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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence

For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing could provide the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required, but yet to be combined with the suite of desired neuromorphic metrics, including linear, symmetric weight update, and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable neuromorphic device based on an electrochemical transistor, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), good state retention (>10 4 s), together with high stretchability of 100% strain. Further integration into a prototype array successfully realized the implementation of vector-matrix multiplication even at 100% strain. Finally, we demonstrate the feasibility of implementing AI-based classification of health signals (as exemplified by electrocardiograms) with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware. Finally, this work breaks the ground for combining AI data analysis into skin-like wearable electronics for achieving human-integrated/mimetic intelligent systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

A generative artificial intelligence framework for long-time plasma turbulence simulations

Generative deep learning techniques are employed in a novel framework for the construction of surrogate models capturing the spatiotemporal dynamics of 2D plasma turbulence. The proposed Generative Artificial Intelligence Turbulence (GAIT) framework enables the acceleration of turbulence simulations for long-time transport studies. GAIT leverages a convolutional variational auto-encoder and a recurrent neural network to generate new turbulence data from existing simulations, extending the time horizon of transport studies with minimal computational cost. The application of the GAIT framework to plasma turbulence using the Hasegawa–Wakatani (HW) model is presented, evaluating its performance via various analyses. Very good agreement is found between the GAIT and the HW models in the spatiotemporal Fourier and Proper Orthogonal Decomposition spectra, the flow topology characterized by the Okubo–Weiss parameter, and the time autocorrelation function of turbulent fluctuations. Excellent agreement has also been obtained in the probability distribution function of particle displacements and the effective turbulent diffusivity. In-depth analyses of the latent space of turbulent states, choice of hyperparameters and alternative deep learning models for the time prediction are presented. Our results highlight the potential of Artificial Intelligence-based surrogate models to overcome the computational challenges in turbulence simulation, which can be extended to other situations such as geophysical fluid dynamics.

Artificial intelligence↗
DOE OSTI2017

An Artificially Intelligent Physical Model-Checking Approach to Detect Switching-Related Attacks on Power Systems

Decentralized and hierarchical microgrid control strategies have lain the groundwork for shaping the future smart grid. Such control approaches require the cooperation between microgrid operators in control centers, intelligent microcontrollers, and remote terminal units via secure and reliable communication networks. In order to enhance the security and complement the work of network intrusion detection systems, this paper presents an artificially intelligent physical model-checking that detects tampered-with circuit breaker switching control commands whether, due to a cyber-attack or human error. In this technique, distributed agents, which are monitoring sectionalized areas of a given microgrid, will be trained and continuously adapted to verify that incoming control commands do not violate the physical system operational standards and do not put the microgrid in an insecure state. The potential of this approach has been tested by deploying agents that monitor circuit breakers status commands on a 14-bus IEEE benchmark system. The results showed the accuracy of the proposed framework in characterizing the power system and successfully detecting malicious and/or erroneous control commands.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Techno-Economic Analysis for Grid Edge Intelligence: A Preliminary Study on Smart Voltage Regulator Controls

Grid edge intelligence has been of interest in both industrial and academic research spaces as the penetration level of distributed energy resources increases. This paper discusses the application of a framework that can perform techno-economic analyses for integrating intelligent autonomous controls into the distribution grid. The framework has been used to provide cost-benefit studies for two smart voltage regulator control algorithms that come as black-box applications. These applications are evaluated within the framework using quasistatic time-series simulations. Results are presented in terms of performance metrics and costs incurred for these two smart voltage regulator control techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Online Junction Temperature Monitoring Using Intelligent Gate Drive for SiC Power Devices

Junction temperature is a prime design/operation parameter, as well as, a main indicator of device's health condition for power electronics converters. Compared to its silicon (Si) counterparts, it is more critical for silicon carbide (SiC) devices due to the reliability concern determined by the immaturity of new material and packaging. This paper presents a practical implementation using an intelligent gate drive for online junction temperature monitoring of SiC devices based on turn-off delay time as the thermo-sensitive electrical parameter. First, the sensitivity of turn-off delay time on the junction temperature for fast switching SiC devices is analyzed. A gate impedance regulation assist circuit is proposed to enhance the sensitivity by a factor of 60 and approach 736 ps/°C tested in the case study with little penalty on the power conversion performance. Next, an online monitoring unit based on gate assist circuits is developed to monitor the turn-off delay time in real time with the resolution less than 104 ps. As a result, the micro-controller is capable of “reading” junction temperature during the converter operation. Finally, a SiC-based half-bridge inverter is constructed with an intelligent gate drive consisting of the gate impedance regulation circuit and online turn-off delay time monitoring unit. Experimental results demonstrate the feasibility and accuracy of the proposed approach.

