Integrating Conversational Artificial Intelligence Systems into Science Gateways
A powerpoint presentation providing information on Integrating Conversational Artificial Intelligence Systems into Science Gateways
97 MATHEMATICS AND COMPUTING↗
SEARCH · Engineering Papers
Results for “artificial”
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.
A Survey of Proposed Standards and Regulations for Artificial Intelligence
Over the last decade, the interest in machine learning and artificial intelligence (A.I) has grown exponentially. Researchers across several industries have explored and proposed uses of machine learning that have the potential to exponentially improve automation, efficiency, etc. The nuclear industry is no exception, with several studies exploring applications in almost every aspect of the field. However, these developments have brought forth a number of concerns. Government officials, industry leaders and many others have called for new regulations to govern the development and implementation of A.I systems. In order for machine learning and A.I to be viable for nuclear applications, there will need to be great regulatory oversight. The purpose of this study is to examine current and proposed regulations that relate to machine learning and provide recommendations for future regulations, specifically for nuclear applications.
99 GENERAL AND MISCELLANEOUS↗
Artificial Intelligence a D and D Enabler - 20552
The paper addresses the development of a specific search engine dedicated to Decommissioning and Dismantling (D and D) projects. The solution combines advanced model based system engineering methodologies and a data centric approach. It is based on an appropriate use of the last Natural Language processing techniques, the most innovative artificial intelligence algorithms and open source framework to handle large volume and diversity of data met in the case of a nuclear infrastructure we have to dismantle. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Applications of Artificial Intelligence in Automating Nuclear Operations and Work Processes
Slides for 2020 industryXchange: Artificial Intelligence and Machine Learning
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Integrating Artificial Intelligence into Science Gateways
Science gateways are altering the manner in which people interact with high performance computing (HPC) by providing a web browser based interface to advanced computing platforms. In particular, science gateways lower the barrier to using HPC by simplifying the process of submitting workloads to such systems and by offloading the efforts required to use HPC to the maintainers of the system. While science gateways decrease the time-to-science that comes with using such advanced systems, progress can still be made in improving the user's experience. In this paper we explore two strategies for integrating artificial intelligence tools commonly found in non-HPC service workflows: voice activated assistants and chatbots. Since August 2021, the HPC group at Idaho National Laboratory answers an average of 581 support tickets per month of which a large percentage could be addressed via these two strategies. This work defines the key capabilities that an HPC voice activated assistant and chatbot would need to address for a userbase consisting of largely non-expert users as well as a design for integration into the Open OnDemand science gateway.
97 MATHEMATICS AND COMPUTING↗
National Renewable Energy Laboratory Supports the U.S. Department of Energy Artificial Intelligence Strategy
The U.S. Department of Energy (DOE) is leading the way in seizing the promise and managing the risks of artificial intelligence (AI), largely through its national laboratories. The National Renewable Energy Laboratory (NREL) is developing and using AI to advance work and productivity across the DOE mission space: science, energy, and security.
artificial intelligence↗
Opportunities for Artificial Intelligence in Emergency Management
This article for the Domestic Preparedness Journal highlights how PNNL's in-depth landscape assessment dug deep into research articles, preprints, code repositories, and surveys at the intersection of applied artificial intelligence and emergency management.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Responsible Adoption of Artificial Intelligence (AI) in Electric Grid Operations
The future of the grid will be powered by AI—or undermined by it. Artificial intelligence is rapidly reshaping grid operations, improving fault detection, forecasting accuracy, and real-time optimization. As AI systems move closer to operational decision loops, however, they introduce new consequence pathways: expanded attack surfaces, model integrity risks, regulatory exposure, and human-automation challenges. This talk presents a consequence-driven framework for deploying AI responsibly in the electric grid. Attendees will gain practical strategies to strengthen resilience, boost reliability, and deploy AI securely — ensuring the grid of the future is not only smarter but safer.
25 - ENERGY STORAGE↗
Ensuring Safe, Effective, and Reliable Use of Artificial Intelligence-Based Applications for Nuclear Electricity Generation through a Systems Approach
There is a significant potential to reduce operating and maintenance cost at nuclear power plants using artificial intelligence (AI) and machine learning (ML). For instance, AI/ML has the potential to significant improve work management processes, condition reporting, and plant surveillance activities. However, the nuclear industry has been slow in adopting AI/ML due to several multifaceted barriers discussed in this paper. This work presents such multidisciplinary approach intended to 1) accelerate industry adoption of AI/ML-based applications at nuclear power plants and 2) ensure their safe, reliable, and effective use. This approach is discussed further in this work and will be used to address overarching challenges associated with AI deployment at scale to provide industry guidance that support accelerated adoption of AI/ML technologies throughout the industry.
