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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 NTRSDate not supplied

Development and Demonstration of a Digital NDE Pipeline for Streamlined Analysis of Ultrasonic Data

Currently when nondestructive evaluation (NDE) is performed on composite structures, the results, although recorded digitally are often manually interpreted and indicated (drawn) on the part being inspected by hand. Following this, a determination must be made on how to disposition that part. This decision could be based on engineering guidelines and best practices, rule-of-thumb, expert opinion or finite-element analysis of the part with some approximate representation of the damage. In order to streamline this process for ultrasonic inspection an effort was undertaken as part of NASA Advanced Composites Project to develop the Digital NDE Pipeline. The Digital NDE Pipeline is an integrated tool suite and associated framework that streamlines the inspection and defect disposition process through model-assisted inspection optimization, automated defect analysis of NDE data, and mapping of NDE data into finite element analysis software. This paper will provide an overview of the Digital NDE Pipeline and provide details of the individual tools developed along with the results of applying these tools to a demonstration test case.

Composites↗
NASA NTRSDate not supplied

Digital Prototyping Methods to Enable Product Development Analysis Cycle Compression in Aerospace Systems

Historically, the product development life cycle (spanning from origination of a systems concept to initial delivery or fielding) for large-scale aerospace systems is 10-25 years. Examples of recent programs exhibiting this timeline are the Space Shuttle (13 years), , International Space Station (18 years), NASA Hubble telescope (16 years), USAF F-35 Strike Fighter (22 years), Missile Defense Agency THAAD (21 years), USAF V-22 Osprey (26 years), USAF B-2 Spirt (19 years), US Army RAH-66 Comanche (22 years, cancelled prior to fielding), James Webb Space Telescope (25 years), Space Launch System (10 years). This list illustrates the challenges of developing and fielding a modern integrated multi-disciplinary aerospace system. These development timelines are often preceded by significant research and development programs and followed by multiple increments, blocks, or spirals to reach planned operational capability. In the modern era of aerospace system acquisition, there is significant pressure to reduce system development timelines to meet system objectives and enable competitiveness in the current industry and landscape. Across the aerospace industry, a range of rapid acquisition and prototyping programs are seeking to achieve system development within timelines considerably less than 10 years. Notably, in September of 2019, NASA issued a solicitation for the development and demonstration of a Human Landing System (HLS) to deliver humans to the lunar surface by 2024 (5 years) and for the development and demonstration of a more sustainable HLS by 2026 (7 years). Lengthy product development cycle timelines are a product of multiple factors ranging from programmatic, sociological, technical, and systems engineering issues. New approaches in systems engineering provide new ways to enable these compressed development timelines. These approaches employ expanded application of advanced systems engineering methods and cross-cutting digital tools to accelerate system development, utilizing digital prototyping to connect maturing sub-system or component technologies into system or system-of-systems hardware prototypes. Approaches such as the use of system integrating physics relationships to reduce the number of design analysis cycle iterations and state analysis modeling to reduce necessary software testing (and improving coverage of system execution scenarios) represent steps forward in reducing the engineering time needed to field new systems. In addition to cost, schedule and performance benefits, expanded digital exploration and demonstration reduce risk in live system test and demonstration. This incremental demonstration approach, where digital prototyping and demonstration leads and informs full system test and demonstration, could be more important for space applications because of the increased difficulty of test and demonstration of space systems and architectures. The Advanced Concepts Office (ACO) at Marshall Space Flight Center merges traditional multi-disciplinary concept definition methods with modern, cross-cutting systems engineering concepts to enable iterative design and analysis of space architectures and systems through coordinated, strategic management of human capital, technical processes, and technology. This paper provides an overview of that approach, including recent examples and a strategic path forward to enabling continued reduction of aerospace system product development life cycles.

