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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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Software interface for controlling and collecting data from the Open PV Electrical Tool (OPET) device. This repository contains everything relating to the control of the OPET device including source code. If you are looking for the hardware or firmware software repositories, links are below: https://github.com/NREL/opet-hardware https://github.com/NREL/opet-firmware
Dynamic nuclear polarization surface enhanced NMR spectroscopy has enabled the determination of high-resolution structures from surface-supported molecules, including singlesite heterogeneous catalysts. Structure determinations have largely mimicked the approaches used in biomolecular NMR spectroscopy, namely, using distance measurements to constrain a conformational search. These early demonstrations made use of purpose-built software, which has limited the adoption of the technique. Herein, we describe the open-source program INTERFACES (Interpret NMR to Elucidate or Reconstruct the Full Atomistic Configurations of External Surfaces) which automates the analysis of RE(SP)DOR data as well as the structure determination for surface sites. Distances, angles, dihedral angles, complex orientation, and distance from the support can all be sampled to find all structures that agree with the experimental data. A χ 2 metric is used to define the error ranges of the REDOR fits and produce structures with an arbitrary level of confidence. Structural solutions are then provided as both overlays and ORTEP-like probability ellipsoids.
We present an implementation of the trimmed serendipity finite element family, using the open-source finite element package Firedrake. The new elements can be used seamlessly within the software suite for problems requiring H 1 , H (curl), or H (div)-conforming elements on meshes of squares or cubes. To test how well trimmed serendipity elements perform in comparison to traditional tensor product elements, we perform a sequence of numerical experiments including the primal Poisson, mixed Poisson, and Maxwell cavity eigenvalue problems. Overall, we find that the trimmed serendipity elements converge, as expected, at the same rate as the respective tensor product elements, while being able to offer significant savings in the time or memory required to solve certain problems.
The MicroEleX code package contains a variety of models and algorithms for physical modeling of microelectronic circuitry, including electrostatics, electrodynamics, superconducting physics, micromagnetics, multi-ferroic systems, and quantum transport. MicroEleX leverages the AMReX software framework to provide scalability on GPU-based supercomputing architectures. The code is open source and designed to be algorithmically flexible so developers can incorporate enhanced or customized physics.
Bluesky Web is a web based interface that provides beam line controls to the end user. It allows users to issue commands to various physical devices at a beam line end station like motors and cameras. It utilizes an open source Python library (Bluesky) as the controller. It uses Bluesky to also allow for running "plans" or a sequence of device operations that can be used when running an experiment. This program is different from other controls technologies because it is intended to be open source and can be accessed from a web browser, as opposed to other paid software that is run as a stand-alone application on a computer.
EnergyPlus-MCP is the first open-source Model Context Protocol server specifically designed for EnergyPlus building energy simulation. This innovative software enables AI assistants and other applications to interact programmatically with EnergyPlus through a standardized, secure interface, eliminating traditional technical barriers in building energy modeling. The software provides specialized tools across five functional domains: server management, model configuration and loading, comprehensive building component inspection, systematic model modification, and simulation execution with results visualization. Key features include automated HVAC system discovery and topology mapping, advanced schedule analysis, intelligent model validation, and interactive visualization capabilities. EnergyPlus-MCP's layered architecture ensures robust separation between protocol communication and domain expertise, enabling scalable deployment across organizations, educational institutions, and research teams. Unlike direct LLM approaches that suffer from inconsistent results and security gaps, EnergyPlus-MCP provides validated, reliable interactions while maintaining scientific rigor. This democratizes sophisticated building energy analysis, making EnergyPlus accessible to broader audiences through conversational interfaces and streamlined workflows.
A Design System is a comprehensive framework of reusable components, standards, and guidelines. Packets is the design system created by developers at ESnet to give our software suite a common look and feel, as well as speed up production time. We would like to integrate it across all our software platforms, including some that are already or are in the process of being open sourced. In order to do this, we would also need to open source a version of Packets.
An embedded system is a small footprint computing unit that typically serves a specific purpose closely associated with measurements and control of hardware devices. These units are designed for reasonable durability and operations in a wide range of operating conditions. Some embedded systems support real-time operations and can demonstrate high levels of reliability. Many have failsafe mechanisms built to handle graceful shutdown of the device in exception conditions. The available memory, processing power, and network connectivity of these devices are limited due to the nature of their specific-purpose design and intended application. Industry practice is to carefully design the software for the available hardware capability to suit desired deployment needs. Volttron is an open source agent development and deployment platform designed to enable researchers to interact with devices and appliances without having to write drivers themselves. Hosting Volttron on small footprint embeddable devices enables its demonstration for embedded use. This report details the steps required and the experience in setting up and running Volttron applications on three small footprint devices: the Intel Next Unit of Computing (NUC), the Raspberry Pi 2, and the BeagleBone Black. In addition, the report also details preliminary investigation of the execution performance of Volttron on these devices.
