Search NASA⌕ Search

SEARCH · Search NASA

Results for “Digital twin (DT)”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

My Virtual Cancer

Since each cancer has its own unique characteristics, each one can respond differently to the same treatments. Therefore, the creation of a digital twin (DT) of cancer can assist us in predicting the evolution of an individual's cancer through modeling each tumor's characteristics and response to treatment. Hence, we propose to take advantage of new advances in computational approaches and combine mechanistic, machine learning, and stochastic modeling approaches to create “My Virtual Cancer", a DT platform. To establish a personalized DT, we use patient-specific data for parameter estimations, sensitivity analysis, and uncertainty quantification. For each patient, we will estimate the values of parameters of their QSP model using the patient's data. We perform a multi-dimensional sensitivity analysis and uncertainty quantification on the mechanistic model to find a set of critical interactions and predict the intervals of confidence. Since this QSP model includes the data-driven mechanistic model of cells and molecules' interaction networks, one of the ultimate results of this DT would be the prediction of evolution of tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Conceptual Machine Learning Strategy for Maintenance of Molten Salt Heat Exchanger

This report addresses the requirements of Milestone M3.2 Define maintenance scenarios and benchmarks. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Machine Learning Classification of Molten Salt Heat Exchanger Channel Plugging using Synthetic Data

This report addresses the requirements of Milestone M3.4 AI capability to identify and predict maintenance events. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating the feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults. To determine the optimal approach to detection and localization of channel plugging, we benchmark seven different ML models: Logistic Regression, K-Nearest Neighbors (KNN), Gaussian Naïve Bayes, Support Vector Machines (SVM), Decision Tree Classifier, Random Forest Tree Classifier, and Feed-Forward Neural Network. ML algorithms are benchmarked using synthetic HX plugging data generated with computational fluid dynamics COMSOL software, with added brown noise to represent experimental noise. We show that the best performance is obtained with the Decision Tree classifier.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Achieving Cyber-Resilience for Power Systems using a Learning, Model-Assisted Blockchain Framework

The secure integration and management of distributed energy resources (DER) and power aggregators in the electric grid requires secure communications and a physics-aware Command and Control (C2) strategy. A Blockchain (BC)-based overlay network was developed to provide a security layer for the existing power grid network that mitigates risks in current and legacy network and C2 protocols. By integrating a Model-Assisted Machine Learning (MAML) framework with a Secure Blockchain Overlay Network (SBON) a defense-in-depth strategy was achieved. In our approach, the MAML framework leveraged a smart contract framework to gather network data and learn the dynamics of DER to develop detection strategies for attacks targeting sensors and actuators used by DER. The MAML framework learned dynamical systems models for individual DERs to detect sensor attacks. For DER we utilized a Digital Twin (DT) to accelerate the learning process for a model resistant to stealthy attacks. The project created DT for PV inverters and BESS. The DTs were coupled with a model-assisted, data-driven learning of DER behavior. Specifically, we evaluated architectures for model-based learning with model-free fine-tuning. Additionally, differential privacy techniques were used to obfuscate data, while still allowing the computation of attack detection results based on obfuscated data. The SBON developed leverages a private permissioned blockchain network orchestrated with the Hyperledger Fabric framework. To connect the cyber world, which orchestrates the blockchain fabric, and the physical world where the power network resides, we developed a system implementation to enable the secure interaction of the physical world and the abstracted blockchain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Environmentally Assisted Fatigue in Light Water Reactor Environment

