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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Integrated Computational Materials and Mechanical Modeling for Additive Manufacturing of Alloys with Graded Structure Used in Fossil Fuel Power Plants

Wire-arc additive manufacturing (WAAM) has demonstrated its unique capability of producing large-size alloy components with a significantly reduced fabrication time and enhanced geometry design freedom. In this project, the team has developed an ICME (Integrated Computational Materials Engineering) modeling framework, which supports the WAAM of the AUSC (Advanced Ultra-Supercritical) power plant components. The manufacturing design has been applied to Inconel 740H, steel P91, as well as the dissimilar alloy components between steel P91 and Inconel 740H. The ICME model framework is developed by considering two types of modeling. First, mechanistic modeling has been applied to control the printing quality and understand the sequence of the dissimilar printing of the wall structure. The following models have been included in the developed ICME framework: finite element thermal model, grain structure model, residual stress simulation, crystal plasticity model, CALPHAD-based precipitation kinetic model, phase stability prediction, thermal expansion predictive model, and heuristic creep model. Secondary, a physics-based machine learning model has also been developed based on the ICME model structure. The machine learning model development is based on the ICME model prediction with calibration of the experiments. In addition, the WAAM has been utilized as a high-throughput experimental tool rapidly generating a gradient of alloy composition to facilitate experimental database generation for process-structure-property relationships. Such a database directly supported the ICME-enhanced machine learning, which further assisted in intermediate composition block design between P91 and 740H. A high-throughput screening study of the oxidation resistance has been performed based on such high-throughput experimentation. Based on the computational design, several dissimilar alloy manufacturing with post-heat treatment have been performed with a comprehensive evaluation of mechanical performance, including hardness mapping, yield strength, creep resistance. In this project, the single component of P91 and 740H processed by WAAM after heat treatment designed by ICME has demonstrated higher performance in yield strength and creep resistance than the wrought materials. The P91 sample prepared by WAAM with ICME-designed heat treatment performs better than P92 in creep resistance. The designed graded alloy printing with intermediate block shows a promising performance that exceeds the traditional welding. Moreover, the current research indicates the high need for location-specific design analysis with uncertainty quantification, an important topic that deserves more dedicated research. The achievement of this project demonstrated the promising future of WAAM in structural alloy manufacturing for energy power plant development. Successful printing requires synergetic efforts made by manufacturing, mechanical, and materials sciences.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Accelerating Multiscale Materials Modeling with Machine Learning

The focus of this project is to accelerate and transform the workflow of multiscale materials modeling by developing an integrated toolchain seamlessly combining DFT, SNAP, LAMMPS, (shown in Figure 1-1) and a machine-learning (ML) model that will more efficiently extract information from a smaller set of first-principles calculations. Our ML model enables us to accelerate first-principles data generation by interpolating existing high fidelity data, and extend the simulation scale by extrapolating high fidelity data (10 2 atoms) to the mesoscale (10 4 atoms). It encodes the underlying physics of atomic interactions on the microscopic scale by adapting a variety of ML techniques such as deep neural networks (DNNs), and graph neural networks (GNNs). We developed a new surrogate model for density functional theory using deep neural networks. The developed ML surrogate is demonstrated in a workflow to generate accurate band energies, total energies, and density of the 298K and 933K Aluminum systems. Furthermore, the models can be used to predict the quantities of interest for systems with more number of atoms than the training data set. We have demonstrated that the ML model can be used to compute the quantities of interest for systems with 100,000 Al atoms. When compared with 2000 Al system the new surrogate model is as accurate as DFT, but three orders of magnitude faster. We also explored optimal experimental design techniques to choose the training data and novel Graph Neural Networks to train on smaller data sets. These are promising methods that need to be explored in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Bench-scale Development of a Transformational Graphene Oxide-based Membrane Process for Post-combustion CO 2 Capture

