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Differentiable physics-enabled closure modeling for Burgers’ turbulence
Abstract Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments in the data sciences. We discuss an approach using the differentiable physics paradigm that combines known physics with machine learning to develop closure models for Burgers’ turbulence. We consider the one-dimensional Burgers system as a prototypical test problem for modeling the unresolved terms in advection-dominated turbulence problems. We train a series of models that incorporate varying degrees of physical assumptions on an a posteriori loss function to test the efficacy of models across a range of system parameters, including viscosity, time, and grid resolution. We find that constraining models with inductive biases in the form of partial differential equations that contain known physics or existing closure approaches produces highly data-efficient, accurate, and generalizable models, outperforming state-of-the-art baselines. Addition of structure in the form of physics information also brings a level of interpretability to the models, potentially offering a stepping stone to the future of closure modeling.
Machine learning of hidden variables in multiscale fluid simulation
Solving fluid dynamics equations often requires the use of closure relations that account for missing microphysics. For example, when solving equations related to fluid dynamics for systems with a large Reynolds number, sub-grid effects become important and a turbulence closure is required, and in systems with a large Knudsen number, kinetic effects become important and a kinetic closure is required. By adding an equation governing the growth and transport of the quantity requiring the closure relation, it becomes possible to capture microphysics through the introduction of ‘hidden variables’ that are non-local in space and time. The behavior of the ‘hidden variables’ in response to the fluid conditions can be learned from a higher fidelity or ab-initio model that contains all the microphysics. In our study, a partial differential equation simulator that is end-to-end differentiable is used to train judiciously placed neural networks against ground-truth simulations. We show that this method enables an Euler equation based approach to reproduce non-linear, large Knudsen number plasma physics that can otherwise only be modeled using Boltzmann-like equation simulators such as Vlasov or particle-in-cell modeling.
Machine Learning-Based Longitudinal Phase Space Prediction of Two-Bunch Operation at FACET-II
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Calcined Waste Storage at the Idaho Nuclear Technology and Engineering Center
This comprehensive report provides definitive volume, mass, and composition (chemical and radioactivity) of calcined waste stored at the Idaho Nuclear Technology and Engineering Center. Calcine composition data are required for regulatory compliance (such as permitting and waste disposal), future treatment of the caline, and shipping the calcine to an off-Site-facility (such as a geologic repository). This report also contains a description of the calcine storage bins. The Calcined Solids Storage Facilities (CSSFs) were designed by different architectural engineering firms and built at different times. Each CSSF has a unique design, reflecting varying design criteria and lessons learned from historical CSSF operation. The varying CSSF design will affect future calcine retrieval processes and equipment. Revision 4 of this report presents refinements and enhancements of calculations concerning the composition, volume, mass, chemical content, and radioactivity of calcined waste produced and stored within the CSSFs. The historical calcine samples are insufficient in number and scope of analysis to fully characterize the entire inventory of calcine in the CSSFs. Sample data exist for all the liquid wastes that were calcined. This report provides calcine composition data based on liquid waste sample analyses, volume of liquid waste calcined, calciner operating data, and CSSF operating data using several large Microsoft Excel (Microsoft 2003) databases and spreadsheets that are collectively called the Historical Processing Model. The calcine composition determined by this method compares favorably with historical calcine sample data.
Equipment Request for the Belleville Agricultural Research and Education Center
The funding provided by the DOE for this project was used exclusively to purchase research equipment involved with the field development and evaluation of crop production technologies and practices for energy crop production. The new equipment has been placed into service on the SIU farms and has significantly enhanced our research capacity and scope for agronomy and precision ag research to support novel seed traits or crop management strategies for improving the efficiency and productivity of corn and soybeans. More specifically, the precision ag capability of the equipment that was purchased has heightened interest by faculty and associated industry partners to develop collaborative projects. In addition, this equipment has provided SIU with a foundation to be more successful at securing competitive grants in energy crop production and precision ag data management. Furthermore, the enhanced capacity for agronomy research in the southern Illinois region has been realized and will benefit crop producers in this region by learning to improve their operations from our research outcomes.
