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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Developing a digital twin for hydropower systems - an open platform framework

The definition of digital twin in Wikipedia says “A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process.” Indeed, the digital twin (DT) concept was initially introduced at the start of the 21st century with intent to create a digital model to reflect physical systems and derive insight from the model to make the decision on system operation. DT is also a promising enabling technology for realising smart manufacturing and industry 4.0. In general, DTs consist of three main parts: physical product, virtual product, and connected data that link physical and virtual product via various data communication schemes.

13 HYDRO ENERGY↗
DOE OSTI2022

Achieving American Leadership in Cybersecurity and Digital Components Factsheet

The Biden Administration’s efforts to meet 100% clean electricity by 2035 and net-zero greenhouse gas emissions by 2050 has created opportunities to rebuild American supply chains create new jobs, strengthen community engagement, and spur U.S. economic growth. DOE’s strategy for success in the transition to a clean energy economy hinges on building and maintaining technology supply chains that are advanced, secure, and resilient to cyber threats. As the energy sector grows increasingly globalized, complex, and digitized, the supply chain for digital components of energy systems – including software, virtual platforms and services, and data – is facing greater threats. Nearly all digital components of U.S. energy sector systems are vulnerable to cyber supply chain instability, stemming from a variety of causes and shared among a broad set of interdependent stakeholders. Overall, supply chain risks for digital components in energy sector systems will continue to evolve and likely increase as these systems are increasingly interconnected, digitized, and remotely operated.

CCE↗
DOE OSTI2017

Development of a bayesian belief network model for the software reliability assessment of nuclear digital I and C safety systems - 206

Since the digital instrumentation and control systems are expected to play an important role for the safety systems in nuclear power plants (NPPs), the need has emerged to not only establish a basis for incorporating software behavior into digital I and C system reliability models, but also to quantify the failure probability of the software used in NPP digital protection systems. In this study, a Bayesian belief network (BBN) model is developed to quantitatively assess software reliability by estimating the number of faults in a software program considering its software development life cycle (SDLC). The model structure and parameters are established based on the information applicable to NPP safety-related systems and the evidence used to construct and quantify the BBN model was collected from three stages of expert elicitation. The software failure probability is estimated from the number of residual defects in a software program at the end of SDLC phase. As a case study, the BBN model was applied to quantify the software reliability of a typical digital protection software having the size of 50 function points and having the Medium development and validation and verification (V and V) qualities. The developed model can be applied to estimate the failure probability for both developing and deployed safety-related NPP software, and such results can be used to evaluate the quality of the digital I and C systems in addition to estimating potential reactor risk due to software failure. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Investigation of instrumentation containing an embedded digital device - 257

The U.S. nuclear power industry primarily relies on mature, well-proven analog technology as the basis for most instrumentation and control (I and C) equipment employed in its nuclear power plants (NPPs). However, the market for this legacy instrumentation is very small and availability of such equipment for spare parts or replacement is increasingly limited. Frequently, modern I and C equipment contains embedded digital devices (EDDs), such as microprocessors and programmable logic devices. While the enhanced functional capabilities provided by the digital components offer improved performance, reliability, and maintainability, the corresponding complexity of sophisticated digital implementations also exacerbate concerns about an increased potential for undetected systematic faults, which can lead to common-cause failure (CCF). As part of a research project sponsored by the U.S. Department of Energy, an investigation has been conducted of commercially available instrumentation marketed for nuclear or industrial application. The objective was to identify the usage of EDDs and to determine the types of functional roles allocated to the devices. Examples of the equipment surveyed include smart sensors, sequencers for emergency diesel generators, pumps, valve actuators, motor control centers, breakers, priority logic modules, and uninterruptible power sources. Where sufficient detail was found, an evaluation was performed to ascertain the nature and role of the digital technology implemented in the instrumentation. The information gained through this investigation serves as technical input for defining a classification approach for EDDs based on the functional impact of the device. For example, the role of an EDD can involve any of the following functions: performance monitoring and diagnostics, information extraction and communication, instrument condition monitoring and protection, or execution of core instrument functions. Classifying digital devices according to their role in the operation of the instrumentation in which they are embedded can enable determination of the potential functional impact of failure of that device and contribute to determination of the potential safety significance of a CCF event disabling all instances of that device. This paper will report on the findings of the investigation of instrumentation with EDDs and indicate considerations for the emerging classification approach. The higher-level goal of this effort is to determine whether a graded approach to qualification testing may be possible based on classification. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Development of a model based assessment process for qualification of embedded digital devices in NPP applications: research approach and current status - 263

