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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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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Design of a readout circuit incorporating a 12-bit analog-to-digital converter for cooled infrared focal plane array

The infrared focal plane array (IRFPA) is gradually becoming more systematic. In order to enhance the signal processing capabilities, analog-to-digital converters (ADC) integrated into the readout integrated circuit (ROIC) is an important development trend. However, the cryogenic effects of MOSFET devices will affect the conversion accuracy of the ADC at 77 K. In this paper, the main parameters of MOSFET are extracted at 77 K in 0.18 μm CMOS process, and a cryogenic model is established. Based on this, a digital ROIC is designed, which integrates a 12-bit single-slope integral ADC in pixel unit. By applying continuous integral ramp generator and mixed-voltage comparator, a high-linearity and low-power consumption single-slope ADC is realized for the cooled readout circuit. The measured dynamic effective number of bits of the ADC at 77 K is 10.92 bits, the static no-missing code resolution reaches 11.99 bits, the maximum differential nonlinear |DNL| < 1LSB and maximum integral nonlinear |INL| < 1LSB. This paper realizes the design of a digital ROIC for IRFPA and improves the simulation accuracy at 77 K.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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 OSTI2018

A digital twin for rapid qualification of 3D printed metallic components

The customized production of complex components by 3D printing has been hailed as a potentially transformative tool in manufacturing with important applications in health care, automotive and aerospace industries. However, after about a quarter of a century of research and development, only a handful of commercial alloys can be printed and the market value of all 3D printed products now amounts to a negligible portion of the manufacturing economy. This difficulty is attributable to a remarkable diversity in structure and properties of the printed components and susceptibility to defects. In addition, the current practice of qualifying components by prolonged trial and error with expensive printing equipment and feed stock material confine the printed products to a niche market where the high product cost and the delay in the qualification are not critical factors. In this report we explain how a digital twin or a digital replica of the printing machine will reduce the number of trial and error tests to obtain desired product attributes and reduce the time required for part qualification to make the printed components cost effective. It is shown that a comprehensive digital twin of 3D printing machine consisting of mechanistic, control and statistical models of 3D printing, machine learning and big data can reduce the volume of trial and error testing, reduce defects and shorten time between the design and production.

36 MATERIALS SCIENCE↗
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 OSTI2016

Comparison of analog and digital pulse-shape-discrimination systems

Pulse shape discrimination (PSD) performance of two optimized PSD systems (one digital and one analog) is compared in this work. One system uses digital charge integration, while the other system uses analog zero crossing. Measurements were conducted with each PSD system using the CAEN V1720 (250 MHz) data acquisition system. An organic-liquid scintillator, coupled to a photo-multiplier tube, was used to detect neutrons and gamma rays from a Cf-252 spontaneous-fission source. The PSD perfor-mance of both systems was optimized and quantified using a traditional figure-of-merit (FOM) approach. FOM's were found for three, 300 keVee light-output bins, spanning from 100 to 1000 keVee, and one larger bin from 100 to 1800 keVee. Digital PSD outperformed analog PSD in the lowest light-output bin by approximately 50%, and by 11% for the highest light-output bin.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Scintillator light yield measurements with waveform digitizers

The proton light yield of organic scintillators has been measured extensively in recent years using fast waveform digitizers and large discrepancies exist in the values reported by different authors. In this letter, we address principles of digital signal processing that must be considered when conducting scintillator light yield measurements. Digitized waveform pulse height values are only proportional to the amount of scintillation light if the temporal shape of the scintillation pulse is independent of the amount of energy deposited. Finally, this is not the case for scintillation pulses resulting from fast neutron interactions in organic scintillators. Authors measuring proton light yield should therefore report pulse integral values and ensure that the integration length is long enough to capture most of the scintillation light.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Investigation of a digitizer for the plastic scintillation detectors of time-of-flight mass measurements

A CAEN DT5742 digitizer has been investigated to process the fast signals from the photomultiplier tubes of the time-of-flight detectors for fast ion beams. A test setup consisting of two plastic scintillation detectors and a pulsed laser source provided signals which were recorded by the digitizer and systematically analyzed with different algorithms to derive the amplitude, rise time and arrival time of the detection signal. To obtain the best amplitude and time resolutions, various optimization techniques including peak fitting and signal smoothing, interpolation improvement, and time-walk correction by amplitude and rise time have been performed and compared in detail. Here we compared the time resolutions obtained by three digital algorithms of leading-edge, zero-crossing constant-fraction (ZC-CFD) and direct constant-fraction discriminations. Finally, we found that the best time-of-flight resolution between two PMTs can be achieved as 12 ps by the method using sample minimum for peak location and using the time-walk-resistant ZC-CFD with 4-point interpolation for timing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants

Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. Here this paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework's capabilities is documented here in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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 OSTI2021

Applications of Digital Twin System in a Smart City System with Multi-Energy

With the increasing number of electric vehicles (EV), the smart transportation system is becoming more closely related to the smart energy system in a smart city. However, because of the high complexity, dynamics, and large-scale of these two systems, it is challenging to study their operation problems systematically, especially in severe conditions, e.g., the 2021 winter Texas power traffic crisis. With artificial intelligence, federated learning, edge computing, and automatic control, a digital twin based smart city is proposed in this paper and it focuses on two applications: smart transportation and smart energy grid. Both systems are presented in a structured design manner with several components in the digital twin system, which also contains many real-implementations of different scenarios. The proposed digital twin of a smart city provides a potential clue for the problems mentioned above.