Michael D Watson↗
NASA NTRS1993

The Trustworthy Digital Camera: Restoring Credibility to the Photographic Image

The increasing sophistication of computers has made digital manipulation of photographic images (as well as other digitally-recorded artifacts, such as sound and video) incredibly easy to perform and, as time goes on, increasingly difficult to detect. Today, every picture appearing in newspapers and magazines has been digitally altered to some degree, with the severity varying from the trivial (cleaning up "noise" and removing distracting backgrounds) to the point of deception (articles of clothing removed, heads attached to other people's bodies, the complete rearrangement of city skylines). As the power, flexibility and ubiquity of image-altering computers continues to increase, the well-known adage that "the photograph doesn't lie" will continue to become an anachronism. A solution to this problem comes from the proposed Digital Signature Standard (DSS), which incorporates modern cryptographic techniques to authenticate electronic mail messages...

Friedman, Gary L.↗
NASA NTRS2022

An Earth System Digital Twin for Flood Prediction and Analysis

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observation data, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions. The NASA’s Advanced Information Systems Technology (AIST)’s Integrated Digital Earth Analysis System (IDEAS) project is to establish an extensible architectural solution to develop digital twins of our physical environment for Earth Science. IDEAS delivers a formal system architecture with mechanisms for the outputs of one model to feed into others; for driving models with observation data; and for harmonizing observation data and model outputs for analysis. To validate and demonstrate the IDEAS architecture, this project collaborates with the Space Climate Observatory (SCO)’s FloodDAM project and the Centre National d’Etudes Spatiales (CNES) to focus on floods detection, prediction and their impacts.

Kettig, Peter↗
NASA NTRS2023

Standards for Interoperable Digital Twins

On September 18, 2023, NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program conducted a mini-workshop that brought together seven experts to survey and discuss "Standards for Interoperable Digital Twins". Since three years, the AIST Program has been developing technologies and prototypes for Earth System Digital Twins (ESDT). During those developments and after the ESDT Workshop held in October 2022, it became clear that standard for ESDT interoperability were needed and should be defined as soon as possible. To address this challenge, the AIST program will survey ongoing development for Digital Twins standards in various domains and the September 18, 2023 Workshop represented the first step towards this understanding. Speakers during this event included: Michael Grieves (Digital Twin Institute), Siri Jodha Singh Khalsa (IEEE GRSS Standards), Trent Tinker (OGC), Ryan Berkheimer (NOAA), John Stone (NVIDIA), Thomas Geenen (ECMWF-DestinE) and Arne Berre (SINTEF-Iliad DTO and EDITO). This movie represents the recording of this event.

Earth Science Remote Sensing↗
NASA NTRSDate not supplied

Decentralized and Asynchronous Planning for Cooperative Practices in High Altitude Digitally Enabled Operations

Cooperative practices will likely be foundational to future digitally enabled airspace operations characterized by decentralized and asynchronous decision-making. This paper explores the implementation of cooperative practices in high-altitude airspace. The digitally enabled operations framework, under exploratory investigation by NASA as a potential new operating mode, which is enabled and dependent on digital technologies and information sharing, may create new operational possibilities for a wide range of vehicle types and missions. An essential element of the operating mode framework under investigation, cooperative practices, could empower operators to manage critical functions such as deconfliction, equitable airspace usage, and demand-capacity balancing. The use of cooperative practices would involve asynchronous flight information sharing and decentralized conflict management by operators, representing a major change from traditional air traffic services that use synchronous surveillance information to support centralized decision-making by air traffic controllers. This paper details three key components for implementing cooperative practices for decentralized, digitally enabled operations: (a) intent modeling and sharing, (b) conflict detection, and (c) cooperative deconfliction.

cooperative operating practics↗
NASA NTRS2024

Enterprise Digital Transformation: Transforming NASA's Workplace

NASA has a long history of serving the Nation and the world, by reaching for new heights and revealing the unknown for the benefit of all humankind. To ensure the agency will deliver on its mission in a hyper-connected, technology-enabled, globally-partnered, and rapidly-changing world, NASA launched a Digital Transformation initiative in late 2020 to modernize the way it works, the experience of its workforce and the agility of its workplace. At the core of its workplace transformation strategy, NASA is on a journey to leverage smart operational technologies, weaving together IoT, AI, intelligent automation, extended reality, and digital twins to build a network of reliable, adaptable and sustainable “Smart Centers” to execute its mission… on planet Earth and beyond. Come hear NASA’s digital strategy, the exciting progress being made, lessons learned from their journey, and how the AIMST community can enable NASA and its partners to serve humanity in a digital future.

