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DOE OSTI2021

Cyber-Power Co-Simulation for End-to-End Synchrophasor Network Analysis and Applications

The resiliency, reliability and security of the next generation cyber-power smart grid depend upon efficiently leveraging advanced communication and computing technologies. Also, developing real-time data-driven applications is critical to enable wide-area monitoring and control of the cyber-power grid given high-resolution data from Phasor Measurement Units (PMUs). North American Synchrophasor Initiative Network (NASPlnet) provides guidance for PMU data exchanges. With the advancement in networking and grid operation, it is necessary to evaluate the performance of different data flow architectures suggested by NASPInet and analyze the impact on applications. Therefore, we need a cyber-power co-simulation framework that supports very large-scale co-simulation capable of running in parallel, high-performance computing platforms and capturing real-life network behavior. This work presents an end-to-end automated and user-driven cyber-power co-simulation using NS3 to model communication networks, GridPACK to model the power grid, and HELICS as a co-simulation engine. Comparative analysis of latency in synchrophasor networks and a performance evaluation of a power system stabilizer application utilizing PMU data in an IEEE 39 bus test system is presented using this cosimulation testbed.

Mustafa, Hussain M.↗

DOE OSTI2014

WE-E-BRE-07: High-Throughput Mapping of Proton Biologic Effect

Purpose: To systematically relate the relative biological effectives (RBE) of proton therapy to beam linear energy transfer (LET) and dose. Methods: Using a custom irradiation apparatus previously characterized by our group, H460 NSCLCs were irradiated using a clinical 80MeV spot scanning proton beam. Utilizing this system allowed for high-throughput clonogenic assays performed in 96-well tissue culture plates as opposed to the traditional 6-well technique. Each column in the 96-well plate received a set LET-dose combination. By altering the total number of dose repaintings, numerous dose-LET configurations were examined to effectively generate surviving fraction (SF) data over the entire Bragg peak. The clonogenic assay was performed post-irradiation using an INCell Analyzer for colony quantification. SF data were fit to the linear-quadratic model for analysis. Results: Irradiation with increasing LETs resulted in decreased cell survival largely independent of dose. A significant correlation between LET and SF was identified by two-way ANOVA and the extra sum-of-squares F test. This trend was obscured at the lower LET values in the plateau region of the Bragg peak; however, it was clear for LET values at and beyond the Bragg peak. Data fits revealed the SF at a dose of 2Gy (SF2) to be 0.48 for the lowest tested LET (1.55keV/um), 0.47 at the end of the plateau region (4.74keV/um) and 0.33 for protons at the Bragg peak (10.35keV/um). Beyond the Bragg peak we measured SF2s of 0.16 for 15.01keV/um, 0.02 for 16.79keV/um, and 0.004 for 18.06keV/um. Conclusion: We have shown that our methodology enables high-content automated screening for proton irradiations over a range of LETs. The observed decrease in cellular SF in high LET regions confirms an increased RBE of the radiation and suggests further evaluation of proton RBE values is necessary to optimize clinical outcomes. Rosalie B. Hite Graduate Fellowship in Cancer Research, NIH Program Project Grant P01CA021239.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-AB-201-03: A Robot for the Automated Delivery of An Electromagnetic Tracking Sensor for the Localization of Brachytherapy Catheters

Purpose: To present a programmable robotic system for the accurate and fast deployment of an electromagnetic (EM) sensor for brachytherapy catheter localization. Methods: A robotic system for deployment of an EM sensor was designed and built. The system was programmed to increment the sensor position at specified time and space intervals. Sensor delivery accuracy was measured in a phantom using the localization of the EM sensor and tested in different environmental conditions. Accuracy was tested by measuring the distance between the physical locations reached by the sensor (measured by the EM tracker) and the intended programmed locations. Results: The system consisted of a stepper motor connected to drive wheels (that grip the cable to move the sensor) and a series of guides to connect to a brachytherapy transfer tube, all controlled by a programmable Arduino microprocessor. The total cost for parts was <$300. The positional accuracy of the sensor location was within 1 mm of the expected position provided by the motorized guide system. Acquisition speed to localize a brachytherapy catheter with 20 cm of active length was 10 seconds. The current design showed some cable slip and warping depending on environment temperature. Conclusion: The use of EM tracking for the localization of brachytherapy catheters has been previously demonstrated. Efficient data acquisition and artifact reduction requires fast and accurate deployment of an EM sensor in consistent, repeatable patterns, which cannot practically be achieved manually. The design of an inexpensive, programmable robot allowing for the precise deployment of stepping patterns was presented, and a prototype was built. Further engineering is necessary to ensure that the device provides efficient independent localization of brachytherapy catheters. This research was funded by the Kaye Family Award.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Assessment of Flow-Enhanced Electrochemical Sensor Testing and Deployments

