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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 37 records · Page 2

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↗

Cross-domain digital twin architecture for predictive maintenance via machine learning and Large Language Models

This research introduces a comprehensive framework for creating and deploying a digital twin platform for continuous monitoring and predictive maintenance within industrial settings. Through utilizing advanced technologies, including Unreal Engine 5, Unity 3D, the Message Queue Telemetry Transport protocol, Random Forest machine learning algorithms, and Large Language Models (LLMs), we establish a platform that digitally reproduces physical equipment and translates digital controls into real-world actions. This facilitates preventive maintenance approaches and improves operational effectiveness. The digital twin platform gathers sensor data from operational equipment, analyzes it using machine learning, and delivers practical insights to prevent potential malfunctions and enhance equipment performance. Furthermore, the incorporation of a web portal enables efficient monitoring and access to historical data, educational materials, and equipment status information. Preliminary findings indicate that digital twins can transform industrial equipment management and maintenance methodologies.

97 MATHEMATICS AND COMPUTING↗

Longitudinal Multi-omics Reveal Phase-Dependent Viral Adaptive Strategies and Functional Potential During Formation of Algal-bacterial Granular Sludge

Virus-host interactions within microbial aggregates critically influence microbiome function and stability, yet how physicochemical stresses shape the interactive dynamics remains largely unexplored. Here, we investigated virus–host dynamics during the transition of algal-bacterial granular sludge (ABGS) from activated sludge under continuous hydraulic shear using integrated metagenomics and metatranscriptomics. Hydraulic stress initially reduced host a-diversity, which coincided with a marked increase in viral lysogenicity. During this host diversity bottleneck, viral microdiversity increased, and genes related to virion structure and DNA packaging were under positive selection (pN/pS >1). As host diversity recovered, viral microdiversity declined, while viral anti-defense systems (ADS) significantly increased in abundance. Lagged correlation analysis revealed a significant positive correlation between viral ADS and host defense systems (DS), suggesting an evolutionary arms race. Furthermore, active lysogenic infections were accompanied by enrichment of DS and auxiliary viral genes (AVGs) involved in genetic information processing and amino acid metabolism, potentially enhancing host fitness. Overall, our study unveils a phase-dependent co-evolutionary interplay between viruses and hosts during ABGS formation, providing insights into the development and maintenance of microbial structural and functional resilience in engineered ecosystems.

Qi, Huiyuan↗

Corrosion and Mechanical Durability Testing in High Temperature Molten Sulfur (CRADA Final Report)

The overall goal of the project is to explore and identify suitable candidate materials for high-temperature molten sulfur thermal energy storage (TES) operating up to a maximum temperature of 400°C–420°C for industrial process heat applications. A detailed corrosion and strength analysis of coupons subjected to long-term thermal testing in molten sulfur will be conducted to characterize the fundamental corrosion mechanism, corrosion rates, and mechanical durability. The results from the project contribute to the engineering design of sulfur TES systems with desired longevity and lower construction and maintenance costs. The research outcome benefits the TES research and development community by providing detailed corrosion and degradation information for more effective engineering design of the molten sulfur TES containment. It improves the diversity and reliability of the renewable technologies available for the U.S. industries and U.S. power grid.

14 SOLAR ENERGY↗

Business Case Analysis for Artificial Intelligence-Large Language Model Technology Integration

AI-assisted processes are expected to enhance operational efficiency and improve decision-making, supporting the long-term economic viability of nuclear power plants. However, detailed business analyses of AI-generated cost savings are rarely performed. Given the recent industry interest in Large Language Model (LLM), the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program has conducted a comprehensive business case analysis of LLM Artificial Intelligence (AI) implementation in nuclear plant engineering workflows. The research employed three complementary business case approaches to evaluate impact of an LLM, using three representative engineering processes as use-cases: Boric Acid Corrosion (BAC) Evaluations, Maintenance Rule Evaluations, and 10 CFR 50.59 Screenings. Through detailed workload analyses and structured interviews, the study quantified significant efficiency improvements ranging from 11% to 59% across these processes. The research further considers how these efficiency gains could translate into tangible reliability improvements through enhanced engineering capacity. Analysis of historical plant trip data indicates that enabling engineers to focus on proactive reliability activities could provide substantial financial benefits through avoided outages, potentially generating greater value than the direct efficiency improvements alone. By documenting successful applications, implementation challenges, and strategic opportunities, this research provides nuclear utilities with a practical framework for evaluating the value of AI technology to support long-term operations through advanced digital technologies.

