PUMA-V - Polyhedral User Mapping Assistant and Visualizer Final Technical Report
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73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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Probing Signal-Based Inertia and Frequency Response Estimation for Power Systems with High Penetration of Inverter-Based Resources: Preprint
Power system inertia is the inherent capability of a power system to resist changes in its frequency during disturbances. Real-time inertia estimation technology has become more important due to the low-inertia issues caused by the increasing integration levels of inverter-based resources (IBRs) from renewable energy; however, existing inertia estimation methods hardly consider multiple frequency response controls that act within the same time frame as conventional inertial response, thus making measured inertia values vary under different testing conditions. To resolve this issue, this paper proposes a novel real-time estimation method to simultaneously estimate a power system's inertia constant and frequency response droop constant using a well-designed probing signal. First, we formulate the inertia and frequency response model of a power system with IBRs. Second, through the integration and manipulation of the developed model, we propose a multivariate linear regression- based estimation method that is resilient to measurement noise. Third, we design a probing signal that can be injected by IBRs to incite the required transients for estimation. Finally, we validate the proposed estimation method through comprehensive power- hardware-in-the-loop experiments using inverter hardware and a realistic island power system model. The results demonstrate that the proposed method can accurately estimate the inertia and droop value of the power system with grid-following IBRs and grid-forming IBRs with virtual synchronous machine control.
frequency response↗
Predict Solid Solution Formation Using Machine Learning
This virtual presentation was given at HEA 2021 Congress.
Gao, Michael↗
The Virtualized Cyber-Physical Testbed for Machine Learning Anomaly Detection: A Wind Powered Grid Case Study
Not Available
Marino, Daniel L. (ORCID:0000000286864752)↗
Virtual Growth of SRF Materials: A Machine Learning Approach to Predict the Crystalline Structural Ordering in Nb Surface Oxides
Niobium's native surface oxide affects SRF cavity and superconducting qubit performance, motivating interest in controlling its crystalline structure. We combine a literature-derived machine-learning analysis with temperature-dependent XRD to study crystalline ordering in Nb2O5. Random Forest models, trained on 74 processing conditions from 17 papers and validated by leave-one-group-out cross-validation, predicted broad crystallinity outcomes well (balanced accuracy 0.809), but struggled with specific polymorph identity (0.577). Annealing temperature was the dominant predictor across all targets; oxygen partial pressure showed negligible importance, reflecting narrow literature coverage rather than physical irrelevance. Temperature-dependent XRD on anodized and H2O2-treated Niobium showed structural evolution consistent with the machine learning predictions. Our model and overall approach provide a data-driven framework for identifying and optimizing conditions that promote crystallization in initially amorphous oxides. This framework can guide the selection of growth and post-annealing conditions for Nb surfaces by narrowing the experimental parameter space, thereby reducing trial-and-error efforts in developing oxide structures relevant to SRF applications.
Tilkin, Anthony [Unlisted, US, IL; Fermilab]↗
Prediction of Material Dielectric Permittivity by Machine Learning
IMPI 55 Virtual Symposium, Virtual, June 28-July 1, 2021
Ellison, Candice↗
Applications of visualization technology in the structural sciences
The structural sciences are undergoing a transformation driven by advancements in visualization technologies that aid researchers in understanding and communicating experimental data from complex molecular systems. New applications of integrative structural biological and biophysical approaches add a wide variety of complementary information from a broad range of scientific disciplines. These approaches extend structural biophysical methodologies to enable research by the incorporation of a variety of data streams and utilization of tools like molecular graphics, virtual reality, and machine learning. To redefine how structural data—particularly from cryo-electron microscopy and x-ray crystallography—are fed forward for scientific exploration and communication, the advances in tools for data visualization and interpretation have been critical. By bringing molecular systems into an interactive three-dimensional space, these novel technologies enhance research workflows, facilitate structure-based drug design, and create engaging educational experiences. Taken together, these visualization innovations are essential tools for advancing the field by making concepts more accessible and compelling.
