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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

Automated and Distributed Monte Carlo Generation for GlueX

MCwrapper is a set of systems that manages the entire Monte Carlo production workflow for GlueX and provides standards for how that Monte Carlo is produced. MCwrapper was designed to be able to utilize a variety of batch systems in a way that is relatively transparent to the user, thus enabling users to quickly and easily produce valid simulated data at home institutions worldwide. Additionally, MCwrapper supports an autonomous system that takes user’s project submissions via a custom web application. The system then atomizes the project into individual jobs, matches these jobs to resources, and monitors the jobs status. The entire system is managed by a database which tracks almost all facets of the systems from user submissions to the individual jobs themselves. Users can interact with their submitted projects online via a dashboard or, in the case of testing failure, can modify their project requests from a link contained in an automated email. Beginning in 2018 the GlueX Collaboration began to utilize the Open Science Grid (OSG) to handle a bulk of simulation tasks; these tasks are currently being performed on the OSG automatically via MCwrapper. This talk will outline the entire system of MCwrapper, its use cases, and the unique challenges facing the system.

Britton, Thomas↗

DOE OSTI2023

Flexible visualization of a 3rd party Intrusion Prevention (Security) tool: A use case with the ELK stack

A difficult aspect of cyber security is the ability to achieve automated real time intrusion prevention across various sets of systems. To this extent, several companies are offering comprehensive solutions that leverage an "accuracy of scale" and moving much of the intelligence and detection on the Cloud, relying on an ever-growing set of data and analytics to increase decision accuracy. Often, they provide tools to visualize the decision workflows in attack prevention (as well as tune the algorithm) but those solutions are not always practical as companies see the problem as "global" that is, from a unified Cyber-security standpoint. However, a key to a successful Cyber-security program is transparency and trust: from an experimental team viewpoint, this specifically means having the ability to immediately see what and from where, who has been blocked and being able to inform the community in case of a revoked access without the need for filing a "ticket" (that may eventually be answered) – in other words, rapid response to their user-base is essential but solutions targeting "sub-groups" in an organization are not often available. We have come up with a versatile solution leveraging the ELK stack (Elasticsearch, Logstash, & Kibana) and an IPS (Intrusion Prevention System) based WAF (Web Application Firewall) from Signal Sciences. Signal Science allows the streaming of detailed logs in a Logstash format suitable for custom solutions for visualization. By combining these two tools, we have strengthened our security posture and enabled individual experiments to monitor their own traffic. Specifically, the IPS WAF provides unique data such as country of origin, protocol, response code, source IP, and paths accessed. In this contribution, we will show how we engineered a visualization solution so experiment groups could access a dashboard with predefined graphs but also, where they can create individual customizable dashboards used to display blocked traffic and troubleshoot latency issues. We will discuss the details and procedures for developing and configuring these tools and how it benefits cyber security postures across our scientific based environment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Towards a RAG-based summarization for the Electron Ion Collider

Abstract The complexity and sheer volume of information — encompassing documents, papers, data, and other resources — from large-scale experiments demand significant time and effort to navigate, making the task of accessing and utilizing these varied forms of information daunting, particularly for new collaborators and early-career scientists.To tackle this issue, a Retrieval Augmented Generation (RAG)-based Summarization AI for EIC (RAGS4EIC) is under development. This AI-Agent not only condenses information but also effectively references relevant responses, offering substantial advantages for collaborators. Our project involves a two-step approach: first, querying a comprehensive vector database containing all pertinent experiment information; second, utilizing a Large Language Model (LLM) to generate concise summaries enriched with citations based on user queries and retrieved data. We describe the evaluation methods that use RAG assessments (RAGAs) scoring mechanisms to assess the effectiveness of responses. Furthermore, we describe the concept of prompt template based instruction-tuning which provides flexibility and accuracy in summarization. Importantly, the implementation relies on LangChain [1], which serves as the foundation of our entire workflow. This integration ensures efficiency and scalability, facilitating smooth deployment and accessibility for various user groups within the Electron Ion Collider (EIC) community. This innovative AI-driven framework not only simplifies the understanding of vast datasets but also encourages collaborative participation, thereby empowering researchers. As a demonstration, a web application has been developed to explain each stage of the RAG Agent development in detail. The application can be accessed athttps://rags4eic-ai4eic.streamlit.app.[A tagged version of the source code can be found inhttps://github.com/ai4eic/EIC-RAG-Project/releases/tag/AI4EIC2023_PROCEEDING.]