Graphene-based materials, such as graphene and graphene oxide (GO), have been considered as next-generation membrane materials. GTI Energy and The State University of New York at Buffalo (UB) have been developing a transformational GO-based membrane process (designated as GO2) that integrates a high CO 2 /N 2 selectivity membrane (GO-1) and a high CO 2 flux membrane (GO-2) for post-combustion CO 2 capture. An innovative membrane structure, consisting of GO nanochannels intercalated by single-walled carbon nanotube (SWCNT), was developed. The membrane prepared on hollow fiber substrate showed CO 2 permeance as high as 1,300 GPU with CO 2 /N 2 selectivity >200. The membranes were successfully scaled up to effective area of 50-100 cm 2 . The 50-100 cm 2 membranes showed CO 2 /N 2 selectivity ≥200 and CO 2 permeance ≥1,000 GPU for the GO-1 type, and CO 2 /N 2 selectivity ≥20 and CO 2 permeance ≥2,500 GPU for the GO-2 type. The CO 2 capture performance of the GO-based membranes was tested using a simulated flue gas. The testing results indicate that the GO-based membranes are stable in the presence of flue gas contaminants. The GO-based membranes were then further scaled up to a surface area of 1,000 cm 2 . Good stability was achieved during an integrated testing with GO-1 and GO-2 membranes using simulated flue gas. A bench-scale system was designed, constructed, and tested at the National Carbon Capture Center (NCCC). Good stability was achieved during testing of a single-stage process with >10 shutdowns/startups at NCCC. During the integrated testing, the membranes showed good stability at 50°C and 57°C. 70-90% CO 2 removal efficiencies and ≥95% CO 2 purity were validated during the steady state operation at NCCC. Techno-economic analysis indicates the GO2 membrane-based process technology provides a reduction in both the levelized cost of electricity (LCOE) and cost of capture when compared to the reference B12B case presented in the Cost and Performance Baseline for Fossil Energy Plants Volume 1: Bituminous Coal and Natural Gas to Electricity study prepared by the National Energy Technology Laboratory (NETL), before considering any system optimization or improvement opportunities. The benefits are primarily driven by a reduction in the equipment costs of the CO 2 capture process vs. the solvent-based reference process in NETL Case B12B as well as a decrease in the base plant size.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Evaluation of AI-enabled Digital Documented Safety Analysis

The National Reactor Innovation Center (NRIC) is leading a transformative initiative to accelerate advanced reactor deployment by fundamentally reimagining how nuclear safety basis documentation is developed, reviewed, and maintained. Traditional Documented Safety Analysis (DSA) processes for DOE-authorized facilities rely on static, document-centric workflows that consume significant time and resources, exemplified by recent major licensing efforts requiring hundreds of thousands of staff hours and millions of pages of documentation review. These conventional approaches create barriers to the rapid, cost-effective deployment of advanced reactors that America's future energy needs demand. NRIC's DOE Authorization Digital Transformation Project addresses these challenges through an innovative framework that integrates artificial intelligence (AI), digital engineering, and systems-based data management into a cohesive digital ecosystem. This white paper presents NRIC's methodology for evaluating AI-enabled document generation capabilities within this broader digital infrastructure, using the Demonstration of Microreactor Experiments (DOME) facility as a pilot case study. The evaluation will assess an AI tool's ability to generate a Preliminary Documented Safety Analysis (PDSA) through progressive integration stages—from standalone document processing to full digital thread connectivity—while maintaining rigorous verification, validation, and regulatory acceptance standards. By establishing dynamic, traceable connections between design data and safety documentation, NRIC's approach has the potential to reduce both document development time and regulatory review cycles by as much as 50%, while simultaneously improving accuracy, consistency, and traceability. This initiative represents a critical step toward establishing reusable digital infrastructure that reactor developers can leverage to accelerate their path from concept to commercial operation, directly supporting NRIC's mission to demonstrate and deploy advanced nuclear energy technologies.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Enabling Innovation in Wind Turbine Design Using Artificial Intelligence

The Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements (INTEGRATE) project is developing a new inverse-design capability for wind turbine rotors using invertible neural networks. This artificial intelligence (AI)-based technology can capture complex nonlinear aerodynamic effects 100 times faster than alternative design approaches.

aerodynamics↗
DOE OSTI2022

Enabling Innovation in Wind Turbine Design Using Artificial Intelligence

The Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements (INTEGRATE) project is developing a new inverse-design capability for wind turbine rotors using invertible neural networks. This artificial intelligence (AI)-based technology can capture complex nonlinear aerodynamic effects 100 times faster than alternative design approaches.

aerodynamics↗
DOE OSTI2026

Commercial integration of advanced nuclear energy with Artificial Intelligence (AI): Possible implications

