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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Grand challenges in the digitalisation of wind energy

The availability of large amounts of data is starting to impact how the wind energy community works. From turbine design to plant layout, construction, commissioning, and maintenance and operations, new processes and business models are springing up. This is the process of digitalisation, and it promises improved efficiency and greater insight, ultimately leading to increased energy capture and significant savings for wind plant operators, thus reducing the levelised cost of energy. Digitalisation is also impacting research, where it is both easing and speeding up collaboration, as well as making research results more accessible. This is the basis for innovations that can be taken up by end users. But digitalisation faces barriers. This paper uses a literature survey and the results from an expert elicitation to identify three common industry-wide barriers to the digitalisation of wind energy. Comparison with other networked industries and past and ongoing initiatives to foster digitalisation show that these barriers can only be overcome by wide-reaching strategic efforts, and so we see these as “grand challenges” in the digitalisation of wind energy. They are, first, creating FAIR data frameworks; secondly, connecting people and data to foster innovation; and finally, enabling collaboration and competition between organisations. The grand challenges in the digitalisation of wind energy thus include a mix of technical, cultural, and business aspects that will need collaboration between businesses, academia, and government to solve. Working to mitigate them is the beginning of a dynamic process that will position wind energy as an essential part of a global clean energy future.

17 WIND ENERGY↗
DOE OSTI2016

On-line, continuous monitoring in solar cell and fuel cell manufacturing using spectral reflectance imaging

A monitoring system 100 comprising a material transport system 104 providing for the transportation of a substantially planar material 102, 107 through the monitoring zone 103 of the monitoring system 100. The system 100 also includes a line camera 106 positioned to obtain multiple line images across a width of the material 102, 107 as it is transported through the monitoring zone 103. The system 100 further includes an illumination source 108 providing for the illumination of the material 102, 107 transported through the monitoring zone 103 such that light reflected in a direction normal to the substantially planar surface of the material 102, 107 is detected by the line camera 106. A data processing system 110 is also provided in digital communication with the line camera 106. The data processing system 110 is configured to receive data output from the line camera 106 and further configured to calculate and provide substantially contemporaneous information relating to a quality parameter of the material 102, 107. Also disclosed are methods of monitoring a quality parameter of a material.

14 SOLAR ENERGY↗
DOE OSTI2026

Simulation and Experimental Validation of an Integrated Heat Pump – Thermal Energy Storage Using a Room-Temperature Phase Change Material

As the dependence on electrical heat pumps (HPs) and intermittent renewables increases, grid strains are expected to grow. This necessitates an energy storage system to reduce the mismatch between energy supply and demand. Thus, a proposed dual-mode commercially available 14.1 kW HP was integrated with a single 22°C phase change material (PCM) thermal storage system (TES) to load-shift both cooling and heating loads. The HP-TES system was manufactured and experimentally tested using a novel test matrix based on AHRI 210/240 psychrometric conditions. Furthermore, transient dual-mode system-level HP-TES models were developed in Modelica and validated using the experimental test conditions. Base HP cooling and heating experimental tests at ambient temperatures of 35°C and −8.3°C show that the modified HP-TES maintained the rated system capacity and performance. The HP-TES discharge provided approximately 30% and 50% reductions in cooling and heating demand, respectively. The transient HP-TES models predicted system capacity and total power input for discharge and recharge operating modes within ±4% mean percentage error, and recharge power input within ±2%, with maximum errors occurring at the equipment startup. During system operation, the sources of model deviations are first-order polynomial fits of the PCM digital scanning calorimetry (DSC) data and unaccounted supercooling in the PCM during solidification. Nonetheless, the model predictions agree with the experimental tests, demonstrating the availability of robust, accurate, and validated transient models that can be used for further validation and the development of system controls.