97 MATHEMATICS AND COMPUTING↗

Technoeconomic Analysis of the Miba Segmented Journal Bearing

This presentation describes a technoeconomic analysis using NLR's Windfarm Operations & Maintenance cost-Benefit Analysis Tool (WOMBAT) to compare operational expenses and levelized cost of energy for wind turbines using standard spherical roller bearings with a novel segmented journal bearing developed by Miba.

17 WIND ENERGY↗

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

42 ENGINEERING↗

Testbed Demonstration of a Microgrid Building Block Prototype

With the adoption of ambitious climate action goals, the penetration level of distributed energy resources (DERs) is rapidly increasing. Microgrids are an efficient way to integrate these DERs, facilitating their operation and control. Additionally, microgrids enhance the overall resilience of the distribution system by serving critical loads both within and outside their boundaries. However, the need for substantial customized engineering leads to a high cost of development, installation and maintenance of microgrids. To address this challenge, Microgrid Building Blocks (MBB) are proposed to reduce the deployment cost of microgrids through modular, standardized design and implementation. This work presents a testbed demonstrating the integrated power conversion, control, and communication functionalities of an MBB. The testbed is formed by a real-time electromagnetic transient (EMT) simulation combined with a hardware and software prototype of MBB. The use cases supported by the MBB testbed are enumerated. The islanded operation, voltage regulation, and optimal dispatch capabilities of an MBB-based microgrid controller are validated through a case study.

Somda, Baza [Virginia Tech]↗

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗

Plasma-Assisted Pre-Chamber Ignition System for Highly Dilute Stoichiometric Heavy-Duty Natural Gas Engines (Final Technical Report)

This project explored advanced ignition technologies to significantly enhance efficiency and reduce operating costs for heavy-duty natural gas engines operating at stoichiometric conditions, while meeting ultra-low NOx emission standards. The main goal was to develop and validate a plasma-assisted pre-chamber ignition system that could deliver at least a 2% increase in brake thermal efficiency (BTE) and a 4% decrease in total cost of ownership (TCO) compared to a typical multi-cylinder engine with three-way catalyst aftertreatment, ensuring compatibility with the expected 2027 EPA/CARB regulations. In the first half of the project, the research team concentrated on developing and testing plasma-assisted pre-chamber ignition using nanosecond pulsed discharges. Extensive experiments were conducted in an optically accessible rapid-compression and expansion machine, a constant-volume chamber, and an optical single-cylinder engine. Experiments were coupled with CFD simulations. The work produced unique insights into pre-chamber flame formation, jet ignition, dilution effects, and flame quenching at pressures, temperatures, and dilution levels relevant to engines. Although plasma-assisted ignition showed promise in controlled lab settings, the research also identified fundamental and practical challenges when applying this technology to real engine conditions. Midway through the project, a crucial pivot was made, guided by three key findings. First, the power electronics required for nanosecond plasma discharges were found to be too costly for commercial use, undermining the project’s cost-of-ownership goals. Second, nanosecond plasma ignition was highly sensitive to turbulent flow in the pre-chamber, resulting in lower ignition reliability than traditional spark under engine-like conditions. Third, achieving a truly diffuse low-temperature plasma at high pressures near top dead center was not possible, reducing the anticipated chemical enhancement benefits. These results collectively suggested that continuing with plasma-assisted ignition was unlikely to meet both efficiency and cost objectives. In response, the project shifted focus to a more realistic approach: enhancing traditional spark-based pre-chamber ignition with significantly less spark energy. Using insights gained earlier in the project, the team redesigned the pre-chamber to maintain high dilution tolerance and quick combustion, even with lower ignition energy. Testing confirmed that with optimized pre-chamber design and combustion timing, a lower-energy spark could reliably ignite highly diluted stoichiometric mixtures, reduce burn time, and boost thermal efficiency. Final engine testing and techno-economic analysis verified that this revised approach successfully achieved the project goals. The optimized pre-chamber ignition system provided over a 2% increase in calculated brake thermal efficiency compared to the baseline engine. Notably, the lower ignition energy and simplified hardware reduced component stress, extended maintenance intervals, and lowered the total cost of ownership. When used with stoichiometric operation and traditional three-way aftertreatment, the system remained compatible with near-zero NOx emissions targets without increasing cost or complexity in the emissions control system. In summary, although the project deviated from its initial plasma-assisted ignition idea, the work produced a more practical and commercially viable solution. The results show that precisely optimized, low-energy pre-chamber spark ignition can significantly improve efficiency and reduce overall ownership costs for heavy-duty natural gas engines. This directly aligns with DOE goals for cleaner, more efficient, and cost-effective transportation technologies.

