Microstructural classification of unirradiated LiAlO2 pellets by deep learning methods
Not Available
Pazdernik, Karl (ORCID:0000000314104006)↗
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Poster — Thur Eve — 69: Computational Study of DVH-guided Cancer Treatment Planning Optimization Methods
Purpose: To compare methods to incorporate the Dose Volume Histogram (DVH) curves into the treatment planning optimization. Method: The performance of three methods, namely, the conventional Mixed Integer Programming (MIP) model, a convex moment-based constrained optimization approach, and an unconstrained convex moment-based penalty approach, is compared using anonymized data of a prostate cancer patient. Three plans we generated using the corresponding optimization models. Four Organs at Risk (OARs) and one Tumor were involved in the treatment planning. The OARs and Tumor were discretized into total of 50,221 voxels. The number of beamlets was 943. We used commercially available optimization software Gurobi and Matlab to solve the models. Plan comparison was done by recording the model runtime followed by visual inspection of the resulting dose volume histograms. Conclusion: We demonstrate the effectiveness of the moment-based approaches to replicate the set of prescribed DVH curves. The unconstrained convex moment-based penalty approach is concluded to have the greatest potential to reduce the computational effort and holds a promise of substantial computational speed up.
60 APPLIED LIFE SCIENCES↗
SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment
Presentation material for a paper presented at the GHGT-17 conference, Calgary, Canada, October 20-24, 2024. The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.
CO2 geologic storage↗
Do Biological and Bedsite Characteristics Influence Survival of Neonatal White-Tailed Deer?
Coyotes recently expanded into the eastern U.S. and potentially have caused localized white-tailed deer population declines. Research has focused on quantifying coyote predation on neonates, but little research has addressed the potential influence of bedsite characteristics on survival. In 2011 and 2012, we radiocollared 65 neonates, monitored them intensively for 16 weeks, and assigned mortality causes.We used Program MARK to estimate survival to 16 weeks and included biological covariates (i.e., sex, sibling status [whether or not it had a sibling], birth weight, and Julian date of birth). Survival to 16 weeks was 0.141 (95% CI = 0.075-0.249) and the top model included only sibling status, which indicated survival was lower for neonates that had a sibling. Predation was the leading cause of mortality (35 of 55; 64%) and coyotes were responsible for the majority of depredations (30 of 35; 86%). Additionally, we relocated neonates for the first 10 days of life and measured distance to firebreak, visual obstruction, and plant diversity at bedsites. Survival of predation to 10 days (0.726; 95% CI = 0.586-0.833) was weakly associated with plant diversity at bedsites but not related to visual obstruction. Our results indicate that neonate survival was low and coyote predation was an important source of mortality, which corroborates several recent studies from the region. Additionally, we detected only weak support for bedsite cover as a covariate to neonate survival, which indicates that mitigating effects of coyote predation on neonates may be more complicated than simply managing for increased hiding cover.
54 ENVIRONMENTAL SCIENCES↗
Destructive Examination of Shipping Package 9975-02731
Destructive and non-destructive examinations were performed on the components of the package 9975-02731 as part of a comprehensive Savannah River Site surveillance program for plutonium materials stored in the K-Area Complex (KAC). The package stored nuclear materials in KAC for 13.8 years. Inspection of the package during opening in KAC noted one non-conforming condition: a large discolored area on the upper fiberboard subassembly. The package was subsequently transferred to Savannah River National Laboratory for more detailed examination. In addition to the observation noted in KAC, the following conditions were observed: (1) Axial gap of 1.021 inch slightly exceeded the 1 inch maximum criterion, and (2) Lead shield height exceeded drawing tolerance of 24.7 inch by 0.019 inch. Visual examination of the upper fiberboard assembly and analysis of the discolored fiberboard indicate the discoloration was due to wood glue residue. The physical, thermal, and mechanical properties of the fiberboard met the acceptance criteria and/or were within the range of previously tested fiberboard and baseline samples. The visual, microscopic and tensile testing results were consistent with previous O-ring and baseline Viton results. The lead shield surface was covered with corrosion product as observed on other shields but there was no evidence of excessive blistering, flaking, or spalling. Besides minor and expected corrosion, no evidence of a degraded condition was found in this package. Examination of the 9975-02731 corroborates existing evidence that the 9975 package is robust and the components can perform their expected functions.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Multidimensional perspectives of geo-epidemiology: from interdisciplinary learning and research to cost–benefit oriented decision-making
