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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

MCP-eGridGPT (MCP-Enabled Chatbot with Electrical Power System Analysis and Interactive Visualization Tool) [SWR-25-126]

This software is an advanced chatbot system that integrates the Model Context Protocol (MCP) to provide intelligent electrical power system analysis and automated visualization generation. The system enables users to interact with complex electrical engineering tools through natural language, automatically analyzes power system data for voltage violations and grid health assessment, and generates professional interactive HTML dashboards and reports. Key features include dynamic tool discovery from MCP servers, multi-LLM provider support, intelligent data interpretation using large language models, automated chart generation, and a web-based interface for real-time analysis. The software bridges sophisticated electrical engineering analysis with user-friendly interfaces, making power system diagnostics accessible through conversational AI.

Choi, Seong [National Laboratory of the Rockies (N↗
DOE OSTI2024

Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models

Uncertainty visualization is a key component in translating important insights from ensemble simulation data into actionable decision-making by visually conveying various aspects of uncertainty within a system. With the recent advent of fast surrogate models trained on ensemble data, we can substitute computationally expensive simulations, which allows users to interact with more aspects of data spaces than ever before. However, the use of ensemble data with surrogate models in a decision-making tool brings up new challenges for uncertainty visualization, namely how to reconcile and communicate the new and different types of uncertainties brought in by surrogates and how to utilize these new data estimates in actionable ways. In this work, we examine these issues as they relate to high-dimensional data visualization, the integration of discrete datasets and the continuous representations of those datasets, and the unique difficulties associated with systems that allow users to iterate between input and output spaces. We assess the role of uncertainty visualization in facilitating intuitive and actionable interaction with ensemble data and surrogate models, and highlight key challenges in this new frontier of computational simulation.

ensemble data↗
DOE OSTI2024

Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models: Preprint

Uncertainty visualization is a key component in translating important insights from ensemble data into actionable decision-making by visually conveying various aspects of uncertainty within a system. With the recent advent of fast surrogate models for computationally expensive simulations, users can interact with more aspects of data spaces than ever before. However, the integration of ensemble data with surrogate models in a decision-making tool brings up new challenges for uncertainty visualization, namely how to reconcile and communicate the new and different types of uncertainties brought in by surrogates and how to utilize these new data estimates in actionable ways. In this work, we examine these issues as they relate to high-dimensional data visualization, the integration of discrete datasets and the continuous representations of those datasets, and the unique difficulties associated with systems that allow users to iterate between input and output spaces. We assess the role of uncertainty visualization in facilitating intuitive and actionable interaction with ensemble data and surrogate models, and highlight key challenges in this new frontier of computational simulation.

ensemble visualization↗
DOE OSTI2026

A Review and Outlook on Experimental Advances and Innovations in Geological CO 2 Storage: Insights from Depleted Gas Reservoirs and Saline Aquifers

Geological storage of carbon dioxide (CO 2 ) in depleted gas reservoirs and deep saline aquifers is a key part of global decarbonization efforts. As carbon capture and storage advances toward commercial-scale deployment, the credibility and scalability of laboratory experiments are increasingly vital for guiding safe and effective field implementation. This review offers a comprehensive, cross-scale evaluation of experimental methodologies, including core flooding, high-pressure, high-temperature systems, microfluidic visualization, and emerging systems such as multilayer commingled/compartmentalized core flooding, 3D-printed micromodels, and AI-powered digital twins. These innovations are demonstrated to enhance representativeness, reproducibility, and real-time insight, thereby addressing the limitations of conventional workflows. A critical analysis of methodological gaps, such as inconsistent pressure–temperature conditions, oversimplified brine chemistry, and a lack of standardization, reveals experimental sources of scale translation errors and performance uncertainty. By comparing the unique challenges of depleted gas reservoirs (such as low water saturation and legacy well leakage) to those of saline aquifers (including pressure buildup and caprock integrity), this review identifies formation-specific priorities for experimental design. Novel contributions include a synthesis of best practices, integration strategies for model calibration, and recommendations for standardizing core handling, saturation procedures, and reporting protocols. Furthermore, this work serves as a guide for developing robust, field-relevant experimental strategies that can increase the deployment and regulatory acceptance of CO 2 storage technologies at scale.

58 GEOSCIENCES↗
DOE OSTI2024

Data Analytics and Visualization of Energy Systems for Critical Infrastructure Insights

Modernization of energy systems including transportation facilities provides opportunities for increased efficiency, expansion of commerce and meeting industry and federal goals. A significant increase in electrical demand is projected to meet these needs, which concentrates at facilities such as airports. For example, Xcel Energy working with two airports in their service area recently published information projecting an up to fivefold increase in electricity demand in the next 25 years [1]. Concurrently, the US Government Accountability Office (GAO) recently surveyed 30 commercial service airports identifying more than 300 outages of more than 5 minutes between 2015 and 2022 [2]. Power, reliability, and resilience planning becomes more important to safely maintain operations and the flow of commerce with fewer energy carriers providing necessary energy to safely move passengers and goods. NREL proposes to develop methodologies to allow owners, utilities, and federal agencies to dynamically analyze, forecast, and manage energy loads at airports, focused upon maintaining the flow of commerce in an efficient, sustainable, and resilient way. To address these energy challenges, a suite of technologies and methodologies can be leveraged to validate concepts, inform design, de-risk solutions and optimize energy management during deployment. These technologies include digitalization of energy systems, microgrid methodologies, and related energy technologies for building and vehicle loads. [1] Electrifying Airport Ecosystems - https://www.enterprisemobility.com/content/dam/enterpriseholdings/marketing/innovation-in-mobility/vehicle-innovation/airport-electrification-study-full-report-2024.pdf [2] Airport Infrastructure: Selected Airport's Efforts to Enhance Electrical Resilience https://www.gao.gov/products/gao-23-105203.