Jill Marlowe↗
NASA NTRS2024

Transfer-AE: A novel autoencoder-based impact detection model for structural digital twin

Accurately detecting the location and intensity of impacts is crucial for ensuring structural safety. Currently, AI-based structural impact detection methods are widely used for their excellent detection accuracy. However, their generalization capability is limited by the scenarios present in the training data. Many complex and dangerous impact scenarios are difficult to conduct real-world experiments on to collect sufficient samples. To capture all impact scenarios and fully leverage the advantages of AI-based detection technologies, advanced methods involve combining real-world structural monitoring data with corresponding numerical models to construct digital twins. These methods continuously refine the created numerical models with limited real-world data and provide diverse impact scenarios through numerical model simulations. However, there are inevitable differences between digital models and physical models that are challenging to correct through mechanical means. This discrepancy in data distribution between the two models significantly hinders the application of digital twin technology in impact/event identification tasks. To address this challenge, this study proposes a novel model based on autoencoders, named Transfer-AE. Transfer-AE encodes the common features of digital twins in the latent space to bridge the uncertainty gap at a macro scale between numerical models and physical models and synchronously fits the magnitude and location of the impact load in the decoder. This enables consistent detection results for the same impact event, whether the sample comes from the numerical model or the physical model. Transfer-AE includes two operating modes: Mode 1 has a fixed computational complexity with stable inference speed, but the training cost and difficulty increase with data distribution. Mode 2's computational complexity increases with data distribution, but it has a fixed training cost and speed. In both cases involving the geodesic dome structure simulating a deep space habitat and the IASC-ASCE benchmark structure, Transfer-AE demonstrated the best performance in impact localization and quantification tasks compared to mainstream domain-adaptive transfer models.

Chengjia Han↗
DOE OSTI2026

Digitizing Today’s Buildings in the Real World: Lessons from Field Demonstrations

Digital twins, created by generating a virtual replica of a building, enable safe evaluation of operational scenarios and applications like fault detection and diagnosis and advanced controls. However, a prerequisite is the creation of a machine-readable digital representation of a building, currently hindered by fragmented information scattered across mechanical drawings, point lists, and natural language sequences. As a result, digital twin development remains labor-intensive, error-prone, and difficult to validate. To address these challenges, two efforts from ASHRAE aim to support the digitalization of buildings. ASHRAE s223 establishes a semantic model of buildings, representing system components, configuration, and data sources. ASHRAE s231 defines a vendor-neutral programming language for expressing their control logic. As the industry evaluates implementing them in their products, understanding the challenges that vendors and implementers may face is crucial. In this paper, we present findings and lessons learned from field demonstrations in five buildings that implemented control applications using ASHRAE s223 and s231. The demonstrations highlight how semantic modeling and formalized control descriptions can significantly reduce software development time, manual point mapping, and hard-coding. Beyond time efficiency, they enable reliable automation by minimizing human interpretation and providing a means for consistency across projects. We describe the processes and best practices for model creation and model usage, from translating heterogeneous building documentation into semantic representations to implementing control logic in real-world systems. Finally, we discuss the challenges that persist, including integration with legacy software environments, gaps in interoperability, and the level of expertise still required to effectively leverage semantic models.