Research typically promotes two types of outcomes (inventions and discoveries), which induce a virtuous cycle: something suspected or desired (not previously demonstrated) may become known or feasible once a new tool or procedure is invented and, later, the use of this invention may discover new knowledge. Research also promotes the opposite sequence—from new knowledge to new inventions. This bidirectional process is observed in geo-referenced epidemiology—a field that relates to but may also differ from spatial epidemiology. Geo-epidemiology encompasses several theories and technologies that promote inter/transdisciplinary knowledge integration, education, and research in population health. Based on visual examples derived from geo-referenced studies on epidemics and epizootics, this report demonstrates that this field may extract more (geographically related) information than simple spatial analyses, which then supports more effective and/or less costly interventions. Actual (not simulated) bio-geo-temporal interactions (never captured before the emergence of technologies that analyze geo-referenced data, such as geographical information systems) can now address research questions that relate to several fields, such as Network Theory. Thus, a new opportunity arises before us, which exceeds research: it also demands knowledge integration across disciplines as well as novel educational programs which, to be biomedically and socially justified, should demonstrate cost-effectiveness. Grounded on many bio-temporal-georeferenced examples, this report reviews the literature that supports this hypothesis: novel educational programs that focus on geo-referenced epidemic data may help generate cost-effective policies that prevent or control disease dissemination.
59 BASIC BIOLOGICAL SCIENCES↗
INL Senior Project
What did my team set out to accomplish:? Can I put a custom Machine Learning Model on FPGA?? Can I analyze network traffic in real time?? Can a QSFP port be used with an FPGA?? Does a visual representation of the latent space enhance our understanding of network traffic?? What is QSFP QSFP (Quad Small Form-Factor Pluggable)? QSFP supports transfer speeds generally up to 100Gb/s? Runs 4 parallel lines running up to 28 Gb/s? Why the latent space is important to our project ?Latent space is the compressed mapping of data points in a non-linear fashion? Create an understanding of the relationship of data collected? Can represent that relationship of a single network packet in 3 points (X, Y, Z) Project Outline FPGA? Custom Xilinx Petalinux Image for the operating system? Python program to collect packets and run them through the DPU (Data Processing Unit)? The program then sends the information over a socket to a computer? Display Program? Python Program that collects the information sent from the FPGA and display it in a graph
99 GENERAL AND MISCELLANEOUS↗
Examining the infrared variable star population discovered in the Small Magellanic Cloud using the SAGE-SMC survey
We present our study on the infrared variability of point sources in the Small Magellanic Cloud (SMC). We use the data from the Spitzer Space Telescope Legacy Program “Surveying the Agents of Galaxy Evolution in the Tidally Stripped, Low Metallicity Small Magellanic Cloud” (SAGE-SMC) and the “Spitzer Survey of the Small Magellanic Cloud” (S{sup 3}MC) survey, over three different epochs, separated by several months to 3 years. Variability in the thermal infrared is identified using a combination of Spitzer’s InfraRed Array Camera 3.6, 4.5, 5.8, and 8.0 μm bands, and the Multiband Imaging Photometer for Spitzer 24 μm band. An error-weighted flux difference between each pair of three epochs (“variability index”) is used to assess the variability of each source. A visual source inspection is used to validate the photometry and image quality. Out of ∼2 million sources in the SAGE-SMC catalog, 814 meet our variability criteria. We matched the list of variable star candidates to the catalogs of SMC sources classified with other methods, available in the literature. Carbon-rich Asymptotic Giant Branch (AGB) stars make up the majority (61%) of our variable sources, with about a third of all of our sources being classified as extreme AGB stars. We find a small, but significant population of oxygen-rich (O-rich) AGB (8.6%), Red Supergiant (2.8%), and Red Giant Branch (<1%) stars. Other matches to the literature include Cepheid variable stars (8.6%), early type stars (2.8%), Young-stellar objects (5.8%), and background galaxies (1.2%). We found a candidate OH maser star, SSTISAGE1C J005212.88-730852.8, which is a variable O-rich AGB star, and would be the first OH/IR star in the SMC, if confirmed. We measured the infrared variability of a rare RV Tau variable (a post-AGB star) that has recently left the AGB phase. 59 variable stars from our list remain unclassified.