Production Facility Prototype Blower 1000 Hour Test Results III
Long duration operations tests of the Aerzen GM 12.4 roots style blower in a closed loop configuration provides valuable data and lessons learned for long-term operation at the Mo-99 production facility. The blower was operated in a closed loop configuration with the flow conditions anticipated in plant operation with a Mo-100 target inline. The additional thermal energy generated from beam heating of the Mo-100 disks was not included in these tests. Eight long duration 500 and 1000 hour tests have been completed since the first test was performed in January of 2016. All eight long duration tests have proven successful in exposing preventable issues related to oil and helium leaks.
Spectral Methods in Time-dependent Data Analysis
We aim to create a new model for time-dependent data analysis, named dynamical learning, that integrates data-driven manifold learning techniques with operator-theoretic methods from dynamical systems theory. This approach has the potential to deliver more efficient methods for analyzing time-dependent data, such as video streams, by naturally separating out the temporal and spatial features of the data. We aim to apply the newly developed methods to video surveillance data related to Sandia mission applications, and particularly focus on the problems of image segmentation and object tracking. This project ended early due to the departure of the PI from Sandia about 18 months into the project. Therefore, this document reports on partial progress towards the initial goals of the project. In addition, this document reports on part of the work conducted during the project; see the Appendix for a summary of all the work conducted during the 18 months.
Production Facility Prototype Blower 1000 Hour Test Results IV
Long duration operations tests of the Aerzen GM 12.4 roots style blower in a closed loop configuration provides valuable data and lessons learned for long-term operation at the Mo-99 production facility. The blower was operated in a closed loop configuration with the flow conditions anticipated in plant operation with a Mo-100 target inline. The additional thermal energy generated from beam heating of the Mo-100 disks was not included in these tests. Twelve long duration 500 to 1000 hour tests have been completed since the first test was performed in January of 2016. All twelve long duration tests have proven successful in exposing preventable issues related to oil and helium leaks. All blower tests to this date have resulted in stable blower performance and consistency. A summary of the results for the end of the ninth test to the twelfth test is included in this report. Complete details for the previous tests are shown in the reports, Production Facility Prototype Blower 1000 Hour Test Results, II, and III.
Production Facility Prototype Blower 1000 Hour Test Results V
Long duration operations tests of the Aerzen GM 12.4 roots style blower in a closed loop configuration provides valuable data and lessons learned for long-term operation at the Mo-99 production facility. The blower was operated in a closed loop configuration with the flow conditions anticipated in plant operation with a Mo-100 target inline. The additional thermal energy generated from beam heating of the Mo-100 disks was not included in these tests. Thirteen long duration 500 to 1000 hour tests have been completed since the first test was performed in January of 2016. All thirteen long duration tests have proven successful in exposing preventable issues related to oil and helium leaks. All blower tests to this date have resulted in stable blower performance and consistency. A summary of the results for the end of the thirteenth test is included in this report. Complete details for the previous tests are shown in the reports, Production Facility Prototype Blower 1000 Hour Test Results, II, III, and IV.
Waste Management Fundamentals for Environmental Professionals (UTrain 50448) [Slides]
At the pilot of this course, we discovered that we need to focus on how we do waste management at LANL. Environmental Professionals have a variety of jobs - most impact waste in some way. Finding a balance between comprehensive enough and too much is key. The goals of this training are for it to be good, consistent, and helpful, not just a requirement. The course modules are: Intro to Waste, Waste Regulations and Requirements, Waste Planning, Waste Generation and Tracking, Waste Characterization, Waste Packaging, Waste Accumulation and Storage, Waste Treatment, Waste Shipping, and Operation Experience and Lessons Learned.
H2@Airports (Workshop Summary Report)
This report serves as the proceedings of the H2@Airports Workshop held virtually by the U.S. Department of Energy (DOE) in collaboration with the U.S. Department of Transportation (DOT) and Department of Defense (DOD), November 4-6, 2020. Presentations from the workshop can be found at H2@Airports Workshop: https://www.energy.gov/eere/fuelcells/h2airports-workshop. The workshop was held to assess the state of the art for electric aircraft and airport applications specifically using hydrogen fuel cells, to discuss operational requirements and lessons learned on early fuel cell aviation and airport projects, to understand current technology gaps, to identify collaborative research and development (R&D) opportunities, to highlight codes, standards, safety, and regulatory challenges, and to identify potential actions that address them. Experts and stakeholders from industry, government, and academia met to discuss the current state of the art of hydrogen and fuel cell technologies and the requirements for using these technologies in aviation applications and land-based applications at airports. This report summarizes the discussions and diverse opinions expressed at the workshop.