42 ENGINEERING↗
DOE OSTI2014

Amplify scientific discovery with artificial intelligence

Computing innovations have fundamentally changed many aspects of scientific inquiry. For example, advances in robotics, high-end computing, networking, and databases now underlie much of what we do in science such as gene sequencing, general number crunching, sharing information between scientists, and analyzing large amounts of data. As computing has evolved at a rapid pace, so too has its impact in science, with the most recent computing innovations repeatedly being brought to bear to facilitate new forms of inquiry. Recently, advances in Artificial Intelligence (AI) have deeply penetrated many consumer sectors, including for example Apple’s Siri™ speech recognition system, real-time automated language translation services, and a new generation of self-driving cars and self-navigating drones. However, AI has yet to achieve comparable levels of penetration in scientific inquiry, despite its tremendous potential in aiding computers to help scientists tackle tasks that require scientific reasoning. We contend that advances in AI will transform the practice of science as we are increasingly able to effectively and jointly harness human and machine intelligence in the pursuit of major scientific challenges.

artificial intelligence↗
DOE OSTI2023

Perspectives on Artificial Intelligence for Predictions in Ecohydrology

Abstract In November 2021, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held, which involved hundreds of researchers from dozens of institutions. There were 17 sessions held at the workshop, including one on ecohydrology. The ecohydrology session included various breakout rooms that addressed specific topics, including 1) soils and belowground areas; 2) watersheds; 3) hydrology; 4) ecophysiology and plant hydraulics; 5) ecology; 6) extremes, disturbance and fire, and land-use and land-cover change; and 7) uncertainty quantification methods and techniques. In this paper, we investigate and report on the potential application of artificial intelligence and machine learning in ecohydrology, highlight outcomes of the ecohydrology session at the AI4ESP workshop, and provide visionary perspectives for future research in this area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Parallel Battery: The Framework and Process for an Intelligent and Ecological Battery System and Related Services

The concept, framework, process methodology and applications of parallel battery were proposed from both virtual and real aspects.The parallel battery was an application of ACP-based parallel intelligence in battery and related energy system areas.The real battery system was running with its equivalent, and the artificial battery system was in a virtual space, in a parallel and interactive manner.The artificial battery system contained the descriptive, predictive, and prescriptive functions on the real battery and related energy systems.There was a closed-loop workflow between the real battery system and the artificial battery system, which iteratively optimizes the battery and related energy systems, leading to a new paradigm of intelligent and ecological parallel battery system management.

ACP approach↗
DOE OSTI2021

Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports. The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files are used with the Geothermal Exploration Artificial Intelligence for the Salton Sea Geothermal Site to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Salton Sea Geothermal Site.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Sensors with Intelligent Measurement Platform and Low-cost Equipment (SIMPLE) for Monitoring and Control of Medium Voltage Distribution Systems with High Penetration of Intermittent Distributed Energy Resources

The primary focus of the SIMPLE project was to develop, commission, test and introduce intelligent voltage and current sensor platform with enhanced characteristics (accuracy, bandwidth and harmonic range) and high measurement granularity for medium voltage distribution system monitoring, protection and control. The enhancements incorporated existing (commercially available) sensors and made them suitable for applications such as voltage regulation, frequency support, fault detection and location, distribution system state estimation, power quality measurements and electrical distribution network topology processing. The development of the intelligent sensor platform was driven by the need for high-fidelity monitoring and control of medium voltage distribution feeder to enable higher penetration of renewable resources and microgrid operation.

14 SOLAR ENERGY↗
DOE OSTI2022

Winter School: Applications of Artificial Intelligence to Topics in Nuclear Physics

The virtual topical Winter School on Artificial Intelligence in Nuclear Physics aimed at giving the participants a deeper understanding on what Artificial Intelligence and Machine Learning are and how they can be used to analyze data, design new detectors, controls, and calibration systems for nuclear physics experiments and perform theoretical calculations of nuclear many-body systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Vulnerabilities in Artificial Intelligence and Machine Learning Applications and Data

Artificial intelligence (AI) applications driven by machine learning (ML) are transformational technologies within the international nuclear security regime. Advancements realized by AI—faster and improved data insights, more efficient and automated processes, reductions in human error—enable nuclear security applications such as behavior analysis for insider threat mitigation, source tracking of stolen nuclear material, and facial recognition software for physical protection. In addition to the advantages, however, there are also inherent vulnerabilities and threats associated with its use and risk mitigations must be built into any AI/ML-enabled systems. This work provides a background on AI and ML and different data types used in the field, including open-source intelligence information (OSINT) that is discoverable by AI tools and application data that are used by AI tools for decision-making and automation. Current and potential AI applications and vulnerabilities related to their use within the nuclear security regime are also discussed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Using Structured Intelligence Graph (STIG) to protect our critical infrastructure against cyber attacks [Poster]