Prakash, Anand Krishnan↗
NASA NTRS2022

NASA Enterprise Digital Transformation Initiative Strategic Framework & Implementation Approach

Since 1958, NASA has delivered its enduring bold purpose, characterized in the 2022 NASA Strategic Plan as a mission to discover, explore, innovate, and advance solutions to the problems of flight, within and outside the Earth’s atmosphere, for the benefit of humankind. The NASA Strategic Plan also recognizes that this mission will be delivered differently as it looks to a future marked by radical global change, which is in part being driven by digital advances. For this reason, in late 2020 NASA established an Enterprise Digital Transformation (DT) agency-level strategic initiative to accelerate and coordinate leveraging digital advances to transform the way the Agency works, the experience of its workforce and the agility of its workplace. This paper documents NASA’s DT strategic framework and associated implementation approach, with the DT strategic initiative serving to ignite, connect, integrate, and facilitate DT progress across a federated organizational operating model.

digital transformation↗
NASA NTRSDate not supplied

NASA’s Four-Layer Cake: Systematic Approach for Developing a Digital Transformation Tactical Plan for Organizations

With the current industry movement to “digitally transform” there is much fervor to do something, to change in some way, to do things differently and digitally; however, the last decade has shown that most organizations do not know where to start or abandon transformation efforts. And often the approaches taken fall in the realm of “low-hanging fruit” or “easy wins” which rarely result in the foundational change needed. NASA was no different in the early years of its digital transformation and this drove the development of what eventually became known as the “Four-Layer Cake”. The Four-Layer Cake provides the critical approach for determining the core areas for investment to gain the greatest return without having to boil the ocean or hope that the low-hanging fruit will provide the foundational elements for a domain’s transformation.

Digital engineering↗
DOE OSTI2024

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling↗
DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗
DOE OSTI2025

Enhancing traffic safety analysis with digital twin technology: integrating vehicle dynamics and environmental factors into microscopic traffic simulation

Traffic safety is a critical concern in transportation engineering and urban planning. Traditional traffic safety analysis requires trained observers to collect data in the field, which is time-consuming, labor-intensive, and sometimes inaccurate. In recent years, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, have been utilized to study traffic safety. However, microscopic traffic simulation only focuses on traffic-related factors, such as traffic volume, traffic signals, and lane configurations, neglecting vehicle dynamics and environment-related factors like weather and lighting conditions, which can significantly impact traffic safety. In light of this, this paper explores the application of digital twin technology in traffic safety analysis, integrating vehicle simulators, which consider vehicle dynamics and environmental factors, and microscopic traffic simulators, which simulate the operations of traffic flow, for enhanced safety evaluations. Various scenarios, including different weather conditions and visibility levels, are simulated using a digital twin of a road segment in Tuscaloosa, Alabama. The simulations employ Surrogate Safety Measures (SSMs) like Time to Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) to assess safety under varying conditions. The results demonstrate that traffic digital twin can identify potential safety issues that traditional microscopic simulation cannot, providing insights for improving traffic control strategies and transportation infrastructure to enhance traffic safety.

Civil engineering↗
DOE OSTI2026

Optimisation of the Kaplan hydropower system via PID 2 and digital twin

Here, this paper proposes a proportional–integral-double–derivative (PID 2 ) optimisation method for the Kaplan hydropower system by building a digital twin. The study first uses one multilayer perceptron (MLP) to model the hydroturbine dynamic and then adopts three connected MLPs to model the generator dynamic, both in an open-loop fashion. Inspired by stochastic distribution control (SDC) theory, we regard the training of the turbine's neural network model as a process control problem, and we propose minimising entropy loss to update the network parameters. The next step is to build the digital twin by connecting the neural network models with a PID 2 controller and a lead-lag exciter and run the whole model in a closed-loop fashion. After that, a binary search approach is applied to optimise the PID 2 parameters based on the obtained digital twin model. The simulation results show that the proposed method can reduce the mean square tracking error by more than 90%. Furthermore, the method is extended to jointly optimise the PID 2 controller and excitation system gains through multiobjective optimisation, leveraging Pareto frontier analysis to balance active power and voltage tracking performance. Simulation results confirm the effectiveness of the proposed method, achieving a 83.46% reduction in relative mean square error of active power, a 47.13% reduction in terminal voltage tracking error, and an 82.78% improvement in the overall scalarized objective.

