Analysis of Application Sensitivity to System Performance Variability in a Dynamic Task Based Runtime
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Shipman, Galen↗
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Analysis of Application Sensitivity to System Performance Variability in a Dynamic Task Based Runtime.
Abstract not provided.
Shipman, Galen↗
Analysis in Motion Initiative – Human Machine Intelligence
As computers and machines become more pervasive in our everyday lives, we are looking for ways for humans and machines to work more intelligently together. How can we help machines understand their users so the team can do smarter things together? The Analysis in Motion Initiative is advancing the science of human machine intelligence — creating human-machine teams that work better together to make correct, useful, and timely interpretations of data.
97 MATHEMATICS AND COMPUTING↗
A Methodology for the Rapid Development of Scalable HPC Data Services
There is growing evidence in the scientifc computing community that parallel file systems are not sufficient for all HPC storage workloads. This realization has motivated extensive research in new storage system designs. The question of which alternative we should turn towards implies that there could be a single answer satisfying a wide range of very diverse applications. We argue that such a generic solution does not exist. Instead, custom data services should be designed and tailored to the needs of specific applications on specific hardware. Furthermore, custom data services should be designed in close collaboration with users. In this paper, we present a methodology for the rapid development of such data services. This methodology promotes the design of reusable building blocks that can be composed together efficiently through a runtime based on high-performance threading, tasking, and remote procedure calls. We illustrate the success of our methodology by showcasing three examples of data services designed from the same building blocks, yet targeting entirely different applications.
Dorier, Mattieu↗
Compositing "Stand Off" Ground Penetrating Radar Scans of Differing Frequencies.
Methods have been developed to combine signals of various frequencies in a manner to produce clearer images in the presence of noise. Ground Penetrating Radar (GPR) scans at various frequencies are no exception. Methods using an optimization problem solver, the Expectation-Maximization (EM) Algorithm, define weights used to perform the task of combining GPR scans. In this paper, we explore using the Gaussian Mixture Model (GMM) feature of the EM Algorithm on GPR scans taken at various heights above ground ("Stand Off' GPR). This method demonstrates the same measured improvement toward producing a cleaner image as GPR scans taken at ground level using the same EM Algorithm method.
97 MATHEMATICS AND COMPUTING↗
Inventory Management Improvement Project
The Lawrence Livermore National Laboratory (LLNL) conducts research and development for the National Nuclear Security Agency (NNSA) and its affiliates. The Polymer AM team at LLNL conducts research and development in 3D printing, specifically direct-ink-write, in accordance with LLNL and NNSA missions. The team is composed of machine operators, chemists, testing engineers, and project engineers across multiple lab spaces with one shared storage area at LLNL. The operations use a variety of different consumables and hardware to conduct research applications for LLNL and NNSA. These items are critical to performing operation tasks; if an item is out of stock, operations associated with the respective item could be suspended for weeks. As such, the Polymer AM team manages an inventory of spare consumables and hardware to ensure these items are always available. However, the current management system, an excel sheet managed by the project engineer group, is not intuitive in providing inventory information despite the high labor utilization needed to maintain the system. As such, the team is looking to improve their inventory management that can send notifications regarding inventory needs, accessible to other team members, provide all relevant information to a specific item, and store historical information for budget and operation planning. The proposed solution is a Computer Maintenance Management System (CMMS): a web-based management software that stores inventory information and provides automated notifications. The system will store information on each item including quantity in stock, technical information, supplier information, costs, lead times, and expiration date. The system has a notifications function that can send notifications to the respective team member’s when an item needs to be counted, item inventory is low, or an item is approaching their expiration date. Information for each item can be tracked over time creating data to be used for budget planning and process improvement purposes. Due to its web-based source, the system can be accessed by the respective team member viewing technical information, quantity, and purchasing information.
42 ENGINEERING↗
Systems, devices, and methods for performing augmented reality responsive to monitoring user behavior
Systems, devices, and methods are described for performing augmented reality (AR) to assist user performing a task in an environment. An AR device may be configured to capture real-time data. An AR engine may be configured to monitor user behavior from the real-time data responsive to feature extraction from the real-time data, compare the user behavior to pre-defined work procedures, and generate augmented reality objects to be output by the AR device.
