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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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DOE OSTI2020

Serial2Parallel

In the era of machine learning, we often need to run the same code/script many times with little or no variations (e.g., performance evaluation, data preprocessing, data generation, hyperparameter tuning, etc.). It is not a problem when you just need to do that a few times, but when the number of repetitions becomes very large, it can be a daunting task. The code “Serial2Parallel” provides an easy way for users to be able to run many numbers of any serial code/scripts in a parallel manner across multiple nodes in an message passing interface (MPI) cluster. The code includes the server program that deals with task pool management and client program that processes task. The server gets the tasks ready and waits for clients' connections. The client code pulls tasks from the server and processes them. The client code will run in parallel.

Sangkeun, MattLee↗

DOE OSTI2020

Lithium-Ion Battery Vent Gas Tools

The spreadsheets and Python scripts are used to determine vent gas hazard metrics (i.e. flammability limits, flame speed, etc.). These scripts and spreadsheets use open source literature to determine these hazard metrics. SAND2020-12540 O 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.

Baird, Austin↗

DOE OSTI2020

Panel-Segmentation [SWR-21-18]

Panel-Segmentation contains the scripts for automated metadata extraction of solar PV installations, using satellite imagery coupled with computer vision techniques. In this package, the user can perform the following actions: *Automatically generate a satellite image using a set of lat-long coordinates, and a Google Maps API key. Users would need to set up a Google Cloud account and get a Maps Static API key. Please refer to Setting Up Google Maps Static API Key section for this process. *Perform image segmentation on the satellite image, to locate the solar array(s) in the image on a pixel-by-pixel basis, using an image segmentation model (panel_detection_model.pth). Get classification of the installation (rooftop, ground mounted fixed-tilt or tracking, carport, etc). *Perform azimuth estimation on each solar array cluster in the masked image. *Detect solar panels and get its latitude, longitude, and address within a geographic bounding box through the SOL-Searcher Pipeline. *Detect and calculate hurricane damage on solar installations given pre-hurricane and post-hurricane satellite imagery through the Hurricane Detection Pipeline. *Detect and calculate hail damage on solar installations given satellite imagery through the Hail Detection pipeline. *Convert NOAA MESH (Maximum Estimated Size of Hail) grib2 files into kml or geojson files. *Estimate tilt and azimuth of a solar array by processing USGS LiDAR data for the array’s location.

Edun, Ayobami↗

DOE OSTI2020

chimes_calculator

The Chebyshev Interaction Model for Efficient Simulation (ChIMES) is a machine-learned interatomic potential targeting chemistry in condensed phase systems. ChIMES models are able to approach quantum-accuracy through a systematically improvable explicitly many-bodied basis comprised of linear combinations of Chebyshev polynomials. Though originally developed as to enable description of organic molecular materials, ChIMES has successfully been applied to systems spanning ambient water to molten carbon, and leveraged as correction for density functional based tight binding simulations. The ChIMES calculator comprises a flexible toolset for evaluating ChIMES interactions (e.g. in simulations, single point calculations, etc). Users have the option of directly imbedding the ChIMES calculator within their codes, or evaluating interactions through the beginner-friendly serial interface, each of which have Python, C++, C, and Fortran API's.

Goldman, Nir↗

DOE OSTI2020

Real-time time-dependent density functional theory in Quantum Espresso and related codes

A set of subroutines that interact with an existing computer program, Quantum Espresso (QE), that takes a set of electronic orbitals from QE and propagates them forward in time in response to a number of perturbations. These perturbations include the motion of ions and a wide spectrum of applied electromagnetic fields (e.g., static magnetic fields, slowly-varying electric fields, x-rays, etc.). From the time evolution of the electronic orbitals comes time-varying values of physical observables like the electronic charge and current densities and atomic forces. These are further post-processed to yield properties of interest like stopping powers, conductivities, and the dynamic structure factor. The electronic orbitals are described in a plane wave basis and the equations of motion are carried out using a unitary or approximately unitary time propagation algorithm. Related work will include subroutines for interfacing this capability, as well as others that already exist within QE, with a fork of QE that is more extensible and modular.SAND2019-12611 M 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.

