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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications

The Modular Autonomous Research System (MARS) was created to address a key challenge in scientific discovery: experiments are often slow, require significant manual labor, and generate data that is not easily integrated across different tools. This limits how quickly scientists can explore new materials, processes, and chemical reactions. Our motivation was to design a system that makes research faster, more reliable, and adaptable by combining automation with artificial intelligence. By doing so, we aimed to reduce human error, accelerate discovery, and allow researchers to quickly test many possibilities that would otherwise take months or years. Our approach was to build a flexible platform that connects laboratory robots, measurement instruments, and a central data system, all guided by artificial intelligence. MARS integrates liquid handling robots, robotic arms, and plate readers with an intelligent decision-making system that chooses the most informative experiments to run next. This creates a closed loop where experiments are performed automatically, the data is analyzed in real time, and new conditions are immediately tested. Through this work, we demonstrated that MARS can carry out multiple experiments with little or no human intervention, adapt to different scientific problems, and handle uncertain or noisy measurements in a robust way. The results show that modular and intelligent automation can significantly accelerate the pace of discovery, providing a model for future self-driving laboratories. This approach addresses the growing scientific need for adaptable, data-driven research platforms that can keep up with the complexity and scale of modern science.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1985

Cockpit automation - In need of a philosophy

Concern has been expressed over the rapid development and deployment of automatic devices in transport aircraft, due mainly to the human interface and particularly the role of automation in inducing human error. The paper discusses the need for coherent philosophies of automation, and proposes several approaches: (1) flight management by exception, which states that as long as a crew stays within the bounds of regulations, air traffic control and flight safety, it may fly as it sees fit; (2) exceptions by forecasting, where the use of forecasting models would predict boundary penetration, rather than waiting for it to happen; (3) goal-sharing, where a computer is informed of overall goals, and subsequently has the capability of checking inputs and aircraft position for consistency with the overall goal or intentions; and (4) artificial intelligence and expert systems, where intelligent machines could mimic human reason.

Wiener, E. L.↗
DOE OSTI2022

Procedure Parsing: A Method for Parsing Handwritten Documents into Computer-Based Procedures

The nuclear industry is heavily procedure driven, where almost everything has a step-by-step instruction that is expected to be followed in detail. Historically, these procedures were printed on paper copies. Recently, the industry transitioned towards electronic copies (i.e., PDFs on tablets). One major drive for this transition is the introduction of human error and loss of situation awareness when using paper copies. However, electronic copies of documents inherently have the same error traps as their paper cousins. Therefore, there is an increased interest in a way to utilize the information in the step-by-step guidance, but to present it in a dynamic manner that guides the user and adapts to any encountered conditions. Researchers at Idaho National Laboratory propose a flexible, automated method based on document parsing and augmented by natural language processing (NLP) techniques, to address these shortcomings and capitalize on these recent advancements in machine learning. The proposed method provides a cost-effective solution for computer-assisted procedure parsing of hand-written control room procedures, originally authored in Word or PDF formats, into instructions that can be displayed as computer-based procedures (CBP) in a modern graphical user interface. The researchers devised, implemented and demonstrated the Operating Procedure Extender for Novel Systems (OPENS) method in 2020. The key to OPENS is to map the original procedure text into a context-free grammar, tying content to equipment, locations, and other steps, actions, etc. This formal grammar is then used to isolate and define keywords and actions verbs, such as “measure” or “evaluate” and tie them to specific equipment referenced within that step or located in other steps, substeps, actions, subactions and tables throughout the procedure. OPENS generates an abstract syntax tree from the document which it uses to store a copy of this information in the open-standard, machine-readable and human-readable file formats XML and JSON. The XML is useful to preserve the relational aspects of the procedure for referencing tables and branching information so the user can be directed to the next appropriate active step based on the values entered for that step and previous steps. The JSON is useful for storing and exchanging data objects used to track responses to previous steps and state changes in simulated environments. In future iterations, these formats can also be used for storing more detailed information about input during plant operation or simulation. The techniques the researcher developed could further be improved by integration of recent advancements in machine learning. NLP methods could standardize documents, correct for grammatical error, and provide automated semantic validation. The researcher expects that self-supervised techniques applied to collections of natural language instructions could strengthen the model with broader context. All these methods together give us a practical way to automatically extract protocols from documents and user interactions, empowering researchers, procedure writers and nuclear operators while moving the industry forward.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2016

