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At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2023

On the co-nucleation of adjoining twin pairs at grain boundaries in hexagonal close-packed materials

Here this work addresses the formation of adjoining twin pairs (ATPs) at grain boundaries (GBs) in hexagonal close-packed (hcp) metals from the perspective of the co-nucleation (CN) of pairs of deformation twins. A continuum defect mechanics model is proposed to investigate the energetic feasibility of the CN of ATPs resulting from the dissociation of GB dislocations. Application of the model to a set of GBs in Mg reveals that CN is largely preferred over the nucleation of a single twin variant for low misorientation angle GBs, which is consistent with previous experimental analyses. The CN events are subsequently analyzed considering the GB character, and the twin system alignment ($m$') and Schmid factors to obtain better insights into the conditions governing the formation of ATPs via CN. The likelihood of CN even at low $m$' values reveals that CN events could be responsible for the formation of ATP pairs in these scenarios.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A digital twin platform for building performance monitoring and optimization: Performance simulation and case studies

Advancements in sensor technology, data analytics, affordable compute, and communication infrastructure have paved the way for Digital Twin technology in optimizing building operations and controls. This study presents the development of an open and interoperable web-based Digital Twin platform for integrating diverse data streams and facilitating effective user interactions. The platform utilizes modern technologies for the web framework and time-series data management, ensuring scalability and responsiveness. The backend supports seamless integration of diverse data sources and emulators, incorporating data from building sensors and meters, external weather Application Programming Interfaces, and advanced EnergyPlus simulation models of the building and its energy systems including the Distributed Energy Resources that are formulated in Functional Mockup Units. A simulation case study was conducted with FlexLab, a test facility on Lawrence Berkeley National Laboratory campus. The case study includes normal operations, Distributed Energy Resource integration, and power outage scenarios, to illustrate the Digital Twin’s ability to provide critical insights into energy performance and thermal resilience. The results demonstrated the platform’s potential as a decision-support tool for optimizing building energy performance and enhancing resilience against extreme weather events. Future work will focus on deploying the Digital Twin platform to a real building for field validation, extending its capabilities to cover more scenarios such as bidirectional Electric Vehicle interactions, and enhancing user engagement.

EnergyPlus↗

DOE OSTI2022

Atikokan Digital Twin: Machine learning in a biomass energy system

The Atikokan Generating Station, operated by Ontario Power Generation, has a 200 MW, biomass-fired tower boiler that operates on a dispatch schedule with a five-minute cycle. The boiler is generally operated in the range of 40–100 MW using two of five burner levels. In order to optimize boiler performance, we propose the implementation of a unique digital twin. Our digital twin abstraction couples Bayesian inference from science-based models and from observations (machine learning) with decision theory to predict operating-variable set points that optimize the physical asset (the boiler) in the presence of uncertainty (artificial intelligence). We focus this paper on the continuous Bayesian machine learning part of the Atikokan Digital Twin; we discuss decision theory in a companion paper. We identify and learn about 12 operational, model, and measured-output parameters and their uncertainties from high-fidelity, science-based simulations of the Atikokan boiler and from the observed measurements at the power plant. Since the goal of the Atikokan Digital Twin is to implement it online in real time, we require fast function evaluations for the quantities of interest extracted from the simulations in the Bayesian analysis. We use Gaussian process regression/interpolation to create accurate, robust surrogate models. We define the Bayesian priors and likelihood function and solve for the posterior distributions of the 12 parameters. Here we then propagate these distributions (i.e., parameters with uncertainty) into the predicted distributions of 790 quantities of interest to learn about the relative importance of various sources of error including experimental, model, and operating-parameter errors.