25 ENERGY STORAGE↗
DOE OSTI2021

Decision-Making Under Uncertainty for a Digital Thread-Enabled Design Process

Abstract Digital thread is a data-driven architecture that links together information from all stages of the product lifecycle. Despite increasing application in manufacturing, maintenance/operations, and design related tasks, a principled formulation of analyzing the decision-making problem under uncertainty for the digital thread remains absent. The contribution of this article is to present a formulation using Bayesian statistics and decision theory. First, we address how uncertainty propagates in the product lifecycle and how the digital thread evolves based on the decisions we make and the data we collect. By using these mechanics, we explore designing over multiple product generations or iterations and provide an algorithm to solve the underlying multistage decision problem. We illustrate our method on an example structural design problem where our method can quantify and optimize different types and sequences of decisions, ranging from experimentation, manufacturing, and sensor placement/selection, to minimize total accrued costs.

Engineering↗
DOE OSTI2023

Skewering the silos: using Brick to enable portable analytics, modeling and controls in buildings

Nearly all large commercial buildings have heating, ventilation and air conditioning (HVAC) systems, lighting systems, safety and other systems controlled by a computer—a dedicated server with a building energy management system (BMS). However, these BMSs are proprietary with each building’s assets (that is, fans, valves, pumps, and their setpoints) named and coded uniquely by the BMS vendor or engineer; building analytics and control algorithms are written specific to the assets and the building. Thus, any control updates or analytics to improve building performance—especially critical to reduce greenhouse emissions or improve load flexibility—are labor intensive and costly. The Brick schema was developed so the same analysis or control algorithms can work on a variety of buildings if each is digitally represented in a Brick data model. The goal of this project was to further the development of Brick to extend it beyond an academic project with demonstrated success in a small field study, to a practical choice for industrial and commercial stakeholders seeking to realize value from building data. To do this, we executed four objectives: (1) expand the Brick schema including its modeling capabilities and vocabulary, (2) develop tools for integrating Brick with existing digital technologies and representations in buildings, (3) develop an open-source analytics platform to facilitate use of Brick in delivering data value, and (4) demonstrate Brick-driven analytics and controls in real settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Parallel database search and prime factorization with magnonic holographic memory devices

In this work, we describe the capabilities of Magnonic Holographic Memory (MHM) for parallel database search and prime factorization. MHM is a type of holographic device, which utilizes spin waves for data transfer and processing. Its operation is based on the correlation between the phases and the amplitudes of the input spin waves and the output inductive voltage. The input of MHM is provided by the phased array of spin wave generating elements allowing the producing of phase patterns of an arbitrary form. The latter makes it possible to code logic states into the phases of propagating waves and exploit wave superposition for parallel data processing. We present the results of numerical modeling illustrating parallel database search and prime factorization. The results of numerical simulations on the database search are in agreement with the available experimental data. The use of classical wave interference may results in a significant speedup over the conventional digital logic circuits in special task data processing (e.g., √n in database search). Potentially, magnonic holographic devices can be implemented as complementary logic units to digital processors. Physical limitations and technological constrains of the spin wave approach are also discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System

Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
DOE OSTI2024

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System: Preprint

Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Engineering Against Digital Risk in CIP Applications: Cyber-Informed Engineering Use Cases

Cyber-Informed Engineering (CIE) addresses the reality that cyber attacks on engineered systems can have consequences far beyond data loss or disruption of digital networks. When control systems are compromised, safety, reliability, and performance of the physical process itself may be threatened. This presentation discusses engineered controls of 7 categories and the CIE database of controls that provides clear examples and guidance for defining and applying engineered controls in CIE. It explains what engineered controls are, how they differ from information security measures, and how they are integrated into system design.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Digital twin framework for PIP-II linac: AI-driven multi-scale modeling from ion source to 800 MeV

