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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Advanced Ceramics for NASA's Current and Future Needs

Ceramic composites and monolithics are widely recognized by NASA as enabling materials for a variety of aerospace applications. Compared to traditional materials, ceramic materials offer higher specific strength which can enable lighter weight vehicle and engine concepts, increased payloads, and increased operational margins. Additionally, the higher temperature capabilities of these materials allows for increased operating temperatures within the engine and on the vehicle surfaces which can lead to improved engine efficiency and vehicle performance. To meet the requirements of the next generation of both rocket and air-breathing engines, NASA is actively pursuing the development and maturation of a variety of ceramic materials. Anticipated applications for carbide, nitride and oxide-based ceramics will be presented. The current status of these materials and needs for future goals will be outlined. NASA also understands the importance of teaming with other government agencies and industry to optimize these materials and advance them to the level of maturation needed for eventual vehicle and engine demonstrations. A number of successful partnering efforts with NASA and industry will be highlighted.

Jaskowiak, Martha H.↗
DOE OSTI2025

Bio-Based Polyurethane Materials: Technical, Environmental, and Economic Insights

Polyurethane (PU) is widely used due to its attractive properties, but the shift to a low-carbon economy necessitates alternative, renewable feedstocks for its production. This review examines the synthesis, properties, and sustainability of bio-based PU materials, focusing on renewable resources such as lignin, vegetable oils, and polysaccharides. It discusses recent advances in bio-based polyols, their incorporation into PU formulations, and the use of bio-fillers like chitin and nanocellulose to improve mechanical, thermal, and biocompatibility properties. Despite promising material performance, challenges related to large-scale production, economic feasibility, and recycling technologies are highlighted. The paper also reviews life cycle assessment (LCA) studies, revealing the complex and context-dependent environmental benefits of bio-based PU materials. These studies indicate that while bio-based PU materials generally reduce greenhouse gas emissions and non-renewable energy use, their environmental performance varies depending on feedstock and formulation. The paper identifies key areas for future research, including improving biorefinery processes, optimizing crosslinker performance, and advancing recycling methods to unlock the full environmental and economic potential of bio-based PU in commercial applications.

Jayalath, Piumi↗
DOE OSTI2016

R&D Plan for RISMC Industry Application #1: ECCS/LOCA Cladding Acceptance Criteria

The Nuclear Regulatory Commission (NRC) is finalizing a rulemaking change that would revise the requirements in 10 CFR 50.46. In the proposed new rulemaking, designated as 10 CFR 50.46c, the NRC proposes a fuel performance-based equivalent cladding reacted (ECR) criterion as a function of cladding hydrogen content before the accident (pre-transient) in order to include the effects of higher burnup on cladding performance as well as to address other technical issues. A loss of operational margin may result due to the more restrictive cladding embrittlement criteria. Initial and future compliance with the rule may significantly increase vendor workload and licensee costs as a spectrum of fuel rod initial burnup states may need to be analyzed to demonstrate compliance. The Idaho National Laboratory (INL) has initiated a project, as part of the DOE Light Water Reactor Sustainability Program (LWRS), to develop analytical capabilities to support the industry in the transition to the new rule. This project is called the Industry Application 1 (IA1) within the Risk-Informed Safety Margin Characterization (RISMC) Pathway of LWRS. The general idea behind the initiative is the development of an Integrated Evaluation Model (IEM). The motivation is to develop a multiphysics framework to analyze how uncertainties are propagated across the stream of physical disciplines and data involved, as well as how risks are evaluated in a LOCA safety analysis as regulated under 10 CFR 50.46c. This IEM is called LOTUS which stands for LOCA Toolkit for US, and it represents the LWRS Program’s response to the proposed new rule making. The focus of this report is to complete an R&D plan to describe the demonstration of the LOCA/ECCS RISMC Industry Application # 1 using the advanced RISMC Toolkit and methodologies. This report includes the description and development plan for a RISMC LOCA tool that fully couples advanced MOOSE tools already in development in order to characterize and optimize plant safety and operational margins. Advanced MOOSE tools that are needed to complete this integrated evaluation model are: RAVEN, RELAP-7, BISON, and MAMMOTH.