The goal of our activities in FY24 was to enable advanced materials accountancy for molten salt reactors (MSRs) through the development and optimization of robust flow-enhanced electrochemical sensors (FEES) using a safeguards-focused sensor testing platform called the modular flow instrumentation testbed (MFIT). Flow-enhanced electrochemical sensors are a type of electroanalytical sensor that have been developed at Argonne National Laboratory to be installed directly into MSR flow conduits to make measurements of the salt composition. These sensors represent a significant improvement in capabilities compared to earlier electroanalytical sensors that instead can only be operated in quiescent conditions. Previous work has focused on testing of the FEES in flowing conditions provided by the MFIT to assess the accuracy and precision of the sensor measurements. This year, the FEES technical readiness was increased by (1) demonstrating the use of optimized sensor techniques to achieve measurements in very high-concentration MSR-relevant salts, (2) leveraging new multimodal sensors and automation approaches to reduce measurement uncertainties, and (3) fabricating and deploying sensors to industrial partners to improve their ability to make measurements in challenging environments. Additionally, we also ran the sensors during complex system operations involving combined flow and purification activities to demonstrate safeguards-relevant measurements during online processing for MSRs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2015

A servo controlled gradient loading triaxial model test system for deep-buried cavern

A servo controlled gradient loading model test system is developed to simulate the gradient geostress in deep-buried cavern. This system consists of the gradient loading apparatus, the digital servo control device, and the measurement system. Among them, the gradient loading apparatus is the main component which is used for exerting load onto the model. This loading apparatus is placed inside the counterforce wall/beam and is divided to several different loading zones, with each loading zone independently controlled. This design enables the gradient loading. Hence, the “real” geostress field surrounding the deep-buried cavern can be simulated. The loading or unloading process can be controlled by the human-computer interaction machines, i.e., the digital servo control system. It realizes the automation and visualization of model loading/unloading. In addition, this digital servo could control and regulate hydraulic loading instantaneously, which stabilizes the geostress onto the model over a long term. During the loading procedure, the collision between two adjacent loading platens is also eliminated by developing a guide frame. This collision phenomenon is induced by the volume shrinkage of the model when compressed in true 3D state. In addition, several accurate measurements, including the optical and grating-based method, are adopted to monitor the small deformation of the model. Hence, the distortion of the model could be accurately measured. In order to validate the performance of this innovative model test system, a 3D geomechanical test was conducted on a simulated deep-buried underground reservoir. The result shows that the radial convergence increases rapidly with the release of the stress in the reservoir. Moreover, the deformation increases with the increase of the gas production rate. This observation is consistence with field observation in petroleum engineering. The system is therefore capable of testing deep-buried engineering structures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Joint attack detection and secure state estimation of cyber-physical systems

Not provided.

Automation & Control Systems↗

DOE OSTI2024

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. Additionally, an error compensation mechanism is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The result show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92% - 95% from one hour to one week ahead forecasting.