Machine Learning for Accelerating Direct-Simulation Monte-Carlo Collision Operators.
Abstract not provided.
GraphAlign: Graph-Enabled Machine Learning for Seismic Event Filtering
This report summarizes results from a 2 year effort to improve the current automated seismic event processing system by leveraging machine learning models that can operated over the inherent graph data structure of a seismic sensor network. Specifically, the GraphAlign project seeks to utilize prior information on which stations are more likely to detect signals originating from particular geographic regions to inform event filtering. To date, the GraphAlign team has developed a Graphical Neural Network (GNN) model to filter out false events generated by the Global Associator (GA) algorithm. The algorithm operates directly on waveform data that has been associated to an event by building a variable sized graph of station waveforms nodes with edge relations to an event location node. This builds off of previous work where random forest models were used to do the same task using hand crafted features. The GNN model performance was analyzed using an 8 week IMS/IDC dataset, and it was demonstrated that the GNN outperforms the random forest baseline. We provide additional error analysis of which events the GNN model performs well and poorly against concluded by future directions for improvements.
Grid-Scale Electricity Storage at Lowest Possible Cost Enabled by Pumped Heat Electricity Storage (Final Report)
Grid-scale energy storage technologies that can provide greater than 10 hours of electricity at full power address a critical need for full penetration of renewables on electricity grids worldwide, minimize renewable curtailment, and maximize grid security and reliability. Pumped Heat Energy Storage (PHES) systems can store energy thermally in any location at the low costs necessary for displacing fossil fuels, reducing foreign fuel imports, and reducing emissions to maintain the energy security of the United States. The award allowed Southwest Research Institute® (SwRI®) and Malta, Inc. (Malta) to advance the PHES concept to a proof-of-concept demonstration for a kW-scale system. The project focused on system operation and integration. The main project objectives were: 1. Address first implementation challenges 2. Explore system control strategies and operation 3. Translate lessons learned to the full-scale system Through these objectives the project completes a first demonstration of this technology and reduces the technical risk associated with a full-scale system.
Uncertainties in ICARUS
This paper explores the challenges in neutrino detection and emphasizes the importance of understanding the ICARUS neutrino detector. It delves into the use of cosmic rays to bridge the gap between experimental and theoretical data, offering insights into the behavior of different components of neutrino detectors. This procedure sheds light on the intricate aspects of cosmic ray detection in ICARUS, contributing to an increased understanding of how this detector's uncertainties operate. More can be learned about neutrino physics in future experiments through this understanding.
Interface Specifications for RAdiation Portal Technology Enhancement & Replacement (RAPTER) Modules
Radiation Portal Monitors (RPMs) were deployed throughout the port and border infrastructure of the United States (U.S.) beginning in 2003 to monitor for the possible presence of uncontrolled radiological and nuclear materials. Since that time, the U.S. Government (USG) has learned much about the operational challenges faced in the field. Principal among the shortcomings has been the lack of flexibility afforded the USG when all Internet Protocol (IP) rights and interfaces of the system are owned by the Original Equipment Manufacturer (OEM).
Electric Grid Security (EGS) FY24 Annual Report
Sandia’s Electric Grid Security program advances a national vision of a secure, resilient, and affordable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY24 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Key accomplishments in this report that support our strategy span our technical program areas and include: • The advancement of energy storage technologies, including creation of a national Long Duration Energy Storage Consortium; • Applications of artificial intelligence and machine learning to enhanced grid operations and planning; • Development of solid-state power conversion technologies and a new medium-voltage research lab; • New technologies to assess wildfire vulnerabilities and mitigate potential impacts; • Advanced applications of new cybersecurity technologies with industry partners; • Contributions to understanding the impacts of electromagnetic pulses and geomagnetic disturbances on grid components; and • Digital twin development for hybrid microgrids with multiple generators, storage, and loads.