Search NASA⌕ Search

SEARCH · Search NASA

Results for “Process Automation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Technical, Economic, Risk, and Adoption Assessment for Evaluating Work Reduction Opportunities in the Nuclear Industry

Automation and cost-saving initiatives, such as process automation with advances in artificial intelligence, are gaining traction in modernization efforts across the nuclear industry. As these innovations are increasingly adopted, it becomes crucial to evaluate their impacts comprehensively. Various technical and economic attributes, along with risk and human readiness factors, must be achieved to ensure that innovative projects enabling automation and modernization are successful. However, no systematic or integrated framework exists that allows plants to evaluate these innovative projects. To address this gap, the Technical, Economic, Risk, and Adoption (TERA) assessment offers a structured method to evaluate innovative technologies, ensuring solutions meet both operational and safety standards. The TERA framework integrates the disparate perspectives to assess modernization opportunities in nuclear operations. It combines qualitative and quantitative models to evaluate the relationship between performance and business impacts, while also enabling continuous re-evaluation during project development. This approach helps plant owners identify high-priority opportunities, optimize cost savings, and minimize risks, ensuring projects remain on track to achieve desired returns. By providing a comprehensive, systematic methodology, TERA enables informed, data-driven decisions that support successful modernization efforts, enhancing efficiency, safety, and cost savings across nuclear operations. This paper explains the TERA framework and its benefits for the nuclear industry.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Informing forest carbon inventories under the Paris Agreement using ground-based forest monitoring data

Human interactions with forests have shaped Earth's climate for millennia and will continue to do so as we target net-zero emission goals. Accurately characterizing these climate impacts requires making reliable forest carbon data available for forest monitoring and planning. Here, we develop a semi-automated process for submitting forest carbon measurements from the largest relevant scientific database to the International Panel on Climate Change's Emission Factor Database, which currently has sparse forest carbon data. Building this bridge from scientific research to international policy is an important step towards managing forests in a net-zero motivated future. Humans have been influencing Earth's climate via transformative impacts on forests for millennia, and forests are now recognized as critical to climate change mitigation under the Paris Agreement. The efficacy of climate change mitigation planning and reporting depends on quality data on forest carbon (C) stocks and changes. The Emission Factor Database (EFDB) of the International Panel on Climate Change (IPCC) is intended to be a definitive source for such data, but needs comprehensive and well-documented data to be so. To facilitate submission of forest C estimates from scientific studies to EFDB, we develop and document a process for semi-automated data submission from the Global Forest C database (ForC v4.0), which is the largest compilation of ground-based forest C estimates. We then assess the data currently available through ForC and provide recommendations for improving forest data collection, analysis, and reporting. As of September 2024, ForC contained ~19,286 records potentially relevant to EFDB, 1068 of which had been submitted and posted to EFDB. These represented 19% of the total EFDB records for forest land. Records were unevenly distributed across variables and geographic regions. ForC records (37%) reviewed could not be submitted because the original publication lacked required information. In the future, ground-based forest C estimates should target gaps in the record, and studies should ensure that they report all information necessary for inclusion in EFDB. Given that climate change is rapidly impacting the world's forests, timely reporting of recent estimates will be critical to accurate forest C inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

An automated design process for short pulse laser driven opacity experiments

Stellar-relevant conditions can be reached by heating a buried layer target with a short pulse laser. Previous design studies of iron buried layer targets found that plasma conditions are dominantly controlled by the laser energy while the accuracy of the inferred opacity is limited by tamper emission and optical depth effects. In this paper, we developed a process to simultaneously optimize laser and target parameters to meet a variety of design goals. We explored two sets of design cases: a set focused on conditions relevant to the upper radiative zone of the sun (electron temperatures of 200 to 400 eV and densities greater than 1/10 of solid density) and a set focused on reaching temperatures consistent with deep within the radiative zone of the sun (500 to 1000 eV) at a fixed density. We found optimized designs for iron targets and determined that the appropriate dopant, for inferring plasma conditions, depends on the goal temperature: magnesium for up to 300 eV, aluminum for 300 to 500 eV, and sulfur for 500 to 1000 eV. The optimal laser energy and buried layer thickness increase with goal temperature. The accuracy of the inferred opacity is limited to between 11% and 31%, depending on the design. Finally, overall, short pulse laser heated iron experiments reaching stellar-relevant conditions have been designed with consideration of minimizing tamper emission and optical depth effects while meeting plasma condition and x-ray emission goals.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Automated pipeline processing X-ray diffraction data from dynamic compression experiments on the Extreme Conditions Beamline of PETRA III

