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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A room-temperature polarization-sensitive CMOS terahertz camera based on quantum-dot-enhanced terahertz-to-visible photon upconversion

Detection of terahertz (THz) radiation has many potential applications, but presently available detectors are limited in many aspects of their performance, including sensitivity, speed, bandwidth and operating temperature. Most do not allow the characterization of THz polarization states. Recent observation of THz-driven luminescence in quantum dots offers a possible detection mechanism via field-driven interdot charge transfer. Here, we demonstrate a room-temperature complementary metal–oxide–semiconductor THz camera and polarimeter based on quantum-dot-enhanced THz-to-visible upconversion mechanism with optimized luminophore geometries and fabrication designs. Besides broadband and fast responses, the nanoslit-based sensor can detect THz pulses with peak fields as low as 10 kV cm–1. A related coaxial nanoaperture-type device shows a to-date-unexplored capability to simultaneously record the THz polarization state and field strength with similar sensitivity.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Observing fish interactions with marine energy turbines using acoustic cameras

Abstract Marine current energy converters such as tidal and riverine turbines have the potential to provide reliable, clean power. The risk of collision of fishes with marine energy turbines is not yet well understood, in part due to the challenges associated with observing fish at turbine sites. Turbidity and light availability can limit the effectiveness of optical sensors like video cameras, motivating the use of acoustic cameras for this task. However, challenges persist in collecting and interpreting data acquired from acoustic cameras. Given the limited number of turbine deployments to date, it is prudent to draw on the application of acoustic cameras to monitor fish in other scenarios. This article synthesizes their use for other fisheries applications to inform best practices and set realistic expectations for the results of acoustic camera monitoring at turbine sites. We discuss six key tasks performed with acoustic cameras: detecting objects, identifying objects as fish, counting fish, measuring fish, classifying fish taxonomically and analysing fish behavior. Specific challenges to monitoring fish at turbine sites are discussed. This article is intended to serve as a reference for researchers, regulators and marine energy developers on effective use of acoustic cameras to monitor fish at turbine sites. The studies detailed in this article provide evidence that, in some scenarios, acoustic cameras can be used to inform the risk of fish collision with marine energy turbines but doing so requires careful study design and data processing.

Cotter, Emma↗
DOE OSTI2021

IoT Devices and Applications for Wire-Based Hybrid Manufacturing Machine Tools

Hybrid manufacturing machine tools have the potential to be a disruptive technology as they can leverage the benefits of both additive and subtractive manufacturing by incorporating both processes on the same machine while limiting the downsides of the individual processes. Since these machines use two very disparate manufacturing processes and hybrid manufacturing is an emerging technology, it will be useful to monitor data coming from the machine and apply it to improve the manufacturing process, the operation of the machine, and to integrate the machine into the larger digital framework of Industrial Internet of Things (IoT). The present work discusses IoT devices that would be beneficial to add to a hybrid machine tool as well as applications for those devices. The proposed methods discussed in this work have not been experimentally implemented on a hybrid machine tool and so there are no performance data available yet. The hybrid machine tool used as a basis to generate these IoT applications is the Mazak VC-500A/5x AM Hot Wire Deposition, which is a 5-axis machine tool incorporated with a wire feedstock 4kW laser deposition system. Methodologies and applications will be outlined for machine health and process monitoring. Other areas covered include process benchmarking, secure networking options for the proposed IoT framework, and hybrid process improvement. Limitations of these methods and future work for new sensor devices and application areas is also discussed.