This report summarizes the Environmentally Assisted Fatigue (EAF) research conducted at ANL under the US DOE Light Water Reactor Sustainability (LWRS) program. Starting from a rich background in theoretical and experimental EAF, ANL previously developed an approach to evaluate fatigue performance of reactor materials in light water reactor environments with the correction factor F en . The approach was based on a large body of experimental work performed at ANL and elsewhere, and was consistent with American Society of Mechanical Engineers (ASME)’s methodology governing the design and construction of reactor components. In recent years, the program was focused on component fatigue prediction and made several major and fundamental contributions in this area. These accomplishments help meet the needs identified by the industry concerning component level fatigue predictions in complex, transient conditions. The main contribution of the ANL program involved the development of a system-level model for estimating residual strain and life of nuclear reactor coolant system components under connected-system-thermal-mechanical boundary conditions. The goal was to predict the stress hotspots, strain residuals, strain amplitudes and the resulting fatigue lives. Thermal-mechanical stress analysis was performed considering thermal stratification and a design-basis reactor loading cycle. Based on the finite element (FE) model results, the strain residuals, strain amplitudes and resulting fatigue lives of reactor coolant system (RCS) components were predicted. The results show that some of the RCS components can have significantly different strain amplitudes, residual strain, and fatigue lives, despite having similar geometry and material. In addition, the simulated component-level strain profile can guide the selection of appropriate test inputs for conducting laboratory-scale EAF tests. Building upon the system-level model, ANL developed a digital twin (DT) framework to predict the structural states and associated fatigue life of components in real-time. This framework is a comprehensive system designed to predict the structural states and fatigue lives of reactor components. It includes multiple models and integrates artificial intelligence (AI), machine learning (ML), and FE based modeling tools to evaluate the structural states and fatigue lives.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Strategy to Develop a Control Scheme for Core Thermal Performance Optimization

This report outlines a three-year research plan for optimizing reactor core thermal performance through use of a Digital Twin (DT) model and a set of neutron detectors to correspondingly adapt the reactor control strategy. It identifies reactor features and achievable neutron measurements that factor into this optimization task. It considers spatial effects that are important and how they can be managed by a real-time algorithm that controls reactivity actuators. The report presents a set of tasks, along with corresponding methods, for accomplishing this objective. The final task in this set is to validate the combined optimization procedure in the Purdue University’s PUR-1 reactor, for which we have an agreement of understanding. Additionally, we describe an alternative approach for overcoming some of the limitations inherent in the above approach, which includes the computational costs of high-fidelity simulations and achievable integration of neutron measurements into the DT. The proposed algorithms lower the technology readiness level (TRL) of the overall approach as some additional analysis and the development of a new neutron detector concept are necessary. In particular, a sensor measuring the gradient of the neutron flux is required, and the prototype is expected to be validated in the PUR-1 reactor. The potential benefits and the opportunity to significantly push forward the state-of-the-art make this approach worthy of further exploration in the upcoming years.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

Digital Twin User Guide for Chelan County Public Utility District

This user manual offers a comprehensive guide for developing a Digital twin (DT) of a Kaplan turbine at Chelan County Public Utility District (Chelan PUD) using neural networks. As variable renewable generation expands, hydropower units must operate with optimal efficiency and stability. For Kaplan machines, this flexibility is achieved through coordinated control of guide vane (wicket gates) opening and runner blade pitch, which amplifies the plant’s inherent nonlinear behavior and challenges traditional physics-only modeling. The efficiency of the Kaplan turbine varies with different combinations of the guide vans (wicket gate) opening and the blade angle. Each guide van opening and blade angle has a corresponding highest efficiency point, forming a cam relationship that represents the optimal combination.The discharge of a hydraulic turbine is controlled by the opening angle of the guide vans. Therefore, for each value of head, there is a certain guide van opening and blade angle that corresponds to the highest efficiency. For a given head, different combinations of the guide van opening and blade angle have different efficiencies. Therefore, coordinate cam curves are used to describe the relationship between the wicket gate opening and blade angle with different water head. To address these challenges, the manual details a data-driven modeling and learning workflow centered on structured neural networks. The approach is designed to forecast critical operational variables—discharge flow, net head, penstock (or scroll-case) pressure, and generator electrical outputs—by leveraging real-time inputs such as the generator power control setpoint, exciter field current and field voltage, together with hydromechanical commands (e.g., gate position and, when available, runner blade-pitch angle). The neural models are trained and validated on operational data from a Kaplan unit operated by Chelan PUD, demonstrating that the structured NN architecture can learn the coupled gate–blade–electrical dynamics. The result is a robust DT that improves situational awareness and supports data-informed decision-making for Chelan PUD’s Kaplan turbine operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System