critcal infrastructure↗
DOE OSTI2021

Visual Modeling for Complex System Valuation: Implementation Guidance

Within the Transactive Systems Program (TSP) at Pacific Northwest National Laboratory (PNNL) the need to incorporate a valuation analysis design early within the research process of transactive energy systems led to development of a valuation methodology. This methodology allows the modeling of economic exchanges within a complex system and supports the evaluation of individual stakeholder economic outcomes in addition to systemwide costs and benefits. The use of visual modeling practices enables the research team to reach common understanding and agreement on the analysis design within the complex system. While this methodology was developed for the valuation of transactive energy systems, it can be applied to any complex system where a granular economic analysis is desired. It allows for the inclusion of equity analyses and ties individual activities and microeconomic outcomes with the systemwide macroeconomic impacts. This document serves as implementation guidance for analysts planning to deploy the methodology within a research study. The appendixes provide specific guidance on how this methodology is deployed within the TSP at PNNL for analysts seeking guidance for deployment within that context.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Supporting Co-Design of Extreme-Scale Systems with In Situ Visual Analysis of Event-Driven Simulations (Final Report)

Extreme-scale systems for compute- and data-centric science are pushing the boundaries of system architectures in order to achieve ambitious goals in terms of computing capability, resilience, and power efficiency. Future systems necessitate emerging designs that incorporate new technologies, system organizations, and computer science techniques, and these changes are not yet well understood. Discrete-event-driven simulation of computing system architectures and subsystems has emerged as a productive and cost-effective means to evaluating potential designs, along with capabilities for executing these simulations of extreme-scale systems. However, techniques for analyzing the behavior of these simulations have not kept pace with our ability to perform the simulations, preventing us from extracting the most value from this promising approach. The objective of this project is to support the codesign of extreme-scale system architectures for compute and data-centric science through research and development of novel methods for analysis and visualization of large-scale event-driven simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Situ: Identifying and Explaining Suspicious Behavior in Networks

Despite the best efforts of cyber security analysts, networked computing assets are routinely compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and major financial damages. Anomaly detection methods are beneficial for detecting new types of attacks and abnormal network activity, but such algorithms can be difficult to understand and trust. Network operators and cyber analysts need fast and scalable tools to help identify suspicious behavior that bypasses automated security systems, but operators do not want another automated tool with algorithms they do not trust. Experts need tools to augment their own domain expertise and to provide a contextual understanding of suspicious behavior to help them make decisions. In this paper we present Situ, a visual analytics system for discovering suspicious behavior in streaming network data. Situ provides a scalable solution that combines anomaly detection with information visualization. The system's visualizations enable operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous. Finally, operators need tools that can be integrated into their workflow and with their existing tools. This paper describes the Situ platform and its deployment in an operational network setting. Furthermore, we discuss how operators are currently using the tool in a large organization's security operations center and present the results of expert reviews with professionals.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Preserving Command Line Workflow for a Package Management System using ASCII DAG Visualization

Package managers provide ease of access to applications by removing the time-consuming and sometimes completely prohibitive barrier of successfully building, installing, and maintaining the software for a system. A package dependency contains dependencies between all packages required to build and run the target software. Package management system developers, package maintainers, and users may consult the dependency graph when a simple listing is insufficient for their analyses. However, users working in a remote command line environment must disrupt their workflow to visualize dependency graphs in graphical programs, possibly needing to move files between devices or incur forwarding lag. Such is the case for users of Spack, an open source package management system originally developed to ease the complex builds required by supercomputing environments. To preserve the command line workflow of Spack, we develop an interactive ASCII visualization for its dependency graphs. Through interviews with Spack maintainers, we identify user goals and corresponding visual tasks for dependency graphs. We evaluate the use of our visualization through a command line-centered study, comparing it to the system's two existing approaches. We observe that despite the limitations of the ASCII representation, our visualization is preferred by participants when approached from a command line interface workflow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Simplifying and Visualizing the Ontology of Systems Engineering Models

The credibility of an engineering model is of critical importance in large-scale projects. How concerned should an engineer be when reusing someone else's model when they may not know the author or be familiar with the tools that were used to create it? In this report, the authors advance engineers' capabilities for assessing models through examination of the underlying semantic structure of a model--the ontology. This ontology defines the objects in a model, types of objects, and relationships between them. In this study, two advances in ontology simplification and visualization are discussed and are demonstrated on two systems engineering models. These advances are critical steps toward enabling engineering models to interoperate, as well as assessing models for credibility. For example, results of this research show an 80% reduction in file size and representation size, dramatically improving the throughput of graph algorithms applied to the analysis of these models. Finally, four future problems are outlined in ontology research toward establishing credible models--ontology discovery, ontology matching, ontology alignment, and model assessment.