10CFR50.46C↗
DOE OSTI2019

Battery Thermal Characterization

A well-designed thermal management system is critical to the life and performance of electric-drive vehicles (EDVs) hybrids (HEVs), plug-in hybrids (PHEVs), and all-electric vehicles (EVs). Temperature and temperature uniformity both significantly affect the performance, lifespan, and safety of vehicle energy storage devices. NREL conducts thermal management research and development (R&D) to optimize battery performance and extend useful life. A recognized leader in battery thermal analysis and characterization, NREL evaluates electrical and thermal performance of battery cells, modules, and packs, full energy storage systems, and the interaction of these systems with other vehicle components. The lab's performance assessments factor in the design of the thermal management system, the thermal behavior of the cell, battery lifespan, and safety of the energy storage system, as well as full integration into a vehicle. The lithium-ion (Li-ion) batteries found in most of today's electric-drive vehicles are smaller and more lightweight than previous nickel-metal hydride (NiMH) technology, but they are also more sensitive to overheating, overcharging, and extreme spikes in temperature known as thermal runaway. High temperatures diminish battery life and increase battery replacement costs, while low temperatures decrease battery power and capacity, all of which impact vehicle range, performance, and affordability. NREL's breakthrough research is focused on reducing thermal barriers to achieve more uniform temperatures. NREL uses its R&D 100 Award-winning Isothermal Battery Calorimeters, the only instruments in the world capable of such precise thermal measurements, for much of this research. Calorimeter readings are paired with thermal imaging of batteries using infrared cameras to evaluate battery temperature distribution. In addition, NREL is working with industry to develop computer-aided engineering software tools to optimize thermal management of batteries. NREL's analysis for the U.S. Advanced Battery Consortium (USABC) show that optimized thermal management can increase battery power by more than 10%. Without proper thermal management, an EDV battery can last more than 10 years in a temperate climate, like in Minnesota, but only 7 years in a hot climate, such as in Arizona. In extreme instances, battery overheating can pose safety hazards, including fires. Performance: Maintaining desired temperature ranges is critical in optimizing EDV battery storage capacity, lifespan, and performance, and eventually impacts cost and consumer acceptance. NREL's breakthrough research focuses on achieving more uniform temperatures while a vehicle is in motion and actively generating heat, as well as when it is parked and exposed to ambient temperatures over prolonged periods of time. Safety: Most energy storage systems deliver maximum performance at higher temperatures. However, this extreme heat can overload systems and create dangerous conditions. NREL evaluation helps to more effectively regulate thermal characteristics, providing greater safeguards and stronger performance. NREL has invented a device that emulates internal short circuits in batteries to better understand how to prevent future failures and related hazards.

25 ENERGY STORAGE↗
DOE OSTI2015

Crystal plasticity analysis of stress partitioning mechanisms and their microstructural dependence in advanced steels

Two-phase advanced steels contain an optimized combination of high yield strength and large elongation strain at failure, as a result of stress partitioning between a hard phase (martensite) and a ductile phase (ferrite or austenite). Provided with strong interfaces between the constituent phases, the failure in the brittle martensite phase will be delayed by the surrounding geometric constraints, while the rule of mixture will dictate a large strength of the composite. To this end, the microstructural design of these composites is imperative especially in terms of the stress partitioning mechanisms among the constituent phases. Based on the characteristic microstructures of dual phase and multilayered steels, two polycrystalline aggregate models are constructed to simulate the microscopic lattice strain evolution of these materials during uniaxial tensile tests. By comparing the lattice strain evolution from crystal plasticity finite element simulations with advanced in situ diffraction measurements in literature, this study investigates the correlations between the material microstructure and the micromechanical interactions on the intergranular and interphase levels. Finally, it is found that although the applied stress will be ultimately accommodated by the hard phase and hard grain families, the sequence of the stress partitioning on grain and phase levels can be altered by microstructural designs. Implications of these findings on delaying localized failure are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Rational optimization of tolC as a powerful dual selectable marker for genome engineering

Selection has been invaluable for genetic manipulation, although counter-selection has historically exhibited limited robustness and convenience. TolC, an outer membrane pore involved in transmembrane transport in E. coli, has been implemented as a selectable/counter-selectable marker, but counter-selection escape frequency using colicin E1 precludes using tolC for inefficient genetic manipulations and/or with large libraries. Here, we leveraged unbiased deep sequencing of 96 independent lineages exhibiting counter-selection escape to identify loss-of-function mutations, which offered mechanistic insight and guided strain engineering to reduce counter-selection escape frequency by ~40-fold. We fundamentally improved the tolC counter-selection by supplementing a second agent, vancomycin, which reduces counter-selection escape by 425-fold, compared colicin E1 alone. Combining these improvements in a mismatch repair proficient strain reduced counter-selection escape frequency by 1.3E6-fold in total, making tolC counter-selection as effective as most selectable markers, and adding a valuable tool to the genome editing toolbox. These improvements permitted us to perform stable and continuous rounds of selection/counter-selection using tolC, enabling replacement of 10 alleles without requiring genotypic screening for the first time. Finally, we combined these advances to create an optimized E. coli strain for genome engineering that is ~10-fold more efficient at achieving allelic diversity than previous best practices.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Sieving Hydrogen Isotopes via Machine Learning Assisted Chemical Vapor Deposition (CVD) of High‐Quality Monolayer Hexagonal Boron Nitride (h‐BN) on Iron Foils