79 ASTRONOMY AND ASTROPHYSICS↗
Post-Closure Report for Closed Resource Conservation and Recovery Act Corrective Action Units, Nevada National Security Site, Nevada, for Fiscal Year 2014
This report serves as the combined annual report for post-closure activities for several Corrective Action Units (CAUs). The locations of the sites are shown in Figure 1. This report covers fiscal year 2014 (October 2013–September 2014). The post-closure requirements for these sites are described in Resource Conservation and Recovery Act Permit Number NEV HW0101 and summarized in each CAU-specific section in Section 1.0 of this report. The results of the inspections, a summary of maintenance activities, and an evaluation of monitoring data are presented in this report. Site inspections are conducted semiannually at CAUs 90 and 91 and quarterly at CAUs 92, 110, 111, and 112. Additional inspections are conducted at CAU 92 if precipitation occurs in excess of 0.50 inches (in.) in a 24-hour period and at CAU 111 if precipitation occurs in excess of 1.0 in. in a 24-hour period. Inspections include an evaluation of the condition of the units, including covers, fences, signs, gates, and locks. In addition to visual inspections, soil moisture monitoring, vegetation evaluations, and subsidence surveys are conducted at CAU 110. At CAU 111, soil moisture monitoring, vegetation evaluations, subsidence surveys, direct radiation monitoring, air monitoring, radon flux monitoring, and groundwater monitoring are conducted. The results of the vegetation surveys and an analysis of the soil moisture monitoring data at CAU 110 are presented in this report. Results of additional monitoring at CAU 111 are documented annually in the Nevada National Security Site Waste Management Monitoring Report Area 3 and Area 5 Radioactive Waste Management Sites and in the Nevada National Security Site Data Report: Groundwater Monitoring Program Area 5 Radioactive Waste Management Site, which will be prepared in approximately June 2015. All required inspections, maintenance, and monitoring were conducted in accordance with the post-closure requirements of the permit. It is recommended to continue inspections and monitoring as scheduled.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Crystalline and Crystalline International Disposal Activities
This report presents the results of work conducted between September 2015 and July 2016 at Los Alamos National Laboratory in the crystalline disposal and crystalline international disposal work packages of the Used Fuel Disposition Campaign (UFDC) for DOE-NE’s Fuel Cycle Research and Development program. Los Alamos focused on two main activities during this period: Discrete fracture network (DFN) modeling to describe flow and radionuclide transport in complex fracture networks that are typical of crystalline rock environments, and a comprehensive interpretation of three different colloid-facilitated radionuclide transport experiments conducted in a fractured granodiorite at the Grimsel Test Site in Switzerland between 2002 and 2013. Chapter 1 presents the results of the DFN work and is divided into three main sections: (1) we show results of our recent study on the correlation between fracture size and fracture transmissivity (2) we present an analysis and visualization prototype using the concept of a flow topology graph for characterization of discrete fracture networks, and (3) we describe the Crystalline International work in support of the Swedish Task Force. Chapter 2 presents interpretation of the colloidfacilitated radionuclide transport experiments in the crystalline rock at the Grimsel Test Site.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Fundamental Impact of Humidity on SOFC Cathode ORR
Although solid oxide fuel cells (SOFC) have demonstrated excellent performance, the durability of SOFCs under real working conditions is still an issue for commercial deployment. In particular cathode exposure to atmospheric air contaminants, such as humidity, can result in long-term performance degradation issues. Therefore, a fundamental understanding of the interaction between water molecules and cathodes is essential to resolve this issue and further enhance cathode durability. In order to study the effects of humidity on the oxygen reduction reaction (ORR), we used in-situ 18 O isotope exchange techniques to probe the exchange of water with two of themost common SOFC cathode materials, (La 0.8 Sr 0.2 ) 0.95 MnO 3±δ (LSM) and La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ (LSCF). In this experiment, heavy water, D 2 O (with a mass/charge ratio of m/z = 20), is used to avoid the overlapping of H 2 O and the 18 O 2 cracking fraction, which both provide a peak at m/z = 18. A series of temperature programmed isotope exchange measurements were performed to comprehensively study the interaction of water with the cathode surface as a function of temperature, oxygen partial pressure, and water vapor concentration. The results suggest that water and O 2 share the same surface exchange sites, leading to competitive adsorption. Our findings show that water prefers to exchange with LSCF at lower temperatures, around 300–450°C. For LSM, O 2 is more favorable than water to be adsorbed on the surface and the presence of O 2 limits water exchange. The experimental data are summarized in a Temperature-PO 2 diagram to help visualize how the exchange of water on each material depends on the operating conditions.