Investigating the Simulink Auto-Coding Process

Model based program design is the most clear and direct way to develop algorithms and programs for interfacing with hardware. While coding "by hand" results in a more tailored product, the ever-growing size and complexity of modern-day applications can cause the project work load to quickly become unreasonable for one programmer. This has generally been addressed by splitting the product into separate modules to allow multiple developers to work in parallel on the same project, however this introduces new potentials for errors in the process. The fluidity, reliability and robustness of the code relies on the abilities of the programmers to communicate their methods to one another; furthermore, multiple programmers invites multiple potentially differing coding styles into the same product, which can cause a loss of readability or even module incompatibility. Fortunately, Mathworks has implemented an auto-coding feature that allows programmers to design their algorithms through the use of models and diagrams in the graphical programming environment Simulink, allowing the designer to visually determine what the hardware is to do. From here, the auto-coding feature handles converting the project into another programming language. This type of approach allows the designer to clearly see how the software will be directing the hardware without the need to try and interpret large amounts of code. In addition, it speeds up the programming process, minimizing the amount of man-hours spent on a single project, thus reducing the chance of human error as well as project turnover time. One such project that has benefited from the auto-coding procedure is Ramses, a portion of the GNC flight software on-board Orion that has been implemented primarily in Simulink. Currently, however, auto-coding Ramses into C++ requires 5 hours of code generation time. This causes issues if the tool ever needs to be debugged, as this code generation will need to occur with each edit to any part of the program; additionally, this is lost time that could be spent testing and analyzing the code. This is one of the more prominent issues with the auto-coding process, and while much information is available with regard to optimizing Simulink designs to produce efficient and reliable C++ code, not much research has been made public on how to reduce the code generation time. It is of interest to develop some insight as to what causes code generation times to be so significant, and determine if there are architecture guidelines or a desirable auto-coding configuration set to assist in streamlining this step of the design process for particular applications. To address the issue at hand, the Simulink coder was studied at a foundational level. For each different component type made available by the software, the features, auto-code generation time, and the format of the generated code were analyzed and documented. Tools were developed and documented to expedite these studies, particularly in the area of automating sequential builds to ensure accurate data was obtained. Next, the Ramses model was examined in an attempt to determine the composition and the types of technologies used in the model. This enabled the development of a model that uses similar technologies, but takes a fraction of the time to auto-code to reduce the turnaround time for experimentation. Lastly, the model was used to run a wide array of experiments and collect data to obtain knowledge about where to search for bottlenecks in the Ramses model. The resulting contributions of the overall effort consist of an experimental model for further investigation into the subject, as well as several automation tools to assist in analyzing the model, and a reference document offering insight to the auto-coding process, including documentation of the tools used in the model analysis, data illustrating some potential problem areas in the auto-coding process, and recommendations on areas or practices in the current Ramses model that should be further investigated. Several skills were required to be built up over the course of the internship project. First and foremost, my Simulink skills have improved drastically, as much of my experience had been modeling electronic circuits as opposed to software models. Furthermore, I am now comfortable working with the Simulink Auto-coder, a tool I had never used until this summer; this tool also tested my critical thinking and C++ knowledge as I had to interpret the C++ code it was generating and attempt to understand how the Simulink model affected the generated code. I had come into the internship with a solid understanding of Matlab code, but had done very little in using it to automate tasks, particularly Simulink tasks; along the same lines, I had rarely used shell script to automate and interface with programs, which I gained a fair amount of experience with this summer, including how to use regular expression. Lastly, soft-skills are an area everyone can continuously improve on; having never worked with NASA engineers, which to me seem to be a completely different breed than what I am used to (commercial electronic engineers), I learned to utilize the wealth of knowledge present at JSC. I wish I had come into the internship knowing exactly how helpful everyone in my branch would be, as I would have picked up on this sooner. I hope that having gained such a strong foundation in Simulink over this summer will open the opportunity to return to work on this project, or potentially other opportunities within the division. The idea of leaving a project I devoted ten weeks to is a hard one to cope with, so having the chance to pick up where I left off sounds appealing; alternatively, I am interested to see if there are any opening in the future that would allow me to work on a project that is more in-line with my research in estimation algorithms. Regardless, this summer has been a milestone in my professional career, and I hope this has started a long-term relationship between JSC and myself. I really enjoy the thought of building on my experience here over future summers while I work to complete my PhD at Missouri University of Science and Technology.