14 SOLAR ENERGY↗

DOE OSTI2025

Improved Treatment of 1–4 Interactions in Force Fields for Molecular Dynamics Simulations

Traditional force fields commonly use a combination of bonded torsional terms and empirically scaled nonbonded interactions to capture 1-4 energies and forces of atoms separated by three bonds in a molecule. While this approach can yield accurate torsional energy barriers, it often leads to inaccurate forces and erroneous geometries and creates an interdependence between dihedral terms and nonbonded interactions, complicating parametrization and reducing transferability. Here, in this paper, we demonstrate that 1-4 interactions can be accurately modeled using only bonded coupling terms, eliminating the need for arbitrarily scaled nonbonded interactions altogether. Furthermore, by leveraging the automated parametrization capabilities of the Q-Force toolkit, we efficiently determine the necessary coupling terms without the need for manual adjustment. Our approach is first validated on a range of small molecule systems, encompassing both flexible and rigid structures, and shows a significant improvement in force field accuracy, obtaining subkcal/mol mean absolute error for every molecule tested. We further extend the bonded-only model for 1-4 interactions to Amber ff14sb, CHARMM36, and OPLS-AA force fields to reproduce ab initio gas and implicit solvent ϕ,ψ surfaces of alanine dipeptide.

Abdullah, Aalim S. [University of California, Berk↗

DOE OSTI2022

Detecting damaged buildings using real-time crowdsourced images and transfer learning

After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged buildings images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~ 6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and when ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important regions on the images that facilitate the decision.

58 GEOSCIENCES↗

DOE OSTI2014

Transmission-Line Fault Analysis Using Synchronized Sampling

An automated analysis approach, which can automatically characterize fault and subsequent relay operation, is the focus of this paper. It utilizes synchronized samples captured during transients from both ends of the transmission line to detect, classify, and locate transmission-line faults and can verify that the tripped line has indeed experienced a fault. The proposed method is tested for several faults simulated on an IEEE 118-bus test system and it has been concluded that it can detect and classify a fault using prefault and postfault recorded samples within 7 ms of fault inception and can accurately locate a fault with 3% accuracy. This time response performance is highly desirable since with the increasing use of modern circuit breakers, which can open the faulty line in less than two cycles, the time window of the captured waveforms is significantly reduced due to the unavailability of measurement signals after breakers open.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

SE2-ANL Subchannel Analysis with New Heating Calculation Based on VARIANT Transport Code

The multi-assembly, steady-state sub-channel analysis code SE2-ANL has been widely used in thermal-hydraulic analysis of sodium cooled fast reactors (SFR). It is used to compute the core-wide temperature profiles for a given flow distribution and to determine the assembly flow rates yielding a desired (assembly-wise) peak temperature distribution. It is a modified version of SUPERENERGY-2 coupled with the DIF3D/GAMSOR/DIF3D procedure for coupled neutron and gamma heating calculations, developed at Argonne National Laboratory (ANL). In the current version of SE2-ANL, the neutron and gamma fluxes are calculated using the finite-difference diffusion theory option of the DIF3D code with six triangular meshes per hexagonal assembly. In addition, the pin power distributions are estimated by assuming that the power distribution within an assembly is separable in the radial and axial directions. Furthermore, the total heat generation rate in an assembly duct wall is simply determined by a user-specified fraction of the total gamma heating within that assembly. In order to determine the pin power distributions more accurately by eliminating these limitations, a new coupled heating calculation procedure has recently developed based on the neutron and gamma transport calculations with the VARIANT code. For this, the GAMSOR code has been extended to generate the intra-nodal gamma source distribution to be consistent with the trial functions of VARIANT. Using the computed neutron and gamma flux distributions, the power distribution in each fuel pin and duct wall is determined using a new utility code CURVE, which has been developed to evaluate the VARIANT flux solution at the center of each fuel pin and along the duct mid-wall. For each pin segment contained in a VARIANT node, the axial power shape is represented as a quadratic profile. The SE2-ANL code has been modified to interface with the new, coupled neutron and gamma heating calculation scheme. In addition, several improvements have been made on the computational methods and models of SE2-ANL. To eliminate the limit on the axial mesh size due to the numerical instability problem of the current explicit scheme, the numerical scheme for discretizing the energy equation in the axial direction has been changed to the {theta}-method of the SLTHEN code. An automated flow allocation scheme has also been implemented in SE2-ANL to determine assembly flow rates such that the peak cladding mid-wall temperatures of individual orifice zones are equalized over the burn cycle. The impacts of the new heating calculation scheme based on VARIANT transport solutions on the cladding and fuel temperatures have been tested against the existing method of SE2-ANL. Using the existing and new heating calculation schemes, full-core subchannel analyses were performed for the metal core design of the 1000 MWt Advanced Burner Reactor (ABR-1000), and the resulting coolant, cladding and fuel temperatures were compared. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