The integration of advanced nuclear technologies (both fission and fusion) with artificial intelligence (AI) presents unprecedented national security challenges and opportunities. As fusion energy approaches commercial viability alongside advanced Small Modular Reactors (SMRs), their integration with AI and Artificial General Intelligence (AGI) systems could fundamentally transform the global energy and AI landscapes — two pillars of national security. This document briefly examines how AI could accelerate nuclear energy development and deployment while altering existing power structures, a lot could be done to deepen the discussions. Simultaneously, it observes how nuclear-powered AI may expedite advances toward AGI and beyond. These issues are deeply interconnected and thus need to be examined as a whole and more comprehensively than what’s being summarized here. For instance, AI-powered autonomous operation of nuclear facilities could reduce human error but introduce new cybersecurity vulnerabilities and uncertainties. Further investigation would also address how AI-enhanced nuclear technologies might complicate proliferation concerns through advanced fuel cycle management, nuclear materials production and safeguard. The strategic advantage gained by first entities achieving successful AI-nuclear integration could reshape global and national security framework. Timely analysis of these implications may be crucial for policymakers seeking to harness these technologies' benefits while effectively mitigating their potential risks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Describing Function Analysis of Transformer Magnetizing Inductance for Direct Power Control of Back-to-Back Modular Multilevel Converters with Advanced Grid Support

This paper provides a detailed investigation into the application of describing function-based analysis for assessing transformer magnetizing inductance and its impact on system performance. The focus is on a back-to-back modular multilevel converter architecture, designed to interconnect systems operating at different frequencies. The study explores the implementation of a Direct Power Control strategy, examining its effects on transformer magnetizing inductance saturation and offering effective mitigation techniques. Furthermore, the integration of advanced grid support functionalities is highlighted, demonstrating how these enhancements bolster the converter's ability to improve grid stability and power quality, positioning it as a robust solution for modern power systems. The proposed approach is validated through extensive computer simulations based MAT LAB/Simulink domain, supported by significant case study results, confirming its practical effectiveness.

back-to-back modular multilevel converters (B2B- M↗
DOE OSTI2025

Stress-dependent χ phase transformation in a Ni-based superalloy

The ongoing push to elevate operating temperatures in aerospace gas turbine engines – driven by goals of enhanced fuel efficiency and reduced CO 2 emissions – mandates advancements in the creep resistance of Ni- and Co-based superalloys, which are integral for critical engine components. This study elucidates the role of stress assisted localized phase transformations in the creep properties of these alloys. By leveraging chemo-mechanical coupling, self-healing γ′ precipitates are designed to immobilize planar defects, thereby increasing creep resistance. Employing advanced characterization techniques such as high-resolution Scanning Transmission Electron Microscopy (HR-STEM), in conjunction with atomistic simulations and thermodynamic calculations, novel deformation pathways facilitated by χ local phase transformation (LPT) strengthening have been uncovered; notably, the formation of χ nano-laths through microtwinning and superlattice intrinsic stacking fault (SISF) shearing. This study highlights critical insights into the compositional boundaries necessary for optimizing LPT strengthening while avoiding deleterious bulk formation of η/χ phases. These advancements will guide the design of new alloys maximizing high-temperature creep strength for advanced aerospace applications.

Egan, Ashton J. [Friedrich-Alexander University Er↗
DOE OSTI2023

Large Language Models (LLMs) for Energy Systems Research

The integration of Large Language Models (LLMs) in energy systems research promises transformative results, as demonstrated in this work, particularly in the realms of information retrieval and legal document analysis. We have developed a chat-based interface, specifically designed to query an extensive corpus of technical reports from the National Renewable Energy Laboratory (NREL). This interface capitalizes on the natural language processing capabilities of LLMs, providing future consumers of NREL research with a user-friendly platform to access and extract valuable information from technical documents, thus enhancing the dissemination of research to the public. In addition to information retrieval, we have employed LLMs to extract renewable energy siting ordinances from a variety of legal documents, a task traditionally driven by significant human labor. This automated extraction not only supports the ongoing development of the high-impact NREL siting ordinance database but also ensures the database's accuracy and comprehensiveness. Crucially, we have augmented the performance of LLMs through the integration of a decision tree framework, resulting in a substantial improvement in extraction accuracy. Comparative analysis with manual efforts has shown that this approach not only rivals but also significantly surpasses human accuracy, heralding increased reliability in legal document analysis for energy systems research. To democratize access to these advancements and foster collaborative research, we introduce the "Energy Language Model" (ELM), an open-source software package. ELM encapsulates the methodologies and tools developed in this work, providing researchers and practitioners with a robust toolkit to conduct similar analyses within their respective domains. Through these contributions, this work underscores the immense potential of LLMs in revolutionizing energy systems research, improving accuracy, efficiency, and accessibility in the field.