Instruments & Instrumentation↗

DOE OSTI2024

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits

Abstract The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein–protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Genetics & Heredity↗

DOE OSTI2025

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

Abstract We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

Kainer, David (ORCID:0000000172714676)↗

DOE OSTI2026

A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance

Artificial agents are increasingly integrated into data analysis workflows, carrying out tasks that were primarily done by humans. Our research explores how the introduction of automation recalibrates the dynamic between humans and automating technology. To explore this question, we conducted a scoping review encompassing twenty years of mixed-initiative visual analytic systems. To describe and contrast the relationship between humans and automation, we developed an integrated taxonomy to delineate the objectives of these mixed-initiative visual analytics tools, how much automation they support, and the assumed roles of humans. Here, we describe our qualitative approach of integrating existing theoretical frameworks with new codes we developed. Our analysis shows that the visualization research literature lacks consensus on the definition of mixed-initiative systems and explores a limited potential of the collaborative interaction landscape between people and automation. Our research provides a scaffold to advance the discussion of human-AI collaboration during visual data analysis. Our integrated taxonomy is available in the form of a web application on https://smonadjemi.github.io/miva.

Monadjemi, Shayan [ORNL] (ORCID:0000000293855969)↗

DOE OSTI2024

Cell‐type‐specific transcriptomics uncovers spatial regulatory networks in bioenergy sorghum stems

SUMMARY Bioenergy sorghum is a low‐input, drought‐resilient, deep‐rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4–5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell‐type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell‐type‐specific and cell‐preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell‐type‐specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell‐specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data ( https://mc‐lab.shinyapps.io/lcm‐dataset/ ).

09 BIOMASS FUELS↗

DOE OSTI2021

Visualizing Vehicle Acceleration and Braking Energy at Intersections along a Major Traffic Corridor

Automobiles approaching a controlled intersection need to brake and come to a full stop when the signal transitions from green to red, and the vehicle must later accelerate to normal speeds after the signal changes back to green. These stops and starts associated with normal signal changes lead to unnecessary energy consumption and vehicle emissions. Previous studies have revealed that the optimization of traffic intersections' signal controls and coordination facilitates smoother traffic flows with reduced stop-and-go driving, which can significantly reduce traffic congestion and unnecessary fuel waste. This paper presents an interactive visual analytics dashboard that allows transportation planners to explore and analyze energy consumption patterns resulting from temporally varying traffic signal phases at multiple intersections along a major transportation corridor using traffic simulation outputs. The visual dashboard is implemented as an accessible and responsive web application and employs a combination of visualization techniques to cover multiple aspects of vehicle acceleration and braking at multiple adjacent intersections along a corridor. The paper presents a case study of a simulated traffic scenario on the Shallowford Road traffic corridor located in Chattanooga, Tennessee to demonstrate the capability of the visual dashboard.

Xu, Haowen↗

DOE OSTI2021

Optvis

Optvis is a web application to visualize control flow graphs, call graphs, disassembly code from a binary or executable. The main use case is visualizing the compiler optimizations in binary code.

Aschwanden, PascalD.↗

DOE OSTI2021

R3IT: Rapid, Rural, Resilient Interconnection Toolkit

R3IT is a free and open-source web application that allows utilities to automate the processing of solar interconnection requests. It handles request submission, request review workflows, payment collection, signature collection, email notifications for any status changes, and optionally an automated engineering screening and approval process.

Pinney, David↗

DOE OSTI2022

s3-browser

Web application that interacts with the Amazon AWS S3 REST API to allow users to manage files and buckets without having to use the aws command line application.

Chu, Vincent↗

DOE OSTI2022

EMSL-Computing/CoreMS-Portal

Software that orchestrate data processing and management for mass spectrometry workflows and data products. Stack presented contains a web application, data processing job scheduling, and data processing workers for data processing and management.