In the development of a digital twin (DT) for hydropower turbines, dynamic modeling of the system (e.g., penstock, turbine, speed control) is crucial, along with all the necessary data interface, virtualization, and dashboard designs. Since the DT must mimic the actual dynamics of the hydropower turbine accurately, adaptive learning is required to train these dynamic models online so that the models in the DT can effectively follow the representation of the actual hydropower turbine dynamics accurately and reliably. This study presents an adaptive learning method for obtaining the hydropower turbine models for DT development of hydropower systems using the recursive least squares algorithm. To simplify the formulation, the hydropower turbine under consideration was assumed to operate near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters. In this context, the well-known six-coefficient model for the Francis turbine was formulated as the starting point to obtain input and output models for the turbine. Then, an adaptive learning mechanism was developed to learn model parameters using real-time data from a hydropower turbine testing system. This led to semi-physical modeling, in which first principles and data-driven modeling are integrated to produce dynamic models for DT development. Applications to a pilot system at the Norwegian University of Science and Technology (NTNU) were made, and the models learned adaptively using the data collected from the university’s pilot system. Desired modeling and validation results were obtained.

13 HYDRO ENERGY↗
NASA NTRS2020

A Digital Twin Feasibility Study (Part II):Non-Deterministic Predictions of Fatigue Life Using In-Situ Diagnostics and Prognostics

The Digital Twin (DT) concept has the potential to revolutionize the way systems and their components are designed, managed, maintained, and operated across a vast number of fields from engineering to healthcare. The focus of this work is the implementation of DT for the health management of fatigue critical structures. This paper is the second part of a two-part series. The first of the series demonstrated the use of multi-scale, initiation-to-failure crack growth modeling to form non-deterministic predictions of fatigue life. In this second part, a general method for reducing uncertainty in fatigue life predictions is presented that couples in-situ diagnostics and prognostics in a probabilistic framework. Monte Carlo methods and high-fidelity finite element models are used to (i) generate probabilistic estimates of crack state throughout the life of the same geometrically complex test specimen and (ii) predict fatigue life with decreasing uncertainty as more of these diagnoses are obtained. The ability to predict accurately and in the presence of uncertainty is demonstrated, suggesting that the proposed DT method is feasible for fatigue life prognosis and should be pursued further with a focus on increasing application realism.

Patrick E Leser↗
DOE OSTI2025

Scenario Generation for Built Environment Decision Support under Uncertainty: Case Studies of Airflow Modeling and Climate-Resilient Infrastructure System Design

When confronted with unforeseen challenges, practicing informed decision making is crucial for enhancing resilience in the built environment. While scan-to-building information modeling (BIM) is a well-established approach for creating detailed digital representations of physical assets, its application in assessing and improving infrastructure resilience remains underexplored. This study addresses this gap by proposing a novel application of scan-to-BIM, namely, scan-to-BIM-to-digital twin (S-BIM-DT) workflow. By integrating reality capture and digital twin technologies, this workflow creates continuously updated and accurate digital representations of physical assets, enabling the generation of various scenarios. Unlike traditional methods, the S BIM-DT workflow facilitates continuous model refinement, supporting informed resilience strategies. By combining these technologies into a cohesive process, the workflow facilitates decision making under uncertainty, enabling stakeholders to evaluate and respond to various scenarios effectively. We demonstrate the implementation of the S-BIM-DT workflow through two use cases that highlight its capability to enhance resilience at different scales. The first use case involves the Combined Transportation, Emergency, and Communications Center (CTECC) in Austin, Texas. BIM-enriched computational fluid dynamics (CFD) modeling simulates airflow and develops alternative scenarios for optimizing the heating, ventilation, and air conditioning (HVAC) systems. This approach enhances resilience against airborne health threats in a postCOVID context. The second use case focuses on designated areas within Beaumont, Texas, as part of the Southeast Texas Urban Integrated Field Laboratory (SETx-UIFL) research. By developing inundation maps to assess extreme weather events, this modeling aids in preparedness efforts and informs the development of climate-resilient infrastructure in vulnerable neighborhoods. Results indicate that the S-BIM-DT workflow effectively generates scenarios that enhance resilience in the built environment by facilitating informed decision making. Furthermore, this study serves as a bridge between advanced scan-to-BIM methodologies and the practical strategies needed to improve built infrastructure resilience.