automation↗
DOE OSTI2024

Importance of Higher Fidelity Model Geometries during Optimization of Critical Experiments

PARADIGM, PARallel Approach of Differential and InteGral Measurements, is a cross-collaborative effort at Los Alamos National Laboratory between nuclear data theorists, differential and integral experimenters, as well as machine learning statisticians to tackle uncertainties in the intermediate region of 239 Pu. In essence, the idea behind PARADIGM is to remove the linear conceptualization of the nuclear data pipeline, shown in Figure 1, and replace it with a far more parallelized approach. The novel approach leverages machine learning to guide which differential measurements and integral experiments will result in the largest decrease in uncertain ties for a nuclide reaction pair in a given energy range. The concept builds off earlier work, EUCLID, which focused on the fast region of 239 Pu. The practical benefit of having evaluation, differential measurement, and integral experiment personnel in collaboration with machine learning is to represent the entire nuclear data in one snapshot. This enable large reduction in the time to deliver improved nuclear data, which using the PARADIGM approach could be done in 3 years. A general outline of PARADIGM and specific topics are available in other papers. The discussion here will pertain directly to the integral experiment design. More specifically, the process of taking a rough design and transforming it into a finalized neutronic model will be discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

PV Inverter Systems Enabled by Monolithically Integrated SiC based Four Quadrant Power Switch (4-QPS) [BiDFET]

The purpose of this project was to develop a new breed of Power Conversion Systems (PCS) for PV integration that is enabled by the newly developed 4-Quadrant Single Die SiC Power Semiconductor Switches (4-QPS) or also referred to as “Bidirectional FET (BIDFET)”. This work includes semiconductor die development, advanced packaging, converter design, development, and testing of 4-QPS enabled hardware prototypes at 1 kW (for single phase residential application) and 10 kW (for three phase commercial application). The BiDirectional Field-Effect Transistor (BiDFET) can enable circuit topologies requiring four-quadrant switches, that were earlier designed using discrete combinations of MOSFETs, IGBTs, GaN HEMTs, and PiN diodes. The monolithic nature of the BiDFET allows lower device count, smaller switch volume, lower inductance, and simpler packaging, and hence more reliable and commercially viable implementation in power electronics converters. The matrix converter topologies, now feasible using BiDFETs, can eliminate the bulky and unreliable dc link capacitors or inductors required for conventional voltage-source or current-source converters in ac–ac and ac–dc applications. The 1.2 kV BiDFET has the potential to disrupt all the applications utilizing 1.2 kV switches, including electric vehicle (EV) drivetrain, bidirectional EV chargers, industrial motor drives, solid-state transformers, datacenter power supplies, elevator drives, dc microgrids, energy storage grid integration, solid-state breakers, etc.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Device Voltage Stress from Ground Leakage Current in Medium-Voltage Solid-State Transformer

Grounding related issues are critical for safe and reliable operation of solid-state transformer (SST) in medium-voltage (MV) applications, e.g., solar photovoltaic and energy storage integration, date center, electric vehicle fast charging, etc. This article presents for the first time the issue of additional device voltage stress due to grounding-loop current in current-source SST, using the soft-switching solid-state transformer (S4T) as an example. The S4T features single-stage isolated AC-AC, AC-DC, or DC-DC conversion with full-range ZVS, derived from flyback converter or current-source converter (CSC). However, the flyback operation for CSC-based SST means that magnetizing current flows through the reverse-blocking devices on only one side of the medium-frequency transformer (MFT) at a time. Then, the voltages across the devices, especially those on the other side of the MFT, can be influenced by parasitic current. A parasitic model of a modular S4T (M-S4T) prototype is developed from direct measurements and datasheets. Using the developed parasitic model and equivalent circuits, the causes of the voltage stress are analyzed. A voltage-stress mitigation scheme of connecting additional grounding capacitors is proposed. Damping resistors are also installed to damp out the grounding-loop resonance. A robust parameter design of the proposed scheme is given. The existence of the voltage stress issue and the effectiveness of the proposed scheme are verified experimentally on an MV SiC M-S4T prototype with inherent parameter variations among the five modules in the prototype. Here, both single-module and stacked-module operation are demonstrated during steady state and dynamic conditions up to 4 kV peak.