Gualdoni, Matthew J.↗
DOE OSTI2023

A Comparison of Three Types of Computer-Based Procedures: An Experiment Using the Rancor Microworld Simulator

The nuclear power industry has historically used paper-based procedures, but a shift towards computer-based procedures (CBPs) has the potential to reduce human errors, alleviate mental workload, and improve work performance. Twenty-seven participants were randomly assigned to one of three CBP types using the Rancor Microworld Simulator, and each performed two different types of operational scenarios (startup and loss of feedwater). The three CBP types varied in levels of digitalization. It was hypothesized that there would be less favorable impressions of the most basic procedures, and that these would demonstrate lower usability than types with greater digitalization. Overall, our predictions were partially supported with some interesting caveats, specifically with some performance benefits for CBPs that provided indicators but not embedded controls. We discuss our findings in terms of optimal levels of digitalization/automation within nuclear operations and suggest pathways for future directions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2013

MPST Software: grl_pef_check

This innovation is a tool used to verify and validate spacecraft sequences at the predicted events file (PEF) level for the GRAIL (Gravity Recovery and Interior Laboratory, see http://www.nasa. gov/mission_pages/grail/main/index. html) mission as part of the Multi-Mission Planning and Sequencing Team (MPST) operations process to reduce the possibility for errors. This tool is used to catch any sequence related errors or issues immediately after the seqgen modeling to streamline downstream processes. This script verifies and validates the seqgen modeling for the GRAIL MPST process. A PEF is provided as input, and dozens of checks are performed on it to verify and validate the command products including command content, command ordering, flight-rule violations, modeling boundary consistency, resource limits, and ground commanding consistency. By performing as many checks as early in the process as possible, grl_pef_check streamlines the MPST task of generating GRAIL command and modeled products on an aggressive schedule. By enumerating each check being performed, and clearly stating the criteria and assumptions made at each step, grl_pef_check can be used as a manual checklist as well as an automated tool. This helper script was written with a focus on enabling the user with the information they need in order to evaluate a sequence quickly and efficiently, while still keeping them informed and active in the overall sequencing process. grl_pef_check verifies and validates the modeling and sequence content prior to investing any more effort into the build. There are dozens of various items in the modeling run that need to be checked, which is a time-consuming and errorprone task. Currently, no software exists that provides this functionality. Compared to a manual process, this script reduces human error and saves considerable man-hours by automating and streamlining the mission planning and sequencing task for the GRAIL mission.