Presented and discussed here is the implementation of a software solution that provides prompt X-ray diffraction data analysis during fast dynamic compression experiments conducted within the dynamic diamond anvil cell technique. It includes efficient data collection, streaming of data and metadata to a high-performance cluster (HPC), fast azimuthal data integration on the cluster, and tools for controlling the data processing steps and visualizing the data using the DIOPTAS software package. This data processing pipeline is invaluable for a great number of studies. The potential of the pipeline is illustrated with two examples of data collected on ammonia–water mixtures and multiphase mineral assemblies under high pressure. The pipeline is designed to be generic in nature and could be readily adapted to provide rapid feedback for many other X-ray diffraction techniques, e.g. large-volume press studies, in situ stress/strain studies, phase transformation studies, chemical reactions studied with high-resolution diffraction etc.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

EXPLAINABLE AND TRUSTWORTHY DIAGNOSTICS ACHIEVABLE THROUGH PROCESS-BASED AUTOMATED REASONING

An approach has been developed that incorporates domain knowledge to obtain a more explainable and trustworthy equipment health monitoring diagnosis than might otherwise be obtained from a purely data-driven method. Physics-based models serve to constrain the realizable solution space and render a more trusted diagnosis. An automated reasoning algorithm performs backward chaining to infer a diagnosis that is consistent with logic statements that have been evaluated as true. This diagnosis is made explainable to an operator by providing the forward chaining path that elucidates for inspection and validity testing those truths implied by the diagnosis.

automated reasoning↗
DOE OSTI2017

Triton: Igiugig Fish Video Analysis (Project Report)

This document summarizes the analysis of video data collected around the Ocean Renewable Power Company’s RivGen® device in the Kvichak River during July and August 2015. Human (manual) analysis of the data was undertaken and used to develop automated algorithms to detect fish in the frames and describe interaction behavior. In addition, a web application, EyeSea, was developed to combine manual and automated processing, so that ultimately the automated algorithms could be used to identify where human analysis was needed (i.e., when fish are present in video frames). The aim of the project was to develop algorithms that could identify video frames with fish present that could then be used to enable faster manual analysis. However, the project has also identified recommendations for future data collection, and research that could help analysis and interpretation. The manual analysis started to look at all data from the start of the deployment; this was to ensure rare events were seen, and initially focused on night-time data when more fish were present. This process highlighted the amount of time taken to identify fish, and ultimately only 42.33 hours of video were reviewed due to the time-consuming analysis. The data were classified as ‘Fish’, when the reviewer was confident it was a fish, and ‘Maybe’ fish, when it was difficult to distinguish. This was based on the movement, shape and color characteristics. ‘Fish’ were further classified as ‘adult’, ‘juvenile’, or ‘unidentifiable’. Behavioral attributes were noted, which were broadly divided into passive and avoidance activities. In over 42 hours data, there were only 20 potential contact interactions, of which 3 were ‘Maybe’ classifications, 12 were juveniles and 5 were adults; on only one occasion was an actual contact confirmed, and this was an adult fish with the camera, not the turbine itself. This also highlights the difficulties associated with confirming a strike or collision event as occurring or being a near-miss. More interactions were detected at night; this was probably biased by nighttime artificial light use, which may have attracted fish, but also could have increased detection probability as the light is reflected from the fish itself. For the algorithm development, background subtraction, optical flow, and deep learning techniques were considered. The deep learning approach was determined as needing too much training data for this application, and therefore was not continued. The optical flow analysis was considered promising, but did not give immediate results, so would need further investigation. Therefore, background subtraction was the main focus. Three methods of background subtraction were tried: Robust Principal Components Analysis (RPCA), Gaussian Mixture Model (GMM) and Video Background Extraction(ViBE). A classification technique was then applied to the foreground images to determine fish presence. Using this combination, it was found that fish could be accurately detected when occupying more pixels (>200 pixels, 98.2% correct; 100-200 pixels, 99.6% correct; 5-100 pixels, 85.4% correct; 2-5 pixels, 66.3% correct). In parallel, EyeSea was developed to convert the video data to a usable form and to enable manual and automated analysis of the data that would have a standardized output. Recommendations for further research, and standardized methods for data collection are given.