The PIP-II superconducting linac at Fermilab is designed to deliver multi-megawatt proton beams for neutrino physics and other high-intensity applications. To expedite commissioning and enhance operational reliability, we have developed an EPICS-based data flow framework that seamlessly integrates digital twins (DT) with physical twins (PT). These digital twins comprise high-fidelity beam dynamics models or data-driven surrogate models connected to their physical counterparts through real-time diagnostics and advanced machine-learning algorithms.Central to this framework is Linac_Gen, an accelerated simulation tool that incorporates convolutional neural networks, random forests, and genetic algorithms to provide up to a tenfold speedup in optimizing the accelerator geometry model. An EPICS translator layer ensures interoperability by efficiently mapping lattice parameters across diverse simulation platforms.Our EPICS-based framework supports multiple operational modes—monitoring, passive learning, closed-loop control, and online learning—covering the entire machine lifecycle. By leveraging HPC resources and multi-objective optimization techniques, the digital twin enables adaptive trajectory correction, real-time fault detection, and predictive modeling of beam stability. This comprehensive approach paves the way for robust, high-intensity operation and data-driven accelerator R&D at Fermilab.

Pathak, Abhishek [Fermilab]↗
DOE OSTI2025

Towards a Robust Adaptive Digital Twin for Fusion Applications

The development of a digital twin system for fusion applications is essential for enhancing the prediction, analysis, and optimization of complex plasma processes. Machine learning (ML), particularly deep learning has demonstrated strong capabilities in modeling such highly nonlinear and intricate systems. However, two critical challenges limit the deployment of deep learning-based digital twins: Uncertainty Quantification (UQ) and data drift. UQ is vital for ensuring trustworthy predictions, especially in decision-support scenarios. Additionally, data-driven models are often sensitive to changes in the underlying data distribution, such as shot-to-shot variations in fusion experiments, which can lead to performance degradation over time. To address these challenges, we are developing an uncertainty-aware, adaptive digital twin framework. Our approach incorporates deep learning models enhanced with Gaussian Process approximations for predictive uncertainty estimation, coupled with an online learning mechanism that enables continuous model adaptation to new experimental data. This adaptive capability allows the data driven models to respond effectively to evolving plasma behaviors and equipment conditions. Specifically, to mitigate the effects of shot-to-shot drift, our system updates itself incrementally as new data becomes available, improving both robustness and fidelity. Our vision is to evolve this data driven model into a self-sustaining digital twin system that leverages UQ based feedback to continuously refine itself and potentially support real-time decision making. This presentation will cover a brief background on uncertainty quantification for ML, our ongoing effort on development of UQ capabilities for ML, our data science pipeline from data collection to model development and analysis and online learning framework for modeling coil deflection at DIII-D. I will also briefly touch upon opportunities and challenges in development of digital twin framework.

Sammuli, Brian [General Atomics]↗
DOE OSTI2025

Determination of Scale Bar for AGHCF Metallography Data

The DOE Nuclear Energy Advanced Reactor Technologies (ART) Fast Reactor Program (FRP) has supported the development of several databases containing information on the safety performance of fast reactors, components, and fuels. This growing collection of legacy experimental data, operating data, and analysis is available online to registered users. Metallography data represents one of the most critical types of post-irradiation examination (PIE) data being collected, organized, and archived in several ART Fast Reactor Databases (https://frdb.ne.anl.gov), including the Metallic Fuels Irradiation & Physics Database (FIPD), Out-of-Pile Transient Database (OPTD), and TREAT (the Transient Reactor Test Facility) Experimental Relational Database (TREXR). These databases contain three principal sets of metallography data. The first set comprises metallography data from Experimental Breeder Reactor-II (EBR-II) and Fast Flux Test Facility (FFTF) irradiated fuel pins examined in the Hot Fuel Examination Facility (HFEF). The second set consists of metallography data from EBR-II irradiated fuel pins examined in the Alpha-Gamma Hot Cell Facility (AGHCF). The third set includes metallography data from transient-tested fuel pins (including both out-of-pile furnace tests and TREAT tests) examined in AGHCF. Since both the second and third sets were generated in AGHCF, they are governed by identical specifications. The metallography data in the databases consist of digital images scanned from either positive or negative photographic films. To analyze the microstructure of a fuel pin, a series of preparatory steps are required, including sectioning, epoxy mounting, mechanical grinding and polishing, and etching. Following sample preparation, specimens are transferred for metallographic examination. The AGHCF and HFEF metallography data were generated using optical microscopes manufactured by Leitz and Bausch and Lomb (B&L). Images were recorded on Polaroid film at preset magnifications. Magnification verification for the Leitz and B&L metallographs was conducted every two months prior to 1989 and at least every six months from 1989 through the conclusion of the IFR program. Magnifications determined from imaging of microslide standards were compared to the instrument settings for magnifications ranging from 50× to 500×. If the magnifications determined from standards deviated from the instrument settings, adjustments were made to the bellows extension until agreement was achieved. The specifications for AGHCF and HFEF legacy metallography data have been established based on available hard-copy and digital records, most of which have been incorporated into the data repositories associated with FIPD, OPTD, and TREXR. Detailed specifications including hard-copy records, digitized records, cutting diagrams and sectioning schemes, high-magnification photographs, photomosaics (composites), information tags, scale bars, and magnification verification procedures can be found in a separate report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Shedding light on U.S. small and midsize data centers: Exploring insights from the CBECS survey