Atomically thin two-dimensional (2D) ceramics, such as monolayer hexagonal boron nitride (h-BN), present potential for disruptive advances in separations. However, sub-atomic scale separation of hydrogen isotopes (H + /D + ) require near pristine 2D material membranes, and scalable synthesis of such high-quality h-BN comparable to mechanically exfoliated crystals remains a significant challenge. Here, we report a scalable Fe-catalyzed chemical vapor deposition (CVD) process for bottom-up synthesis of large-area, high-quality monolayer h-BN films, overcoming key limitations of conventional ammonia-based routes. By leveraging mechanistic insights and higher CVD temperatures, we suppress multilayer formation and achieve uniform monolayer h-BN coverage on commercially available Fe foils. Machine learning enables systematic exploration of the complex, multi-dimensional CVD parameter space (growth time, temperature, precursor temperature, multilayer faction, coverage), providing data-driven approaches to visualize and identify process regimes facilitating predominantly monolayer h-BN growth with minimal secondary nuclei/ad-layers. The optimized Fe-catalyzed CVD h-BN membranes show high-quality as observed by proton/deuteron (H + /D + ) selectivity ≈8.45, approaching the highest quality benchmark of mechanically exfoliated h-BN (H + /D + selectivity ≈10) as well as significantly outperforming Cu-catalyzed CVD h-BN membranes (H + /D + selectivity ≈3.62, control selectivity ≈1.7). Our work provides a scalable cost-effective route for high-quality monolayer h-BN synthesis for sub-atomic scale separations (H + /D + ) and demonstrates the broader potential of machine learning-guided optimization of CVD for advancing synthesis of 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Power Application Developer’s Guide to the Common Information Model: An Introduction for Power Systems Engineers and Application Developers – CIM17v40

A key issue in creating the next generation of energy management system (EMS) and advanced distribution management system (ADMS) platforms will be the ability to represent and exchange power system network model data in a consistent manner. To this end, the Common Information Model (CIM) stands out as the only standardized vocabulary (or ontology) for defining power system network models and asset data in a comprehensive, consistent manner across the generation-transmission-distribution boundary. The CIM is freely available to use and extend. The CIM is maintained by the UCAiug (informally known as the CIM User’s Group) under an Apache 2.0 license. The CIM Users Group collaborates with the IEC and other standards communities for the development of technical and informative specifications. Although portions of the information model are referred to by the corresponding IEC standards naming, it is not necessary to purchase any of the IEC standards to use the CIM information model. This document provides a roadmap for power system engineers and application developers not familiar with semantic modeling to start using the CIM for modeling, simulation, optimization, and development of advanced power applications. The key classes needed for defining power system topology and equipment are explained systematically. Key focus areas include modeling of lines, transformers, generators, switching equipment, loads, and distributed energy resources (DERs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Parametric reduced order models for graded lattice structures

Graded lattice structures, characterized by smoothly varying mechanical properties, hold significant promise for optimizing material distribution in advanced engineering applications. However, accurately modeling these structures poses substantial computational challenges due to the continuous geometric variations within their unit cells. Here, to address these challenges, this paper introduces a novel Efficient Reduced Order Model (EROM) that integrates the Matrix Discrete Empirical Interpolation Method (MDEIM) and Discrete Empirical Interpolation Method (DEIM) with polynomial regression to efficiently manage geometric parametrization in lattice structures. Unlike traditional reduced order models (ROMs) that require extensive precomputed libraries for each geometric configuration, our approach enables continuous geometric variations through a flexible algebraic formulation, significantly reducing computational costs while preserving high accuracy. The method constructs projection matrices for individual unit cells that can be efficiently assembled into global systems, leveraging the repetitive nature of lattice structures. Numerical studies demonstrate that our EROM achieves displacement errors below 1% and von Mises stress prediction errors below 4%, coupled with computational speedups exceeding two orders of magnitude compared to full-order simulations. The proposed method's modularity and scalability make it particularly suitable for design optimization and real-time simulation of functionally graded lattice structures, with applications spanning aerospace to biomedical engineering.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Optimal Wind Turbine Design for H2 Production

