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DOE OSTI2015

WE-D-BRD-02: Tracking Coils for Brachy MR Guidance

The advantages that make MR highly desirable for in-room treatment guidance, i.e. soft-tissue visualization and physiological assessment of healthy/tumor tissues, are largely recognized in the research community. However, the MR absorption in practical clinical workflows is still hindered by lack of sufficient technological infrastructure. Recent advances have been made towards filling that gap such as the development of integrated therapy systems relying on an MR scanner and a radiation source (linac, Co-60) and/or brachytherapy. The session is intended to present new efforts and methods aimed at facilitating the adoption of MR guidance for treatment delivery. Specifically, the discussion will focus on a) linac designs for reduced footprint and seamless integration with MR/linac systems, b) tracking coils for brachytherapy applications, and c) procedures for the commissioning and monitoring of MR and MR/linac performance. Learning Objectives: Understand limitations and proposed solutions for linac designs and associated integration with MR/linac systems; Understand current issues with MR-guided brachytherapy procedures Understand the issues and needs for new MR commissioning and QC. License agreement with Modus Medical Devices to develop a phantom for the quantification of MR image distortions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-D-BRD-03: Novel Methods and Tools for MR Commissioning and QC

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Dataset of U.S. School Bus Depots

A large body of public health literature describes how undesirable or dangerous facilities, such as truck depots and industrial plants, located in or near communities can lead to health harms. Research also describes the high levels of traffic-related air and noise pollution that is linked to health harms and may be disproportionately distributed near many schools. Therefore, a primary use case for this dataset is to analyze the location of school bus depots and to create an evidence base that would better enable the work of community members, advocates, and other stakeholders toward improving air quality and public health. Other possible uses for this school bus depot dataset include electricity grid planning and reliability, given recent momentum toward school bus electrification. This dataset was created using an object-based approach with remote sensing data. The primary source of aerial imagery was the National Agriculture Imagery Program (NAIP) dataset. NAIP imagery was analyzed to locate individual school buses based on their color and size, and then classified clusters of school buses as potential depots, which were then verified visually. The resulting dataset contains 11,309 depots across the 48 contiguous U.S. states and Washington, D.C. Fifty-one percent (5,730 depots) are at schools, defined as being 350 meters or less from the nearest school. The accuracy of the dataset was assessed by comparing it with independent reference datasets containing 506 depots from the records of two school transportation companies. We found good agreement, with an omission error rate of 15.2% (77 depots). This dataset represents one of the only remote sensing projects to conduct object detection using data at the sub-meter to 1-meter resolution for a continental-scale application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

AlgaeOrtho, a bioinformatics tool for processing ortholog inference results in algae

Introduction: Microalgae constitute a prominent feedstock for producing biofuels and biochemicals by virtue of their prolific reproduction, high bioproduct accumulation, and the ability to grow in brackish and saline water. However, naturally occurring wild type algal strains are rarely optimal for industrial use; therefore, bioengineering of algae is necessary to generate superior performing strains that can address production challenges in industrial settings, particularly the bioenergy and bioproduct sectors. One of the crucial steps in this process is deciding on a bioengineering target: namely, which gene/protein to differentially express. These targets are often orthologs which are defined as genes/proteins originating from a common ancestor in divergent species. Although bioinformatics tools for the identification of protein orthologs already exist, processing the output from such tools is nontrivial, especially for a researcher with little or no bioinformatics experience. Methods: The present study introduces AlgaeOrtho, a user-friendly tool that builds upon the SonicParanoid orthology inference tool (based on an algorithm that identifies potential protein orthologs based on amino acid sequences) and the PhycoCosm database from JGI (Joint Genome Institute) to help researchers identify orthologs of their proteins of interest in multiple diverse algal species. Results: The output of this application includes a table of the putative orthologs of their protein of interest, a heatmap showing sequence similarity (%), and an unrooted tree of the putative protein orthologs. Notably, the tool would be instrumental in identifying novel bioengineering targets in different algal strains, including targets in not-fully annotated algal species, since it does not depend on existing protein annotations. We tested AlgaeOrtho using three case studies, for which orthologs of proteins relevant to bioengineering targets, were identified from diverse algal species, demonstrating its ease of use and utility for bioengineering researchers. Discussion: This tool is unique in the protein ortholog identification space as it can visualize putative orthologs, as desired by the user, across several algal species.

