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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 OSTI2023

Closed Loop Geothermal Working Group: GeoCLUSTER App, Subsurface Simulation Results, and Publications

To better understand the heat production, electricity generation performance, and economic viability of closed-loop geothermal systems in hot-dry rock, the Closed-Loop Geothermal Working Group -- a consortium of several national labs and academic institutions has tabulated time-dependent numerical solutions and levelized cost results of two popular closed-loop heat exchanger designs (u-tube and co-axial). The heat exchanger designs were evaluated for two working fluids (water and supercritical CO2) while varying seven continuous independent parameters of interest (mass flow rate, vertical depth, horizontal extent, borehole diameter, formation gradient, formation conductivity, and injection temperature). The corresponding numerical solutions (approximately 1.2 million per heat exchanger design) are stored as multi-dimensional HDF5 datasets and can be queried at off-grid points using multi-dimensional linear interpolation. A Python script was developed to query this database and estimate time-dependent electricity generation using an organic Rankine cycle (for water) or direct turbine expansion cycle (for CO2) and perform a cost assessment. This document aims to give an overview of the HDF5 database file and highlights how to read, visualize, and query quantities of interest (e.g., levelized cost of electricity, levelized cost of heat) using the accompanying Python scripts. Details regarding the capital, operation, and maintenance and levelized cost calculation using the techno-economic analysis script are provided. This data submission will contain results from the Closed Loop Geothermal Working Group study that are within the public domain, including publications, simulation results, databases, and computer codes. GeoCLUSTER is a Python-based web application created using Dash, an open-source framework built on top of Flask that streamlines the building of data dashboards. GeoCLUSTER provides users with a collection of interactive methods for streamlining the exploration and visualization of an HDF5 dataset. The GeoCluster app and database are contained in the compressed file geocluster_vx.zip, where the "x" refers to the version number. For example, geocluster_v1.zip is Version 1 of the app. This zip file also contains installation instructions. **To use the GeoCLUSTER app in the cloud, click the link to "GeoCLUSTER on AWS" in the Resources section below. To use the GeoCLUSTER app locally, download the geocluster_vx.zip to your computer and uncompress this file. When uncompressed this file comprises two directories and the geocluster_installation.pdf file. The geo-data app contains the HDF5 database in condensed format, and the GeoCLUSTER directory contains the GeoCLUSTER app in the subdirectory dash_app, as app.py. The geocluster_installation.pdf file provides instructions on installing Python, the needed Python modules, and then executing the app.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Interactive visualization of multi-data-set Rietveld analyses using Cinema:Debye-Scherrer [nteractive Visualization of Multi-Dataset Rietveld Analyses using Cinema:Debye-Scherrer]

A tool named Cinema:Debye-Scherrer to visualize the results of a series of Rietveld analyses is presented. The multi-axis visualization of the high-dimensional data sets resulting from powder diffraction analyses allows identification of analysis problems, prediction of suitable starting values, identification of gaps in the experimental parameter space and acceleration of scientific insight from the experimental data. The tool is demonstrated with analysis results from 59 U–Nb alloy samples with different compositions, annealing times and annealing temperatures as well as with a high-temperature study of the crystal structure of CsPbBr 3 . A script to extract parameters from a series of Rietveld analyses employing the widely used GSAS Rietveld software is also described. Finally, both software tools are available for download.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Visualizing degradation mechanisms in a gas-fed CO 2 reduction cell via operando X-ray tomography