NREL and industrial partners GE and Nel seek to advance affordable green hydrogen production by optimizing wind turbine design specifically for hydrogen production and validating designs using NREL's ARIES facilities. This project will couple wind turbine, wind plant, solar plant, and electrolyzer models to predict hydrogen production from variable, renewable power sources. It will then use gradient-based optimization with exact-analytic gradients to optimize wind turbine rotor diameter, hub height, and power rating for optimal hydrogen production. Finally, it will validate the optimal turbine designs using the ARIES research platform, where it will compare a baseline turbine to the optimal design for a variety of scenarios and resources.

electrolyzer↗
DOE OSTI2020

Do optimization methods in deep learning applications matter?

With advances in deep learning, exponential data growth and increasing model complexity, developing efficient optimization methods are attracting much research attention. Several implementations favor the use of Conjugate Gradient (CG) and Stochastic Gradient Descent (SGD) as being practical and elegant solutions to achieve quick convergence, however, these optimization processes also present many limitations in learning across deep learning applications. Recent research is exploring higher-order optimization functions as better approaches, but these present very complex computational challenges for practical use. Comparing first and higher-order optimization functions, in this paper, our experiments reveal that Levemberg-Marquardt (LM) significantly supersedes optimal convergence but suffers from very large processing time increasing the training complexity of both, classification and reinforcement learning problems. Our experiments compare off-the-shelf optimization functions(CG, SGD, LM and L-BFGS) in standard CIFAR, MNIST, CartPole and FlappyBird experiments.The paper presents arguments on which optimization functions to use and further, which functions would benefit from parallelization efforts to improve pretraining time and learning rate convergence.

Ozyildirim, Buse Melis↗
DOE OSTI2015

WE-EF-BRA-03: Catheter- Free Ablation with External Photon Radiation: Treatment Planning, Delivery Considerations, and Correlation of Effects with Delivered Dose

Purpose: To plan, target, and calculate delivered dose in atrioventricular node (AVN) ablation with volume-modulated arc therapy (VMAT) in an intact porcine model. Methods: Seven pigs underwent AVN irradiation, with prescription doses ranging between 25 and 55Gy in a single fraction. Cardiac CT scans were acquired at expiration. Two physicians contoured AVN targets on 10 phases, providing estimates of target motion and inter-physician variability. Treatment planning was conducted on a static phase-averaged CT. The volume designated to receive prescription dose covered the full extent of AVN cardiac motion, expanded by 4mm for setup uncertainty. Optimization limited doses to risk structures according to single-fraction tumor treatment protocols. Orthogonal kV images were used to align bony anatomy at time of treatment. Localization was further refined with respiratory-gated cone-beam CT, and range of cardiac motion was verified under fluoroscopy. Beam delivery was respiratory-gated for expiration with a mean efficiency of 60%. Deformable registration of the 10 cardiac CT phases was used to calculate actual delivered dose for comparison to electro-anatomical and visually evident lesions. Results: The mean [minimum,maximum] amplitude of AVN cardiac motion was LR 2.9 [1.7,3.9]mm, AP 6.6 [4.4,10.4]mm, and SI 5.6 [2.0,9.9]mm. Incorporating cardiac motion into the dose calculation showed the volume receiving full dose was 40–80% of the volume indicated on the static planning image, although the contoured AVN target received full dose in all animals. Initial results suggest the dimensions of the electro-anatomical lesion are correlated with the 40Gy isodose volume. Conclusion: Image-guidance techniques allow for accurate and precise delivery of VMAT for catheter-free arrhythmia ablation. An arsenal of advanced radiation planning, dose optimization, and image-guided delivery techniques was employed to assess and mitigate effects of cardiac and respiratory motion. Feasibility of delivery to the pulmonary veins and left ventricular myocardium will be investigated in future studies. D. Packer Disclosures: Abiomed, Biosense Webster, Inc., Boston Scientific Corp., CardioFocus, Inc., Johnson and Johnson, Excerpta Medica, Ortho-McNeil-Jannsen, Sanofi Aventis, CardioInsight Technologies, InfoBionic, SIEMENS, Medtronic, Inc., CardioDx, Inc., CardioInsight Technologies, FoxP2 Medica, Mediasphere Medical, Wiley-Blackwell, St. Jude Medical, Endosense, Thermedical, EP Advocate LLC, Hansen Medical, American Heart Association, EpiEP, NIH.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2005

Orbit Modification of Earth-Crossing Asteroids/Comets Using Rendezvous Spacecraft and Laser Ablation