Built environment↗
DOE OSTI2022

Development and assessment of prognosis digital twin in a NAMAC system

The nearly autonomous management and control (NAMAC) system is a comprehensive control system to assist plant operations by furnishing control recommendations to operators. Prognosis digital twin (DT-P) is a critical component in NAMAC for predicting action effects and supporting NAMAC decision-making during normal and accident scenarios. To quantifying and reducing uncertainty of machine-learning-based DT-Ps in multi-step predictions, this work investigates and derives insights from the application of three techniques for optimizing the performance of DT-P by long short-term memory recurrent neural networks, including manual search, sequential model-based optimization, and physics-guided machine learning. Finally, sequential model-based optimization and physics-guide machine learning result in smallest errors when the predicting transients are similar to the training data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Canonical and noncanonical Hamiltonian operator inference

Here, a method for the nonintrusive and structure-preserving model reduction of canonical and noncanonical Hamiltonian systems is presented. Based on the idea of operator inference, this technique is provably convergent and reduces to a straightforward linear solve given snapshot data and gray-box knowledge of the system Hamiltonian. Examples involving several hyperbolic partial differential equations show that the proposed method yields reduced models which, in addition to being accurate and stable with respect to the addition of basis modes, preserve conserved quantities well outside the range of their training data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2024

MBSE (SysML) and Digital Twin Integration to Support Engineering Analysis & Design

Problem Statement: The complexity of Gateway systems and of managing the integrated safety analysis have made traditional methods of engineering analysis and design increasingly challenging. To address these challenges, a prototype that integrate Model-Based Systems Engineering (MBSE) with Digital Twin technology has been developed to demonstrate the utilities and functions. Project Goal: Develop a prototype of a Digital Twin Of Gateway ECLSS IMV system to enhance fault management analysis through collaboration, visualization, and simulation. Overall Project Results / Accomplishments: - Developed extension to streamline the integration of SysML models with the Digital Twin platform - Demonstrated the capability to aggregate any relevant information and provide simulation integration within the DT prototype. - Generated digital twin simulation using SysML Activity Diagrams - Implemented a complex trade study use case based on the integrated SysML-Digital Twin infrastructure

MBSE↗
DOE OSTI2025

Deployment of Digital Twins and Advanced Instrumentation to Identify Looseness Between Rotor Spider Arms and Rim Attachment in Hydro Turbines

The project aimed to develop a Virtual Sensor Digital Twin (VS-DT) to monitor rotor rim float displacement (RRFD) in legacy hydropower turbines. RRFD, if undetected, can cause rotor and stator damage, leading to costly repairs and safety concerns. The purpose of the VS-DT was to predict RRFD signals using existing conventional sensors instead of installing dedicated, costly physical sensors, thereby reducing operational costs and enhancing safety.

13 HYDRO ENERGY↗
NASA NTRSDate not supplied

NASA Earth System Digital Twins (ESDT) For a Sustainable Future

An Earth System Digital Twin (or ESDT) is an Information System for Understanding, Forecasting, and Conjecturing the complex interconnections among Earth systems, including anthropomorphic forcings and impacts to humanity. More specifically, an Earth System Digital Twin is a dynamic and interactive information system that first provides a digital replica of the past and current states of the Earth or Earth system, as accurately and timely as possible (i.e., the “What Now”); second allows for computing forecasts of future states under nominal assumptions and based on the current replica (i.e., the “What Next”); and third offers the capability to investigate many hypothetical scenarios under varying impact assumptions (i.e., the “What If”). The full power of digital twins is that, through an integrated representation and standardized tools and software technologies, the same digital replica can address the needs of multiple users at various resolutions (spatial and temporal) and for various applications (science, economic, policy, etc.) – “from farmer to scientist”. Since 2020, NASA’s Earth Science Technology’s Intelligent Systems Programs have started defining and investigating ESDTs. After several workshops, more than 15 projects have been funded to develop technologies and components as well as a few prototype ESDT systems. An ESDT architecture framework has been defined, and it is now being used to develop a Coastal Zone Digital Twin (CZ-DT), in coordination with NOAA and CNES. Additional projects have also just been selected to build additional ESDT technologies and prototypes. The details of these Programs will be detailed at the conference.

Earth Science Remote Sensing; Information Systems↗