42 ENGINEERING↗
DOE OSTI2024

Integrating Electric Vehicles into the Grid

Historically, transportation and power systems operated independently, but the rise of electric vehicles is transforming this relationship. In the United States, EV demands are expected to become the largest source of electricity load growth, posing challenges for the grid if not properly managed. This presentation highlights how managed charging strategies and strategic infrastructure deployment can integrate EVs into the power system to enhance grid efficiency and support renewable energy.

ADVANCED PROPULSION SYSTEMS,DIRECT ENERGY CONVERSI↗
DOE OSTI2025

Securing Smart Manufacturing: Detection of Cyber-Physical Attacks in CNC-Based Systems

As Industry 4.0 advances, the integration of computer numerical control (CNC) machines and advanced manufacturing technologies is transforming production into smart manufacturing systems that blend physical and digital processes as cyber-physical systems. However, this increased cyber-physical connectivity exposes manufacturing systems to cyber threats that can cause severe operational and financial disruptions. This paper presents a comparative study on cyber attacks and anomaly detection techniques in manufacturing, focusing on network traffic from CNC machines. The data extracted from network packets includes machine commands and control signals exchanged between the machine's interface and control system, crucial for maintaining operational integrity. We explore two types of cyber attacks, design modification and command injection, which pose substantial risks to CNC machine productivity and system integrity. Our investigation involves experiments on a real CNC system, highlighting the urgent need for effective detection mechanisms. To address these threats, we evaluate three anomaly detection methods: dynamic time warping (DTW), rolling average, and a deep learning, long short-term memory (LSTM) time-series-based autoencoder. Each is assessed for its effectiveness in identifying anomalous behaviors caused by the attacks. Our findings demonstrate the unique strengths and limitations of each detection technique, providing a deeper understanding of their applicability in realworld manufacturing environments. The comparative analysis indicates that while certain methods are highly effective against specific attack types, others offer broader applicability across different attacks. This study contributes to the accurate detection of anomalies in CNC machining processes, thereby enhancing the reliability and security of smart manufacturing systems against diverse cyber threats.

Williams, Bethanie [Tennessee Technological Univer↗
DOE OSTI2023

Assessment of Existing OpenStudio Measures: Reviews, Interviews, and Future Developments

OpenStudio Measure development is continuously in progress and greatly propelled by the collaborative efforts within the building energy modeling community. To ensure the widespread adoption and benefit of OpenStudio Measures, developers must understand the current status of Measure development and the needs of OpenStudio Measure users. The first step involves a comprehensive review of existing content to prevent redundancy and gain insights into how OpenStudio Measures are used in the building energy modeling community. This knowledge can then be integrated into the Measure development process, and the expertise of practitioners and OpenStudio Measure users can be leveraged to shape future Measures. Numerous OpenStudio Measures have been created and shared on the Building Component Library (BCL). The BCL is an open-source repository housing various OpenStudio-related resources, including building component blocks, descriptive metadata, and Measures describing modifications to building energy models. The OpenStudio Measures in BCL encompass a wide range of energy conservation Measures from basic lighting power reduction to complex HVAC model transformation. They also enable users to generate customized reports and facilitate the integration of energy simulation with other analytical processes. This report presents review of 272 currently available OpenStudio Measures in BCL. The OpenStudio Measures were reviewed by category and subcategory. These Measures are summarized by their functionalities and keywords. To gain insights into how OpenStudio Measures are used in building energy modeling community, interviews were conducted. A total of 12 interview responses were collected from 6 individuals in the industry and 6 individuals in academia. The knowledge acquired from reviewing the existing Measures and interview results will be integrated into the Measure development process, and the expertise of practitioners and OpenStudio Measure users will be leveraged to shape future Measures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