Thien, Austen↗
DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

Varela, Sebastian↗
DOE OSTI2024

Design and construction of the CMS Outer Tracker for the Phase-2 Upgrade

he High Luminosity LHC (HL-LHC) is expected to deliver an integrated luminosity of $3000-4000$~fb$^{-1}$ after 10 years of operation with peak instantaneous luminosity reaching about $5-7.5\times10^{34}$cm$^{-2}$s$^{-1}$. During Long Shutdown 3, several components of the CMS detector will undergo major changes, called Phase-2 upgrade, to be able to operate in the challenging environment of the HL-LHC. The current CMS silicon strip tracker has to be replaced with a new detector. The Phase-2 Outer Tracker (OT) will have higher radiation tolerance, higher granularity, and the capability to handle higher data rates compared to the current system. Another key feature of the OT will be to provide tracking information to the Level-1 (L1) trigger, allowing trigger rates to be kept at a sustainable level without sacrificing physics potential. For this, the OT will be made out of modules with two closely spaced sensors read out by front-end ASICs, which can correlate hits in the two sensors creating short track segments called stubs. The stubs will be used for tracking in the L1 track finder. The modules come in two flavors: strip-strip (2S) and pixel-strip (PS), which contain different sensor configurations and multiple ASICs. In this contribution, the design of the CMS Phase-2 OT, the technological choices, and the quality assurance (QA) procedures used to ensure the functionality of the modules will be reported. The contribution will cover the first results with pre-production devices and the different aspects taken into account during the QA: from fulfilling the precision specification of the module assembly procedure to ensuring the proper communication between the different ASICs on the module. The module noise performance is also checked and the full module functionality is verified at different temperatures.

Zoi, Irene↗
DOE OSTI2020

Passive bias temperature compensation circuit module

A passive bias temperature compensation module for silicon photomultiplier, avalanche photodiodes and similar photodetectors that possess a moderately linear temperature coefficient of gain and that may be compensated by varying an applied bias voltage. The module includes an electrical circuit and a method for determining component values to provide a constant voltage source to stabilize the gain of one or more photodetector devices. A temperature sensor in the module is held in close thermal contact with the photodetector and a filter capacitor is electrically close to the photodetector. The module is based on the concept of temperature sensitive voltage division which is applicable to situations in which large numbers of photodetectors must be gain-compensated for temperature variations over a wide range while maintaining excellent gain matching. The passive bias temperature compensation method enables multiple photodetectors to share a single constant voltage supply without loss of matching performance.

42 ENGINEERING↗
DOE OSTI2024

Wind and structural loads data measured on parabolic trough solar collectors at an operational power plant

Abstract Wind loading is a primary contributor to structural design costs of concentrating solar-thermal power collectors, such as heliostats and parabolic troughs. These structures must resist the mechanical forces generated by turbulent wind, while the reflector surfaces must maintain optimal optical performance. Studying wind-driven loads at a full-scale, operational concentrating solar-thermal power plant provides insights into the wind impact on the solar collector field beyond the capabilities of wind tunnel tests or state-of-the-art simulations. We conducted comprehensive field measurements of the atmospheric turbulent wind conditions and the resulting structural wind loads on parabolic troughs at the Nevada Solar One plant over a two-year period. The measurement setup included meteorological masts and structural load sensors on four trough rows. Additionally, a lidar scanned the horizontal plane above the trough field. In this study, we describe the high-resolution dataset characterizing the complex flow field and resulting structural loads. This first-of-its-kind dataset will enhance the understanding of wind loading on collector structures and will help in designing the next-generation solar collectors and photovoltaic trackers.

14 SOLAR ENERGY↗
DOE OSTI2025

Thermally induced PtOx interfacial layer enhances stability of Pt/β-Ga2O3 vertical Schottky diodes

This study investigates the long-term stability of Pt/β-Ga2O3 field-plated Schottky barrier diodes at high temperatures, with extended thermal cycling and soaking stress. The device characteristics reveal a strong dependence on operating temperature, leading to an ON/OFF current ratio of ∼10× at ±3 V and 600 °C. Extensive thermal cycling (25 cycles over 100 h) between room temperature and 350 °C shows that leakage currents stabilize after the initial ten cycles. This stabilization is likely due to the formation of a thin PtOx layer at the Pt/β-Ga2O3 interface, as confirmed by high-resolution scanning transmission electron microscopy. Further exposure to a constant 350 °C environment for 10 h reveals a slight increase in the leakage current from 10−7 to 10−6 A at −5 V and degradation of the forward current, due to potential thermodynamic instability of the Ohmic contacts. This study provides insights into the changes in the Pt/β-Ga2O3 diode performance with thermal stress, offering a basis for predicting the device’s operational reliability at high temperatures for sensing and power applications.