09 BIOMASS FUELS↗

DOE OSTI2026

Effects of interstitial oxygen on ω transformations and twin formation in bcc NbTaTiHf multi-principal element alloy from first-principles

Transformation- and twinning-induced plasticity (TRIP and TWIP) have been reported to contribute to the low-temperature deformation of some body-centered cubic (bcc) multi-principal element alloys (MPEAs) containing large fractions of group IV transition metals. The influence of interstitial solutes on the mechanisms underlying these forms of plasticity, however, remains unclear. Using first-principles calculations, we study the effects of interstitial O atoms on the relative stability of bcc and ω phases and on unstable and twin boundary stacking fault energy profiles in a representative bcc MPEA with high group-IV elemental fraction: NbTaTiHf. We find that O additions generally promote the relaxation of ω configurations back to their parent bcc structure, therefore inhibiting ω transformation. Calculations of the Rice parameter for bulk bcc and phases, as well as bcc-ω interfaces, further show that ω formation is a potent embrittlement factor, an effect that is enhanced by O additions, suggesting that the formation of bcc-ω interfaces is energetically preferred over the formation of the bulk ω phase. By contrast, the Rice parameter for twin boundaries indicates that these interfaces do not embrittle the material, even with O atoms at twin boundaries, providing a more favorable pathway for plastic deformation compared to ω transformation.

Density functional theory↗

DOE OSTI2026

Deep learning–based digital twins for heat pumps

Heat pumps are effective cooling and heating appliances to save energy in buildings. However, traditional heat pump models are challenging to integrate with building demands in a co-simulation environment because of the nonlinear thermodynamics of refrigerants. Developing digital twin representatives for heat pumps capable of faster calculations with good accuracy is desirable. This study aimed to establish a generic deep learning–based digital twin for heat pumps with a large amount of high-fidelity data. Two refrigerants for two different heat pumps were considered: an air source heat pump with refrigerant R-410A, an air source heat pump with refrigerant CO 2 , a water source heat pump with refrigerant R-410A, and a water source heat pump with refrigerant CO 2 . Furthermore, results showed that the deep learning (long short-term memory) models effectively represented these four heat pumps as a digital twin: (a) accuracy for training and testing showed smaller than 0.02 for heating electricity and heating demands, and (b) the digital twins showed good consistency with original data for heating electricity and heating demands (root mean square errors of less than 0.12 W and 0.19 W, respectively). Therefore, deep learning–based heat pump models can be used in the co-simulation of building mechanical systems.

Air source heat pump↗

DOE OSTI2024

Utilizing a Virtual Sodium-Cooled Fast Reactor Digital Twin to Aid in Diversion Pathway Analysis for International Safeguards Applications

We report digital twin technology has the potential to improve the effectiveness of international safeguards inspectors by providing a tool which can: first, perform an accurate diversion path analysis, identify their indicators, and required sensors to detect them; and second, monitor facilities in real-time using critical data streams that benefit from this safeguards-by-design approach. Safeguards inspectors are required to visit facilities and verify the nuclear material to ensure no diversion has taken place and detect misuse of the facility; however, this analysis and verification effort is time consuming, and with limited funding it is imperative that time spent at a nuclear facility is focused on key areas. A virtual digital twin of three prototypic sodium fast reactors was developed, where diversion and misuse scenarios were explored to determine how a digital twin could provide inspectors with an understanding of how proliferation may occur and where the most likely areas for proliferation would be. For each of the three reactors, an optimization algorithm was able to find core designs which would be difficult to detect via sensors alone; however, the use of a machine learning adapter provided by the digital twin was able to show general trends in where proliferation as likely to take place.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Twin-boundary-mediated flexoelectricity in LaAlO 3

Flexoelectricity has garnered much attention owing to its ability to bring electromechanical functionality to nonpiezoelectric materials and its nanoscale significance. In order to move towards a more complete understanding of this phenomenon and improve the efficacy of flexoelectric-based devices, it is necessary to quantify microstructural contributions to flexoelectricity. Here we directly measure the flexoelectric response of bulk centrosymmetric LaAlO 3 crystals with different twin-boundary microstructures. We show that twin-boundary flexoelectric contributions are comparable to intrinsic contributions at room temperature and enhance the flexoelectric response by ~4× at elevated temperatures. Additionally, we observe time-dependent and nonlinear flexoelectric responses associated with strain-gradient-induced twin-boundary polarization. Finally, these results are explained by considering the interplay between twin-boundary orientation, beam-bending strain fields, and pinning site interactions, and directly demonstrate that macroscopic flexoelectric responses are very sensitive to structural defects.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Digital Twins for Data Centers