03 NATURAL GAS↗

Cyber-Informed Engineering (CIE) Power Generation Guide [Slides]

This guide offers suggestions for applying CIE principles to technologies used to generate electric power. It addresses issues of design, implementation, and maintenance, preemptively addressing cybersecurity threats to electric generation. The intended audience for this guide includes practitioners across the energy and cybersecurity sectors, such as energy industry professionals (e.g., engineers, system designers, operators, and researchers) and cybersecurity experts (e.g., communication system designers, information technology/operational technology [IT/OT] administrators, and penetration testers).

13 HYDRO ENERGY↗

Development of Digital Twin-Informed Predictive Maintenance for Critical Components in Advanced Reactors

Small modular reactors (SMRs) and microreactors, along with other advanced reactor (AR) technologies, are key to the future of nuclear energy. For these systems to achieve low operating costs, high reliability, and flexibility across applications, their operation and maintenance must be optimized. Digital twin (DT) technology is one of the technologies that enables real-time (or faster than real-time) monitoring and prognosis of critical components which are vital for operational efficiency, low costs, and enhanced safety of ARs, accelerating their deployment. DT technology provides dynamic virtual representation of physical assets by integrating real-time data, physics-based models, and advanced analytics, which is critical to optimizing the performance of the entire energy system throughout the life cycle. DTs empower engineers and operators to virtually explore different scenarios, configurations, and control strategies, allowing for the identification of optimal solutions that maximize reactor efficiency, safety, and economics.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Key insights from US Department of Energy Better Plants workforce development bootcamps (2022–2025)

This study examines the effectiveness of the US Department of Energy’s Better Plants Program Bootcamps, which are designed to enhance participants’ technical skills in improving energy efficiency and optimizing operations in manufacturing facilities. Through the analysis of survey data collected from 529 participants across 9 bootcamps, the research investigates the motivations, benefits, and demographic trends of attendees. The findings reveal that skill acquisition and improvement are primary drivers for participation, with key benefits including hands-on training on diagnostic equipment and software tools, networking opportunities, and access to technical resources. The analysis shows strong participation from sectors characterized by high energy consumption and employment, such as chemical and transportation equipment manufacturing. Over 50% of participants have job titles that include “EHS” or “Energy” showing their key roles in leading energy efficiency and energy management efforts in manufacturing. Furthermore, the analysis highlights the distribution of participants across managerial, engineering, and technical roles, revealing a higher representation of managers and engineers. This observation suggests a need for targeted outreach to engage technicians, equipment operators, maintenance staff, and floor workers to ensure comprehensive workforce development. The post-bootcamp survey showed that the participants highly valued the opportunities for peer learning and idea exchange, and the benefits they gained from them. This research contributes to the advancement of manufacturing education by demonstrating the efficacy of specialized training in addressing critical industry challenges and fostering a more competent and empowered workforce.