artificial intelligence↗
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↗
DOE OSTI2024

Digital risk analysis in nuclear engineering projects: Designing for safety, performance, reliability, and security

Cyber-informed engineering and security-by-design frameworks are important in promoting the need to identify cybersecurity concerns early in the systems engineering lifecycle so risks from adversarial cyber-attacks can be eliminated or reduced through engineering design practices. In addition to adversarial risk, risk in operational technology systems also includes non-adversarial and unintentional risk from other factors such as human performance errors, environmental conditions, design flaws, and device degradation or failure. This paper introduces a new concept for characterizing digital risk, both adversarial and non-adversarial, and provides the basis for initial research into a novel digital risk analysis approach focused on incorporating attack difficulty into a multi-attribute analysis technique using robust decision-making. This digital risk characterization is also used to frame a discussion on the challenges of competing objectives and competing stakeholder requirements in an integrated energy system project that incorporates a small modular reactor and industrial facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis

The proliferation of digital cameras co-located with eddy covariance instrumentation provides new opportunities to better understand the relationship between canopy phenology and the seasonality of canopy photosynthesis. In this paper we analyze the abilities and limitations of canopy color metrics measured by digital repeat photography to track seasonal canopy development and photosynthesis, determine phenological transition dates, and estimate intra-annual and interannual variability in canopy photosynthesis. Here, we used 59 site-years of camera imagery and net ecosystem exchange measurements from 17 towers spanning three plant functional types (deciduous broadleaf forest, evergreen needleleaf forest, and grassland/crops) to derive color indices and estimate gross primary productivity (GPP). GPP was strongly correlated with greenness derived from camera imagery in all three plant functional types. Specifically, the beginning of the photosynthetic period in deciduous broadleaf forest and grassland/crops and the end of the photosynthetic period in grassland/crops were both correlated with changes in greenness; changes in redness were correlated with the end of the photosynthetic period in deciduous broadleaf forest. However, it was not possible to accurately identify the beginning or ending of the photosynthetic period using camera greenness in evergreen needleleaf forest. At deciduous broadleaf sites, anomalies in integrated greenness and total GPP were significantly correlated up to 60 days after the mean onset date for the start of spring. More generally, results from this work demonstrate that digital repeat photography can be used to quantify both the duration of the photosynthetically active period as well as total GPP in deciduous broadleaf forest and grassland/crops, but that new and different approaches are required before comparable results can be achieved in evergreen needleleaf forest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Light Water Reactor Sustainability Program: Digital Architecture Project Plan

There are many technologies available to the nuclear power industry to improve efficiency in plant work activities. These range from new control room technologies to those for mobile field workers. They can make a positive impact on a wide range of performance objectives – increase in productivity, human error reduction, validation of results, accurate transfer of data, and elimination of repetitive tasks. It is expected that the industry will more and more turn to these technologies to achieve these operational efficiencies to lower costs. At the same time, this will help utilities manage a looming staffing problem as the inevitable retirement wave of the more seasoned workers affects both staffing levels and knowledge retention. A barrier to this wide-scale implementation of new technologies for operational efficiency is the lack of a comprehensive digital architecture that can support the real-time information exchanges needed to achieve the desired operational efficiencies. This project will define an advanced digital architecture that will accommodate the entire range of system, process, and plant worker activity to enable the highest degree of integration, thereby creating maximum efficiency and productivity. This pilot project will consider a range of open standards that are suitable for the various data and communication requirements of a seamless digital environment. It will map these standards into an overall architecture to support the II&C developments of this research program.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Digital Architecture Planning Model

As part of the U.S. Department of Energy’s Light Water Reactor Sustainability Program, the Digital Architecture (DA) Project focuses on providing a model that nuclear utilities can refer to when planning deployment of advanced technologies. The digital architecture planning model (DAPM) is the methodology for mapping power plant operational and support activities into a DA that unifies all data sources needed by the utilities to operate their plants. The DA is defined as a collection of information technology capabilities needed to support and integrate a wide spectrum of real-time digital capabilities for performance improvements of nuclear power plants. DA can be thought of as integration of the separate instrumentation and control and information systems already in place in nuclear power plants, which are brought together for the purpose of creating new levels of automation in plant work activities. A major objective in DAPM development was to survey all key areas that needed to be reviewed in order for a utility to make knowledgeable decisions regarding needs and plans to implement a DA at the plant. The development was done in two steps. First, researchers surveyed the nuclear industry in order to learn their near-term plans for adopting new advanced capabilities and implementing a network (i.e., wireless and wire) infrastructure throughout the plant, including the power block. Secondly, a literature review covering regulatory documents, industry standards, and technical research reports and articles was conducted. The objective of the review was to identify key areas to be covered by the DAPM, which included the following: 1. The need for a DA and its benefits to the plant 2. Resources required to implement the DA 3. Challenges that need to be addressed and resolved to implement the DA 4. Roles and responsibilities of the DA implementation plan. The DAPM was developed based on results from the survey and the literature review. Model development, including the survey results and conclusions made about the key areas during the literature review, are described in this report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