As demand for digital services accelerates, the energy and environmental footprint of data centers faces increasing scrutiny. While hyperscale cloud facilities have driven efficiency gains, small and midsize U.S. data centers remain a critical yet underexamined segment with significant untapped potential for energy savings. This study leverages data from the Commercial Buildings Energy Consumption Survey (CBECS) to analyze trends in server stocks, computing customers, cooling system adoption and efficiency, and geospatial distribution from 2012 to 2018. Findings reveal a sharp decline in small and midsize data centers, from 1.764 million to 1.398 million, with server counts dropping from 5.177 million to 4.262 million—aligning with the broader shift toward cloud computing. More than 40 % of servers in small data centers and 55 % in midsize data centers are housed in office buildings, and over half of all servers are concentrated in climate zones 5A (cold), 3A (mixed-humid), and 4A (mixed-humid), with the highest densities in metropolitan hubs. While direct expansion units remain the dominant cooling system, a clear transition toward more energy-efficient solutions, particularly air economizers, is evident. By integrating server and cooling system distributions, we estimate Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) for U.S. data centers by size and year. Results show that midsize data centers are more energy-efficient but more water-intensive due to the widespread use of water-cooled chillers. These findings highlight the trade-offs in cooling system selection and provide a critical foundation for policies aimed at enhancing efficiency in an evolving data center landscape.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Quasar UV Luminosity Function at 3.5 < z < 5.0 from SDSS Deep Imaging Data

We present a well-designed sample of more than 1000 type 1 quasars at 3.5 < z < 5 and derive UV quasar luminosity functions (QLFs) in this redshift range. These quasars were selected using the Sloan Digital Sky Survey (SDSS) imaging data in the Stripe 82 and overlap regions with repeat imaging observations that are about 1 mag fainter than the SDSS single-epoch data. The follow-up spectroscopic observations were conducted by the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS) as one of the BOSS ancillary programs. Reaching i ~ 21.5 mag, our sample bridges previous samples from brighter and deeper surveys. We use a 1/V a method to derive binned QLFs at 3.6 < z < 4.0, 4.0 < z < 4.5, and 4.5 < z < 4.9 and then use a double power-law model to parameterize the QLFs. We also combine our data with literature QLFs to better constrain the QLFs across a much wider luminosity baseline. The faint- and bright-end slopes of the QLFs in this redshift range are around –1.7 and –3.7, respectively, with uncertainties from 0.2 to 0.3 to >0.5. The evolution of the QLFs from z ~ 5 to 3.5 can be described by a pure density evolution model (∝10 kz ) with a parameter k similar to that at 5 < z < 7, suggesting a nearly uniform evolution of the quasar density at z = 3.5–7.

79 ASTRONOMY AND ASTROPHYSICS↗