We utilize operando X-ray computed tomography, coupled with real-time electrochemical analysis, to reveal the underlying failure mechanisms of membrane electrode assemblies (MEAs) for electrochemical CO 2 reduction (eCO 2 R). Through operando imaging, we can obtain unprecedented insights into the dynamic behavior of the MEA under different operating conditions, revealing critical changes in interface interactions, phase distribution, and structural integrity over time. Our findings identify phenomena giving rise to the transition from CO 2 R to the hydrogen evolution reaction (HER), as evidenced by shifts in cathode potential and CO 2 R selectivity. The formation of inhomogeneous precipitates at the gas diffusion electrode disrupts the CO 2 supply and reduces the active sites for eCO 2 R, resulting in a shift toward H2 production during low current density operation. Additionally, under high current density conditions, rapid water crossover up to the microporous layer/gas diffusion layer promotes the transition from CO 2 R to HER, further shifting cell potential toward anodic direction. Oscillating voltage conditions reveal the dissolution and regrowth of precipitates, providing direct visualization of the competing selectivity of CO 2 R and HER. This work offers new insight into the degradation mechanisms of MEAs, with implications for the design of more durable CO 2 R systems.

Lee, Sol A [California Institute of Technology (Ca↗
DOE OSTI2026

MSD CoP Webinar: "Advances in MSD-LIVE to Support the MSD Community of Practice"

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Advances in MSD-LIVE to Support the MSD Community of Practice Presenters: Casey Burleyson and Zoe Guillen (Pacific Northwest National Laboratory) Abstract: The MultiSector Dynamics Living, Intuitive, Value-adding, Environment (MSD-LIVE; msdlive.org) is a cloud-based data management system and advanced computing platform that enables MSD researchers to document and archive their data, run their models and analysis tools, and share their data, software, and workflows within the MSD Community of Practice. Recently, several high-profile datasets have attracted many new users to MSD-LIVE. This webinar has two goals: 1) To refamiliarize the MSD community and new users with the components of the platform (e.g., the data repository, model training notebooks, and data dashboards) and to highlight examples of how these components are advancing MSD science and 2) To demonstrate new features in v3 of the platform, released in late 2025. The main new feature in v3 is the ability to interactively explore data in MSD-LIVE without downloading it. MSD-LIVE users can now click a button in our data repository and launch a blank Jupyter notebook with access to the underlying data on AWS. Users can use the notebook to write analysis, visualization, or subsetting routines that process the data directly on the AWS cloud. We also added a GitHub integration feature that allows users to share analysis or visualization code they develop with the community of MSD-LIVE users. The webinar will wrap up with a look at what's coming next for MSD-LIVE in 2026. Moderator: Patrick M. Reed (MSD CoP Facilitation Team) This webinar was held on: May 12th, 2026 from 1-2 PM EST.

Open Science↗
DOE OSTI2022

Interactively Assessing Disentanglement in GANs

Abstract Generative adversarial networks (GAN) have witnessed tremendous growth in recent years, demonstrating wide applicability in many domains. However, GANs remain notoriously difficult for people to interpret, particularly for modern GANs capable of generating photo‐realistic imagery. In this work we contribute a visual analytics approach for GAN interpretability, where we focus on the analysis and visualization of GAN disentanglement. Disentanglement is concerned with the ability to control content produced by a GAN along a small number of distinct, yet semantic, factors of variation. The goal of our approach is to shed insight on GAN disentanglement, above and beyond coarse summaries, instead permitting a deeper analysis of the data distribution modeled by a GAN. Our visualization allows one to assess a single factor of variation in terms of groupings and trends in the data distribution, where our analysis seeks to relate the learned representation space of GANs with attribute‐based semantic scoring of images produced by GANs. Through use‐cases, we show that our visualization is effective in assessing disentanglement, allowing one to quickly recognize a factor of variation and its overall quality. In addition, we show how our approach can highlight potential dataset biases learned by GANs.

Jeong, Sangwon↗
DOE OSTI2022

Processes in DNA damage response from a whole-cell multi-omics perspective

Technological advances have made it feasible to collect multi-condition multi-omic time courses of cellular response to perturbation, but the complexity of these datasets impedes discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we report a whole-cell mechanistic analysis of HL-60 cellular response to bendamustine. We integrate both enrichment and network analysis to show the progression of DNA damage and programmed cell death over time in molecular, pathway, and process-level detail using an interactive analysis framework for multi-omics data. Our framework, Mechanism of Action Generator Involving Network analysis (MAGINE), automates network construction and enrichment analysis across multiple samples and platforms, which can be integrated into our annotated gene-set network to combine the strengths of networks and ontology-driven analysis. Taken together, our work demonstrates how multi-omics integration can be used to explore signaling processes at various resolutions and demonstrates multi-pathway involvement beyond the canonical bendamustine mechanism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Web application for machining parameter recommendation using visual analytics