99 - GENERAL AND MISCELLANEOUS↗
Risk-informed Graded Approach for Reliability and Performance Assessment of Machine Learning and Artificial Intelligence for Advanced Condition Monitoring Techniques
With the shift away from time-based maintenance and toward condition-based maintenance, and to reduce overall maintenance costs, there has been an upsurge in the usage and development of advanced condition monitoring (ACM) techniques for real-time monitoring of nuclear power plant (NPP) components. ACM is particularly useful in the development of digital twins, which are designed to predict the failure or degradation of plant components. Successful implementation of ACM requires an assessment to inform the development of a risk-informed approach to evaluate the use of ACM to meet Nuclear Regulatory Committee (NRC) regulations for in-service testing (IST) programs. This includes the monitoring and diagnostics of reactor components and systems in current, new, and advanced reactors. A key component in ACM is the usage of machine learning (ML) and artificial intelligence (AI) algorithms that can employ real-time data from instrumentation and sensors to detect and predict reactor component degradations. Such predictive capabilities enable early detection of component degradation so as to help plant personnel plan and execute necessary maintenance. For successful implementation of ML/AI in ACM such that regulatory requirements are met, a risk-informed graded approach is needed to assess the reliability and performance of ML/AI for ACM. The American Society for Mechanical Engineers (ASME) developed their Operations and Maintenance (O&M) Code to provide guidance on safe, reliable O&M of NPPs. The IST section of the O&M Code specifically establishes requirements for IST and examination to gauge operational readiness of components in water-cooled NPPs. This paper presents a state-of-the-art review of how reliability and risk assessment can be integrated with ACM to assess component performance by non-nuclear industries. This is followed by different methodologies and approaches for conducting performance and reliability assessments so as to meet IST requirements for NPP components.
97 - MATHEMATICS AND COMPUTING↗
Artificial Intelligence and Digital Engineering as Enablers for System Engineering in the Energy Sector
Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.
42 - ENGINEERING↗
Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector
Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world?s energy demands and build energy security.
42 - ENGINEERING↗
Poster: Responsible Adoption of Artificial Intelligence (AI) in Electric Grid Operations
The rapid integration of artificial intelligence (AI) in the utility transmission and distribution (T&D) sector is revolutionizing traditional grid management practices. As utilities encounter complexities from evolving consumer behaviors and energy integration, AI becomes a critical solution for enhancing grid monitoring, fault detection, and operational optimization. However, increased reliance on interconnected technologies introduces significant cybersecurity risks, regulatory compliance challenges, and human factors concerns. This study proposes a strategic, responsible and consequence-driven approach to AI implementation, examining the dual nature of AI adoption by highlighting its transformative benefits for utilities and associated risks. It provides utilities with a framework for evaluating AI integration, enabling them to navigate challenges and capitalize on opportunities to achieve greater reliability, efficiency, and resilience in an increasingly complex energy landscape.
24 - POWER TRANSMISSION AND DISTRIBUTION↗
Leveraging Artificial Intelligence in Federal Projects
Artificial intelligence (AI) has the potential to transform grid operations. As energy demand rises, weather patterns shift, and foreign threats to critical infrastructure grow, it is essential to harness advanced technology to modernize the grid and increase overall resiliency. This paper examines the integration of AI in federally funded grid infrastructure projects. By analyzing project data, it evaluates the penetration and use cases of AI technologies within key funding initiatives and explores opportunities and challenges associated with their deployment. This analysis categorizes AI adoption in recent federal grid investments, establishing a baseline for measuring near-term impacts and identifying promising AI applications. The research found that 16% of recent federal energy projects included AI integration, and approximately 75% of these projects used AI for more than one primary application.