WE-G-204-07: Automated Characterization of Perceptual Quality of Clinical Chest Radiographs: Improvements in Lung, Spine, and Hardware Detection

Purpose: To develop and validate more robust methods for automated lung, spine, and hardware detection in AP/PA chest images. This work is part of a continuing effort to automatically characterize the perceptual image quality of clinical radiographs. [Y. Lin et al. Med. Phys. 39, 7019–7031 (2012)] Methods: Our previous implementation of lung/spine identification was applicable to only one vendor. A more generalized routine was devised based on three primary components: lung boundary detection, fuzzy c-means (FCM) clustering, and a clinically-derived lung pixel probability map. Boundary detection was used to constrain the lung segmentations. FCM clustering produced grayscale- and neighborhood-based pixel classification probabilities which are weighted by the clinically-derived probability maps to generate a final lung segmentation. Lung centerlines were set along the left-right lung midpoints. Spine centerlines were estimated as a weighted average of body contour, lateral lung contour, and intensity-based centerline estimates. Centerline estimation was tested on 900 clinical AP/PA chest radiographs which included inpatient/outpatient, upright/bedside, men/women, and adult/pediatric images from multiple imaging systems. Our previous implementation further did not account for the presence of medical hardware (pacemakers, wires, implants, staples, stents, etc.) potentially biasing image quality analysis. A hardware detection algorithm was developed using a gradient-based thresholding method. The training and testing paradigm used a set of 48 images from which 1920 51×51 pixel{sup 2} ROIs with and 1920 ROIs without hardware were manually selected. Results: Acceptable lung centerlines were generated in 98.7% of radiographs while spine centerlines were acceptable in 99.1% of radiographs. Following threshold optimization, the hardware detection software yielded average true positive and true negative rates of 92.7% and 96.9%, respectively. Conclusion: Updated segmentation and centerline estimation methods in addition to new gradient-based hardware detection software provide improved data integrity control and error-checking for automated clinical chest image quality characterization across multiple radiography systems.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-141: Automated Dose Point Placement for Cervical Cancer Brachytherapy Using Tandem and Ovoid Applicators

Purpose: To develop a standalone application, which automatically and consistently calculates the coordinates of points A and H based solely on the implanted applicator geometry for cervical cancer HDR brachytherapy. Methods: Manchester point A and ABS point H are both located 2cm lateral from the central tandem plane. While both points are located 2cm above the cervical os, surrogates for the os differ. Point A is defined relative to the anatomical cervical os. Point H is defined relative to the intersection of the tandem with the superior aspects of the ovoids. The application takes an input text file generated by the treatment planning system (TPS, BrachyVision, Varian) that specifies the source geometries. It then outputs the 3D coordinates of points A and H in both the left and right directions. The algorithm was implemented and tested on 34 CT scans of 7 patients treated with HDR brachytherapy delivered using tandem and ovoids. A single experienced user retrospectively and manually placed points A and H on the CT scans, whose coordinates were used as the gold standard for the comparison to the automatically calculated points. Results: The automatically calculated coordinates of points A and H agree within 0.7mm with the gold standard. The averages and standard deviations of the 3D coordinate difference between points placed by the two methods are 0.3±0.1 and 0.4±0.1mm for points A and H, respectively. The maximum difference in 3D magnitude is 0.7mm. Conclusion: The algorithm consistently calculates dose point coordinates independently of the planner for cervical cancer brachytherapy treated with tandem and ovoids. Automated point placement based on the geometry of the implanted applicators agrees in sub-millimeter with careful manual placements by an experienced user. This algorithm expedites the planning process and eliminates dependencies on either user input or TPS visualization tools.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Replace Human Intelligence with Fast and Smart Geometric Reasoning and Graph Neural Network to Accelerate Next Gen ModSim Workflows