This project implements coupled simulation of electric power grid transmission and distribution (T&D) by instantiating the co-simulation platform HELICS using a commercial distribution planning software CYMDIST1 by Eaton Inc., a power management company, and by leveraging the open-source transmission grid simulator GridDyn and LLNL’s High Performance Computing (HPC). The project is funded by DOE’s Technology Commercialization Funding (TCF) and supported by DOE Office of Technology Transitions (OTT).
BioSHOT is an internally developed SCEPTRE emulation for bio lab environments, meant to train operators to better defend their environments. This project aims to take the existing base topology, add additional realisitic bio software (e.g. GNU Health) and add the final documentation and packaging for release to open source.
In this work, we present a computational tool for mesoscale applications using open-source exascale- computing compatible adaptive-mesh-refinement (AMR) library, AMReX [2]. AMReX is software library that enables development of application solvers with block-structured Cartesian AMR. Our tool has capabilities to include realistic geometry representation, chemical species transport, reactions and thermodynamics that are critical for capturing mesoscale physics. A significant achievement is the ability of our solver to automatically import electron microscopy data in the form of a stereolithography (STL) or pixelated file format (mrc, tiff) without undergoing the tedious task of unstructured mesh generation. This feature allows for rapid simulation of catalyst particles with complex morphologies using an immersed-boundary formulation. The use of AMR allows for higher resolutions at catalyst surface interfaces, which in turn provides an accurate description of surface reactions and transport. Our solver uses a hybrid distributed and shared memory parallelism (OpenMP/GPU-based) with which strong scaling up to 10,000 processors for realistic catalyst particle simulations have been demonstrated.
This document describes the capabilities of the System Advisor Model (SAM) developed and distributed by the U.S. Department of Energy's National Renewable Energy Laboratory. The document is for potential users and others wanting to learn about the model's capabilities. SAM is a techno-economic computer model that calculates performance and financial metrics of renewable energy projects. Project developers, policy makers, equipment manufacturers, and researchers use graphs and tables of SAM results in the process of evaluating financial, technology, and incentive options for renewable energy projects. SAM simulates the performance of photovoltaic, concentrating solar power, solar water heating, wind, geothermal, biomass, and conventional power systems. The financial models are for projects that either buy and sell electricity at retail rates (residential and commercial) or sell electricity at a price determined in a power purchase agreement (PPA). SAM's simulation tools facilitate parametric and sensitivity analyses, Monte Carlo simulation and weather variability (P50/P90) studies. SAM can also read input variables from Microsoft Excel worksheets. For software developers, the SAM software development kit (SDK) makes it possible to use SAM simulation modules in their applications written in C/C plus plus, C sharp, Java, Python, MATLAB, and other languages. NREL provides both SAM and the SDK as free downloads at https://sam.nrel.gov. SAM is an open source project, so its source code is available to the public. Researchers can study the code to understand the model algorithms, and software programmers can contribute their own models and enhancements to the project. Technical support and more information about the software are available on the website.
The National Biosurveillance Integration Center (NBIC) is developing a scalable, flexible open source data collection, analysis, and dissemination tool to support biosurveillance operations by the U.S. Department of Homeland Security (DHS) and its federal interagency partners. The NBIC integrates, analyzes, and distributes key information about health and disease events to help ensure the nation’s responses are well-informed, save lives, and minimize economic impact. NBIC serves as a bridge between Federal, State, Local, Territorial, and Tribal entities to conduct biosurveillance across human, animal, plant, and environmental domains. The integration of information enables early warning and shared situational awareness of biological events to inform critical decisions directing response and recovery efforts. To meet its mission objectives, NBIC utilizes a variety of datasets, including open source information, to provide comprehensive coverage of biological events occurring across the globe. NBIC Biofeeds is a digital tool designed to improve the efficiency of reviewing and analyzing large volumes of open source reporting by biosurveillance analysts on a daily basis; moreover, the system provides a mechanism to disseminate tailored feeds allowing NBIC to better meet the specific information needs of individual, interagency partners. The tool is currently under development by the Department of Energy (DOE), Pacific Northwest National Laboratory (PNNL)and it is in a testing and evaluation phase supported by NBIC biosurveillance subject matter experts. Integration with the Defense Threat Reduction Agency (DTRA), Biosurveillance Ecosystem(BSVE) is also underway. NBIC Biofeeds Version 1 is expected to be fully operational in Fiscal Year 2017. The PNNL is applying agile methodology to streamline the build of NBIC Biofeeds to specifications required for operational use by NBIC and its federal interagency partners. Biosurveillance, analytics, and system engineering subject matter experts provide guidance on the implementation of features in the tool to ensure functionality aligns with operational workflows and production support. PNNL is leveraging software from a previous government effort to repurpose the technology to meet NBIC needs. NBIC Biofeeds incorporates the open source, document-orientated MongoDB database to capture user- and system-generated metadata on hundreds of thousands of records, in part, to establish baselines to aid prospective and retrospective analysis on emerging biological events. NBIC Biofeeds integrates a biosurveillance taxonomy (uniquely developed by NBIC),which includes input from interagency partners to recognize critical characteristics of a biological event. In NBIC Biofeeds Version1, metadata capture of reported events is done manually by NBIC analysts; however, moving forward in Version 2, the tool will be further automated to flag significant reporting on biological events with a human remaining in the loop to confirm the validity of the system-generated tags. To serve as a one-stop tool for open source biosurveillance, NBIC Biofeeds automatically harvests information from thousands of websites, utilizing third party aggregators, paid subscriptions to data feeds, and scraping of high priority sources. Users can develop desired queries for automatic updating, leverage a unique review and curation mechanism, and further analyze data from topical, geographic, and temporal visualization features in the tool. To meet NBIC’s information sharing needs, the tool allows for design of tailored RSS feeds and electronic message-based delivery of analysis on biological events, intended for recipients in the government with unique missions around human, animal, plant, and environmental health. Through current testing and evaluation – underway by biosurveillance subject matter experts – NBIC Biofeeds is demonstrating value in supporting open source biosurveillance by the Center for more rapid recognition and sharing of key event characteristics. Centralizing access and analysis of this data set into a single system is increasing the efficiency of daily, global biosurveillance, while enhancing the value of information identified through use of the querying, curation, and production support features in the tool.