The computer codes create a web application including multiple user interfaces, visualizations, interaction modules between the user, and visualizations. The visualizations will be interactive, allowing users to query the data directly, and they will be arranged in a dashboard. When selections are made in one visualization, filters will be applied to all other visualizations providing a coordinated system for asking questions about the data. The visualization can be utilized for manufacturing data management, analysis, and recommendation.

Chae, Junghoon↗
DOE OSTI2021

Anomalous nanoparticle surface diffusion in LCTEM is revealed by deep learning-assisted analysis

Significance Liquid cell transmission electron microscopy (LCTEM) is an emerging technique, which enables nanoscale visualization and tracking of single nanoparticles near interfaces with unprecedented spatial resolution. Here, we studied the diffusion of nanoparticles in LCTEM experiments using techniques powered by deep neural networks and statistical tests. We observed two underlying regimes of diffusive behavior which are governed by the interaction of the electron beam, the nanoparticle, the nearby substrate, and the liquid environment. This understanding forms the foundation to use LCTEM for single-nanoparticle tracking for a broad range of nanoparticles, interfaces, and liquids.

Jamali, Vida↗
DOE OSTI2023

Ultrafast relaxation of lattice distortion in two-dimensional perovskites

Direct visualization of ultrafast coupling between charge carriers and lattice degrees of freedom in photoexcited semiconductors has remained a long-standing challenge and is critical for understanding the light-induced physical behaviour of materials under extreme non-equilibrium conditions. Here we obtain a direct visualization of the structural dynamics in monocrystalline 2D perovskites. We achieve this by monitoring the evolution of wavevector-resolved ultrafast electron diffraction intensity following above-bandgap high-density photoexcitation. Our analysis reveals a light-induced ultrafast reduction in antiferro-distortion resulting from a strong interaction between the electron–hole plasma and perovskite lattice, which induces an in-plane octahedra rotation towards a more symmetric phase. Correlated ultrafast spectroscopy performed at the same carrier density as ultrafast electron diffraction reveals that the creation of a dense electron–hole plasma triggers the relaxation of lattice distortion at shorter timescales by modulating the crystal cohesive energy. Then we show that the interaction between carrier gas and lattice can be altered by tailoring the rigidity of the 2D perovskite by choosing an appropriate organic spacer layer.

36 MATERIALS SCIENCE↗
DOE OSTI2025

GenomeDepot v1.0

GenomeDepot is a web-based platform for annotation, management, and comparative analysis of microbial genomic sequences and associated data including ortholog families, protein domains, operons, regulatory interactions, strain taxonomy, and sample metadata. GenomeDepot supports rapid creation of web-sites for user-defined genome collections that include bioinformatic tools for interactive genome browsing, BLAST search, annotation search, comparative genomic neighborhood visualization, and sequence download. Gene function annotations are generated by a customizable annotation pipeline. The pipeline runs annotation tools in Conda environments and can be easily extended with additional user-specified tools.

Kazakov, Alexey [Lawrence Berkeley National Labora↗
DOE OSTI2024

Foldy: An open-source web application for interactive protein structure analysis

Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. In our manuscript, we detail Foldy’s interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldy’s interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training) v1

SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training) is a comprehensive data visualization and analysis application focused on working with COLTRIMS (COLd Target Recoil Ion Momentum Spectroscopy) data, which is used in atomic and molecular physics experiments. The application offers several powerful features: - Data uploading and processing capabilities for COLTRIMS files - Multiple visualization methods using UMAP (Uniform Manifold Approximation and Projection) for dimensionality reduction - Interactive selection of data points across multiple views - Feature engineering through various methods: - Manual feature selection from calculated physics parameters - Deep autoencoder for dimension reduction - Genetic programming for discovering meaningful features - Mutual information-based feature selection - Multiple clustering approaches (DBSCAN, KMeans, Agglomerative) - Quality metrics for evaluating clustering results - Export capabilities for selections and generated features