08 HYDROGEN↗
DOE OSTI2022

Narrow-linewidth laser cooling for rapid production of low-temperature atoms for high data-rate quantum sensing

We present a proof-of-concept demonstration of a narrow linewidth $^{87}$Rb magneto-optical trap (MOT) operating on the narrow linewidth $5S_{1/2}$ → $6P_{3/2}$ transition at 420 nm. We stabilized the absolute frequency of the 420 nm laser to an atomic transition in $^{87}$Rb and demonstrate a MOT using 420 nm light driving the $5S_{1/2}$, $F = 2$ → $6P_{3/2}, F' = 3$ transition. We then use tome-of-flight measurements to characterize the 420 nm MOT temperature, observing a minimum temperature of about $T^{(420)}_{horizontal}$ = 150μK and $T^{(420)}_{vertical}$ = 250μK before the opportunity to perform significant characterization and optimization. Although this temperature is significantly higher then the expected 420 nm Doppler cooling limit ($T_D^{(420)}$ ≈ 34 μK), these are already approaching the Doppler limit of a standard 780 nm MOT ($T_D^{(780)}$ ≈ 146 μK). We believe that with further optimization the Doppler cooling limit of ≈ 34 μK can be achieved. This initial result answers our key research question and demonstrates the viability of applying narrow linewidth laser cooling as a robust technique for future fieldable quantum sensors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Electrical Fault Detection, Power Quality, Distributed Energy Resource Use Cases, and Cyber Event Applications with the Cyber Grid Guard System Using Distributed Ledger Technology

Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation and are associated with distributed energy resources (DERs). The integrity and confidentiality of data from IEDs is crucial, and distributed ledger technology (DLT) could improve the resilience of microgrids by helping to make these data more secure. The most popular applications using blockchain technology for electrical utilities is in the field is based on energy trading. However, the dynamism of the penetration of customer owned DERs and the deployment of sensors with IEDs have led to the identification of new applications using DLT that are focused on other areas, such as monitoring, operation and management of the grid and its assets. In addition, the majority of studies on electrical grid applications with blockchain were validated with software simulations. Although general monitoring of power systems for using DLT could be evaluated in operational electric grids, other DLT research applications such as defense against cyber-attacks and/or electrical fault detection are not likely to be performed in a real infrastructure because of possible risks to the network/equipment security. This report summarizes the application of power system applications using distributed ledger technology (DLT), providing a secure DLT framework for collecting data from IEDs like power meters and protective relays inside and outside of an electrical substation and/or between two different electrical utilities. In this study, the use case scenarios were created and assessed for different power system application by using DLT. The electrical fault detection for faulted phases (1), power quality monitoring of phase voltage magnitudes, frequency levels and load power factor (2), DERs use case monitoring (3), and cyber-event applications (4) were performed in a test bed with a Cyber-Grid Guard (CGG) system using DLT. It had a real-time simulator with power meters and protective relays in-the-loop. The first section of this report presents a literature review of power system applications using blockchain at research level. The second section shows the theory and equations used on this report. The third section shows the description of the test bed, equipment, architecture, and electrical grid diagrams. The fourth section shows the experimental models and use case scenarios that were performed for the electrical fault detection, power quality, DERs use case, and cyber event applications with the CGG system using DLT. The fifth section shows the results collected from the tests based on comparing the time stamped events of the analog signals from the IEDs, DLT computer and real time simulator. The sixth section performed the discussion of the results for the use case scenarios. Finally, section seven presents the conclusions for this report were presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

NPFTURBULENCE: Fast Response Temperature by Airborne Measurements

The original data were collected during the field campaign of the “Turbulent layers promoting New Particle Formation” experiment (NPFTURBULENCE; https://www.arm.gov/research/campaigns/aaf2024npfturbulence; https://www.arm.gov/publications/programdocs/doe-sc-arm-25-004.pdf ) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Atmospheric Observatory (https://www.arm.gov/capabilities/observatories/sgp ) in north-central Oklahoma. The ARM Aerial Facility ArcticShark uncrewed aerial system (UAS, https://www.arm.gov/capabilities/observatories/aaf/uas) was based at Blackwell–Tonkawa Municipal Airport (IATA: BWL, ICAO: KBKN, FAA LID: BKN, 36.74475° N, 97.34918° W, 313.9 m MSL), for the field campaign from May 5 through May 29, 2024. The ArcticShark UAS performed 11 flights, including 10 research flights over the Central Facility of the ARM SGP to measure atmospheric state, turbulence, surface IR temperature and imagery, aerosol number concentration and size distribution. The current dataset presents fast response temperature in the atmospheric boundary layer and lower free troposphere measured on the airborne platform throughout the field campaign. The primary instruments used to create the current dataset were the fine wire thermocouple probe, Pitot-static system (part of the UAS control), and infrared gas analyzer sensor for H2O and CO2 (LI-840A). All parameters used in the temperature calculations (static and dynamic pressure, absolute humidity in form of dew point temperature) were included in the data set. For user convenience, one additional parameter was also included: the type of flight flag (level, up, down, turn, and combination of thereof).