Fueled by an unprecedented adoption of AI (Artificial Intelligence), data centers are becoming the largest growing consumers of energy. Digital Twins provide living digital models of physical systems that enable data-driven analysis and application of AI to better manage selective aspects of the data center and drive efficiency for sustainability. Digital twins have emerged as a way to create virtual prototypes of physical artifacts, which may be used in a variety of contexts. Physical artifacts include airplanes, factories, or even static objects, such as bridges or dams. Digital twin helps monitor changes and assist in predicting planned or unplanned behaviors of physical objects. In this paper, we discuss digital twins for data centers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Digital Twin and Cybersecurity Requirements for Beartooth

This report summarizes the requirements for the Cybersecurity and the Digital Twin for Beartooth. The Beartooth digital twin will ultimately allow for both comprehensive offline data analysis and transformational autonomy. This document places limits on the IPL-funded work scope to end at a predictive digital twin stage but allow for future expansion to support full autonomy. It is expected that this work will support both currently funded LDRD and future direct-funded digital twin research.

42 ENGINEERING↗

DOE OSTI2025

Immersive Digital Twin Laboratory for Engineering Education (CRADA Final Report)

This project aimed to create an immersive digital twin laboratory that incorporates advanced tracking and visualization capabilities. In collaboration with Fort Lewis College, NREL designed a state-of-the-art physical visualization laboratory, developed a software platform to enable interaction with tracked physical objects in the laboratory, and provided proof-of-concept curricula that included manipulating these tracked objects. The project was initiated to address the growing need for innovative educational tools in engineering education. As renewable energy systems, particularly solar installations, become more complex, there is a pressing need to bridge the gap between theoretical knowledge and practical application. Traditional methods of teaching solar engineering concepts often fall short of providing students with a comprehensive, hands-on understanding. This immersive digital twin laboratory was conceived to fill that gap by creating a safe, non-energized setting where students can interact with augmented solar installation objects, gaining valuable insights into system performance, design, and maintenance. The project utilized extended reality (XR) technologies, including head-mounted displays (HMDs) and a whole-room optical motion tracking system, to connect physical objects with their digital twins in real time. The laboratory was equipped with MagicLeap 2 HMDs, supported by a Vicon Vero 2.2 Optical Tracking System, which provided precise 6-degrees-of-freedom (6-DOF) tracking. We developed a software platform to manage the interaction between the tracked physical objects and their virtual counterparts, enabling real-time data synchronization, object recognition, and virtual overlays. We designed the system to be flexible and extendable, allowing for future integration of additional objects and curriculum. This research advances the field of engineering education by demonstrating the potential of immersive digital twin environments. The laboratory provides a dynamic learning space where students can experiment, collaborate, and learn without the risks associated with live experimentation. The ability to simulate and manipulate solar installation objects under various conditions has broad implications for workforce development, particularly in renewable energy. The project also highlights the economic feasibility of using XR technologies in educational settings, offering a cost-effective solution for institutions looking to enhance their curriculum. By fostering a deeper understanding of solar energy systems, this work contributes to the broader goal of supporting the global energy transition and preparing the next generation of engineers and technicians.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

High Fidelity Digital Twins for BWRX-300 Critical Systems

This project developed and demonstrated digital-twin technology for advanced nuclear systems under the ARPA-E GEMINA program, with a focus on the BWRX-300 small modular reactor. The primary objective was to reduce operation and maintenance (O&M) costs through predictive health monitoring and model-based fault detection. The project combined MIT’s high-fidelity physics models and machine-learning and digital-twin algorithms, and GE Hitachi’s digital-twin algorithms and operational expertise to build physics-based digital twins capable of real-time system monitoring and maintenance scheduling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Data Management for Digital Twin Implementation: Enhancing Existing SCADA Architecture for INL's CITRC Test Distribution System

The United States power grid will continue to experience stress from aging infrastructure, extreme weather, and increasing loads. In order to strengthen resiliency, researchers need to run tests and simulations that model real-life infrastructure. Idaho National Laboratory (INL) is creating a digital twin of its Critical Infrastructure Test Range Complex (CITRC). CITRC is an at-scale grid testbed which can be configured in utility-realistic distribution scenarios for a variety of tests, such as advanced grid protection and control. A digital twin of this infrastructure would support in-depth tests/simulations without risking physical consequences, before trialing devices under test at-scale. This project focuses on the data acquisition part of constructing a digital twin. It investigates the Supervisory Data Acquisition and Control (SCADA) system of CITRC and explores ways to utilize this system for the twin. Future “ideal” at-scale implementations such as upgraded equipment, modern communication protocols, and automation applications are also explored.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