Daoud, Hazem [Lawrence Berkeley National Laborator↗
DOE OSTI2018

Sensing and Sampling of Trace Contaminations by a Dexterous Hexrotor UAV at Nuclear Facilities - 18600

Safe and efficient cleanups are a priority for decontamination and decommissioning of nuclear facilities. The contaminations of byproducts and wastes in such nuclear facilities pose a serious problems while decommissioning, to both the operators and public. Sensing these contaminations and cleaning them is an essential part of the safety procedures. The contaminations might range from low-energy to high-energy byproducts. Sensing these byproducts would require contact based or non-contact based sensing, based on its energy levels. Byproducts such as americium, are low-energy which makes it difficult to sense and sample without contact. Therefore, the workers need to physically swab the surfaces to collect the samples of these contaminations. These tasks pose a great danger for the safety of the human workers which includes the risk to reach greater heights, exposure to the radiation and sampling the entire building. A robotic solution is much more desirable in these cases, where the worker can safely handle the contaminations from a safe area. Use of robotic technologies inside these hazardous environments ensures the safety and enhances the performance. The more suitable robotic solution for reaching great heights and covering large areas of nuclear facilities, are aerial vehicles. Multi-rotor unmanned aerial vehicle (UAV) are chosen for their ability to hover at a position and also fly around in confined spaces. Quadrotors are a common choice of multi-rotor UAVs both research and industry because of its ease to fly and commercial availability. For the applications during the cleanups we need the UAV to not only hover and fly in confined spaces, but also interact with the physical world. Despite of quadrotor's advantages, they are not a best suit as they can't interact with the physical world. The tasks involving physical interactions would require the UAV to apply arbitrary forces and torques in all six degrees of freedom (DOF). As quadrotors are under-actuated and non-holonomic in motion, they cannot exert all the required forces. At Collaborative Robotics Lab, Purdue University, we developed a novel multi-rotor UAV solution, Dexterous Hexrotor, which can exert arbitrary forces and torques in all six DOF, independently and instantaneously. This enables the UAV platform to quickly respond to the external disturbances and precisely hold its position during the mission. The Dexterous Hexrotor serves as a robotic tool for the workers to perform sensing and sampling during the cleanups. Workers can operate Dexterous Hexrotor manually and/or semi-autonomously with human intervention. Manual control requires the worker to fly the Dexterous Hexrotor using a remote control. So the workers should possess knowledge on flying the UAV and to perform physical interaction. A semiautonomous Dexterous Hexrotor requires human input to some extent and requires very less knowledge of flying. The autonomy is designed to operate in flight mode and sampling mode. In flight mode, Dexterous Hexrotor takes off from a 'safe' area, reaches the desired altitude, and fly autonomously towards a sampling point. This sampling point is the target position specified on the 2D reference map. This mode also includes navigation towards the sampling point, avoiding the obstacles in the constrained environment. After reaching a stopping point, Dexterous Hexrotor waits for the human input for a target location to swab and collect samples. The Dexterous Hexrotor is now in sampling mode and waits for the human input. The human operator selects the swabbing location through the live streaming video from onboard camera. Through visual servoing, aided by the map, the UAV will move closer towards the target position. It then transitions to the impact control scheme to perform physical interaction with the wall and collects samples. Once the sample is collected, it autonomously returns back to the takeoff position in 'safe' area for the analysis by mass spectrometry. In this paper, we majorly focus on the physical interaction strategy and the required components which enables the autonomy. Decommissioning of nuclear facilities requires decontamination inside the entire buildings. Decontamination of nuclear facilities involves sensing and sampling the huge buildings, long shafts, etc. Sampling the entire facility is not possible due to the limitation in Dexterous Hexrotor's flight time. But this can be achieved by using a swarm of Dexterous Hexrotors to cover larger areas. The onboard robotic arm can be designed based on the applications such as cleaning, applying sealants, etc. The modularity of the design allows us to switch components as per the requirements of the task. These kind of robotic solutions functioning as tools for the workers, aims to make the work environment safer and playful. (authors)

