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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

TripleGraph

RDF triplestores are great tools for online graph analytic processing (i.e., graph pattern query processing), but they do not provide graph mining capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.). The software title “TripleGraph” is a graph analysis toolkit, which uses an RDF triplestore as its backend for creating, manipulating, mining, and programming with large scale property graphs. It allows users to run various graph mining algorithms easily. User can import edgelist-formatted (homogeneous graph) or JSON-formatted graph (property graph) into the RDF triplestore using the provided tool and perform various analysis such as (1) Node/edge retrieval and manipulation, (2) Pathfinding between two given nodes, (3) Running graph mining algorithms (PageRank/Personalized PageRank, Single Source Shortest Path/Multi-Source Shortest Path, Connected Component, Node Eccentricity, Peer Pressure Clustering). It supports standard graph data format and works with a standard SPARQL endpoint like Jena Fuseki. It allows users to perform online graph analytic processing and graph mining on the same platform (a triplestore).

Sangkeun, MattLee↗
DOE OSTI2020

ESS-DIVE File Level Metadata Extractor

The File-level Metadata (FLMD) parser is a lightweight python script that can produce a summary of fields based on column headers within a Comma-separated Values (CSV) file. The script enables a way to extract certain metadata fields from CSV data files. An optional configuration file can be used to select specific column headers to extract a smaller subset of values. The script can be used on a single file, or it can be given a directory to process groups of files in bulk. The FLMD parser outputs the metadata as a JSON and can be configured to updated the source file or produce an external companion file.

McNelis, Jack↗
DOE OSTI2020

CatCost™ Data Tools [SWR-20-59]

CatCost™ enables catalyst researchers to develop comprehensive estimates of the cost to produce pre-commercial catalysts early in the catalyst development process. It includes both web browser and Excel-based tools, each with distinct strengths. The web tool includes powerful visualization capabilities, while the Excel tool allows for high degrees of control and customization by power users. The CatCost™ Python Data Tools are designed to facilitate the transfer of data from the Excel tool to the web tool, so that users can take advantage of both the powerful visualization in the web tool and the higher degree of customization available in the excel tool. Rather than painstakingly copying entries from estimates, the materials, equipment, and spent catalyst libraries from Excel to a JSON format for the web tool, the CatCost™ Data Tools provide an intuitive applet which allows them to simply choose which library or estimate they wish to transfer, and then returns files that integrate smoothly with the web version of the tool.

Baddour, Frederick↗
DOE OSTI2022

Link Parser Library

Link Parser Library is a Python library for parsing HTTP Link header format. Link Parser extracts information about relationships to other resources from Link Header and translates it into an easy to use Json structure.

Balakireva, Lyudmila↗
DOE OSTI2022

Memento Tracer Extension

This browser extension records a user's interactions with a webpage so that it can be applied during the selective crawling of similar classes of webpages. This extension currently supports Chrome Browser only. The navigation steps (or trace) captured in JSON format.

Balakireva, Lyudmila↗
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

epJSON Editor

The software provides an interface to the EnergyPlus input file, providing similar functionality to the IdfEditor tool but written in Python and specifically targeting the JSON-formatted input file. This interface occupies (as IdfEditor did previously) the niche between directly editing the input file and use of a more full-featured graphical user interface, providing both advanced and novice users with a simple interface. An extensive effort was made to gather stakeholder feedback in order to avoid merely duplicating the features of IdfEditor and to build an interface that met the needs of users both now and into the future.

Glazer, Jason↗
DOE OSTI2023

pnnl/PHANTOM

A topology-based anomaly detection tool for temporal varying data. The input is json or CSV formatted data with a timestamp field identified. The algorithm computes vectorizations (using a vectorization config file) of time windows of data and computes a topological measure of anomalousness. The output is a sequence of anomaly scores for each time window

Seppala, Garret↗
DOE OSTI2023

ctrl-flow v0.0.1

ctrl-flow is a software that assists users in selecting a building control sequence from a library of control sequences that are implemented in the Modelica Buildings Library. The software allows selecting, configuring and editing the control sequences. It also allows exporting the sequence for use in building project design documents in Modelica-based simulations. The export will be in a json intermediate format, in the Modelica Language, in the Control Description Language and in English language format, such as in a Word document. The software functions as a web based application that includes a graphical user interface.

Wetter, Michael↗
DOE OSTI2024

Mneme

A simple tool allowing recording the execution of a GPU (CUDA) kernel and replaying that kernel as an independent executable. The tool operates in 3 phases. During compile time the user needs to apply a provided LLVM pass to instrument the code. The pass detects all device global variables and device functions and stores this information with the respective LLVM-IR in the global device memory. The compilation generates a record-able executable. The second phase involves running the application executable with a desired input and using LD_PRELOAD to enable recording. When recording before invoking a device kernel the pre-loaded library stores device memory in persistent storage and associates the memory with the device kernel and an LLVM IR file. At the end of the recorded execution the pre-load library generates a database in the form of a JSON file containing information regarding the LLVM-IR files and the snapshots of device memory. During the third and last phase the user can replay the execution of an kernel as a separate independent executable. Besides executing it the user can modify the LLVM IR file and auto-tune parameters such as kernel launch-bounds or kernel runtime execution parameters (e.g. Kernel Block and Grid Dimensions). Is