Call, Jared A.↗
NASA NTRSDate not supplied

Supervisory Control with Dual Tasking in Post G-Transition Vehicle Landings

BACKGROUND Landing during exploration spaceflight may consist of both planned automated supervisory control and unplanned crew override. Supervisory control, particularly when performed under cognitive load with additional monitoring tasks, is essential for ensuring overall mission success during landing contingencies. Evaluating performance in a relevant Human Landing System (HLS) supervisory landing task after long-duration microgravity exposure can help identify potential risks from human error and sensorimotor alterations. Adaptive changes in the sensorimotor system can manifest during g-transitions as spatial disorientation. Although training and landing aids facilitate successful landings despite disorientation, these adaptive changes may heighten cognitive demand, which must be considered in the landing strategy. It is important to characterize these effects as soon as possible following the G-transition while the sensorimotor system remains in a state of adaptive flux to inform appropriate countermeasures. METHODS A Multi-attribute Lunar Table Battery (MALTB) task was developed for an iOS tablet device to provide flexible crew testing and training capabilities in-flight and on the ground. Elements of the tablet task were derived from the Multi-Attribute Task Battery (MATB, Cegarra et al. 2020). The task requires crew members to study a map of a planned landing site and memorizing the terrain and surface landmarks to inform potential divert maneuvers during landing. The user will oversee a series of approaches through touchdown simulations on the tablet with an external camera view of the Lunar surface. The primary responsibility of the crew member will be to execute a divert if the guidance recommended site is erroneous (e.g., the guidance projected landing target is not within 10m of the planned landing site center), or the projected landing site is no longer suitable due to surface obstacles. Considering vehicle maneuverability and fuel reserves, the divert capabilities will diminish as the task progresses. In cases where a divert is initiated, a new landing target will need to be designated by the user and will be evaluated for the proximity to the original pre-planned site. A secondary operational monitoring task will challenge the user's cognitive reserve by requiring the user to maintain several gauges within acceptable limits and respond to a visual indicator while completing the landing approach. Outcome measures include distance from the planned landing site to the user-initiate divert landing location, ground slope at the new landing site, time to divert, the ability to accomplish the secondary monitoring tasks, and perceived workload. The tablet task is being evaluated in a ground-based study to determine the learning effect of first-time users. The tablet task will be utilized in a flight study to test performance multiple times postflight. Future potential testing in-flight have been identified for capsules with iOS tablet devices. Multi-attribute Lunar Table Battery Task MALTB comprises video footage of thirty distinct landing conditions. The landing scenarios feature various landing sites (n = 3), hazardous object sizes (n = 6), sun azimuth degrees (n = 4), camera modes (i.e., fixed or gimbaled), and navigation bias (i.e., true or false). Each seventy second trial uses a sixty-degree constant glideslope trajectory. The application architecture and layout include user identification setup and data storing, a guided walkthrough of the task and interface components, practice mode for task familiarization, and a modified Bedford workload scale administered following task completion. The time-based dependent measures are saved locally to the iOS Files application and post-processing scripts have been developed to evaluate the remaining measures of performance. RELEVANCE This project will deliver an operational demonstration of crew monitoring capability following spaceflight and identify potential deficits that may require remediation. Comparison of individual vestibular and cognitive changes with crew performance will help better characterize the landing risks associated with sensorimotor alterations. ACKNOWLEDGEMENTS: This project is funded by NASA’s Human Research Program Human Health Countermeasures Element. REFERENCES Cegarra J, Valery B, Avril E, Calmettes C, Navarro J (2020) OpenMATB: A Multi-Attribute Task Battery promoting task customization, software extensibility and experiment replicability. Behav Res Methods 52:1980-1990 doi: 10.3758/s13428-020-01364-w

Matthew McDonnell↗
DOE OSTI2023

Three-dimensional phenotyping of peach tree-crown architecture utilizing terrestrial laser scanning

Tree training systems for temperate fruit have been developed throughout history by pomologists to improve light interception, fruit yield, and fruit quality. These training systems direct crown and branch growth to specific configurations. Quantifying crown architecture could aid the selection of trees that require less pruning or that naturally excel in specific growing/training system conditions. Regarding peaches [Prunus persica (L.) Batsch], access tools such as branching indices have been developed to characterize tree-crown architecture. However, the required branching data (BD) to develop these indices are difficult to collect. Traditionally, BD have been collected manually, but this process is tedious, time-consuming, and prone to human error. These barriers can be circumnavigated by utilizing terrestrial laser scanning (TLS) to obtain a digital twin of the real tree. TLS generates three-dimensional (3D) point clouds of the tree crown, wherein every point contains 3D coordinates (x, y, z). To facilitate the use of these tools for peach, we selected 16 young peach trees scanned in 2021 and 2022. These 16 trees were then modeled and quantified using the open-source software TreeQSM. As a result, “in silico” branching and biometric data for the young peach trees were calculated to demonstrate the capabilities of TLS phenotyping of peach tree-crown architecture. The comparison and analysis of field measurements (in situ) and in silico BD, biometric data, and quantitative structural model branch uncertainty data were utilized to determine the reconstructive model’s reliability as a source substitute for field measurements. Mean average deviation when comparing young tree (YT) height was approx. 5.93%, with crown volume was approx. 13.26% across both 2021 and 2022. All point clouds of the YTs in 2022 showed residuals lower than 12 mm to cylinders fitted to all branches, and mean surface coverage greater than 40% for both the trunk and primary branching orders.