Yoon, SuJong↗
ABF Past Accomplishments - TEA/LCA
The Integrated Analysis task in the Agile BioFoundry (ABF) conducts techno-economic analysis (TEA) and life cycle assessment (LCA) to quantify the economics and environmental impacts of bioprocesses under development. The team from Argonne National Laboratory (ANL) and the National Renewable Energy Laboratory (NREL) develops TEA and LCA models of selected compound targets and hosts of interest to the ABF in an effort to provide an analysis-based foundation to the R&D. To date, the Integrated Analysis task has performed process modeling and analyses on multiple pairings of bioproducts and hosts, centered around major metabolic "beachheads" and "exemplar" molecules chosen for each of the metabolic pathways of interest. This presentation provides an overview of the approach used for TEA and LCA in the ABF and of the progress, outcomes, and impacts of TEA and LCA to support ABF goals, with a focus towards developing bio-based products that are both environmentally and economically viable.
BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
LaserSnake2: An Innovative Approach to Nuclear Decommissioning - 17080
Nuclear decommissioning challenges exist the world over and require innovative approaches to ensure safe and cost effective dismantling of high hazard nuclear facilities. LaserSnake technology makes use of the flexibility and dexterity of snake arm robots to deploy high-powered laser cutting, lifting and manipulation tools. This paper describes the development and use of the system to achieve the successful size reduction and removal of a substantial stainless steel vessel within the First Generation Reprocessing Plant (FGRP) at Sellafield in the UK. Due to the environment, nuclear decommissioning is an extremely challenging activity. High ambient radiation levels often preclude entry to the areas involved, and as industry tries to minimise dose uptake, even moderate radiation levels now require remote solutions. 'In-situ' size reduction of process plant, including vessels and pipework, will allow the removal of the nuclear inventory from a building prior to further decontamination and demolition. The term 'in situ' is in this case used to mean work done on plant items within their existing cells as opposed to moving items from their original location to a dedicated size reduction facility. Traditional size reduction cutting methods, such as reciprocating saws or grinders, pose a significant challenge for remote deployment platforms. This includes the loss of haptic feedback and the length of time taken to perform the task, and difficulty dealing with the reaction forces. Although remote engineered solutions for these tools are possible, they are are typically complicated, unreliable and expensive. Robot deployed laser cutting is widely used in non-nuclear industrial sectors, and offers a simpler method for size reduction. LaserSnake2 is a UK collaborative project, co-funded by the Nuclear Decommissioning Authority, Innovate UK and the Department of Energy and Climate Change. The project combined the skills of robotic experts and laser and optic specialists, to develop a remote handling technology suited for deployment within active nuclear environments. As well as meeting strict safety case requirements, the system provides a safe and cost effective method for decommissioning of complex nuclear spaces. An overview of the system development and deployment is presented in this paper. In July 2016, a successful decommissioning campaign was conducted in the FGRP at Sellafield. The LaserSnake2 system was used to size reduce a double-skinned dissolver vessel within a radiologically active nuclear cell. Planning of the size reduction method and processes, as well as results of the campaign itself are described in this paper. The containment procedures and radiological considerations for the system are presented along with the effects of operations on the cell and ventilation systems. LaserSnake, developed as a modular arm system with up to 4.5 m of articulation, carries an integrated tool comprising a high power, decommissioning specific, laser cutting head, high definition cameras and high powered illumination LEDs. It is designed for precise dismantling work within confined and hazardous environments. The tool can be interchanged, allowing the snake-arm robot also to carry grippers, environmental mapping sensors and other tools which may prove advantageous during a decommissioning campaign. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Tele-robotics Requirements for Remote Handling in Nuclear Facilities - 17538