SAND2024-00944O Qthread Support for MPICH is software that provides additions needed to enable the use of the Qthreads library. The high-performance message passing interface (MPICH) is an open-source implementation of MPI mainly developed and distributed by Argonne National Laboratory. Qthreads is a lightweight, user-level threading library developed and distributed by Sandia National Laboratories. MPICH currently supports Posix threads, Windows threads, and Argobots. This software enables parallel programs built with the MPICH implementation of MPI to use Qthreads user-level threads rather than Posix system-level threads. The software uses existing infrastructure in MPICH to interface to the Qthreads library. The existing interfaces in MPICH allow for creating, destroying, and managing the execution of multiple threads within a process and this software translates these calls to the equivalent Qthreads library functions. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
The control and protection functions of inverter-based resources (IBR) have raised concerns with bulk system reliability. Most of the current interest lies with solar photovoltaic generation but increasing amounts of storage would pose the same risks. Newer North American Electric Reliability Corporation (NERC) guidelines call for electromagnetic transient (EMT) studies of IBR and recommend that transmission operators collect distributed energy resource (DER) data to support such modeling. IEEE Standard P2800.1 is defining tests for model parameterization, so good model data should become available from inverter vendors. (EMT studies also apply to large power transformer reliability, and transformer vendors can provide EMT models.) Utilities don’t currently have the rest of the bulk system represented for EMT studies at large scale. An International Electrotechnical Commission (IEC) standard Common Information Model (CIM) provides a way of supporting these detailed models from physical asset data, e.g., conductors, towers, transformer data sheets, control block diagrams, while avoiding software vendor lock-in. CIM-for-EMT, with proposed schema extensions and open-source converters, provides a way to exchange EMT data between organizations and tools. This project leverages Office of Electricity (OE) funding of CIM-for-EMT code base through the GridAPPS-DTM project, and of GridPACKTM (parallelized transmission solver), for interoperability testing in CIM-for-EMT. The project also leverages partner PGSTech investments in EMTP® interoperability with CIM.
Many organizations are tasked with the collection and processing of large quantities of data from various measurement devices. Data reported from these sources are often not interoperable with datasets and software used by analysts and other organizations in the same domain, introducing barriers for collaboration on large-scale projects. This poses a particular problem for cross-device comparisons and machine learning applications, which rely on large quantities of data from multiple sources. To address these challenges, the open-source Time-Series Data Pipelines (Tsdat) Python framework was developed by Pacific Northwest National Laboratory, with strategic guidance and direction provided by the National Renewable Energy Laboratory and Sandia National Laboratories to facilitate collaboration and accelerate advancements in the marine energy domain through the development of an open-source ecosystem of tools. This paper will describe the Tsdat framework and the data standards within which it operates. A beta version of Tsdat has been released and is being used by several projects in marine energy, wind energy, and building energy systems.
WEC-Sim is a DOE-funded software tool being jointly developed by NREL and SNL. WEC-Sim computationally models wave energy converters (WEC), devices that generate electricity using movement of water systems such as oceans, rivers, etc. There is great potential for WECs to generate electricity, but as of yet, the industry has yet to establish a commercially viable concept. Modeling, design, and simulations tools are essential to the successful development of WECs. Commercial WEC modeling software tools can't be modified by the user. In contrast, WEC-Sim is a free, open-source, and flexible enough to be modified to meet the rapidly evolving needs of the WEC industry. By modeling the power generation performance and dynamic loads of WEC designs, WEC-Sim can help support the development of new WEC devices by optimizing designs for cost of energy and competitiveness. By being easily accessible, WEC-Sim promises to help level the playing field in the WEC industry. Importantly, WEC-Sim is also excellent at its job! In 2014, WEC-Sim was used in conjunction with NREL’s FAST modeling software to win a hydrodynamic modeling competition. WEC-Sim and FAST performed very well at predicting the motion of a test device in comparison to other modeling tools. The most recent version of WEC-Sim (v1.1) was released in April 2015.
As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.