Hydropower system↗
DOE OSTI2025

First-of-a-Kind Risk-Informed Digital Twin for Operational Decision Making

A digital twin (DT) is a digital model or a collection of models of a physical entity. DTs in the nuclear arena can be used from plant design through decommissioning. Decisions are typically a priori or made offline. Risk-informed decision making is identifying what can go wrong, its frequency, and the consequences of its failure. Ideally risk-informed decision making reflects the current state of the plant and provides a decision in real time. Traditionally, probabilistic risk assessments (PRAs) evaluate the failures of safety systems, the risk of core damage, and the offsite dose as the consequence. However, this DT evaluates the decisions on the control side rather than the protection side. It uses the same risk methods to probabilistically inform the decision-making process but in a different way. Rather than evaluating the risk of core damage, this DT evaluates the likelihood of avoiding a trip set point while maintaining plant safety. Performance-based assessments are identified via its probabilistic evaluation of operational alternatives based on system status. Because the purpose of the control system is to maintain system variables within prescribed operating ranges, upsets or challenges that can exceed a trip set point resulting in a plant transient and a challenge to plant mitigating systems based on actual plant conditions, are evaluated to safely maintain the plant within the operating ranges. The probabilistic portion of the model is autonomously and automatically adjusted, and the metric of interest (i.e. likelihood of avoiding a trip set point) is recalculated. The digital representation of the physical system (i.e. the DT) performs a deterministic performance–based assessment of the probabilistically identified alternatives identified to validate the probabilistic assessment. A decision-making algorithm selects the appropriate option based on the probabilistic and deterministic assessments and transmits a control signal to a component(s) to initiate a corrective action or informs an operator of its decision.

digital twin↗
DOE OSTI2024

Data-Driven Digital Twin for Reliability Assessment of DC/DC Buck Converter

In commercial applications, the operation of DC/DC converters significantly impacts overall system performance and long-term reliability. This study introduces a data-driven digital twin (DT) approach for estimating critical degradation parameters of DC/DC BUCK converter under steady-state condition. Initially, a circuit-level MATLAB/Simulink digital model (DM C ) is refined against a hardware prototype’s switching model dataset using offline particle swarm optimization. The optimized digital model’s steady-state response is then verified with its average model response while varying the duty and load. Subsequently, degradation profiles are imposed on the inductor, capacitor, MOSFET in the DMC. A large dataset is generated from this model, allowing training, validation, and testing of machine learning (ML) models for component health regression tasks. The proposed method employs random forest ML models, achieving impressive regression results with a squared R value as high as 0.99978 and a root mean square error of 4.2× 10 –6 . The method is further validated on a medium power level DC/DC BUCK prototype with varying load conditions, and includes the analysis of MOSFET’s on-resistance under degradation conditions. This data-driven DT method shows promise for identifying parasitic degradation and ohmic loss parameters, enhancing converter reliability assessments in a non-invasive, generalized, and computationally efficient manner.

14 SOLAR ENERGY↗
DOE OSTI2025

A Digital Twin for an Inverter-Based Resource Power Plant: Real-time data streaming unlocks situation awareness

Here, this study presents the development and successful implementation of a digital twin specifically designed for a grid-connected IBR power plant. By integrating a reduced-order model of the IBR system and dynamically updating the grid impedance with real-time data, the digital twin effectively captures and replicates the behavior of the physical system. Its accuracy and reliability are validated through critical test scenarios, including a three-phase fault and a line-tripping event. The results confirm that the digital twin closely emulates its physical counterpart, demonstrating its strong potential for real-time analysis, system monitoring, and predictive decision making in modern power systems.

Digital twins↗