The instrumentation and control (I and C) equipment used in currently operating U.S. nuclear power plants (NPPs) is primarily based on mature analog technologies that are progressing towards obsolescence. The continued reliance on this legacy analog technology, which is also being propagated into new NPP designs, imposes performance penalties and maintenance burdens in comparison with modern digital I and C instrumentation. In many instances, currently available I and C equipment contain embedded digital devices (EDDs) such as microprocessors and programmable logic devices. Experience in other industries, such as avionics, has shown that digital I and C equipment containing EDDs can provide significant benefits over analog-based equipment in terms of performance, reliability, and maintainability. In recent years, due to the high demand for digital technologies in the industrial I and C marketplace, it is becoming increasingly difficult for NPPs to acquire instrumentation that is not equipped with an EDD. However, the nuclear power industry has been slow to adopt digital technology, especially in safety-related systems, in large part as a result of regulatory concerns about common-cause failure (CCF) vulnerabilities of equipment with EDDs. Consequently, there is a clear need to develop cost effective qualification methods to contribute to the assessment of CCF vulnerability posed by EDDs in modern instrumentation that could be used in NPPs. This paper describes the research regarding qualification methods for equipment with EDDs that is sponsored by the Nuclear Energy Enabling Technologies (NEET) Advanced Sensors and Instrumentation (ASI) program of the U.S. Department of Energy (DOE). The purpose of the current research is to develop an effective approach employing science-based methods to resolve concerns about CCF vulnerability that serve to inhibit deployment of advanced instrumentation (e.g., sensors, actuators, microcontrollers) with EDDs in nuclear power applications. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Addressing embedded digital devices in safety-related systems of nuclear power plants - 307

The purpose of this paper is to provide an overview of the development of the approach to addressing embedded digital devices (EDD) in safety-related systems of nuclear power plants in the U.S. and around the world. In April, 2016, the U.S. Nuclear Regulatory Commission (US NRC) issued RIS 2016-05 to heighten awareness that EDDs might exist in procured equipment used in safety-related systems without the devices having been explicitly identified in procurement documentation. Inadequate consideration of these devices in digital technology system upgrades, component replacements, and new equipment applications could lead to an adverse safety consequence. The US NRC request was for addressees to implement early efforts to identify these devices. An area where this is being addressed is in regard to the performance of Diversity and Defense-In-Depth (D3) analyses, as addressed in US NRC Branch Technical Position 7-19. Nuclear facilities have increased their use and reliance on digital technology in systems and equipment (e.g., I and C, electrical systems, and fluid systems). In addition to digital instrumentation and control (I and C) systems, examples of safety-related equipment that may use digital technology include emergency diesel generators, pumps, valve actuators, motor control centers, breakers, priority logic modules, time-delay relays, and uninterruptible power sources. In the U.S. and around the world, engineering and licensing activities in standards and guidance have been, and are being developed to address this important consideration in protecting safety-related systems. This paper addresses development of an extended approach to evaluating common-cause failure vulnerability and mitigation in regard to equipment with an EDD. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

IBR Digital Supply Chain Gap Analysis and Recommendations

The adoption of clean energy technologies, including solar photovoltaics, continues to introduce non-traditional stakeholders to the operations and planning of the electric system. Stakeholders such as manufacturers, vendors, owners, aggregators, and others are enabling the adoption, integration, and optimum operations of solar technologies at accelerated rates. Inverters form the foundation of many digitally controlled energy sources for clean energy technologies, including Solar, Battery Energy Storage Systems, Hybrid Systems, and Hydrogen Fuel Cells. Their supply chain is complex, a series of microchips, electronic switches and other components making up its primary functions. The complexity of this space and the growing digitization associated with these components can create supply chain cyber risks. One measure to mitigate cybersecurity attacks is proper digital supply chain security. The U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO), in partnership with the Cybersecurity, Energy, Security, and Emergency Response (CESER) office, is hosting a workshop to bring together solar vendors and services providers to discuss digital supply chain security for solar systems and challenges and opportunities in the transitioning to a fully domestic supply chain for solar energy in the U.S. This workshop will support the Securing Solar for the Grid (S2G) and Energy Cyber Sense program activities. During the workshop, industry experts and researchers from DOE National Laboratories will discuss the current solar supply chain landscape and the transition to domestic manufacturing of solar components in the U.S. Tools and techniques to better manage and secure the digital supply chain of solar devices and systems will be discussed.