42 ENGINEERING↗
DOE OSTI2020

In Situ Chemical Monitoring and Imaging of Contents within Microfluidic Devices Having a Porous Membrane Wall Using Liquid Microjunction Surface Sampling Probe Mass Spectrometry

The ability to observe dynamic chemical processes (e.g., signaling, transport, etc.) in vivo or in situ using nondestructive chemical imaging opens a new door to understanding the complex dynamics of developing biological systems. With the advent of “biology-on-a-chip” devices has come the ability to monitor dynamic chemical processes in a controlled environment, using these engineered habitats to capture key features of natural systems while allowing visual observation of system development. Having the capability to spatially and temporally map the chemical signals within these devices may yield new insights into the forces that drive biosystem development. Here, a porous membrane sealed microfluidic device was designed to allow normal microfluidic operation while enabling continuous, location specific sampling and chemical characterization by liquid microjunction surface sampling probe mass spectrometry (LMJ-SSP MS). LMJ-SSP was used to extract fluids with nL-to-μL/min flow rates directly from selected areas of the microfluidic device without negatively impacting the device function. These extracts were subsequently characterized using MS. This technique was used to acquire MS images of the entirety of several multi-input microfluidic devices having different degrees of fluid mixing. LMJ-SSP MS imaging visualized the spatial distribution of chemical components within the microfluidic channels and could visualize chemical reactions occurring in the device. These microfluidic devices with a porous membrane wall are wholly compatible with the construction of biology-on-a-chip devices. This ultimately would enable correlation of biosystem physical structure with an evolving chemical environment

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Method and system for information retrieval and aggregation from inferred user reasoning

The visual analytic system enables information retrieval within large text collections. Typically, users have to directly and explicitly query information to retrieve it. With this system and process, the reasoning of the user is inferred from the user interaction they perform in a visual analytic tool, and the appropriate information to query, process, and visualize is systematically determined.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

A Heterogeneous System for Eagle Detection, Deterrent, and Wildlife Collision Detection for Wind Turbines (Final Technical Report)

This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360° camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described. Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms. As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.

17 WIND ENERGY↗
DOE OSTI2019

Vulnerable to misinformation? Verifi!

We present Verifi2, a visual analytic system to support the investigation of misinformation on social media. On the one hand, social media platforms empower individuals and organizations by democratizing the sharing of information. On the other hand, even well-informed and experienced social media users are vulnerable to misinformation. To address the issue, various models and studies have emerged from multiple disciplines to detect and understand the effects of misinformation. However, there is still a lack of intuitive and accessible tools that help social media users distinguish misinformation from verified news. In this paper, we present Verifi2, a visual analytic system that uses state-of-the-art computational methods to highlight salient features from text, social network, and images. By exploring news on a source level through multiple coordinated views in Verifi2, users can interact with the complex dimensions that characterize misinformation and contrast how real and suspicious news outlets differ on these dimensions. To evaluate Verifi2, we conduct interviews with experts in digital media, journalism, education, psychology, and computing who study misinformation. Our interviews show promising potential for Verifi2 to serve as an educational tool on misinformation. Furthermore, our interview results highlight the complexity of the problem of combating misinformation and call for more work from the visualization community.

Karduni, Alireza↗
DOE OSTI2019

TexTonic: Interactive Visualization for Exploration and discovery of Very Large Text Collections

TexTonic is a visual analytic system for interactive exploration of very large unstructured text collections. TexTonic visualizes hierarchical clusters of representative terms, snippets, and documents in a single, multi-scale spatial layout. Exploration is supported by interacting with the visualization and directly manipulating the terms in the visualization using semantic interactions. These semantic interactions steer the underlying analytic model by translating user interactions within the visualization to contextual updates to the supporting data model. The combination of semantic interactions and information visualization at multiple levels of the data hierarchy helps users manage information overload so that they can more effectively explore very large text collections. In this paper we describe TexTonic’s data processing and analytic pipeline, user interface and interaction design principles, and the results of a user study conducted mid-development with experienced data analysts. We also discuss the implications TexTonic could have on visual exploration and discovery tasks

human centered computing, Visual analytics, Inform↗
DOE OSTI2021

Knocksideways in plants: an inducible system for in planta visualization of protein interactions

Understanding protein-protein interactions (PPIs) is key to unlocking the secrets of various cellular processes. While many tools have been developed to detect PPIs, such as the yeast two-hybrid assay, bimolecular fluorescence complementation, and affinity purification, there is great demand for additional methods to complement existing ones. As all approaches have limitations, evidence from at least two independent assays is required to validate PPIs. In a Breakthrough Report, Joanna Winkler and colleagues present here a promising method, named “knocksideways in plants” (KSP), for inducible visualization of PPIs in planta.

59 BASIC BIOLOGICAL SCIENCES↗