30 DIRECT ENERGY CONVERSION↗
Designs for Risk Evaluation and Management
The Designs for Risk Evaluation and Management (DREAM) tool was developed as part of the effort to quantify the risk of geologic storage of carbon dioxide (CO 2 ) under the U.S. Department of Energy's National Risk Assessment Partnership (NRAP). DREAM is an optimization tool created to identify optimal monitoring schemes that minimize the time to first detection of CO 2 leakage from a subsurface storage formation. DREAM acts as a post-processer on user-provided output from subsurface leakage simulations. While DREAM was developed for CO 2 leakage scenarios, it is applicable to any subsurface leakage simulation of the same output format. The DREAM tool is comprised of three main components: (1) a Java wizard used to configure and execute the simulations, (2) a visualization tool to view the domain space and optimization results, and (3) a plotting tool used to analyze the results. A secondary Java application is provided to aid users in converting common American Standard Code for Information Interchange (ASCII) output data to the standard DREAM hierarchical data format (HDF5). DREAM employs a simulated annealing approach that searches the solution space by iteratively mutating potential monitoring schemes built of various configurations of monitoring locations and leak detection parameters. This approach has proven to be orders of magnitude faster than an exhaustive search of the entire solution space. The user's manual illustrates the program graphical user interface (GUI), describes the tool inputs, and includes an example application.
(Ruprecht) Yonkofski, Catherine↗
The ARM Data-oriented Metrics and Diagnostics Package for Climate Models - A New Tool for Evaluating Climate Models with Field Data
The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Inter-comparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.
54 ENVIRONMENTAL SCIENCES↗
EnergyPlus-MCP: A model-context-protocol server for ai-driven building energy modeling
Traditional building energy modeling with the EnergyPlus building performance simulation engine requires domain expertise, programming skills, and intensive manual efforts limiting its effective adoption. This paper introduces EnergyPlus-MCP, the first open-source Model Context Protocol (MCP) server specifically designed for EnergyPlus simulation workflows, establishing a new foundational infrastructure for AI-driven building energy modeling. The MCP server implements a layered architecture with 35 specialized tools spanning model management, editing and analysis, HVAC and other systems configuration inspection, and simulation execution, enabling Large Language Models to interact with EnergyPlus through conversational interfaces. The server addresses critical workflow barriers by automating model validation, streamlining energy efficiency measures modification, and providing intelligent output management with interactive visualization. Through practical demonstrations using a multi-zone building retrofit analysis, we show how the EnergyPlus-MCP server significantly reduces manual efforts while maintaining full simulation rigor. By providing accessible natural language interfaces to sophisticated building energy analysis, this approach enables scalable deployment of simulation expertise across public and private organizations, educational institutions, and research teams, fundamentally transforming traditional building energy modeling practices.
AI↗
Preliminary Report on Compositional Specification for Printed 316SS
The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing (AM). A key challenge for the widespread adoption of AM is the variability in material properties, which can originate from powder feedstock, process parameters, geometry, and the machine. AM powder feedstock compositions are based on those developed for conventional manufacturing. However, the smaller melt pools combined with multiple melt cycles over the course of a build can have substantial effects on the local phase selection resulting from minor batch-to-batch feedstock variability. Therefore, this report focuses on coupling CALPHAD calculations with data mining and visualization to aid the development of compositional specifications for stainless steel 316, an important alloy for nuclear applications and suitable for AM. This work reports that δ-ferrite formation has a stronger dependence on the amount of nickel in the alloy despite nickel being an austenite stabilizer. These findings show the need for exercising a tighter control on the amount of nickel in the alloy compared with the amount of chromium, which is a ferrite stabilizer.