Eng, Edward T. [New York Structural Biology Center↗
Saturn's inner satellites: Orbits, masses, and the chaotic motion of atlas from new Cassini imaging observations
We present numerically derived orbits and mass estimates for the inner Saturnian satellites, Atlas, Prometheus, Pandora, Janus, and Epimetheus from a fit to 2580 new Cassini Imaging Science Subsystem astrometric observations spanning 2004 February to 2013 August. The observations are provided as machine-readable and Virtual Observatory tables. We estimate GM{sub Atlas} = (0.384 ± 0.001) × 10{sup −3} km{sup 3} s{sup −2}, a value 13% smaller than the previously published estimate but with an order of magnitude reduction in the uncertainty. We also find GM{sub Prometheus} = (10.677 ± 0.006) × 10{sup −3} km{sup 3} s{sup −2}, GM{sub Pandora} = (9.133 ± 0.009) × 10{sup −3} km{sup 3} s{sup −2}, GM{sub Janus} = (126.51 ± 0.03) × 10{sup −3} km{sup 3} s{sup −2}, and GM{sub Epimetheus} = (35.110 ± 0.009) × 10{sup −3} km{sup 3} s{sup −2}, consistent with previously published values, but also with significant reductions in uncertainties. We show that Atlas is currently librating in both the 54:53 co-rotation-eccentricity resonance (CER) and the 54:53 inner Lindblad (ILR) resonance with Prometheus, making it the latest example of a coupled CER-ILR system, in common with the Saturnian satellites Anthe, Aegaeon, and Methone, and possibly Neptune's ring arcs. We further demonstrate that Atlas's orbit is chaotic, with a Lyapunov time of ∼10 years, and show that its chaotic behavior is a direct consequence of the coupled resonant interaction with Prometheus, rather than being an indirect effect of the known chaotic interaction between Prometheus and Pandora. We provide an updated analysis of the second-order resonant perturbations involving Prometheus, Pandora, and Epimetheus based on the new observations, showing that these resonant arguments are librating only when Epimetheus is the innermost of the co-orbital pair, Janus and Epimetheus. We also find evidence that the known chaotic changes in the orbits of Prometheus and Pandora are not confined to times of apse anti-alignment.
79 ASTRONOMY AND ASTROPHYSICS↗
Large-Scale Inverter Integration in Bulk Power Grids Using Heterogeneous Grid-Forming Control Strategies
The conventional generation within the grid is gradually being replaced by inverter based resources. However, the integration of inverters into realistic transmission grid models has not been thoroughly explored. To address this gap, this work develops an automation framework aimed at facilitating the integration of a large number of inverter-based resources into a large-scale grid. Two types of grid-forming controlled inverters—namely droop-controlled inverters and virtual synchronous machine-controlled inverters—are developed for use in positive-sequence simulation packages. Various penetration levels ranging from 23% to 100% of grid-forming inverters are tested in a grid setup in the US Western Interconnection. The dynamic performance of inverters under different control schemes is compared in response to various disturbances.
Lyu, Xue [BATTELLE (PACIFIC NW LAB)]↗
Measuring coal thickness
Laboratory tested concept, for measuring thickness of overhead coal using noncontacting sensor system coupled to controller and high pressure water jet, allows mining machines to remove virtually all coal from mine roofs without danger of cutting into overlying rock.
Barker, C.↗
Creating better virtual worlds
The topics discussed include the following: virtual reality, man machine systems, and human-computer interface.