This report describes the approach and results of an end-to-end simulation to deflect a long-period comet (LPC) by using a rapid rendezvous spacecraft and laser ablation system. The laser energy required for providing sufficient deflection DELTA V and an analysis of possible intercept/rendezvous spacecraft trajectories are studied in this analysis. These problems minimize a weighted sum of the flight time and required propellant by using an advanced propulsion system. The optimal thrust-vector history and propellant mass to use are found in order to transfer a spacecraft from the Earth to a targeted celestial object. One goal of this analysis is to formulate an optimization problem for intercept/rendezvous spacecraft trajectories. One approach to alter the trajectory of the object in a highly controlled manner is to use pulsed laser ablative propulsion. A sufficiently intense laser pulse ablates the surface of a near-Earth object (NEO) by causing plasma blowoff. The momentum change from a single laser pulse is very small. However, the cumulative effect is very effective because the laser can interact with the object over long periods of time. The laser ablation technique can overcome the mass penalties associated with other nondisruptive approaches because no propellant is required to generate the DELTA V (the material of the celestial object is the propellant source). Additionally, laser ablation is effective against a wide range of surface materials and does not require any landing or physical attachment to the object. For diverting distant asteroids and comets, the power and optical requirements of a laser ablation system on or near the Earth may be too extreme to contemplate in the next few decades. A hybrid solution would be for a spacecraft to carry a laser as a payload to a particular celestial body. The spacecraft would require an advanced propulsion system capable of rapid rendezvous with the object and an extremely powerful electrical generator, which is likely needed for the propulsion system as well. The spacecraft would station-keep with the object at a small standoff distance while the laser ablation is performed.

Park, Sang-Young↗
DOE OSTI2018

Technology for Building Systems Integration and Optimization – Landscape Report

BTO's Commercial Building Integration (CBI) program helps advance a range of innovative building integration and optimization technologies and solutions, paving the way for high-performing buildings that could use 50-70% less energy than typical buildings. CBI’s work focuses on early stage technology innovation, with an emphasis on how components and systems work together and how whole buildings are integrated and optimized. This landscape study outlines the current body of knowledge, capabilities, and the broader array of solutions supporting integration and optimization in commercial buildings. CBI seeks to support solutions for both existing buildings and new construction, which often present very different challenges.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

Theory and applications of optimal control in aerospace systems

This AGARD graph addresses the advances effected in the theory and design of modern optimal guidance and control systems, in the following areas: Part I: Theory, Part II: Design Techniques, Part III: Applications and should provide an aid in the application of these modern techniques. This AGARD graph was prepared at the request of the Guidance and Control Panel of AGARD.

Pieter Kant↗
DOE OSTI2024

Equally high efficiencies of organic solar cells processed from different solvents reveal key factors for morphology control

The power conversion efficiency of organic solar cells (OSCs) is exceeding 20%, an advance in which morphology optimization has played a significant role. It is generally accepted that the processing solvent (or solvent mixture) can help optimize morphology, impacting the OSC efficiency. Here we develop OSCs that show strong tolerance to a range of processing solvents, with all devices delivering high power conversion efficiencies around 19%. By investigating the solution states, the film formation dynamics and the characteristics of the processed films both experimentally and computationally, we identify the key factors that control morphology, that is, the interactions between the side chains of the acceptor materials and the solvent as well as the interactions between the donor and acceptor materials. Our work provides new understanding on the long-standing question of morphology control and effective guides to design OSC materials towards practical applications, where green solvents are required for large-scale processing.

14 SOLAR ENERGY↗
DOE OSTI2024

Advanced manufacturing and digital twin technology for nuclear energy*

Advanced manufacturing techniques and digital twin technology are rapidly transforming the nuclear industry, offering the potential to enhance productivity, safety, and cost-effectiveness. Customized parts are being produced using additive manufacturing, automation, and robotics, while digital twin technology enables the virtual modeling and optimization of complex systems. These advanced technologies can significantly improve operational efficiency, predict system behavior, and optimize maintenance schedules in the nuclear energy sector, leading to heightened safety and reduced downtime. However, the nuclear industry demands the highest levels of safety and security, as well as intricate manufacturing processes and operations. Thus, challenges such as data management and cybersecurity must be addressed to fully realize the potential of advanced manufacturing techniques and digital twin technology in the nuclear industry. This comprehensive review highlights the critical role of digital twin technology with advanced manufacturing toward nuclear energy to improve performance, minimize downtime, and heighten safety, ultimately contributing to the global energy mix by providing dependable and low-carbon electricity.

36 MATERIALS SCIENCE↗