cybersecurity↗
DOE OSTI2015

Virology: The Next Generation from Digital PCR to Single Virion Genomics

In the past 25 years, virology has had major technology breakthroughs stemming first from the introduction of nucleic acid amplification testing, but more recently from the use of next-generation sequencing, digital PCR, and the possibility of single virion genomics. These technologies have and will improve diagnosis and disease state monitoring in clinical settings, aid in environmental monitoring, and reveal the vast genetic potential of viruses. Using the principle of limiting dilution, digital PCR amplifies single molecules of DNA in highly partitioned endpoint reactions and reads each of those reactions as either positive or negative based on the presence or absence of target fluorophore. In this review, digital PCR will be highlighted along with current studies, advantages/disadvantages, and future perspectives with regard to digital PCR, viral load testing, and the possibility of single virion genomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Technical Specification Surveillance Interval Extension of Digital Equipment in Nuclear Power Plants: Review and Research. Light Water Reactor Sustainability Program report

The nuclear industry has been slow to incorporate digital technology into nuclear plant designs for several reasons, including: The high cost of design change modifications to implement them, versus simply replacing them with like for like technology when the components fail; Digital technology qualification issues, particularly in safety related applications that are susceptible to software common cause failure. This is a concern both for current operating plants as well as new builds; and, General familiarity and comfort with the existing analog technology on the part of the nuclear plant engineering staff, in spite of the superior performance and reduced maintenance costs of the digital replacements. At the same time, the nuclear industry is under significant cost pressure in the electric marketplace due to the abundance of natural gas generation and renewables. The industry would benefit by investment in new technologies that could lower future operating costs while addressing obsolescence and reliability issues of the current analog technologies. However, the industry has been unable to formulate the business cases to take advantage of these labor savings and production reliability improvements. This paper discusses another potential cost savings benefit of a transition to digital technology, extending the surveillance intervals of technical specification required equipment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Field Insights: Strengthening Digital Assurance Through On-Site Network Monitoring

The accelerating deployment of digital energy infrastructure, ranging from inverter-based resources (IBRs), battery energy storage systems (BESS), to advanced grid control platforms, has brought unprecedented visibility, flexibility, and efficiency to the electric grid. However, this digital transformation also introduces new cybersecurity challenges, particularly in the form of supply chain risks and operational blind spots at the grid edge. Over the past year, the Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER), through its Rapid Risk Assessment initiative, along with the Grid Deployment Office (GDO), through its Technical Assistance for Digital Assurance (TADA) initiative, have supported a series of on-site network engagements led by Idaho National Laboratory (INL). These engagements, conducted in partnership with asset owners across the country, have focused on identifying real-world vulnerabilities and misconfigurations in operational environments, many of which are not detectable through remote assessments or traditional compliance audits. The goal of this report is to distill key findings and lessons learned during network hunt engagements from INL’s fiscal year (FY) 2024 - 2025. It is intended to help asset owners—regardless of their participation in the program—better understand the evolving threat landscape and adopt practical measures to secure their digital energy infrastructure.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2011

Digitally Calibrated TR Modules Enabling Real-Time Beamforming SweepSAR Architectures for DESDynI-Class Radar Instruments

Real-time digital beamforming, combined with lightweight, large aperture reflectors, enable SweepSAR architectures such as that of the proposed DESDynI [Deformation, Ecosystem Structure, and Dynamics of Ice] SAR [Synthetic Aperture Radar] Instrument (or DSI). SweepSAR promises significant increases in instrument capability for solid earth and biomass remote sensing, while reducing mission mass and cost. This new instrument concept requires new methods for calibrating the multiple channels, which must be combined on-board, in real-time. We are developing new methods for digitally calibrating digital beamforming arrays to reduce development time, risk and cost of precision calibrated TR modules for array architectures by accurately tracking modules' characteristics through closed-loop Digital Calibration, thus tracking systematic changes regardless of temperature

SweepSar↗
DOE OSTI2017

A qualitative assessment of current CCF guidance based on a review of safety system digital implementation changes with evolving technology - 148