09 BIOMASS FUELS↗

DOE OSTI2018

Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network

Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning-based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset show that the results of the proposed method have very small resolution loss and achieves better performance relative to state-of-the-art methods both quantitatively and visually.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

SMART-COM – Scalable Multi-Agent Adaptive Resolution Tools for Collaborative Outage Management

The purpose of this grant was to conduct scientific research and prototype applications to support NPP outage staff in their adaptive decision-making in efficient scheduling and resource allocation while preventing violation of safety technical specifications. The project contributed to scientific knowledge and engineering methods in (1) user interface design, (2) scheduling optimization and risk estimation, and (2) natural language processing that would benefit the nuclear power plants in minimizing schedule overruns and even unexpected shutdowns. The research team conducted site visits at a test reactor facility and an operating nuclear power plant to gather necessary information and inputs for research and development of a software application to support NPP staff in executing their outages. The final software application consisted of three modules. First, the natural language processing module supports interactive processing of technical documentation to build a database for outage staff to query non-permissible actions on system components. This module can alleviate outage staff from reviewing extensive documentation and minimize violation of technical specifications, especially in time-sensitive situations. Second, the schedule optimization module schedules outage activities and compute risk indices that outperform existing software and current practice. This module can reduce completion time of an outage that typically have too many activities for human to optimize based on current practice that does not apply the latest operations research. Finally, the visualization module presents progress and risk information of the overall outage and individual activities, as well as enabling access to the natural language processing and schedule optimization modules. This module can provide outage staff with situation awareness that are necessary to make risk-informed decisions in response to unexpected events during the execution of an outage.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Flowable Cementitious Grouts for False Bottoms in Hanford Waste Tanks – 26085

Cementitious grouts with epoxy coatings are being evaluated as an approach to refurbish the bottom of waste tanks at the Hanford Site. This work studies four grout concept materials for this application, two grouts being from commercial vendors and two grouts based off formulations used to close reactors and tanks at the Savannah River Site. All the candidate grouts demonstrate flowability and pumpability at the 0.5 cu ft scale. The commercial vendor products demonstrate the best flow and have lower water/premix ratio and are less likely to have bleed, which would impact epoxy bonding to the grout. The commercial products also demonstrate increased compression strengths compared to the reactor closure grouts. Additional fresh and cured properties for all the grout formulations were similar. The grouts in the tank waste simulant without epoxy coating do not crack but show visual changes to the grout and the surrounding simulant solution, and simulant absorption into the grout. The grout/epoxy bond in ambient conditions has strengths of over 7 MPa and needs to be evaluated for simulant chemical durability. Overall, the results to date justify testing the flowability, pumpability, and placement of the grouts at larger, engineering scale and for further studies of the interactions between the grout and epoxy.

Valdes, Nicholas [Savannah River National Laborato↗

DOE OSTI2020

Visualizing a viral genome with contrast variation small angle X-ray scattering

Despite the threat to human health posed by some single-stranded RNA viruses, little is understood about their assembly. The goal of this work is to introduce a new tool for watching an RNA genome direct its own packaging and encapsidation by proteins. Contrast variation small-angle X-ray scattering (CV-SAXS) is a powerful tool with the potential to monitor the changing structure of a viral RNA through this assembly process. The proteins, though present, do not contribute to the measured signal. As a first step in assessing the feasibility of viral genome studies, the structure of encapsidated MS2 RNA was exclusively detected with CV-SAXS and compared with a structure derived from asymmetric cryo-EM reconstructions. Additional comparisons with free RNA highlight the significant structural rearrangements induced by capsid proteins and invite the application of time-resolved CV-SAXS to reveal interactions that result in efficient viral assembly.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Plasmon 3D Electron Tomography and Local Electric-Field Enhancement of Engineered Plasmonic Nanoantennas

Plasmonic nano-antennas are pushing the limits of optical imaging resolution capabilities in near-field scanning optical microscopy (NSOM). Accordingly, these techniques are driving the basic understanding of photonic and optoelectronic nanoscale devices with applications in sensing, energy conversion, solid-state lighting and information technology. Imaging the localized surface plasmon resonance (LSPR) at the nanoscale is a key to understanding the optical responses of a given tip geometry in order to engineer better plasmonic nano-antennas for near-field experiments. In recent years the advancement of focused ion beam technology provides the ability to directly modify plasmonic structures with nanometer resolution. Also, scanning transmission electron microscopy (STEM) with electron energy loss spectroscopy (EELS) is an established technique allowing imaging of LSPR. Specifically, the combination of these two techniques provides spectrally sensitive two-dimensional (2D) imaging information to better visualize and understand LSPR on the nanometer scale. This can be combined with electron tomography to provide the three-dimensional LSPR distribution. Here in this paper we demonstrate the fabrication of Au nano-pyramids using helium ion microscopy, and analyze the LSPR in 3D reconstructions produced by total variation (TV)-norm minimization of a set of 2D STEM-EELS maps. Additionally, a boundary element simulation method was used to verify the experimentally observed nanopyramid LSPR modes. Finally, we show that the point-spread-functions (PSF) of LSPR mode hot spots in nanopyramids differ to local electric-field enhancement under optical excitation making direct comparison to NSOM experimental resolution difficult. Furthermore, the STEM-EELS results show how LSPR modes are influenced by the tip characteristics, which can inform the development of new nano-antenna designs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