The main constraint in scientific nuclear facilities is the environmental conditions inside the operational areas which imply restrictive equipment requirements to resist high radiation levels and other restrictions related to magnetism, ultra-vacuum, and/or temperature. Robots for remote handling systems are required to perform inspection and maintenance operations since direct human intervention is not allowed, or constrained, under these environmental conditions. Several challenges have to be solved in tele-operation technologies in order to properly perform remote tasks in scientific nuclear radiation facilities. The main solutions described in this paper are based on using multiplexer for the transmission of signals and applying hybrid rate/position control algorithm so as to improve tele-manipulation in these environments. Both developments have been evaluated and discussed. (authors)
42 ENGINEERING↗
Best practices mobile application for D and D KM-IT - 15712
The Deactivation and Decommissioning Knowledge Management Information Tool (D and D KM-IT) is the repository of useful information built specifically for D and D community. The knowledge provided by the D and D KM-IT is the experience and expertise gained by people within the field and fellow employees of the Department of Energy (DOE). This information has been the result of gathering, analyzing, storing, and sharing knowledge to help reduce time spent on discovering previously found data. In order to help in organizing all the data from all the professional contributors, the D and D KM-IT has been divided into 12 different modules. Each individual module pertains to a different area of information for users, such as vendor and technology information, collaboration tools, and even a hotline that contains problems posted by the users. Purpose: - Design and develop the module for the Best Practices on D and D KM-IT mobile platform deployed at https://m.dndkm.org; - The Best Practices mobile module should integrate with existing modules seamlessly; - Use the D and D KM-IT existing database to consume information for the mobile system. jQuery Mobile framework was used to develop Best Practices module for mobile devices. jQuery Mobile framework provides support for responsive web design (RWD). RWD allows adaptation of the layout and user interface of web sites and web apps to different device resolutions, screen densities, and views. UI design was implemented with three fundamental elements: CSS media queries, An adaptable grid, Flexible images and media. Advantages/benefits: - A responsive web design adapts to any-sized screen / resolution and orientation view; - ASP.NET compatible which allows the D and D KM-IT database to support all platforms accessing the system; - Navigating pages is more bandwidth efficient by refreshing data in small section with Ajax instead of reloading the entire page; - Multiple parts of a page can perform different tasks resulting in improved user's experience. Results: - Mobile system is accessible on compatible devices running Android, Apple, Blackberry, Palm, and Windows devices; - Best Practices searches through Characterization, Decontamination, Dismantlement, Material Disposition, and Worker Safety; - Keyword search is enabled for the best practices that are available on both the desktop and mobile systems - Documents are formatted into a PDF file format to ensure compatibility on any device. Conclusion: - Fully integrated with the desktop Best Practices module and displays documents that are uploaded on web platform; - Fast-loading with D and D KM-IT mobile system and downloadable documents; - Responsive design which gives the same user experience when either viewed on smart phone or tablet.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
A Simple, Revolutionary Approach to Waste Packaging Utilizing Proven Technology - 20201
Historically, removing hazardous waste from gloveboxes involves the use of bags as the primary containment method. This method can prove tedious, repetitive, and time consuming to ensure the process follows all safeguards required to contain the waste and mitigate any breach of containment. Layers of bags, yards of tape, and multiple filters are added to the waste stream in order to transfer the hazardous waste from the glovebox safely into the drum package. Then, finally, it can be shipped and disposed of. The process to cut, tie, and tape each bag for disposal involves manual effort by multiple operators. Following Administrative Safety Controls, good operators are able to safely perform those tasks; but, since the bag is the only confinement layer, incidents can occur. Operators, in some cases, are also at close proximity to this waste which may increase the risk of radiation exposure. Once the waste is transferred out through these bags, it is nearly impossible to remove packaged waste that may contain higher dosage levels, thus increasing drum disposal costs incurred by the facility. Exceeding the allowable radiation levels, and drums not fully filled, both contribute significantly to excess disposal costs. Central Research Laboratories (CRL), based in Red Wing Minnesota, has developed the Waste Drum Transfer System (WDTS) building upon the proven technology and containment of the Rapid Transfer Port (RTP) as an alternate, more efficient method of waste transfer. This solution consists of an RTP Alpha port mounted inside a glovebox and Beta assembly unit