Baczewski, Andrew↗

DOE OSTI2020

Counting Occupants Using Network Technology (COUNT) v0.1

With this COUNT software, we can run a simple query on a campus's Wireless Local Area Network controller (WLAN controller) to obtain the number of connected devices per access point across the whole campus and this can be used as a proxy for the actual number of occupants in the campus. The software supports user defined functions to parse access point names to retrieve more information (e.g.: building names, room numbers etc.) and also allows post-processing of the data.

Pritoni, Marco↗

DOE OSTI2020

ExchCXX v0.1.0

ExchCXX is a modern C++ library for the evaluation of exchange-correlation (XC) functionals required for density functional theory (DFT) calculations. In addition to providing high-level wrappers for the ubiquitous Libxc library for the CPU evaluation of XC functionals, ExchCXX also provides locally developed implementations of a small subset of XC functionals which may be evaluated either on the host (CPU) or device (GPU, FPGA, etc).

Williams-Young, David↗

DOE OSTI2021

K_Road (KRoad)

KRoad is a 2-dimensional, lightweight, vehicle driver simulator that uses 3-dimensional vehicle driving model considerations, i.e. state-of-the-art friction models, and enables the driver to make control decisions with less computation time and programmer effort required than is with full-fidelity 3d simulators. KRoad is fully compatible with OpenAI gym, and contains a factored gym framework for composing gyms from various components (process, reward function, observation function, termination conditions, etc.). KRoad is additionally compatible with the RLlib distributed reinforcement learning environment, running under the Ray distributed python framework.

Tripp, CharlesEdison [National Renewable Energy La↗

DOE OSTI2021

HALOS (Heliostat Aimpoint and Layout Optimization Software) [SWR-21-41]

Heliostat Aimpoint and Layout Optimization Software (HALOS) is an open-source software package that allows users to explore solar field layout optimization, aimpoint strategy optimization, and performance characterization of concentrating solar power tower plants. Users interface with the tool through python, and results are reported in time series tables, plots, runtime logs, and flat-file outputs. Users choose from a list of variables such as tower height, receiver capacity, flux limits, design-point irradiance, etc., and specify information about the system using a small collection of flat files. The software can then optimize the specified variables (e.g., aimpoints for each heliostat) to maximize the thermal energy delivered to the receiver while adhering to flux limits. HALOS is implemented to be flexible with respect to flux characterization methods, but includes a direct connection to NREL's SolarPILOT™ software via its python API so that users can utilize high-fidelity flux simulation methods that have already been developed.

Zolan, Alexander↗

DOE OSTI2021

AUTOPERF

AutoPerf is a collection of modules for low-overhead performance monitoring on HPC systems, the modules collect monitoring data such as MPI data, network counter data etc. Autoperf modules interoperate with the Open source Darshan software (xgitlab.cels.anl.gov/darshan/) as submodules and leverages the Darshan's log recording and analysis framework.

CHUNDURI, DEVISUDHEER KUMAR↗

DOE OSTI2021

stacked predictive sparse decomposition (SPSD) v1.0

It is an unsupervised representation algorithm used to learn morphometric properties from cellular objects. Many similar/identical open source implementations are widely available in tensorflow, pytorch, etc. And ours is implemented in matlab.

Chang, Hang↗

DOE OSTI2021

DASSA v0.0.1

DASSA (Parallel DAS Data Storage and Analysis) provides a data storage engine and analysis engine for data from distributed acoustic sensing and other methods. It supports various data analysis operations, from FFT , signal filter, cross-correlation, compression, stacking etc.

Dong, Bin↗

DOE OSTI2020

Heat Generation versus Heat Dissipation Maps for Batteries (Battery Safety Maps) [SWR-21-27]

NREL's Battery Safety Map (Heat Generation versus Heat Dissipation Maps for Batteries) generates a response surface that is grouped into regions where the battery is deemed safe from the propagation of thermal runaway, those where an engineering solution would render the battery safe, and finally instances where thermal runaway is inevitable. The demarcation for the different regions is provided by specifications or constraints imposed by the end user. NREL's Battery Safety Map is a user-defined subroutine that sweeps through a set of parameters describing the operating conditions and short-circuit scenarios for battery safety simulations. For each run, the electrochemical-thermal model implementing reaction kinetics and gas generation are run to evaluate the amount of heat generated. A thermal model then captures the amount of heat dissipation based on boundary conditions defined (e.g., forced convection, constant temperature at the surface, etc.). Based on the rate of heat transfer and the amount of heat generated, each run is classified into "safe," "mitigation possible," or "unsafe" zones. These categories are defined based on constraints or tolerance specifications provided by the end user.