36 MATERIALS SCIENCE↗
Molecular radiotherapy: The NUKFIT software for calculating the time-integrated activity coefficient
Purpose: Calculation of the time-integrated activity coefficient (residence time) is a crucial step in dosimetry for molecular radiotherapy. However, available software is deficient in that it is either not tailored for the use in molecular radiotherapy and/or does not include all required estimation methods. The aim of this work was therefore the development and programming of an algorithm which allows for an objective and reproducible determination of the time-integrated activity coefficient and its standard error.Methods: The algorithm includes the selection of a set of fitting functions from predefined sums of exponentials and the choice of an error model for the used data. To estimate the values of the adjustable parameters an objective function, depending on the data, the parameters of the error model, the fitting function and (if required and available) Bayesian information, is minimized. To increase reproducibility and user-friendliness the starting values are automatically determined using a combination of curve stripping and random search. Visual inspection, the coefficient of determination, the standard error of the fitted parameters, and the correlation matrix are provided to evaluate the quality of the fit. The functions which are most supported by the data are determined using the corrected Akaike information criterion. The time-integrated activity coefficient is estimated by analytically integrating the fitted functions. Its standard error is determined assuming Gaussian error propagation. The software was implemented using MATLAB.Results: To validate the proper implementation of the objective function and the fit functions, the results of NUKFIT and SAAM numerical, a commercially available software tool, were compared. The automatic search for starting values was successfully tested for reproducibility. The quality criteria applied in conjunction with the Akaike information criterion allowed the selection of suitable functions. Function fit parameters and their standard error estimated by using SAAM numerical and NUKFIT showed differences of <1%. The differences for the time-integrated activity coefficients were also <1% (standard error between 0.4% and 3%). In general, the application of the software is user-friendly and the results are mathematically correct and reproducible. An application of NUKFIT is presented for three different clinical examples.Conclusions: The software tool with its underlying methodology can be employed to objectively and reproducibly estimate the time integrated activity coefficient and its standard error for most time activity data in molecular radiotherapy.
61 RADIATION PROTECTION AND DOSIMETRY↗
ActiveSpaces: Exploring dynamic code deployment for extreme scale data processing: ActiveSpaces: Exploring dynamic code deployment for extreme scale data processing
Managing the large volumes of data produced by emerging scientific and engineering simulations running on leadership-class resources has become a critical challenge. The data have to be extracted off the computing nodes and transported to consumer nodes so that it can be processed, analyzed, visualized, archived, and so on. Several recent research efforts have addressed data-related challenges at different levels. One attractive approach is to offload expensive input/output operations to a smaller set of dedicated computing nodes known as a staging area. However, even using this approach, the data still have to be moved from the staging area to consumer nodes for processing, which continues to be a bottleneck. In this paper, we investigate an alternate approach, namely moving the data-processing code to the staging area instead of moving the data to the data-processing code. Specifically, we describe the ActiveSpaces framework, which provides (1) programming support for defining the data-processing routines to be downloaded to the staging area and (2) runtime mechanisms for transporting codes associated with these routines to the staging area, executing the routines on the nodes that are part of the staging area, and returning the results. We also present an experimental performance evaluation of ActiveSpaces using applications running on the Cray XT5 at Oak Ridge National Laboratory. Finally, we use a coupled fusion application workflow to explore the trade-offs between transporting data and transporting the code required for data processing during coupling, and we characterize sweet spots for each option.
97 MATHEMATICS AND COMPUTING↗
Adapting to Artificial Intelligence
There is no need to be afraid of the emerging advances in artificial intelligence, says Argonne’s Marius Stan, Program Lead for Intelligent Materials Design. Although artificial intelligence may upend the job market in some ways, he says, it may also create new industries and positions as employees begin to designing and making robots. “It’s the problem and the solution,” Stan says. Meanwhile, artificial intelligence is playing a significant role in defining new research methods that bring us to resolution or discovery more quickly than ever before. Argonne is using or developing many of these technologies as AI extends to new branches of science and technology, sprouting new buds that represent various facets of human function and cognition — from learning to language processing to visual recognition.
97 MATHEMATICS AND COMPUTING↗