24 - POWER TRANSMISSION AND DISTRIBUTION↗
Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks
Abstract Empirical modeling approaches are frequently used to upscale local eddy covariance observations of carbon, water, and energy fluxes to regional and global scales. The predictive capacity of such models largely depends on the data used for parameterization and identification of input‐output relationships, while prediction for conditions outside the training domain is generally uncertain. In this work, artificial neural networks (ANNs) were used for the prediction of gross primary production (GPP) and latent heat flux (LE) on local and European scales with the aim to assess the portion of uncertainties in extrapolation due to sample selection. ANNs were found to be a useful tool for GPP and LE prediction, in particular for extrapolation in time (mean absolute error MAE for GPP between 0.53 and 1.56 gC m −2 d −1 ). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7–1.41 gC m −2 d −1 ), while extrapolation in areas with different seasonal cycles and controlling factors (e.g., the tropical regions) showed noticeably higher errors (GPP MAE 0.8–2.09 gC m −2 d −1 ). The distribution and the number of sites used for ANN training had a remarkable effect on prediction uncertainty in both, regional GPP and LE budgets and their interannual variability. Results obtained show that for ANN upscaling for continents with relatively small networks of sites, the error due to the sampling can be large and needs to be considered and quantified. The analysis of the spatial variability of the uncertainty helped to identify the meteorological drivers driving the uncertainty.
Papale, Dario↗
Detection of Artificially Generated Seismic Signals Using Balloon-Borne Infrasound Sensors
Here, we conducted an experiment in Pahrump, Nevada, in June 2017, where artificial seismic signals were created using a seismic hammer, and the possibility of detecting them from their acoustic signature was examined. In this work, we analyze the pressure signals recorded by highly sensitive barometers deployed on the ground and on tethers suspended from balloons. Our signal processing results show that wind noise experienced by a barometer on a free-flying balloon is lower compared to one on a moored balloon. This has never been experimentally demonstrated in the lower troposphere. While seismoacoustic signals were not recorded on the hot air balloon platform owing to operational challenges, we demonstrate the detection of seismoacoustic signals on our moored balloon platform. Our results have important implications for performing seismology in harsh surface environments such as Venus through atmospheric remote sensing.
58 GEOSCIENCES↗
In Situ Conversion of Artificial Proton‐Rich Shell to Inorganic Maskant Toward Stable Single‐Crystal Ni‐Rich Cathode
Single-crystal high-nickel oxide with an integral structure can prevent intergranular cracks and the associated detrimental reactions. Yet, its low surface-to-volume ratio makes surficial degradation a more critical factor in electrochemical performance. Herein, artificial proton-rich (ammonium bicarbonate) shell is successfully introduced on the nickel-rich LiNi 0.92 Co 0.06 Mn 0.02 O 2 single crystals for in situ electrochemically conversing into inorganic maskant to enhance stability of cathode. The process is that the surficial enriched proton, once released from the ammonium bicarbonate shell (proton reservoir) during 1st charge, is immediately captured by LiPF 6 , in situ electrochemically conversing to LiF and Li 3 PO 4 sub-nano particle dense maskant (sub-nano F-&P-maskant). The in situ formed compact nano F-&P-maskant significantly resists the cathode against electrolyte attack and improves the surface stability of particles during long-term cycling. Consequently, this surface modification enables 95% capacity retention after 100 cycles at a high voltage of 4.5 V in the half cell and 83% capacity retention after 800 cycles in the full cell. In conclusion, this work demonstrates a strategy for reconstructing the protective layer using the rational design of surficial enriched proton shells for advanced lithium batteries.
25 ENERGY STORAGE↗
Artificial Intelligence and Multiscale Modeling for Sustainable Biopolymers and Bioinspired Materials
Abstract Biopolymers and bioinspired materials contribute to the construction of intricate hierarchical structures that exhibit advanced properties. The remarkable toughness and damage tolerance of such multilevel materials are conferred through the hierarchical assembly of their multiscale (i.e., atomistic to macroscale) components and architectures. Here, the functionality and mechanisms of biopolymers and bio‐inspired materials at multilength scales are explored and summarized, focusing on biopolymer nanofibril configurations, biocompatible synthetic biopolymers, and bio‐inspired composites. Their modeling methods with theoretical basis at multiple lengths and time scales are reviewed for biopolymer applications. Additionally, the exploration of artificial intelligence‐powered methodologies is emphasized to realize improvements in these biopolymers from functionality, biodegradability, and sustainability to their characterization, fabrication process, and superior designs. Ultimately, a promising future for these versatile materials in the manufacturing of advanced materials across wider applications and greater lifecycle impacts is foreseen.
Wang, Xing Quan [Department of Mechanical Engineer↗