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Data Visualization: Augmented Reality
In recent years, there has been an increasing interest in developing new technologies for automated characterization and visualization of condition monitoring data. Augmented Reality (AR) is a technology that is being developed to improve such data visualization. Augmented reality has been defined as a technology that merges virtual and physical components in real-time, and in three dimensions. Wearable, commercially-available AR devices allow onsite engineers and technicians to perform inspection tasks with significantly more available information such as comparisons of past and present sensor and imager data, onsite data analysis and result displays, and various forms of metadata including technical drawings, previous inspection reports and maintenance histories, operation manuals, codes and standards, and holograms representing data analysis results superimposed onto the in situ monitored system.
Site A2 - PNNL Surface Flux Station / Raw Data
Raw time series of fast response wind and virtual sonic temperature data (nominal 20Hz) for calculation of the turbulent fluxes; measured at 4 mAGL.
Site A5 - PNNL Surface Flux Station / Raw Data
Raw time series of fast response wind and virtual sonic temperature data (nominal 20Hz) for calculation of the turbulent fluxes; measured at 4 mAGL.
Site G - PNNL Surface Flux Station / Raw Data
Raw time series of fast response wind and virtual sonic temperature data (nominal 20Hz) for calculation of the turbulent fluxes; measured at 4 mAGL.
Site A1 - PNNL Surface Flux Station / Raw Data
Raw time series of fast response wind and virtual sonic temperature data (nominal 20Hz) for calculation of the turbulent fluxes; measured at 4 mAGL
Site H - PNNL Surface Flux Station / Raw Data
Raw time series of fast response wind and virtual sonic temperature data (nominal 20Hz) for calculation of the turbulent fluxes; measured at 4 mAGL
CACO Site - PNNL Surface Flux Station at 4m / Raw Data
This dataset contains raw time series of fast response wind and virtual sonic temperature data (nominal 20Hz) for calculation of the turbulent fluxes; measured at 4 mAGL.
CACO Site - PNNL Surface Flux Station at 10m / Raw Data
This dataset contains raw time series of fast response wind and virtual sonic temperature data (nominal 20Hz) for calculation of the turbulent fluxes; measured at 10 mAGL.
RHOD Site - PNNL Surface Flux Station at 4m / Raw Data
This dataset contains raw time series of fast response wind and virtual sonic temperature data (nominal 20Hz) for calculation of the turbulent fluxes; measured at 4 mAGL.
Adaptive primal–dual control for distributed energy resource management
With the increased adoption of distributed energy resources (DERs) in distribution networks, their coordinated control with a DER management system (DERMS) that provides grid services (e.g., voltage regulation, virtual power plant) is becoming more necessary. One particular type of DERMS using primal–dual control has recently been found to be very effective at providing multiple grid services among an aggregation of DERs; however, the main parameter, the primal–dual step size, must be manually tuned for the DERMS to be effective, which can take a considerable amount of engineering time and labor. To this end, we design a simple method that self-tunes the step size(s) and adapts it to changing system conditions. Additionally, it gives the DER management operator the ability to prioritize among possibly competing grid services. Here we evaluate the automatic tuning method on a simulation model of a real-world feeder in Colorado with data obtained from an electric utility. Through a variety of scenarios, we demonstrate that the DERMS with automatically and adaptively tuned step sizes provides higher-quality grid services than a DERMS with a manually tuned step size.
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.
Real-Time Optimization Workflow Status Update
Economically optimal and safe operation of integrated energy systems (IES) requires optimization at many different time scales. A real-time optimization (RTO) workflow will attempt to maximize revenue and minimize operational costs on a time scale of minutes to hours. Such a workflow requires the use of a digital twin (DT), which is a virtual representation of a physical system. The DT is updated using real-time data from the physical system, and serves as a model in an optimization framework. The optimization results are then sent back to the physical system to complete the loop. This report details the progress made in developing building blocks for a DT/RTO framework. The Risk Analysis Virtual Environment (RAVEN) platform within the Framework for Optimization of Resources and Economics (FORCE) tool suite can perform many of the tasks required for building a DT and performing RTO. The first item of this report details RAVEN enhancements that enable RAVEN workflows to be run in various environments. Data communication between the physical system and its DT is essential for successful RTO. This includes preprocessing real-time data, loading data into a data warehouse, and querying the stored data. The second section of this report describes the progress made in implementing an adapter in Python in order for Deep Lynx to handle the data communication. Typical dispatch optimization frameworks are built on linear programming (LP). The prototype RTO workflow developed in this report uses an LP problem as a part of a receding-horizon- or economic model predictive control (EMPC) based optimization. The third section of this report details the framework of an RTO workflow in which the system consists of a simple electrical storage device. A DT can be built from a reduced-order model (ROM). Integrating a ROM into a typical LP optimization framework has been challenging because most optimization packages require the user to write algebraic expressions for the system model. The final section of this report shows how an externally built RAVEN ROM can be integrated in an RTO framework by using the Python package Pyomo. This demonstrates the RTO workflow capability from a software-only perspective and is an important step in demonstrating the capability to implement an RTO workflow for a physical system.