Air temperature↗
DOE OSTI2024

Optimality and Noise Resilience of Critical Quantum Sensing

We compare critical quantum sensing to passive quantum strategies to perform frequency estimation, in the case of single-mode quadratic Hamiltonians. We show that, while in the unitary case both strategies achieve precision scaling quadratic with the number of photons, in the presence of dissipation this is true only for critical strategies. We also establish that working at the exceptional point or beyond threshold provides suboptimal performance. This critical enhancement is due to the emergence of a transient regime in the open critical dynamics, and is invariant to temperature changes. When considering both time and system size as resources, for both strategies the precision scales linearly with the product of the total time and the number of photons, in accordance with fundamental bounds. However, we show that critical protocols outperform optimal passive strategies if preparation and measurement times are not negligible. Our results are applicable to a broad variety of critical sensors whose phenomenology can be reduced to that of a single-mode quadratic Hamiltonian, including systems described by finite-component and fully connected models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI1994

Nuclear Safety [Vol. 35, No. 2, July-December 1994]

Nuclear Safety is a journal that covers significant issues in the field of nuclear safety. Its primary scope is safety in the design, construction, operation, and decommissioning of nuclear power reactors worldwide and the research and analysis activities that promote this goal, but it also encompasses the safety aspects of the entire nuclear fuel cycle, including fuel fabrication, spent-fuel processing and handling, and nuclear waste disposal, the handling of fissionable materials and radioisotopes, and the environmental effects of all these activities. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 179 Consideration of Postaccident Consequences in the Determination of Safety Objectives for Future Nuclear Power Plants in France, D. Queniart, A. Sugier, and J. Lochard; ACCIDENT ANALYSIS: 187 Nuclear Safety Research: The Phebus FP Severe Accident Experimental Program, P. von der Hardt, A. V. Jones, C. Lecomte, and A. Tattegrain; 205 Containment Performance Analysis of the Advanced Neutron Source Reactor at the Oak Ridge National Laboratory, S. H. Kim, R. P. Taleyarkhan, and V. Georgevich; 213 Assessment of Fission Product Deposits in the Reactor Coolant System: The DEVAP Program, G. Le Marois and M. Megnin; 222 Erratum to “A Review of the Available Information on the Triggering Stage of a Steam Explosion," Vol. 35, No. 1, CONTROL AND INSTRUMENTATION: 223 Effects of Normal Aging on Calibration and Response Time of Nuclear Plant Resistance Temperature Detectors and Pressure Sensors, H. M. Hashemian; DESIGN FEATURES: 235 Defense in Depth Against the Hydrogen Risk—A European Research Program, F. Fineschi; ENVIRONMENTAL EFFECTS: 246 Technical Note: A Preliminary Analysis of the Risks to Hong Kong Resulting from Potential Accidents of Daya Bay Nuclear Power Plant, Z. Shi and X. Wei; OPERATING EXPERIENCES: 253 Reactor Shutdown Experience, Compiled by J. W. Cletcher; SPECIAL SECTION ON TMI-2 VESSEL INVESTIGATION PROJECT: 256 Three Mile Island—New Findings 15 Years After the Accident A. M. Rubin and E. Beckjord; 269 Relocation of Molten Material to the TMI-2 Lower Head, J. R. Wolf, D. W. Akers, and L. A. Neimark; 280 Insight Into the TMI-2 Core Material Relocation Through Examination of Instrument Tube Nozzles, L. A. Neimark; 288 Physical and Radiochemical Examinations of Debris from the TMI-2 Lower Head, D. W. Akers and B. K. Schuetz; 301 Results of Metallographic Examinations and Mechanical Tests of Pressure Vessel Samples from the TMI-2 Lower Head, D. R. Diercks and G. E. Korth; 313 Margin-to-Failure Calculations for the TMI-2 Vessel, J. Rempe, L. Stickler, S. Chavez, G. Thinnes, R. Witt, and M. Corradini; U.S. NUCLEAR REGULATORY COMMISSION INFORMATION AND ANALYSES: 328 1993 Accident Sequence Precursor (ASP) Program Results, L. N. Vanden Heuvel, J. W. Cletcher, D. A. Copinger, J. W. Minarick, B. W. Dolan, and P. D. O’Reilley; RECENT DEVELOPMENTS: 339 Reports, Standards, and Safety Guides, D. S. Queener; 345 Proposed Rule Changes as of June 30, 1994; ANNOUNCEMENTS: 234 Thirty-First Annual Meeting of the National Council on Radiation Protection and Measurements; 356 1995 International Incineration Conference; 358 Fifth International Controls and Instrumentation Conference; 359 ANS International Topical Meeting on Safety of Operating Reactors; 359 Fifth International Conference on Nuclear Criticality Safety; 360 International Conference on Probabilistic Safety Assessment Methodology and Applications; 351 The Authors; 357 Reviewers of Nuclear Safety, Vol. 35.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Modeling Savings for ENERGY STAR Smart Home Energy Management Systems