13 HYDRO ENERGY↗
DOE OSTI2017

Triton: Igiugig Video Analysis (Project Report)

This document summarizes the analysis of video data collected around the Ocean Renewable Power Company’s RivGen® device in the Kvichak River during July and August 2015. Human (manual) analysis of the data was undertaken and used to develop automated algorithms to detect fish in the frames and describe interaction behavior. In addition, a web application, EyeSea, was developed to combine manual and automated processing, so that ultimately the automated algorithms could be used to identify where human analysis was needed (i.e., when fish are present in video frames). The aim of the project was to develop algorithms that could identify video frames with fish present that could then be used to enable faster manual analysis. However, the project has also identified recommendations for future data collection, and research that could help analysis and interpretation. The manual analysis started to look at all data from the start of the deployment; this was to ensure rare events were seen, and initially focused on night-time data when more fish were present.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Virtual Resource Management Framework (CRADA Final Report)

There is a need for advanced and widespread business automation within the nuclear industry to drive down operational costs while sustaining or improving safe operations. While there are many business process automation platforms commercially available, the difficulty is that business processes typically rely on a mixture of resource types to accomplish the desired activities and there is no universal software framework virtualizing diverse resource types for the purpose of automation. In this context, we are referring to any capability, physical or intangible, that can be used by an organization to achieve its objectives as a resource. To deploy business automation broadly and enable integrated operations for nuclear (ION), a framework is needed to represent all resource types and their associated disparate data within a plant and to enable seamless flow of resource information to the technologies used for process automation and resource optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

BESTEST-GSR (Building Energy Simulation Test - Generation Simulation and Reporting) [SWR 18-23]

Building Energy Simulation Test (BESTEST) is an NREL-developed method to validate the qualitative performance of different whole building simulations engines relative to each other. https://www.nrel.gov/docs/legosti/old/6231.pdf For EnergyPlus® and OpenStudio®, generation of test cases, simulation, and reporting has been a manual or partially automated process that typically took a number of months after a new version of EnergyPlus came out. Building Energy Simulation Test - Generation Simulation and Reporting (BESTEST-GSR) was developed to fully automate this process so it is less error-prone, less time-consuming, and can be quickly run on request for any development branch of EnergyPlus and OpenStudio. The initial use case is to directly support OpenStudio and EnergyPlus development, but the software is configured to support use by external software developers that build tools on top of EnergyPlus and/or OpenStudio. Tools built on top of OpenStudio can leverage the test generation, simulation, and reporting, while tools built on EnergyPlus can leverage the test simulation and reporting. The model generation is accomplished by running OpenStudio measures for envelope, heating, or cooling test cases. At this stage the specified weather file required for a specific test case is assigned to the model. Simulation is enabled using the OpenStudio Command Line Interface (CLI) to run a series of OpenStudio and Reporting measures to a specified seed model. The specified workflow is defined with an OpenStudio OSW file. The first measure generates the test case model, while the second measure is a reporting measure that runs after the simulation to collect necessary simulation results from the EnergyPlus SQL file. The Reporting stage gathers data from individual OpenStudio OSW files and populates a series of BESTEST Microsoft Excel spreadsheets. BESTEST-GSR also gathers zip files from individual simulations for diagnostic purposes.

Goldwasser, David↗
DOE OSTI2025

Light-powered end-to-end neutron detection and imaging with an edge-deployed optical AI chip

Neutron detection is widely used in many applications including nuclear physics, nuclear energy, nuclear technologies and nuclear safeguards. Developing an end-to-end neutron detection and imaging workflow paves way towards fully automated processes for many applications. We implemented an automated workflow for neutron detection experiments which use a solid state image sensor to capture neutron hits as a digital image. We deploy the workflow to an edge-based optical neural network (ONN) to increase the radiation-hardness and lifetime of neutron detection instruments. We present a two-stage neural network framework for detection of neutrons at sub-pixel resolution. The first stage uses a region proposal network to efficiently detect and extract neutron hits from the input camera image. The second stage feeds the extracted hits into a fully connected neural network to predict the sub-pixel hit position. The performance of the two-stage framework is evaluated using the edge-based ONN. The results show that we can achieve above 96% neutron detection accuracy as well as sub-pixel and sub-micron position resolution, while enjoying the advantages of the ONN hardware including radiation-hardness, low energy consumption and high computing speed for integrated edge camera and hardware deployment, when compared with electronic counterparts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Ingrained: An Automated Framework for Fusing Atomic-Scale Image Simulations into Experiments

To fully leverage the power of image simulation to corroborate and explain patterns and structures in atomic resolution microscopy, an initial correspondence between the simulation and experimental image must be established at the outset of further high accuracy simulations or calculations. Furthermore, if simulation is to be used in context of highly automated processes or high-throughput optimization, the process of finding this correspondence itself must be automated. In this work, "ingrained," an open-source automation framework which solves for this correspondence and fuses atomic resolution image simulations into the experimental images to which they correspond, is introduced. Here, the overall "ingrained" workflow, focusing on its application to interface structure approximations, and the development of an experimentally rationalized forward model for scanning tunneling microscopy simulation are described.