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Closing the Loop between In Situ Stress Complexity and EGS Fracture Complexity

42 ENGINEERING↗

DOE OSTI2026

Methods development towards automated, physics-informed, quantitative quality control of TRISO-SiC

Tristructural-isotropic (TRISO) fuel particles have been developed as a high-performance fuel for use in high-temperature gas-cooled reactor (HTGR) systems due to their high efficiency and stability under both normal and off-normal conditions. Broader deployment of this technology in advanced nuclear applications may benefit from quantitative quality assurance and quality control (QA/QC) methods that directly link TRISO properties to downstream performance. A key TRISO property is the SiC layer microstructure, which influences fission product retention during irradiation. However, existing QA/QC for the TRISO-SiC microstructure comprises only a qualitative visual inspection; therefore, there is a clear opportunity for the development of quantitative methods for TRISO QA/QC. Here, to this end, previous work has demonstrated an image processing approach to grain boundary (GB) identification and subsequent extraction of microstructural metrics; however, extensive twinning within the SiC layer complicates such analyses because twin GBs significantly influence microstructural metrics but are not expected to contribute to fission product transport. This study presents the initial development, training, and testing of an ML-based image segmentation algorithm designed to identify and remove twin GBs from standard backscattered electron micrographs, providing an industrially applicable, quantitative, and physically meaningful QA/QC approach for the TRISO-SiC microstructure. Although pixel-wise performance metrics for the twin predictions are low, the change in grain area and the number of GB pixels after twin removal predicted by the ML workflow are within 1% of the true values calculated using crystallographic data. This suggests that the model is well capable of predicting overall twin boundary structures and grain morphology, and continued advancement of this approach could enable automated, scalable, and physics-informed QA/QC for TRISO-SiC microstructures, supporting the reliable qualification of coated particle fuels for next-generation reactor systems.

Computer vision↗

DOE OSTI2016

Autonomous Sampling Platform Development: Radiological Contamination Mapping at SRS

From 1961 to 1964, radioactive elements were released from the Savannah River Site into local bodies of water via cooling water charges from the reactors on site. In 1983, the extent of the radioactive contamination was first studied, and elements such as 137 Cs, 90 Sr, 238 Pu, 241 Am, 244 Cm, and tritium were found to have seeped from local bodies of water into sediment and the surrounding flora and fauna. The current method of tracking and monitoring radioactive contamination at the SRS is to gather samples and conduct measurements in a laboratory. A cheaper, and safer, method to conduct such measurements would be to automate the process by using an autonomous boat that can travel to locations, conduct measurements, and return home all without human intervention. To introduce this idea, the construction of an autonomous boat prototype was completed to demonstrate the practicality and feasibility of such an idea. The prototype travels to a set of waypoints, stops at each waypoint, and returns when all waypoints have been reached. It does this by employing a simple battery-powered boat with an Arduino controller that steers the boat using a steering algorithm incorporated into a Proportional Integral Derivative (PID) function. A total of three tests were conducted at two different bodies of water; and after working out some hardware problems, the boat drone was able to successfully steer and reach all programmed waypoints. With the prototype complete, the next steps to realizing the final product of the boat drone will include adopting a processing unit with higher-bit architecture, using a bigger boat with a more powerful trolling motor, and incorporating a solar panel for continuous power and round-the-clock performance.

42 ENGINEERING↗

DOE OSTI2014

Automated identification of crystallographic ligands using sparse-density representations

A novel procedure for identifying ligands in macromolecular crystallographic electron-density maps is introduced. Density clusters in such maps can be rapidly attributed to one of 82 different ligands in an automated manner. A novel procedure for the automatic identification of ligands in macromolecular crystallographic electron-density maps is introduced. It is based on the sparse parameterization of density clusters and the matching of the pseudo-atomic grids thus created to conformationally variant ligands using mathematical descriptors of molecular shape, size and topology. In large-scale tests on experimental data derived from the Protein Data Bank, the procedure could quickly identify the deposited ligand within the top-ranked compounds from a database of candidates. This indicates the suitability of the method for the identification of binding entities in fragment-based drug screening and in model completion in macromolecular structure determination.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