture and protocol suite that allows organizations to exchange data and information. It does this by defining relations between a set of client nodes that share data via a set of server nodes. The primary focus of UUDEX is to facilitate communications between control centers, operations centers, and other trusted organization.
Welsh, Jeff↗
pnnl/grid_prediction
Two datadriven predictive approaches, namely, {\em Koopman Operator Theoretic (KOT)-based} model and {\em Graph Neural Network (GNN)-based model}, to enable effective power system state predictions. The KOT-based approaches (Robust DMD, deepDMD) capture the power system evolution as a linear dynamical system on an abstract space. The GNNs model the spatio-temporal correlations using graph convolutional network and are called Spatio-Temporal Graph Convolutional Network (STGCN). These predictive models are trained, tested and compared rigorously based on their predictions of frequencies in the IEEE 68 bus system when subjected to a disturbance. GridSTAGE framework developed at Pacific Northwest National Laboratory is leveraged to generate multiple datasets (in the form of PMU measurements) for training and testing by strategically creating load changes across the spatial locations of the network
Nandanoor, Sai Pushpak↗
pnnl/pecblocks
ConverterBlocks uses the output of detailed electromagnetic transient (EMT) simulations to produce generalized block diagram models of power electronic systems. The process uses deep learning with customized block architectures. The outputs are reduced-order models that meet specified accuracy requirements, while providing important advantages over the original EMT models: ConverterBlock models have fewer nodes and can take longer time steps, resulting in shorter simulation times ConverterBlock models are continuously differentiable, making them compatible with control design methodsGeneralized block diagram modeling of power electronic converters for grid solar and storage applications.
McDermott, Tom↗
pnnl/diet-parselantro
A data annotation tool that integrates with the python Jupyter notebook environment.
Central, PNNL Developer↗
pnnl/DM-Sim
DM-Sim: Quantum Simulator on GPU Cluster using Density Matrix: a new simulation approach that significantly reduces communication overhead.
Li, Ang↗
pnnl/SOSAT
State of Stress Analysis Tool (for carbon sequestration). The SOSAT tool provides value during GCS site screening and characterization phases to target collection of specific additional data to constrain uncertainties in geomechanical risk and to help operators to make informed decisions during the operational phase (Appriou et al., 2019). The capability represented in SOSAT, therefore, helps build stakeholder confidence that geomechanical risks are understood and manageable
Central, PNNL Developer↗
pnnl/galas
Codebase for analyzing large atomistic simulation results using graph analytics. Analysis of large molecular simulations is difficult due to size and memory constrains in commonly used analysis software. This code was developed to analyze an ~8 million atom polycrystalline Al system under shear, with a particular emphasis on identifying defect structures. This code applies graph theory to reduce the system to components of interest and applies associated algorithms to characterize these components.
Pope, Jenna (Bilbrey)↗
pnnl/EXPERT
The EXPERT toolkit contains next-gen AI capabilities to detect, anticipate, and reason about proliferation expertise evolution from unstructured dynamic multilingual real-world data.
Glenski, Maria↗
pnnl/solubility-prediction-paper
We are using existing deep learning modeling architectures and data representation techniques, but we are consolidating the methods into a single set of training and evaluation scripts. Additionally, we test and evaluate the performance of the models on the largest set of solubility measurements available to date.
Central, PNNL Developer↗
pnnl/arena
CFA ARENA is a novel programming model with the support of a runtime targeting asynchronous data-centric execution paradigm in a distributed system. All the machine nodes in ARENA are connected by a ring network to bring the specialized computation to the data rather than the reverse to minimize data movement. The programming interfaces are implemented using C++
Tan, Cheng↗
pnnl/DeepDataProfiler
Python library for analyzing the internal structure of deep neural networks.
Praggastis, Brenda↗
pnnl/mol_dgnn
Molecular dynamics simulations often rely on parameterized atomistic potentials, which can be inaccurate compared to ab initio simulation methods. However, dynamic ab initio simulations come with high computational cost. Deep learning techniques offer a method to obtain both high accuracy and low cost predictions of dynamics. Our method gives a prediction of the structural dynamics of a molecule accounting for the prior dynamics, giving high-accuracy predictions at low computational cost
Ashby, Michael↗