Twinac: A Universal Framework for Virtual Accelerator Controls
We propose a universal framework for a system of virtual accelerator controls (Twinac), a standard toolkit for research institutions to design, maintain, and use a real-time, end-to-end "digital twin" of their particle accelerator facility. This virtual counterpart will mirror any physical accelerator to provide (1) predictive maintenance; (2) surveillance of hidden environmental factors, such as seasonal temperature variations, which could impact performance of power supplies, magnets, and other instruments; and (3) a capability to model novel ways to operate the accelerator without risking equipment damage. Twinac is envisioned as accelerator facility agnostic, allowing institutions to share and reuse myriad simulation approaches (analytics-based, A.I.-driven, or combinations of the like) across facilities. The Twinac system lays the groundwork for a collaborative network of institutions to maintain and update this shared virtual accelerator technology.
Improved measurement of the decays η ′ → π + π − π + ( 0 ) π − ( 0 ) and search for the rare decay η ′ → 4 π 0
Using a sample of 10 billion J / ψ events collected with the BESIII detector, the decays η ′ → π + π − π + π − , η ′ → π + π − π 0 π 0 and η ′ → 4 π 0 are studied via the process J / ψ → γ η ′ . The branching fractions of η ′ → π + π − π + π − and η ′ → π + π − π 0 π 0 are measured to be ( 8.56 ± 0.25 ( stat ) ± 0.23 ( syst ) ) × 10 − 5 and ( 2.12 ± 0.12 ( stat ) ± 0.10 ( syst ) ) × 10 − 4 , respectively, which are consistent with previous measurements but with improved precision. No significant η ′ → 4 π 0 signal is observed, and the upper limit on the branching fraction of this decay is determined to be less than 1.24 × 10 − 5 at the 90% confidence level. In addition, an amplitude analysis of η ′ → π + π − π + π − is performed to extract the doubly virtual isovector form factor α for the first time. The measured value of α = 1.22 ± 0.33 ( stat ) ± 0.04 ( syst ) , is in agreement with the prediction of the vector meson dominance model. Published by the American Physical Society 2024
Study of Communication Boundaries of Primal-Dual Based Distributed Energy Resource Management Systems (DERMS)
As the coordination of distributed energy re-sources becomes more necessary to provide grid services through a distributed energy resource management system (DERMS), studying the communication requirements for suc-cessful real-world, low-cost implementation becomes increasingly important. Specifically, this paper studies the necessary communication time resolutions between the different system components for a primal-dual-based DERMS, a highly de-veloped DERMS framework. We design a metric to evaluate the functionality of a DERMS with respect to providing grid services. Using numerical simulations based on a real-world feeder in Colorado, we show that the upper bound on the time resolutions is on the order of minutes instead of the previously assumed order of seconds.
Optimization Of Real-time Capacity Allocation
ORCA is a modeling toolset to accelerate real-time control and optimization of digital twins, including virtual models of facilities, physical facilities, and interconnections to allow optimal control of physical facilities using virtual models. ORCA is enabled by INL's RAVEN and DeepLynx software codes.
Oak Ridge National Laboratory's Strategic Research and Development Insights for Digital Twins
Oak Ridge National Laboratory (ORNL) is pleased to provide our response to the NITRD RFI on Digital Twins Research and Development. Digital twins are virtual representations of physical systems, leveraging real-time data to simulate and predict behaviors. ORNL is advancing digital twin technology across various disciplines, including neutron scattering, networking, science ecosystems, supercomputing, secure facilities, mobility technologies, materials design and discovery, power systems, fusion reactors, biological sciences, and earth observation. These efforts aim to enhance scientific research, operational efficiency, and decision-making processes. ORNL facilities, such as the High Flux Isotope Reactor (HFIR), Grid-C, Spallation Neutron Source (SNS), and Oak Ridge Leadership Computing Facility (OLCF), provide the infrastructure to develop and demonstrate these digital twin technologies. In this document, we lay out key challenges, research gaps, and future opportunities based on our experience with digital twins that aim to serve as useful contributions towards a National Digital Twins R&D Strategic Plan. In the remaining document, we address nine of the thirteen topic areas specified in the RFI.