Santhanagopalan, Shriram↗

DOE OSTI2020

RODeO (Revenue Operation and Device Optimization Model) [SWR 20-67]

The Revenue, Operation, and Device Optimization (RODeO) model explores optimal system design and operation considering different levels of grid integration, equipment cost, operating limitations, financing, and credits and incentives. RODeO is a price-taker model formulated as a mixed-integer linear programming (MILP) model in the GAMS modeling platform. The objective is to maximizes the net revenue for a collection of equipment at a given site. The equipment includes generators (e.g., gas turbine, steam turbine, solar, wind, hydro, fuel cells, etc.), storage systems (batteries, pumped hydro, gas-fired compressed air energy storage, long-duration systems, hydrogen), and flexible loads (e.g., electric vehicles, electrolyzers, flexible building loads). The input data required by RODeO can be classified into three bins: 1) utility service data, which refers to retail utility rate information (meter cost, energy and demand charges), 2) electricity market data, which include energy and reserve prices, 3) other inputs, which refer to additional electrical demand, product output demand, technological assumptions, financial properties, and operational parameters.

Guerra Fernandez, Omar Jose↗

DOE OSTI2020

Obtain3D_open

This computer code uses the concept of the parallax to compute the x, y and z coordinates of points found using transmission electron microscopy (TEM), or any transmission imaging technique, using two images, each taken at a different perspective of the region containing the points. Points correspond, but are not limited, to the center of cavities or precipitates, positions of irradiation black dot damage, positions along a dislocation line, or positions along where an interface meets a free surface. Features can include dislocations, interfaces, cavities, precipitates, inclusions etc. The x, y and z coordinates of the points are output in a text file. The code also allows the user to visualize the features containing the points in three dimensions.

Eftink, Benjamin↗

DOE OSTI2021

CUDAMicroBench

CUDAMicroBench is a collection of fourteen microbenchmarks that demonstrate performance challenges in CUDA programming and techniques to optimize the CUDA programs to address these challenges. It also includes examples and techniques for using advanced CUDA features such as data shuffling between threads, dynamic parallelism, etc that can help users optimize the CUDA program for performance.

Liao, Chunhua↗

DOE OSTI2022

Trace Crawler SOFTWARE

The trace crawler is a tool for selective web crawling to archive web resources with well-defined boundaries. The specific web navigation steps (or trace) are formulated for the families of webpages, where layout or HTML structure can be similar but the content is different, for example, GitHub, Slideshare, blogs, etc. The trace is recorded in a json file format.

Balakireva, Lyudmila↗

DOE OSTI2022

Tomo2Mesh: Fast Reconstruction and Visualization of Tomography Data in Mesh Format

Tomo2Mesh is an open-source project targeted towards real-time reconstruction, segmentation, and visualization of computed tomography (CT) data in mesh format. The CT reconstruction scheme is based on filtered back-projection of voxel subsets. The segmentation scheme uses a 3D convolutional neural network. To allow for fast, real-time reconstruction, voxel subsets are first identified by coarse reconstruction. The detail in specific regions of interest is improved through full reconstruction of voxel subsets in that region. Data structures are implemented to store and process voxel subsets. Finally, voxel data is labeled using connected components to detect disconnected regions such as voids whose morphological attributes (e.g., Feret diameter, principal axis orientation, local number density, size, etc.) can be measured also in real-time. Finally, a fast marching cubes implementation processes labeled voxel data into a triangular face mesh (vertices and faces) in .ply format for visualization in Paraview or other mesh visualization tools. The code provides a simple programming interface for detecting, classifying, and visualizing regions of interest based on morphology. For example, detected voids can be classified as round pores or extended cracks. Highly porous neighborhoods can be identified based on local number density. The mesh texture (or color) is assigned based these morphological attributes to allow smart visualization scenarios in real-time (e.g., show only long cracks). At the time of first release (July 2022), extraction of face mesh for visualization for raw CT data from a 2 megapixel camera would take between 1-5 minutes for most scenarios.

TEKAWADE, ANIKET↗