Furness, Thomas A., III↗
Mobile Hot Cell Digital Twin: End-of-life Management of Disused High Activity Radioactive Sources – 23598
Sealed radioactive sources are utilized for a wide range of applications across nuclear facilities, universities, hospitals, and industry. When these sources reach the end of serviceable life, they become waste. As waste, this radioactive material then goes through a process of recapture and then transfer to long term storage. With the advancement of technology in conjunction with better accessibility of technology, industries are exploring the use of digital automation to enhance productivity, efficiency, and safety while minimizing operation and maintenance costs, health and environmental risks, and uncertainty in the project life cycles. One area for exploration is the use of a digital twin to help design a robust, versatile, and safe solution for recapturing spent sources. We believe this avenue can also provide further advantages in the operations aspect of end-of-life management of spent radioactive sources by reducing the deployment time, increasing operator safety, and reducing operation & maintenance cost. We present a novel digital twin framework for end-of-life management of disused high activity radioactive sources. We have designed and developed a framework that houses a digital twin for visualizing and monitoring the recapture process to inform the engineering and design of a new Mobile Hot Cell. Furthermore, we demonstrate the feasibility of the proposed framework by providing a prototypical implementation, supporting the Mobile Hot Cell and human-machine interface's virtual replication.
61 RADIATION PROTECTION AND DOSIMETRY↗
System and method for error detection and correction in virtual reality and augmented reality environments
Embodiments of the present disclosure are related to training one or more of machine learning algorithms in a virtual reality environment for error detection and correction and/or for employing one or more trained machine learning models in an augmented reality environment to detect and/or correct user errors associated the performance of one or more tasks.
97 MATHEMATICS AND COMPUTING↗
A virtual simulator designed for collision prevention in proton therapy
Purpose: In proton therapy, collisions between the patient and nozzle potentially occur because of the large nozzle structure and efforts to minimize the air gap. Thus, software was developed to predict such collisions between the nozzle and patient using treatment virtual simulation. Methods: Three-dimensional (3D) modeling of a gantry inner-floor, nozzle, and robotic-couch was performed using SolidWorks based on the manufacturer’s machine data. To obtain patient body information, a 3D-scanner was utilized right before CT scanning. Using the acquired images, a 3D-image of the patient’s body contour was reconstructed. The accuracy of the image was confirmed against the CT image of a humanoid phantom. The machine components and the virtual patient were combined on the treatment-room coordinate system, resulting in a virtual simulator. The simulator simulated the motion of its components such as rotation and translation of the gantry, nozzle, and couch in real scale. A collision, if any, was examined both in static and dynamic modes. The static mode assessed collisions only at fixed positions of the machine’s components, while the dynamic mode operated any time a component was in motion. A collision was identified if any voxels of two components, e.g., the nozzle and the patient or couch, overlapped when calculating volume locations. The event and collision point were visualized, and collision volumes were reported. Results: All components were successfully assembled, and the motions were accurately controlled. The 3D-shape of the phantom agreed with CT images within a deviation of 2 mm. Collision situations were simulated within minutes, and the results were displayed and reported. Conclusions: The developed software will be useful in improving patient safety and clinical efficiency of proton therapy.
60 APPLIED LIFE SCIENCES↗
RAM as a Network Managed Resource
This paper describes an architecture for and several application examples of using a dynamically allocable RAM pool over the network as part of a deep memory hierarchy. We present four different use cases, including in situ analysis, machine learning, quantum chemistry simulations, and virtualization. In each use case, we modified or implemented software to use a network RAM pool, and then evaluated the performance. The cases of in situ analysis, machine learning, and virtualization demonstrated good performance. While the quantum chemistry experiments exhibited an average slowdown of 53% for all runs, including those where resources were clearly oversubscribed, the scalability results showed promise.