This paper is a summary of a study performed in support of the US Nuclear Regulatory Commission (NRC) to evaluate current policy on software common-cause failure (CCF). The study reported in this paper is one of several that contributed to the technical basis to inform NRC staff. The study first reviewed policies and assessment guidance as discussed in the Staff Requirements Memorandum to the Secretary of the Commission, Office of the NRC (SECY) 93-087, Policy, Technical, and Licensing Issues Pertaining to Evolutionary and Advanced Light Water Reactor (ALWR) Designs, and Branch Technical Position (BTP) 7-19, Guidance on Evaluation of Defense-in-Depth and Diversity in Digital Computer-Based Instrumentation and Control Systems, as well as Chapter 7, 'Instrumentation and Controls', in NUREG-0800, Standard Review Plan for Review of Safety Analysis Reports for Nuclear Power Plants. The study then examined instrumentation and controls (I and C) technology implementations in nuclear power plants in the light of current CCF guidance. The intent was to assess whether the current position on CCF remains appropriate given the evolutions in digital safety system implementations and, if gaps in the guidance were found, to provide recommendations as to how these gaps could be closed. The methodology adopted was to review the vendors' technology and software implementation processes for digital safety systems as technology evolved. The following three representative safety systems were selected to provide illustrative examples: - Eagle 21 was selected to represent vintage microprocessor-based technology used from the 1980's to mid-1990's, - TELEPERM XS (TXS) was selected to represent second generation microprocessor-based technology used from the mid-1990's to the 2000's, and - The advanced logic system (ALS) was selected to represent the latest trend of using technology based on field programmable gate arrays (FPGAs). Technology implementations were reviewed in light of the basic premise of the NRC in BTP 7-19 (Revision 6), that software-based or software-logic-based digital system development errors are a credible source of CCF and therefore are susceptible to CCF because identical copies of the software-based logic and architecture are present in redundant divisions of safety-related systems. BTP 7-19 categorizes firmware and logic developed from software-based development systems all under software. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Advanced Transmission Technologies –GETs and HPCs Session 3: HPCs and Building Actions Plans to Digital Assurance Risks

The third session of the Idaho National Laboratory’s (INL) Technical Assistance for Digital Assurance (TADA) program, held on November 11, 2025, centered on High Performance Conductors (HPCs) and the formulation of action plans to address digital assurance risks associated with Grid-Enhancing Technologies (GETs). This session convened experts from utilities, vendors, and government agencies to examine the technical, operational, and cybersecurity aspects of HPC deployment. Discussions highlighted the benefits of HPCs, such as their ability to rapidly increase transmission capacity using existing corridors, improve grid resilience, reduce system losses, and align with FERC Orders 2023 and 1920. Participants evaluated supply chain and digital assurance risks, including reliance on imported materials, limited domestic manufacturing capacity, workforce shortages, and traceability issues. The session also emphasized the importance of digital trust, integration-layer cybersecurity, and unified risk frameworks, introducing tools like intrusion detection systems, encryption, zero trust networking, and firmware integrity. Recaps of earlier workshops on Dynamic Line Ratings (DLRs), Advanced Power Flow Control (APFC), and Transmission Topology Optimization (TTO) underscored institutional barriers and integration challenges. Action plans were proposed to mitigate issues such as inconsistent cybersecurity practices, SBOM usage, supply chain visibility, operator trust, and misaligned incentives. Additionally, INL presented its supply chain risk management tools and Cyber-Informed Engineering (CIE) principles to support secure procurement and system design. The session concluded with a commitment to share key takeaways, incorporate cohort feedback into future policy development, and continue collaborative engagement through upcoming pilot activities. Session 3 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2021

Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. Here, we argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration.

36 MATERIALS SCIENCE↗
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

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 OSTI2021

Convolution-based frequency domain multiplexing of SiPM readouts using the $\mathrm{DRS4}$ digitizer

Here we present 4:1 multiplexing of organic scintillators, each coupled to a silicon photomultiplier (SiPM), to reduce the need for a large number of digitizer input channels to readout highly pixelated radiation detection systems. Frequency domain multiplexing (FDM) encodes a detector pulse by assigning it a unique frequency via convolution before combining the encoded signal into a single channel. The combined signal is then read through a digitizer input channel. We have designed an FDM system to multiplex four SiPMs using DRS4 digitizer evaluation board from Paul Scherrer Institute (PSI). We demonstrate 4:1 multiplexing of the SiPM fast output signals and pulse recovery from the digitized multiplexed signal using deconvolution. The noise in the recovered pulse introduces a bias and uncertainty in the estimate of energy and timing that changes with pulse height. The relative uncertainty in the estimated energy from the recovered pulse decreases with pulse height with a maximum uncertainty of 3.1% for the low energy pulses (corresponding to 100 keV); the uncertainty in the estimated time pick-off also decreases with pulse height with a maximum uncertainty of 110 ps for the low energy pulses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