composed of a drum liner fitted inside a standard 55 gallon drum. The use of the Beta drum liner bypasses the typical use of bags, sleeves or tape to provide leak tight containment. The process to connect and disconnect a drum from the glovebox is achieved by the self-docking and rotating system controls of the RTP Alpha, all done with only one operator standing in an upright ergonomic position. This system also allows for greater flexibility in available Beta connection options, expanding the overall functionality of the system. This system can be disconnected and reconnected multiple times to maximize volume containment. This will minimize drum disposal costs and reduce the overall volume of waste output by more efficiently filling the drums. Radiation levels inside each drum can be monitored and adjusted more efficiently by operators utilizing the reconnect option. Minimal As Low As Reasonably Achievable (ALARA) levels are also achievable with this system. This paper will show how highly efficient packaging and disposal of hazardous waste can be accomplished in a cost-effective manner utilizing the proven RTP technology. Maximized operator productivity and safety, as well as minimized radiation dose exposure, are all attainable through the system's engineered controls. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Machine Vision-based Robot Manipulators for Nuclear Applications - 20370
Decommissioning and dismantling of nuclear facilities are major challenges facing the nuclear industry. Robot manipulators with capabilities of restoring nuclear structures and significantly prolonging nuclear power generation in addition to their applications in decommissioning and dismantling (D and D) would immensely ease the above-mentioned challenge. Ability to remotely sense/monitor and analyze dangerous and hazardous environments such as nuclear reactors as well as performing reparatory tasks in such environments using robot manipulators require additional information from vision sensors. Structural sample collection and inspection as well as restoration require object and position information. Computer vision is used to obtain precise 3D position information. The current work uses the Sawyer robot with the integrated Cognex camera. The research presents an approach to facilitate and improve operations in a nuclear reactor using robotic vision control. The vision control system is modeled with Augmented Image Space-based visual servoing approach. Results showing accurate robot control. Object recognition is used to recognize the image and calculate it poses. To transform coordinates of the object's pose from the camera frame to the robotic frame transformation matrices are employed. Future work will employ a 3D camera (with depth information) using Denso robot. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Artificial Intelligence Application to D and D - 20492
As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Real-time supervisory control implementation of SmAHTR power plant - 143
This paper discusses an implementation of a supervisory control system for a nuclear power plant model using Small modular Advanced High Temperature Reactors (SmAHTR). The simulation operates in real-time using a network of computer agents, implemented on Raspberry Pi's (RPi's), where each major task is performed by one or more RPi's running calculations in parallel. The system architecture contains local, module, master, and operator actions to automate all aspects of the plant. Local level controllers run individual control loops to ensure the desired reference signals are met. The module level controllers set the local level reference signals within each SMR unit and detect sensor and actuator failures to adjust those reference signals as needed. Master level controllers balance plant power by calculating required heat power from each reactor and electric power from each generator and sending that data to the module controllers. Finally, the operator level contains an operator interface to summarize data and faults gathered through the controller levels, with options to review plant and reactor details as desired. This control implementation allows the plant to run autonomously, load follow a desired electrical power curve, and detect a growing number of fault scenarios. Ultimately, this type of supervisory control system will reduce labor-intensive surveillance and testing and allow fewer operators to more safely monitor an entire plant. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Synthesis and Analysis of Glass Analogs for Solid Fallout Debris