A Confidence-Guided Technique for Tracking Time-Varying Features

Application scientists often employ feature tracking algorithms to capture the temporal evolution of various features in their simulation data. However, as the complexity of the scientific features is increasing with the advanced simulation modeling techniques, quantification of reliability of the feature tracking algorithms is becoming important. One of the desired requirements for any robust feature tracking algorithm is to estimate its confidence during each tracking step so that the results obtained can be interpreted without any ambiguity. To address this, we develop a confidence-guided feature tracking algorithm that allows reliable tracking of user-selected features and presents the tracking dynamics using a graph-based visualization along with the spatial visualization of the tracked feature. Here, the efficacy of the proposed method is demonstrated by applying it to two scientific datasets containing different types of time-varying features.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

SU-E-J-215: Towards MR-Only Image Guided Identification of Calcifications and Brachytherapy Seeds: Application to Prostate and Breast LDR Implant Dosimetry

Purpose: To identify and analyze the appearance of calcifications and brachytherapy seeds on magnitude and phase MRI images and to investigate whether they can be distinguished from each other on corrected phase images for application to prostate and breast low dose rate (LDR) implant dosimetry. Methods: An agar-based gel phantom containing two LDR brachytherapy seeds (Advantage Pd-103, IsoAid, 0.8mm diameter, 4.5mm length) and two spherical calcifications (large: 7mm diameter and small: 4mm diameter) was constructed and imaged on a 3T Philips MR scanner using a 16-channel head coil and a susceptibility weighted imaging (SWI) sequence (2mm slices, 320mm FOV, TR/ TE= 26.5/5.3ms, 15 degree flip angle). The phase images were unwrapped and corrected using a 32×32, 2D Hanning high pass filter to remove background phase noise. Appearance of the seeds and calcifications was assessed visually and quantitatively using Osirix (http://www.osirix-viewer.com/). Results: As expected, calcifications and brachytherapy seeds appeared dark (hypointense) relative to the surrounding gel on the magnitude MRI images. The diameter of each seed without the surrounding artifact was measured to be 0.1 cm on the magnitude image, while diameters of 0.79 and 0.37 cm were measured for the larger and smaller calcifications, respectively. On the corrected phase images, the brachytherapy seeds and the calcifications appeared bright (hyperintense). The diameter of the seeds was larger on the phase images (0.17 cm) likely due to the dipole effect. Conclusion: MRI has the best soft tissue contrast for accurate organ delineation leading to most accurate implant dosimetry. This work demonstrated that phase images can potentially be useful in identifying brachytherapy seeds and calcifications in the prostate and breast due to their bright appearance, which helps in their visualization and quantification for accurate dosimetry using MR-only. Future work includes optimizing phase filters to best identify and delineate seeds and calcifications.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Effect of Alkali-Silica Reaction on Shear Strength of Reinforced Concrete Structural Members

Alkali-silica reaction (ASR) was discovered in the early 40s by Stanton (1940) of the California Division of Highways. Since, it has been recognized as a major degradation mechanism for concrete dams and transportation infrastructures. Sometimes described as the ’cancer of concrete’, this internal swelling mechanism causes expansion, cracking and loss of mechanical properties. There are no known economically viable solutions applicable to massive concrete to prevent the reaction once initiated. The e ciency of the mitigation strategies for ASR subjected structures is limited. Several cases of ASR in nuclear generating stations have been disclosed in Japan (Takatura et al. 2005), Canada at Gentilly 2 NPP (Tcherner and Aziz 2009) 1, and more recently, in the United States for which the U.S. Nuclear Regulatory Commission issued Information Notice (IN) 2011-20, ’Concrete Degradation by Alkali Silica Reaction,’ on November 18, 2011, to provide the industry with information related to the ASR identified at Seabrook. Considering that US commercial reactors in operation enter the age when ASR degradation can be visually detected and that numerous non nuclear infrastructures (transportation, energy production) have already experienced ASR in a large majority of the States (e.g., Department of Transportation survey reported by Touma (Touma 2000)), the susceptibility and significance of ASR for nuclear concrete structures must be addressed in the perspective of license renewal and long-term operation beyond 60 years. The aim of this report is to perform an extensive parametric series of 3D nonlinear finite element analyses of three di erent “beam-like” geometries, including two di erent depths, three di erent types of boundary conditions, and four other parameters: namely, the ASR volumetric expansion, the reinforcement ratio, the loss of elastic modulus induced by ASR and the loss of tensile strength caused by ASR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Data Integration Aggregated Model and Ontology for Nuclear Deployment (DIAMOND): Preliminary Model and Ontology