The objective of this study was to develop a repeatable and defensible methodology to analyze the energy savings for Home Energy Management Systems (HEMS) that meets the minimum requirements for certification under ENERGY STAR ® Smart Home Energy Management System (SHEMS) Version 1. Mandatory connected loads include a smart thermostat, two smart lights, and one smart power strip or smart outlet. Control strategies must include feedback to occupants through an in-home display, user programming, occupancy sensor-based controls, and responsiveness to utility signals such as demand response programs. Several occupant behavior patterns were selected to quantify the range of energy savings potential for a HEMS with this basic functionality. A literature review was conducted to establish realistic room-by-room occupancy levels and usage patterns for connected devices. A series of event-driven hourly profiles were created, followed by adjustments based on application of HEMS control strategies to thermostats, interior lighting, and plug load schedules. EnergyPlus modeling was performed using these hourly schedules in three locations (Boston, Houston, and Phoenix) to examine climate dependence of energy savings. Total site energy savings ranged from 4.3 to 27.1 MBtu/year (7%-35%), and utility bill savings ranged from $\$$123 to $\$$670/year (6%-29%). The highest predicted savings was realized by occupants that were not energy conscious prior to HEMS installation, but highly engaged with the HEMS controls once the system was installed. The smart thermostat accounted for most of the savings, followed by the smart power strip. Smart lighting did not save a significant amount of energy in our analysis, based on an assumption that efficient LEDs with no standby power would normally be installed anyway.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Digital Twin for Hydropower System Object Modeling: Alder Dam (FY2023)

Hydropower is the world's largest source of renewable electricity, and hydropower plants are distributed all over the world. Typical major components of a hydropower plant are the governor, excitation, generator, thrust bearing, hydraulic turbine, transformer, the main lead, metering and control, tailwater depression, and dissolved oxygen. For each component, various measures are taken. The measurements are acquired by various heterogeneous systems, including standalone sensors, programmable logic controllers (PLC), Supervisory control and data acquisition (SCADA), Internet of Things (IoT), and data acquisition and integration platforms such as OSI/PI. The measured data are often archived within the plant by a data management platform, and many institutions have cloud-based archive systems, such as Hydropower Research Institution (HRI), U.S. Army Corps of Engineers (USACE), and Columbia River Data Access in Real Time (DART). Object Modeling is a general framework for designing information systems. It focuses on objects, the actions they perform, and the messages they send to one another to cause those actions to be taken. The major differences among object modeling, network modeling, data modeling, and process modeling are that in the first we focus on the actions in response to information, objects which form the system, the actions they perform, and how they pass information to one another, while in the second we concentrate on where, when and how much information is moved, while in the third we focus on what information is moved and where it is moved, while in the last we focus on how it is moved and when it is moved. Object modeling was developed basically as a method to develop object-oriented systems and to support object-oriented programming. It describes the static structure of the system. The object Modeling Technique is easy to draw and use. That is why we choose object modeling to connect physical hydropower plants to Digital Twin. It recognizes the objects and the relationship between them. It identifies the attributes and functions of each class. Dynamic Modeling: It explains how objects respond to events. Functional Modeling indicates the processes executed in an object and how data changes when it moves to objects. It has been used in many applications like telecommunication, transportation, etc.

13 HYDRO ENERGY↗
DOE OSTI2024

Perspectives on AI Architectures and Co-design for Earth System Predictability

Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.

58 GEOSCIENCES↗