Following a nuclear detonation, radiochemists and post-detonation debris diagnosticians are tasked with performing high-quality analysis of fallout debris samples and accurate device interpretation. Debris formed following a nuclear detonation commonly consists of a heterogeneous glassy matrix containing uranium, plutonium, neptunium, americium, and fission/activation products. Homogeneous and well-characterized reference materials are critical for accurate post-detonation debris analysis. Such reference materials can include aged nuclear explosion debris from the Nevada Test Site (e.g., Trinitite) as well as 'fresh' doped-glass material. Recent work has highlighted the often heterogeneous nature of historical debris, which presents a problem from the aspects of safeguards accountancy and verification as well as micro-scale characterization (i.e., microbeam analysis). In this regard, our work focuses on the creation of homogeneous, 'fresh' doped-glassy standards containing uranium, plutonium, surrogate fission products (e.g., Sr, Cs, Pm, Sm, Eu), and urban material (e.g., Fe and Ca from construction material, stainless steel, aluminosilicates phases as a surrogate for dirt). (authors)
07 ISOTOPE AND RADIATION SOURCES↗
Artificial Intelligence for Event Reconstruction and Higgs Physics at CMS and Future Colliders
This dissertation charts a trajectory in which advances in artificial intelligence (AI) play a central role in pushing the high-energy physics frontier, complementing progress driven by higher collision energies and larger colliders. The discovery potential of the LHC and future colliders relies on accurate reconstruction of increasingly complex particle collision events. In the CMS experiment, this task is performed by the particle-flow (PF) algorithm. This dissertation presents the first implementation of a machine-learning-based particle-flow (MLPF) reconstruction in the CMS detector based on transformer architectures. In simulated top quark--antiquark pair (ttbar) events under LHC Run~3 (2023--2024) conditions, MLPF improves jet energy resolution by 10--20\% compared to standard PF for jets with transverse momentum between 30--100\GeV. Runtime performance is evaluated using simulated multijet events, with a median inference time of 20\unit{ms} per event on an NVIDIA L4 GPU, compa red to approximately 110\unit{ms} for standard PF. The MLPF algorithm is also validated on Run~3 collision data, representing the first data-validated ML-based reconstruction pipeline at any LHC experiment. We then extend MLPF toward future electron--positron colliders and introduce the first full-simulation cross-detector transfer learning workflow for PF reconstruction. The model is pre-trained on simulated events from the Compact Linear Collider detector (CLICdet) and fine-tuned on the CLIC-like detector (CLD) proposed for the Future Circular Collider (FCC). This approach achieves up to a 40\% improvement in jet energy resolution over rule-based reconstruction while reducing the required training dataset size by an order of magnitude, demonstrating the potential of AI to accelerate detector development and optimization. This dissertation also demonstrates how modern AI techniques enhance the sensitivity of LHC physics analyses. A CMS search for highly Lorentz-boosted Higgs bosons decaying to \textrm{W} boson pairs is presented, focusing on the single-lepton final state. A dedicated fine-tuning strategy for \ParT yields an approximately 70\% increase in expected sensitivity relative to the baseline model. The analysis uses proton--proton collision data at a center-of-mass energy of \ensuremath{\sqrt{s}=13\TeV} collected by CMS between 2016 and 2018, corresponding to an integrated luminosity of 138\ensuremath{\ \mathrm{fb}^{-1}}. The expected significance of the search is $1.86\sigma$, with an observed signal strength of $-0.19^{+0.48}_{-0.46}$. Finally, explainable AI techniques are applied to the MLPF and \ParticleNet algorithms using layerwise relevance propagation, showing that both models base their predictions on physically meaningful features consistent with our physics intuition. Together, these results demonstrate how advanced AI methods can enhance reconstruction, analysis sensitivity, and interpretability, shaping the next era of experimental parti cle physics.
Mokhtar, Farouk [UC, San Diego]↗
Code for multi-shape Gaussian process (GP) fitting with uncertainty quantification (UQ)
This is code associated with the publication “Nonparametric Multi-shape Modeling with Uncertainty Quantification,” authored by Hengrui Luo (Lawrence Berkeley National Laboratory) and Justin Strait (Los Alamos National Laboratory). The code is used to fit multiple-output Gaussian process (GP) models of planar closed curves to collections of ordered point sets, allowing for flexible nonlinear prediction of the underlying curve under dense or sparse point set samplings and with or without noise, as well as tractable uncertainty quantification. To do this, we employ use of a periodic kernel to account for the nonlinear input space of closed curves, and combine with coregionalization models to account for dependence both (i) between curve coordinates, and (ii) between pairs of curves. Functions in the code are capable of fitting these models, as well as performing additional tasks with the fitted curves such as (a) shape registration and alignment, (b) shape averaging, and (c) fitting for curve sub-populations / clusters.
Strait, Justin↗