The data of a nuclear utility or plant are stored in various isolated forms in different systems. These data have different structures and tools and are, therefore, used independently. The integration of data sources is often performed manually and on an as-needed basis to achieve a targeted cost saving. A holistic approach to the integration of the data would enable creating a data warehouse for a single nuclear power plant (NPP) or multiple plants. A data warehouse results in direct and indirect cost saving because it automates the manual search for data, enables sharing and comparison of data from various tools of a single or multiple plants, enable digital transformation of data, increase frequencies of needed data that are sparse in nature (such as failures signatures), reduces the need for training on various tools for plant staff, enables a holistic staff perception of plant activities, reveals cost-saving opportunities, and improves the visual perception of all parts a plant necessary for operations and maintenance. One of the main challenges to deploy a data warehouse is associated with the integration of data sources into a consistent data model using a standard ontology that is developed by and for the nuclear power industry. This model is non-existent and is a necessity to enable the nuclear data warehouse role. A data model contains objects that make up a given domain along with their attributes and relationships. It provides domain standardization and maintainability, setting forth the objects, attributes, and relationships within that domain and acting as the guide by which the various applications within that domain will structure their data and interchange. An ontology is defined as “a set of concepts and categories in a subject area or domain that shows their properties and the relations between them”. Ontologies consist of a taxonomy or directed rooted tree. A survey of the models and ontologies available to support a data integration aggregated model and ontology for nuclear deployment (DIAMOND) was performed and different standards that address specific aspects of the needed model and ontology were leveraged and integrated in this effort. In specific, the Basic Formal Ontology (BFO) and Lifecycle Modeling Language (LML) were leveraged for the base ontology. The ISO 4224 and 15926 were leveraged in the development of the overlapping nuclear and fossil data aspects. The electric power research institute (EPRI) power distribution common information model (CIM) was also reviewed and the experience gained was leveraged in the power generation scope of DIAMOND. The initial aim targeted by this effort was to develop the ontology definitions and model structure. This report documents the resulting model and ontology to develop DIAMOND. The report describes the various elements of the model development based on a growing tree development approach, i.e. DIAMOND’s tree structure is established with the core data aspects and is populated with a preliminary level of details. Fifteen sources of data in a nuclear power plant were considered and incorporated into DIAMOND at this stage of development, representing the majority of operations data sources in nuclear power plants. Additional data sources, scope, and a higher level of fidelity will be incorporated into DIAMOND next in collaboration with the nuclear industry and relevant stakeholders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning

Coulomb explosion imaging (CEI) is a powerful technique for capturing the real-time motion of individual atoms during ultrafast photochemical reactions. CEI generates high-dimensional data with naturally embedded correlations that allow mapping the coordinated motion of nuclei in molecules. This enables reliable separation of competing reaction pathways and makes this approach uniquely suited for characterizing weak reaction channels. However, rich information contained in experimental CEI patterns remains largely underexploited due to challenges in visualizing correlations between multiple observables in multi-dimensional parameter space. Here we present a new approach to CEI of intermediate-sized polyatomic molecules, detecting up to eight ionic fragments in coincidence and leveraging machine-learning-based analysis to identify patterns and correlations in the resulting high-dimensional momentum-space data, enabling robust molecular structure identification and differentiation. Our approach provides high-dimensional background-free data encoding exceptionally rich structural information and establishes an automated, scalable framework for extracting insightful information from the data. As a demonstration, we apply this method to image and distinguish dichloroethylene isomers, showcasing its potential for broader applications in molecular imaging. Our results pave the way for channel-specific analysis of ultrafast structural dynamics in chemically relevant systems, particularly for disentangling mixed reaction pathways and detecting contributions from weak channels and minority species.