STIG is a revolutionary cybersecurity tool developed by researchers at the U. S. Department of Energy's Idaho National Laboratory and it is a software that allows utility owners and operators to easily visualize, create, and edit cyberthreat intelligence information. STIG uses Structured Threat Information eXpression (STIX) and converts complex data on cybersecurity vulnerabilities into a visualization that is easy to understand and act on. With STIG, utility owners and operators have a common system for sharing threat intelligence information, thus increasing the chances of detecting and mitigating cyber exploits before they lead to a cyberattack.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Artificial Intelligence for Natural Gas Utilities: A Primer

Modern natural gas utilities face numerous challenges and competing priorities from various stakeholders. Policymakers, customers, and advocacy groups want to see gas utilities improve performance on safety, reliability, resilience, affordability, and environmental stewardship. State utility regulators — public utility commissions — are responsible for overseeing utility performance, ensuring that ratepayer funds are being spent in the public interest, and aligning utility goals with public goals. The use of new technologies is critical to enabling cost-effective performance on these attributes. Artificial intelligence (AI) is a widely used term among utilities and regulators, but the term means different things to different stakeholders, and it is often used to describe data analytics approaches that fall short of the formal definition of AI, which is: “…the ability of a machine to receive inputs and produce a behavior or reaction similar to that of an intelligent human being.” AI (and related tools, techniques, and technologies) can help utilities solve current and emerging challenges. By combining customer and system data with analytical tools and technologies, AI can augment human decision-makers by assisting in identifying problems and events before they occur, enabling resources to be more efficiently directed across utility infrastructure. The intended primary audience for this primer is state regulators, although utilities and other stakeholders might also find it useful and relevant to improve their awareness of AI. The objectives of this primer are to: (a) offer a set of broadly applicable definitions for AI and related terms, allowing regulators, utilities, and other stakeholders to speak the same language; (b) discuss how AI is currently being implemented in the gas utility sector; and (c) understand the challenges affecting AI solutions and how tools might be implemented in the future. This primer fits within NARUC’s goals of providing impartial information to improve the ability of public utility commissions to regulate in the public interest. As such, this primer does not seek to recommend AI over any other investment, nor does it endorse any particular vendor, product, or approach. It does seek to prepare state regulators to oversee AI investments by sharing information about the current landscape of commercially available tools. To these ends, the primer is organized as follows: Section I discusses the current environment in which natural gas utilities operate and how AI, when thoughtfully designed and implemented, can enable utilities to achieve performance goals; Section II offers definitions of AI and related terms within the data analytics discipline; Section III provides three current opportunities for which AI can offer solutions: replacing aging gas distribution infrastructure, preventing excavator damage to gas distribution infrastructure, and improving energy efficiency programs. This section discusses each problem statement in detail. Second, Section III includes a discussion of how costs and benefits of investments to solve each problem are measured. And third, this section offers real-world examples of utility implementation of AI solutions; Section IV discusses challenges with implementing AI, both from the perspective of utilities and regulators; Section V suggests areas in which AI could feasibly be implemented in the near future; Finally, Section VI offers concluding thoughts and areas for further research.

03 NATURAL GAS↗
DOE OSTI2024

Intelligent Manufacturing for Extreme Environments Conference Proceedings

The Intelligent Manufacturing for Extreme Environments (IMEE) workshop was held at the Center for Advanced Energy Studies (CAES) in Idaho Falls, Idaho, May 2–3, 2023, in support of the United States (U.S.) National Science Foundation (NSF) Established Program to Stimulate Competitive Research: Workshop Opportunities (EPSCoR-WO) program. This workshop featured keynote speakers, panels, and breakout sessions with 58 participants. Nuclear reactors need to operate under extreme service conditions, such as high temperatures, corrosive environments, and high-radiation doses. Hence, reactor components must be able to withstand those conditions. The participants envision a future where on demand manufacture of components for small modular reactors (SMRs), microreactors, and other advanced reactor designs are possible. In this future, regulatory bodies accept validated manufacturing processes and standardized feedstocks, thus eliminating the need for individual component testing. However, the necessary technologies and regulatory policies needed for this future do not exist today. Successful innovation would revolutionize the nuclear power sector, enable fast commercial development, create economic opportunities in the U.S., reduce the carbon footprint and associated risks, and promote a skilled and highly competitive workforce. The objective of the workshop was to convene world-class experts, researchers, educators, and students to identify gaps and envision solutions for five interrelated challenges for intelligent manufacturing in extreme environments. The key outcomes of the conference were: (1) to take the opportunity for researchers and educators to network and form collaborations; and (2) to produce a full report to inform policy-makers, industry, and the academic community of various challenges and opportunities in the nuclear energy sector.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