Allcock, William↗
TU-CD-304-05: 4Ï€ Non-Coplanar Radiotherapy: From Mathematical Modeling to Clinical Implementation
Purpose: To develop and clinically implement 4π radiotherapy, an inverse optimization platform that maximally utilizes non-coplanar intensity modulated radiotherapy (IMRT) beams to significantly improve critical organ sparing. Methods: A 3D scanner was used to digitize the human and phantom subject surfaces, which were positioned in the computer assisted design (CAD) model of a TrueBeam machine to create a virtual geometrical model, based on which, the feasible beam space was calculated for different tumor locations. Beamlets were computed for all feasible beams using convolution/superposition. A column generation algorithm was employed to optimize patient specific beam orientations and fluence maps. Optimal routing through all selected beams were calculated by a level set method. The resultant plans were converted to XML files and delivered to phantoms in the TrueBeam developer mode. Finally, 4π plans were recomputed in Eclipse and manually delivered to recurrent GBM patients. Results: Compared to IMRT utilizing manually selected beams and volumetric modulated arc therapy plans, markedly improved dosimetry was observed using 4π for the brain, head and neck, liver, lung, and prostate patients. The improvements were due to significantly improved conformality and reduced high dose spillage to organs mediolateral to the PTV. The virtual geometrical model was experimentally validated. Safety margins with 99.9% confidence in collision avoidance were included to the model based model accuracy estimates determined via 300 physical machine to phantom distance measurements. Automated delivery in the developer mode was completed in 10 minutes and collision free. Manual 4 π treatment on the GBM cases resulted in significant brainstem sparing and took 35–45 minutes including multiple images, which showed submillimeter cranial intrafractional motion. Conclusion: The mathematical modeling utilized in 4π is accurate to create and guide highly complex non-coplanar IMRT treatments that consistently and significantly outperform human-operator-created plans. Deliverability of such plans is clinically demonstrated. This work is funded by Varian Medical Systems and the NSF Graduate Research Fellowship DGE-1144087.
60 APPLIED LIFE SCIENCES↗
SU-F-BRD-11: A Virtual Simulator Designed for Collision Prevention in Proton Therapy
Purpose: In proton therapy, collisions between patient and nozzle potentially occur in attaining minimal air gap due to the large nozzle structure. Thus, we developed software predicting the collisions of the nozzle and patient by simulating treatments. Methods: 3D modeling of a gantry inner-floor, nozzle and robotic-couch was done by using the SolidWorks based on the manufacturer’s machine data. To obtain patient body information, a 3D-scanner was utilized to scan a patient right before CT scanning. From the acquired images, a 3D-image of the patient’s body contour was reconstructed. The accuracy of the image was confirmed against the CT image for a humanoid phantom. The machine components and the virtual patient were combined on the treatment-room coordinate system, resulting in a virtual simulator. The simulator simulated the motion of its components such as rotation and translation of gantry, nozzle and couch, in real scale. Collision, if any, was examined both in static mode and dynamic mode. The static mode checks only at fixed positions of the machine’s components while dynamic mode examines while one component is in motion. Collision was notified if any voxel of two components, for example a nozzle and a patient or couch, overlapped when calculating volume locations. The event and collision point are visualized and colliding volumes are reported. Results: All components were successfully assembled and the motions could be accurately controlled. The 3D-shape of a phantom agreed with CT images within a deviation of 2 mm. Collision situations can be simulated within minutes and the results are displayed and reported. Conclusion: The developed software will be useful in improving patient safety and clinical efficiency for proton therapy. This work was supported by the National Research Foundation of Korea funded by Ministry of Science, ICT & Future Planning (2012M3A9B6055201, 2013M2A2A7043507), and Samsung Medical Center grant (GFO1130081)
60 APPLIED LIFE SCIENCES↗
Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics
Abstract Longitudinal phase space (LPS) provides a critical information about electron beam dynamics for various scientific applications. For example, it can give insight into the high-brightness X-ray radiation from a free electron laser. Existing diagnostics are invasive, and often times cannot operate at the required resolution. In this work we present a machine learning-based Virtual Diagnostic (VD) tool to accurately predict the LPS for every shot using spectral information collected non-destructively from the radiation of relativistic electron beam. We demonstrate the tool’s accuracy for three different case studies with experimental or simulated data. For each case, we introduce a method to increase the confidence in the VD tool. We anticipate that spectral VD would improve the setup and understanding of experimental configurations at DOE’s user facilities as well as data sorting and analysis. The spectral VD can provide confident knowledge of the longitudinal bunch properties at the next generation of high-repetition rate linear accelerators while reducing the load on data storage, readout and streaming requirements.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