09 BIOMASS FUELS↗
DOE OSTI2025

Automated Fire Detection for Industrial Settings with Pretrained Convolutional Networks

Early fire detection in industrial environments is critical to preventing equipment damage, personal injury, and operational disruptions. Traditional smoke detectors, while effective, often experience delays due to the time required for smoke to reach sensors, allowing fires to spread. Manual fire watch operations and human surveillance of camera feeds are resource-intensive and prone to human error. To address these challenges, this paper explores the application of convolutional neural networks for automated fire detection, specifically in industrial settings. By leveraging 11 different pre-trained machine vision models from TensorFlow and enhancing them with transfer learning on a custom-built industrial fire dataset, we optimized fire detection performance. Here, we analyzed each machine vision model architecture in terms of its depth, width, and input image resolution, considering both resource requirements and detection accuracy. We further explored the option of combining multiple models into an ensemble classifier to evaluate whether the performance improvements could justify the much greater computational complexity and other practical impacts. A cost-benefit analysis is presented to evaluate the trade-offs between performance and computational expense. Our findings identify that EfficientNetV2L, specifically tailored for industrial applications, provides the optimal balance between costs involved in training and using the model versus the overall fire detection performance. Additionally, we present a qualitative analysis of model performance using the technique of gradient-based class activation mapping to provide explainability by visualizing model decisions.

artificial intelligence↗
DOE OSTI2023

A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films

Abstract Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Uncertainty quantification of a physics-informed model based on sparse identification of a Thermal Energy Distribution System

Integrated energy systems (IES)s are crucial for enhancing the economy and efficiency of power generation sources (e.g., nuclear energy) necessary to unleash American energy dominance. These systems can be integrated with thermal energy storage (TES) and intermittent renewable energies to optimize overall energy use, peak-load regulation, and demand-side responses. However, the stabilization of energy generation, transport, and utilization introduces operational complexities that exceed the challenges of managing each sub-component individually. Currently, though IESs rely on human operators for efficiency and stability, reducing human error risk and enhancing performance through automation is highly desirable. Recent advances at Idaho National Laboratory have demonstrated successful control of the Thermal Energy Distributed System (TEDS). However, the automatic control system depends on a deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC) model, which are trained based on simulation data from physics-based simulations. Because of uncertainties in physics-based simulation, SINDyC model results in large discrepancies against experimental data and cannot be reliably used in automatic control. In this paper, we present an innovative approach to address these discrepancies by quantifying uncertainties and developing a more robust model. We first generated trajectories by using first-principles physics codes to encapsulate the experiment. Next, we trained thousands of models by randomly sampling these trajectories. We then collapsed all those models into one probabilistic SINDyC by fitting a multivariate Gaussian distribution onto the resulting coefficient’s distribution. Despite its simplicity, our approach successfully produced 95% confidence intervals that captured the experimental trajectories. It even did so with a higher probability and better U-pooling score across six of the seven relevant quantities of interest (QoIs), as compared to other classical approaches. In conclusion, ongoing research is focusing on generating new experimental trajectories to validate this approach, and on employing Bayesian calibration to refine parametric uncertainties and guide future model development efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

What, why and when to go virtual: An international analysis of early adopters of virtual building energy codes inspections