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↗

DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

42 ENGINEERING↗

DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

actuated signal control↗

DOE OSTI2023

Twin Boundaries Contribute to The First Cycle Irreversibility of LiNiO 2

Abstract LiNiO 2 remains a target for layered oxide Li‐ion cathode development as it can theoretically deliver the highest energy density of this materials class. In practice, LiNiO 2 suffers from poor capacity retention due to electrochemically‐induced structural changes. While the impact of Ni off‐stoichiometry on the electrochemical performance has been extensively studied, that of planar defects present in the as‐synthesized cathode is not well understood. Advanced ex situ and operando structure probes are used to identify and quantify point and planar defects present in as‐synthesized Li 1‐y Ni 1+y O 2 cathodes and monitor their evolution during the first cycle. Specifically, a 7 Li nuclear magnetic resonance (NMR) signature characteristic of Li environments near twin boundaries is identified; an assignment supported by first‐principles calculations and scanning transmission electron microscopy (STEM) images of twin boundary defects. The NMR results suggest that the concentration of twin boundaries depends on the amount of Ni excess. Moreover, operando magnetometry and ex situ synchrotron X‐ray diffraction and NMR demonstrate that these planar defects impede Li reinsertion into the bulk cathode at reasonable discharge rates and contribute to the first cycle irreversible capacity. These findings provide new design rules for Li 1‐y Ni 1+y O 2 cathodes, whereby a reduced concentration of twin boundaries in the pristine material leads to reduced kinetic limitations and improved cathode utilization.

Nguyen, H.↗

DOE OSTI2024

Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing

Detecting anomaly in fatigue and fracture experimental materials science is an interesting yet challenging topic. The reasons are threefold. First, the anomalous microstructure feature that gives rise to structural failure is small, sometimes in the order of 10 -7 of the interrogated volume. This, in turn, results in a highly imbalanced classification problem in machine learning (ML). Second, the consequence is high, in the sense that the test specimen is destructed in such case. Third, the convolution between microstructure stochasticity and the small probability of void nucleation, growth, and coalescence makes failure and fracture a hard-to-predict and challenging problem in materials science due to its irreproducibility, even experimentally. In this paper, we developed a materials digital twin and applied anomaly detection methods to detect voids and anomaly in additive manufacturing (AM). The materials digital twin is driven by two integrated computational materials engineering (ICME) models, which are kinetic Monte Carlo (kMC) and crystal plasticity finite element method (CPFEM). In conclusion, we demonstrated that by using anomaly detection, it is possible to detect voids and other defects in materials digital twin, which paves way for future research in integrating materials digital twin with its physical counterpart.

ICME↗

DOE OSTI2021

In situ monitoring of dislocation, twinning, and detwinning modes in an extruded magnesium alloy under cyclic loading conditions

This work investigates the microscopic deformation mechanisms of an extruded, precipitation-strengthened AZ80 magnesium (Mg) alloy subjected to strain-controlled low-cycle fatigue using in situ neutron diffraction measurements. Results demonstrate that the plastic deformation during cyclic loading is dominated by the alternating {10.2} extension twinning and detwinning mechanisms. The observed deformation mode is strongly texture and precipitate dependent. For the initial texture, the tested material has two major texture components which result in the occurrence of extension twinning during both compression and reverse tension in the first two cycles. The prolonged detwinning process in the following cycles is proposed to relieve the shear stress field of {00.2} grains, leading to the disappearance of twinning. The precipitation strengthening results in an increase of the critical resolved shear stress (CRSS) by similar to 33 MPa for the extension twinning in this AZ80 alloy. Here, the synergistic effects of the initial texture, precipitation strengthening, and load sharing of various grain families and phases contribute to the complicated evolution of dominant deformation mechanisms, among which elevated dislocation activities are believed to be responsible for the relatively poor low-cycle-fatigue lifetime when compared to other Mg alloys.

36 MATERIALS SCIENCE↗