Parasyris, Konstantinos↗
DOE OSTI2024

Collection And Analysis Of Telemetry For The Cyote Heuristic

CATCH CLI focuses on gathering telemetry data, storing it in the Neo4j database, querying for Mitre ATT&CK patterns, and creating STIX 2.1 reports. Key Components: Analysis Modules: Analyze data to detect attack patterns. GoSTOTS Collection Engines: Collect telemetry data. These tools can be used together or individually. Analysis modules rely on data from specific engines to identify attack patterns. Source Code Organization: Engines: CATCH/catch/cmd/collection Modules: CATCH/catch/cmd/analysis CGUI Overview CATCH Graphical User Interface (CGUI) offers a graphical shell to execute CATCH CLI, allowing easy editing of: Analysis Modules Database configurations Profiles (collection and device settings) Neo4j Overview Neo4j is a graph database using the Cypher query language, storing data in JSON. It seamlessly integrates with STIX 2.1 data for: Data Submission: CATCH Collection Engines Data Querying: Analysis Modules CATCH modifies STIX 2.1 data for Neo4j submission and reverts it back during querying. STIG Overview Structured Threat Intelligence Graph (STIG) is a tool for creating, editing, querying, analyzing, and visualizing threat intelligence using STIX 2.1 and storing data in Neo4j. Usage Tools can be run: Manually (CLI): Refer to CATCH documentation User Interface: Run ./cgui/CGUI or go run ./cgui/ Additional Information Logging System: Detailed in the config documentation Further Documentation: Available for CATCH and CGUI

Madsen, MichaelJ. [Idaho National Laboratory (INL)↗
DOE OSTI2024

Nvd Search To Stix

This code is a python based application that queries the National Vulnerability Database (NVD) API search term and CPE endpoints. It then sifts through the API response and uses the STIX2 python package to create STIX SDOs, SROs, and SCOs from the applicable data. If there are CWEs associated with the bundle, it queries the OpenCVE API for information on the weakness, then translates that data to STIX as well. It then combines all the data into a STIX bundle and outputs it to a JSON file.

Beckman, BryanR [Idaho National Laboratory (INL), ↗
DOE OSTI2024

DER Translate

SAND2024-13723O This software translates and maps any .xlsx-based distributed energy resource (DER) device from the SunSpec certification registry (https://sunspec.org/certified-registry/) to a JSON-based DER models map. It is then read by DER monitoring tools for applications such as network intrusion detection and system health monitoring. 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.

Fragkos, Georgios↗
DOE OSTI2025

building-physics/refrigeration-system

The existing refrigeration system for OpenStudio was originally designed for a prototype supermarket model. To support the development of new modular space types, it is crucial to refactor the system. This process includes refining the design by utilizing the manufacturer's existing data, along with incorporating the latest data from manufacturers, to enhance the system’s adaptability and functionality. This repository currently contains the refrigeration system database and Python-based automation for generating OpenStudio-compatible refrigeration JSON files.

Bae, Yeonjin [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2025

Ocpp 2.0.1. Interim Kpi Calculator

The project is split into four pieces. The first is a raw OCPP log parser. The second is a file splitter. The third is a message parser. The final piece is the Interim KPI calculator. The OCPP log parser was created from two different formats of raw OCPP 2.0.1 data. Its intended purpose is to extract device IDs and OCPP event messages from nontabular text logs. The parser looks for specific substrings in the logs to identify which of the two "standards" it should select from. The KPI generator does not perform any of its calculations in parallel. Instead, we opt for a naive batching approach. The splitter takes the file generated from the parser and creates many smaller files for each of the device IDs in the dataset. This allows the pandas queries in the log formatter to be iterate over a significantly smaller slice of data, increasing performance significantly. The message parser step takes messages from each of the files (containing distinct device IDs) and breaks the message out into pieces. The final result is a file with different columns specifying different attributes of the JSON message. The file is an aggregation of all different devices. This is the most complex portion of the code. The KPI calculator takes the parsed messages, as a single file, and calculates the KPI from that data. An excel file is produced with four sheets. These contain the metrics for Session Success, Charge Start Success, Charge End Success, and Charge Start Time. It includes the metrics for the different equations in the Interim KPI Implementation Guide as well as a weighted sum of the different equations for each KPI (excluding Charge End Success and Charge Start Time).

Quinn, Casey↗
DOE OSTI2025

rmon (Resource monitor) [SWR-24-128]

The resource monitor application provides monitoring, collection, and visualization of resource utilization in compute nodes. This package contains utilities to monitor system resource utilization (CPU, memory, disk, network). Here are the ways you can use it: -Monitor resource utilization for a compute node for a given set of resource types and process IDs. -Start a process and monitor its resource utilization. -Monitor resource utilization for a compute node asynchronously with the ability to dynamically change the resource types and process IDs being monitored. -Produce JSON reports of aggregated metrics. -Produce interactive HTML plots of the statistics.

Thom, Daniel [National Renewable Energy Laboratory↗
DOE OSTI2024

Machine Learning Automation Pipeline

Machine Learning Automation Pipeline (MLAP) is a package to perform machine learning (ML) analysis in a step by step manner, starting with data extraction until analysis and prediction. The scripts provide the users option to chose an action such as "Extract", "Prep", and "Train" and numerous cases can be launched with just a single command. The inputs for each case are provided using a JSON file. The simulation results of several cases can be assessed using an automated process and analyzed for various metrics pertinent to ML analysis.

Jha, Pankaj↗
DOE OSTI2025

NREL-AltDSS [SWR-25-58]

This repository builds upon the original work by PMeira at AltDSS-Schema, which aimed to define a structured JSON-based schema for DSS circuit representation. The schema has been packaged in this repository with slight modifications, enabling users to add the package as a dependency for downstream applications

Elgindy, Tarek [National Renewable Energy Laborato↗