42 ENGINEERING↗
DOE OSTI2018

Investigating the effect of supramolecular gel phase crystallization on gel nucleation

Supramolecular gel phase crystallization offers a new strategy for drug polymorph screening and discovery. In this method, the crystallization outcome depends on the interaction between solute and gel fibre. While supramolecular gels have shown success in producing new polymorphs and crystals with novel morphologies, role of the gel and nature of gel–solute interaction remains largely unexplored. The present study aims to provide a comprehensive picture of the structural evolution of a supramolecular gel produced from a bis(urea) based gelator (G) in the presence of a polymorphic drug carbamazepine (CBZ). The structural aspects of the gel have been assessed by single crystal X-ray analysis, X-ray powder diffraction (XRPD) and solid state NMR spectroscopy. Small Angle Neutron Scattering (SANS) has been used to follow the changes in gel structure in the presence of CBZ. Visual evidence from morphological study and structural evolution observed at a macroscopic level from rheological measurements, shows good agreement with the SANS results. The concentration of the gelator and the relative proportion of G to CBZ were found to be crucial factors in determining the competitive nucleation events involving gelation and crystallization. At a critical G to CBZ ratio the effect of CBZ on gel structure was maximum and fiber bundling in the gel was found to be critically affected. Here, this study offers important information about how the interplay of gelator assembly and gel–solute interactions can fine-tune the nucleation events in a supramolecular gel phase crystallization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Multidisciplinary concurrent optimization framework for multi-phase building design process

Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. Here this paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

GenomeDepot: data management system for microbial comparative genomics

Summary GenomeDepot is an open-source web-based platform for annotation, management, and comparative analysis of microbial genomic sequences and associated data including ortholog families, protein domains, operons, regulatory interactions, strain taxonomy, and sample metadata. GenomeDepot supports rapid creation of websites for user-defined genome collections that include bioinformatic tools for interactive genome browsing, Basic Local Alignment Search Tool (BLAST) search, annotation search, comparative genomic neighborhood visualization, and sequence download. Gene function annotations are generated by a customizable annotation pipeline. The pipeline runs annotation tools in Conda environments and can be easily extended with additional user-specified tools. Availability and implementation GenomeDepot is open source and distributed under the GNU General Public License via GitHub (https://github.com/aekazakov/genome-depot). GenomeDepot is implemented in Python and was tested in Ubuntu Linux. Full installation instructions and documentation are available at https://aekazakov.github.io/genome-depot/. GenomeDepot demo server is freely accessible at https://iseq.lbl.gov/demogd/.

Kazakov, Alexey [Lawrence Berkeley National Labora↗
DOE OSTI2019

Enabling immersive engagement in energy system models with deep learning

Complex ensembles of energy simulation models have become crucial components of renewable energy research in recent years. Often the increasing computational cost, high-dimensional structure, and other complexities hinder researchers from fully utilizing these data sources for knowledge building. Researchers at National Renewable Energy Laboratory have determined an immersive visualization workflow to dramatically improve user engagement and analysis capability through a combination of low-dimensional structure analysis, deep learning, and custom visualization methods. We present case studies for two energy simulation platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Standard operating procedure for post-operation component disassembly and observation of benchtop water electrolyzer testing

Post-operation component disassembly and observation of electrolyzer parts is useful in understanding the interactions of the components and the electrochemical environment beyond the systems electrochemical output. We report a standard protocol for post-operation component disassembly and observation, including directions for cell-component preservation, preliminary visual inspection of cell components, and a guide for the advanced inspection of specific components with suggestions for further analysis if necessary. The procedures outlined here allow for a standardized method that can be used and compared between different laboratories and for literature comparison to experimental results.

03 NATURAL GAS↗