Chemical Physics (physics.chem-ph)↗

DOE OSTI2019

Acoustic emission Bayesian source location: Onset time challenge

Robust identification of the most accurate observed input data among a pool of observations is key in modeling and decision making. A statistically biased observed measurement deteriorates the predictive power of a model and affects decision-making ability based on the prediction of the model. When two competing methods of measurement are available, such as methods which identify arrival times in acoustic emission (AE) signals, a principal question is whether one of the two obtained datasets, or a combination of the two, should be used later on, for example, to localize an AE source. This question becomes more important when collecting not repeatable data such as AE signals created by a propagating crack. Here this paper considers an inverse source location problem in a concrete block to address the mentioned issue, a proposed methodology which also has wider application in competitive data selection. Elastic energy released by an AE event, such as a propagating crack, is recorded by acoustic emission data acquisition system. The onset time of AE signals is often used to locate the source of the event, and its accuracy directly affects the precision of source identification. This research proposes an innovative approach to select the most probable onset time obtained from two automatic picker methods. The proposed method selects the most probable onset times, which are observed by each picker for each sensor, in a probabilistic fashion. To validate the proposed method, the most accurate onset time observed by each picker is identified by visual inspection and is compared with the one is selected by the proposed method. Finally, the dataset is used for source location identification. Results show that picked onset times determined by the proposed method generate more accurate source identification when compared with coordinates obtained using each dataset individually.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Physical discovery in representation learning via conditioning on prior knowledge

Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of complex systems. In many cases, the resulting data sets are underpinned by low-dimensional simple representations encoding the factors of variability within the data. The representation learning methods seek to discover these factors of variability, ideally further connecting them with relevant physical mechanisms. However, generally, the task of identifying the latent variables corresponding to actual physical mechanisms is extremely complex. Here, we present an empirical study of an approach based on conditioning the data on the known (continuous) physical parameters and systematically compare it with the previously introduced approach based on the invariant variational autoencoders. The conditional variational autoencoder (cVAE) approach does not rely on the existence of the invariant transforms and hence allows for much greater flexibility and applicability. Interestingly, cVAE allows for limited extrapolation outside of the original domain of the conditional variable. However, this extrapolation is limited compared to the cases when true physical mechanisms are known, and the physical factor of variability can be disentangled in full. We further show that introducing the known conditioning results in the simplification of the latent distribution if the conditioning vector is correlated with the factor of variability in the data, thus allowing us to separate relevant physical factors. We initially demonstrate this approach using 1D and 2D examples on a synthetic data set and then extend it to the analysis of experimental data on ferroelectric domain dynamics visualized via piezoresponse force microscopy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Elastic depths for detecting shape anomalies in functional data

This this paper, we propose a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data. Shape anomalies are functions that have considerably different geometric forms or features from the rest of the data. Identifying them is generally more difficult than identifying magnitude anomalies because shape anomalies are often not distinguishable from the bulk of the data with visualization methods. The proposed elastic depths use the recently developed elastic distances to directly measure the centrality of functions in the amplitude and phase spaces. Measuring shape outlyingness in these spaces provides a rigorous quantification of shape which, in turn, gives the elastic depths a strong theoretical and practical advantage over other methods in detecting shape anomalies. A simple boxplot and thresholding method are introduced to identify shape anomalies using the elastic depths. We assess the elastic depth's detection skill on simulated shape outlier scenarios and compare them against popular shape anomaly detectors. Finally, bond yields, image outlines, and hurricane trajectories are used to demonstrate our method's applicability to functional data observed on three different manifolds.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Preliminary Assessment of Tecplot Chorus for Analyzing Ensemble of CTH Simulations

The exploration of large parameter spaces in search of problem solution and uncertainty quantifcation produces very large ensembles of data. Processing ensemble data will continue to require more resources as simulation complexity and HPC platform throughput increase. More tools are needed to help provide rapid insight into these data sets to decrease manual processing time by the analyst and to increase knowledge the data can provide. One such tool is Tecplot Chorus, whose strengths are visualizing ensemble metadata and linked images. This report contains the analysis and conclusions from evaluating Tecplot Chorus with an example problem that is relevant to Sandia National Laboratories. This report documents a preliminary evaluation of Tecplot Chorus for analyzing ensemble data from CTH simulations. The project that funded this report and evaluation is also evaluating and guiding development with SNL’s Slycat. Slycat and Tecplot Chorus each have their strengths, weaknesses, and overlapping capabilities. It is quite likely that, as the scale of ensemble data increases, both of these tools (and possibly others) will be needed for different processing goals. This report will focus on Tecplot Chorus and its application to an example ensemble of data supplied by David J. Peterson and John P. Korbin; this example is of a flyer plate impact and weld study henceforth referred to as CTH Impact Example. This evaluation also defines a workflow for analysts that can help reduce the time and resources for processing ensemble data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