Corilo, Yuri↗

DOE OSTI2022

pnnl/simple-building-calculator

The Simple Building Calculator is a web application that estimates annual energy use using regression models that have been fit to simulation results for the DOE Commercial Prototype Building Models. The tool is a single-page application that will allow a user to rapidly analyze the impact of energy efficiency measures and design options on building performance. As it uses linear regression models rather than more complicated machine learning models or physics based simulation, the tool can deploy easily and run client-side.

Xu, Weili↗

DOE OSTI2024

DQL (Django Query Logger) [SWR-24-96]

The Django Query Logger (DQL) is a tool that allows developers of Django web applications to stream the raw database queries that are being executed in real-time from any Django application for review, profiling, filtering, formatting, and analysis. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or Alliance for Sustainable Energy, LLC. The views and opinions of authors expressed in the available or referenced documents do not necessarily state or reflect those of the United States Government or Alliance.

Swindler, Alexander↗

DOE OSTI2025

pnnl/GeoCLUSTER

GeoCLUSTER is a Python-based web application that provides a collection of interactive methods for streamlining the visualization of the technical and economic modeling of closed-loop geothermal systems.

Buchko, Alex [Pacific Northwest National Laborator↗

DOE OSTI2025

NOVA Trame Library

The nova-frame library provides a simple themed web application based on the Trame Framework

Watson, Gregory [Oak Ridge National Laboratory (OR↗

DOE OSTI2025

Decayheatml

This code is designed to predict and analyze the decay heat generated in molten salt reactors (MSRs) using a hybrid approach that combines machine learning and segmented polynomial fitting. The accurate prediction of decay heat is essential for reactor safety and the optimization of spent fuel storage. The code operates through several key components: 1) Data Architecture: It incorporates a modular data architecture that handles various MSR-specific operational parameters such as power density, humidity content, and air ingress. These parameters are sampled using Sobol sequences to ensure comprehensive coverage of operational uncertainties. 2) Machine Learning Framework: The code employs a diverse set of machine learning models, including polynomial regression, decision trees, random forests, gradient boosting, support vector regression, k-nearest neighbors, multi-layer perceptrons, and symbolic regression. These models are trained to predict decay heat over a wide temporal range, from immediate shutdown up to 10,000 years. 3) Region-Optimized Training: The temporal domain is divided into multiple regions, each modeled separately to capture distinct decay heat characteristics across different time scales. This approach significantly improves the accuracy and interpretability of predictions. 4) Segmented Polynomial Interpretation (SPI): The SPI method translates machine learning predictions into piecewise polynomial equations. These equations are physically interpretable and can be directly integrated into existing engineering workflows and safety analyses. 5) Front-End Interfaces: The code includes both a Jupyter notebook interface for research development and a Streamlit web application for operational deployment. These interfaces allow users to interactively explore decay heat predictions, adjust operational parameters, and visualize results in real-time. 6) Applications: The framework supports various applications, including safety system validation and spent fuel container optimization. It enables real-time evaluation of worst-case decay heat scenarios, informing the design of passive safety systems and optimizing container designs for long-term storage. Overall, this code provides a robust, accurate, and user-friendly tool for predicting decay heat in MSRs, enhancing reactor safety, and optimizing spent fuel management.

Retamales, Mauricio Eduardo Tano [Idaho National L↗

DOE OSTI2025

stor4build

The EnergyPlus simulation engine supports modeling and simulation of thermal energy storage (TES) systems in several ways, including using the Python-EMS feature, which extends the operation of the engine with custom code written in Python. Creation of models using this feature can be tedious and error prone, with the connection of the model components to the Python code a particularly troublesome area. The stor4build Python package simplifies this process by modifying an input model to add a selected TES technology (implemented with the Python-EMS feature) and runs the simulation. The package leverages the OpenStudio middleware software development kit to automate this process as much as possible, eliminating potential errors and simplifying usage of EnergyPlus. The package provides objects, functions, and OpenStudio measures that implement the necessary operations to automate the creation of EnergyPlus models that integrate TES technologies with building systems. In addition, two user interfaces are provided: a command line interface and a web application programming interface. The automated process implemented by the package greatly simplifies the modeling and simulation process, allowing for parametric studies to be executed much more efficiently and effectively. The OpenStudio-based workflow is also very flexible and will allow for future additions of new technologies.

DeGraw, JasonWilliam [Oak Ridge National Laborator↗