Energy efficiency↗

COG Software Architecture Design Description Document

This COG Software Architecture Design Description Document describes the organization and functionality of the COG Multiparticle Monte Carlo Transport Code for radiation shielding and criticality calculations, at a level of detail suitable for guiding a new code developer in the maintenance and enhancement of COG. The intended audience also includes managers and scientists and engineers who wish to have a general knowledge of how the code works. This Document is not intended for end-users.

61 RADIATION PROTECTION AND DOSIMETRY↗

Total Cost of Ownership Reduction Using Passive Pre-Chamber Ignition in Natural Gas Engines

Spark plug durability is a factor affecting the total cost of ownership (TCO) of spark-ignited natural gas engines, with some heavy-duty platforms requiring plug replacement after only 750 hours of operation. The high ignition energy demand under lean or diluted conditions accelerates electrode wear, shortening plug life and increasing maintenance frequency. This work evaluates passive pre-chamber (PC) ignition operating at lowered spark energies as a strategy to reduce spark energy requirements and extend plug durability, thereby lowering TCO. Experiments were conducted on a medium-duty Cummins 6.7L ISB engine at 1600 RPM and 50% load under varying exhaust gas recirculation (EGR) dilution levels (0–40%). Two passive pre-chambers with 1.1 mm and 1.6 mm nozzle diameters were compared with conventional spark ignition (SI). SI was operated with a fixed coil dwell of 4 ms (~90 mJ), while the PC configuration was tested across 2–4 ms dwell times (~30–90 mJ). Cylinder pressure analysis revealed that PC ignition sustained stable combustion at significantly lower spark energies than SI, with improved combustion stability at ~30 mJ compared with SI at ~90 mJ. The PC system also extended the dilution tolerance beyond that achievable with SI, while delivering up to ~2% higher indicated thermal efficiency and reducing the COV by nearly 90% at 30% EGR. A TCO analysis was conducted to assess the economic benefit of adopting a passive PC system operating at reduced spark energies compared to SI. It was found that adopting a passive PC system could reduce TCO by approximately $11,000 per engine over a five-year operational period, primarily due to fuel savings, extended spark plug life, and reduced maintenance frequency. These projections were weighed against the additional hardware cost of the pre-chamber, yielding a rapid estimated return on investment of ~300 operating hours. Therefore, this work motivates continued research and development of passive PC technology and its commercial adoption in natural gas engines used in transportation applications.

Dhotre, Akash [University of Minnesota]↗

Transportability of exogenous microbial community correlates with interwell connectivity in deep aquifers

Subsurface resource engineering operations often utilize continuous injection of externally-sourced water into geological reservoirs for formation pressure maintenance, resource recovery or energy/waste storage. Such injected water generally contains naturally occurring microbes. Little is known, however, about how the injectate microbes transport through geological media as a community, how such transportability is affected by injector-producer connectivity, and whether such knowledge can be utilized for flowpath characterization. In this study, we analyzed daily-to-weekly timeseries microbial community data from the injected- and produced-fluids of a ten-month flow test at a deep, well-characterized engineered aquifer. We found that the injectate microbial community was distinct from the indigenous community at the amplicon sequence variant (ASV) level, and that the transportability of injectate community towards a given producer, quantified by an “nASV-Overlap” metric we propose, had strong and significant positive correlation with known injector-producer connectivities at our site. This suggests that the better the connectivity, the higher the probability for more injectate species to flow through the interwell region and arrive at a producer. Because interwell connectivity is an important yet usually unknown parameter in subsurface resource engineering, such correlation in turn points to nASV-Overlap as a useful indicator of interwell connectivity for aquifer characterization and long-term monitoring. Based on our findings, an nASV-Overlap-based microbial tracing approach was developed for characterizing and monitoring the relative connectivities across multiple producers with a given injector. A side-by-side comparison between the new nASV-Overlap approach and traditional artificial tracer methods is presented, and their respective strengths and limitations are discussed.

Deep biosphere↗