36 MATERIALS SCIENCE↗
DOE OSTI2022

libwfa: Wavefunction analysis tools for excited and open‐shell electronic states

Abstract An open‐source software library for wavefunction analysis, libwfa, provides a comprehensive and flexible toolbox for post‐processing excited‐state calculations, featuring a hierarchy of interconnected visual and quantitative analysis methods. These tools afford compact graphical representations of various excited‐state processes, provide detailed insight into electronic structure, and are suitable for automated processing of large data sets. The analysis is based on reduced quantities, such as state and transition density matrices (DMs), and allows one to distill simple molecular orbital pictures of physical phenomena from intricate correlated wavefunctions. The implemented descriptors provide a rigorous link between many‐body wavefunctions and intuitive physical and chemical models, for example, exciton binding, double excitations, orbital relaxation, and polyradical character. A broad range of quantum‐chemical methods is interfaced with libwfa via a uniform interface layer in the form of DMs. This contribution reviews the structure of libwfa and highlights its capabilities by several representative use cases. This article is categorized under: Software > Quantum Chemistry Theoretical and Physical Chemistry > Spectroscopy

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Diagnostics for Undersampling and Clustering in Monte Carlo Criticality Calculations

For the past 40 years, it has been known from the theoretical work of Gelbard [1] and Brissenden [2] that the results from Monte Carlo (MC) criticality calculations are biased if the number of neutrons per generation is not sufficiently large. The computed k-effective eigenvalue will be lower than the actual eigenvalue, and the computed eigenfunction will be too low in high-importance regions and too high in low-importance regions. This difficulty is commonly called the undersampling problem for criticality calculations. The biases for undersampling were confirmed for practical, realistic problems with modern MC codes by Brown [3]. The solution to undersampling is simple - use a larger number of neutrons per generation to reduce the biases to levels that are negligible. Over the past few years, the OECD-NEA-WPNCS Expert Group on Advanced Monte Carlo Techniques (EGAMCT) [4,5,6] investigated an extreme form of undersampling called clustering. When the number of neutrons per generation is small, neutrons in successive generations tend to group together (cluster) due to correlations, and the clusters tend to migrate through the problem space. While the problems of undersampling and clustering are well known and well understood theoretically, there were no existing diagnostics for MC codes to determine whether a given calculation is using sufficient neutrons per generation to eliminate these problems. That is, no tools were available to diagnose biases due to undersampling or clustering. The tools described herein resolve this longstanding problem by providing 2 robust methods for detecting undersampling and clustering in MC criticality calculations. The diagnosis of undersampling and clustering is part of a larger effort to thoroughly overhaul the calculational methods for criticality problems, providing for automated sampling of the initial fission distribution, acceleration of the iteration convergence process, automated detection of convergence in the iterations and starting of the active tally cycles, and the diagnosis of undersampling. All of these new methods have been prototyped in a local modified version of mcnp6.2 [7] and tested on a variety of critical systems.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

WELLBASE - An Interactive Platform for Wellbore Material Assessment

This project seeks to build an open-source wellbore material data repository with adequate material performance and contextual data to support Geological Carbon Storage (GCS). By appropriately evaluating the data types as mentioned earlier made available by the WELLBASE tool, stakeholders can make more informed decisions regarding well selections, risk assessment, and economic analysis for geologic carbon storage projects. Advanced Natural Language Processing models and other custom python scripts will be deployed in an automated process to extract unstructured data from documents, reports, and web applications and subsequently parse to more usable formats. The processed data will then be integrated into a robust and comprehensive database architecture, optimizing data accessibility, and usability for analytical purposes. The final data products will be accessible through a user-friendly visualization platform that will allow users to query and visualize the data, as well as download data in usable formats.

Tetteh, Daniel A.↗
DOE OSTI2024

A Dataset of CFD Simulated Industrial Furnace Images for Conditional Automatic Generation with GANs

The steel industry is constantly looking for ways to automate processes and improve efficiency. A standard practice in industry is to simulate how complex systems will operate before they are actually used. Some complex systems, including steel industry processes such as blast furnaces, require complex physics-based simulations utilizing computational fluid dynamics (CFD). These CFD physics-based simulations are very accurate but can take significant time and computational resources to process, resulting in challenges for the implementation of the models in real-world operational environments. In recent years, deep learning (DL) has been considered as a substitute for these CFD models. DL models can be trained on validated CFD simulation data and then used for industrial process inference. Previous DL-based solutions have made great contributions for industrial automation but are currently missing the additional visualization component that CFD simulations also provide. In this paper, we propose a dataset for simple DL generative approaches that can help to address this issue. The dataset and methodology under development to approach this prediction are discussed in this work.

Calix, Ricardo↗