To meet greenhouse gas reduction targets, several countries are pursuing more ambitious policies in their buildings and construction sectors, such as introducing zero net energy/carbon building codes. Countries often report not having enough qualified staff for performing building energy code inspections and many are exploring faster, easier, and more reliable methods to check the compliance of buildings with their codes. Building inspections are a critical element for ensuring code compliance and they have traditionally been performed in person. However, in-person inspections can be labor and travel intensive, costly, and prone to human error. In this paper, the authors explore how virtual inspections, particularly in light of the recent COVID-19 pandemic, have impacted processes for building code compliance checks in jurisdictions and communities around the world. Here, the authors collected data on four key parameters (time and financial savings, scope of inspections, changing practices and technological innovation, and benefits to consumers) from six jurisdictions and communities in five countries (Australia, Canada, Singapore, United Arab Emirates, and the United States) to analyze the impacts of virtual inspections on code compliance checks. The analysis found the greatest value from virtual inspections in geographically dispersed regions and for cities experiencing rapid building construction. The study also explored emerging technologies that are being piloted for virtual inspections. Although many of these technologies hold promise, more resources and capacity are needed to make them viable for use in building energy code inspections.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Limitations of traditional tools for beyond design basis external hazard PRA

Probabilistic risk assessment (PRA) is being used increasingly by the nuclear industry for safety during normal operations as well as for the protection against external hazards. Computation of total risk in an external hazard PRA is dependent on hazard assessment, fragility assessment, and systems analysis. A systems analysis for propagation of component fragilities is conducted using event and fault trees. The event and fault trees for an actual power plant can be fairly large in size, which imposes computational challenges. Hence, certain assumptions are employed for computational efficiency. These assumptions typically represent the conditions imposed during the design basis (DB) scenario. The traditional PRA tools based on these assumptions are also widely applied to perform risk assessment in the context of beyond design basis (BDB) scenarios. However, some of these assumptions may not be valid for certain BDB scenarios. In addition, the probability of dependent failures also increases in BDB scenarios due to common cause failures (CCF) which usually results from design modifications, human errors, etc. In this manuscript, a simple and a relatively more complex illustrative examples are used to show the limitation of these assumptions in the numerical quantification of risk for the case of BDB conditions. Case studies with CCF events across multiple fault trees are also presented to illustrate the effect of these assumptions when traditional approach is used in BDB risk assessment. It is shown that the assumptions are valid for the case of DB conditions but may lead to excessively conservative risk estimates in the case of BDB conditions. Finally, a Bayesian network based top-down algorithm is proposed as an alternative tool for accurate numerical quantification of total risk in systems analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Streaming Large-Scale Microscopy Data to a Supercomputing Facility

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s -1 (87,000 frames s -1 ⁠), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera’s data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

4D-STEM↗
DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2d materials↗
DOE OSTI2023

Scaling Automatic Vector Data Alignment to Satellite Imagery

Given the tremendous volume of accessible Earth Observation (EO) data, there is a need to develop scalable Geospatial Artificial Intelligence (GeoAI) solutions for time-sensitive applications. Scalability in this context refers to rapidly processing large-scale EO data using high performance computing resources. Accurate mapping of the built environment from remote sensing (RS) imagery has been one of the crucial components in GeoAI workflows for a wide spectrum of humanitarian applications. Derived vector data of built environment is often leveraged for disaster preparedness and response activities. However, factors such as differences in ortho-rectification, atmospheric conditions and human error, results in spatial misalignment between vector data and the timely available RS imagery. Model training for downstream tasks such as object detection, change analysis, etc., is negatively impacted due to such spatial misalignment. Although there has been progress towards automatic alignment of vector data, the lack of scalability remains an open research challenge. This paper proposes to leverage parallel computing to optimize an automatic vector data alignment workflow. It further employs CPU-level multi-core parallelism for improving the performance of the workflow for scalable built environment mapping. We report observations and discuss findings from the preliminary experiments performed on the Summit Supercomputer.

Potnis, Abhishek↗
DOE OSTI2023

Smart Data Mapping for Connecting Power System Model and Geospatial Data

Knowing the geospatial locations of power system model elements is the foundation for analyzing system vulnerability to natural hazards and connecting loads with end users and their communities. However, power system models and geospatial data for power grid assets may have been developed asynchronously without close coordination. Creating a direct mapping between the two may be a challenging task, considering heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect power system model elements and geospatial data for transmission network, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.

Resilience, geospatial data, grid transmission net↗