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The objectives of this project are as follows: 1. Reduce uncertainty in PV performance models by developing and validating new and improved models and submodes. 2. Create and manage an open source repository of modeling functions and data. 3. Build and grow the PV Performance Modeling Collaborative; and, 4. Represent the US in the IEA PVPS Task 13 Working group.
The objectives of this project are as follows: 1) Reduce uncertainty in PV performance models by developing and validating new and improved models and submodes. 2) Create and manage an open source repository of modeling functions and data. 3) Build and grow the PV Performance Modeling Collaborative 4) Represent the US in the IEA PVPS Task 13 Working group.
LANL was tasked with working with NREL to implement QUIC within the TAP API by the end of Q1, FY2023. If QUICURB cannot be implemented in a way that it can be run outside of the QUIC platform, there will be no further R&D funding for QUIC development. QUIC includes a graphical user interface that imports and preprocesses the various input data streams (3D building databases, ambient wind, vegetation, etc.) and writes them in a format that the QUICURB Fortran executable requires. In order to interface with the TAP API, a Python script was developed that would replace the functionality previously only available within QUICGUI. LANL worked with NREL to test the Python script to ensure that it was operation and fulfilled the requirements.
PNNL staff performed an independent technical review (ITR) of the aluminum-clad spent nuclear fuel (ASNF) program Task 2. The objective of Task 2 is to determine G-values for the radiolytic production of gaseous molecular hydrogen (H 2 ) from the aluminum oxyhydroxide layers present on ASNF cladding. The G-values need to be defensible, bounding, but not overly conservative. As part of the ITR, the team reviewed program documents, Task 2 work plans, and results to date. In addition, the team held three separate video conferences with the program research staff to ask clarifying questions and obtain additional detail. The PNNL ITR team concludes that if the program follows the methodology and rigor outlined in the test matrices, the program will develop G-values for a defensible technical basis of extended storage of ASNF. Recommendations for improvement are given.
Environmental barrier coatings (EBCs) are required coatings for utilization of SiC/SiC ceramic matrix composite (CMC) components in gas turbines, where the EBC represents the life-limiting factor for such components. EBC/CMC systems have shown success in aero-engine applications with increased turbine inlet temperatures and improved efficiencies, which are achieved through higher temperature stability, lower density, and decreased reliance on cooling air compared to traditional superalloys. While industrial gas turbines (IGTs) do not currently utilize SiC/SiC CMCs, the current shift towards low-carbon or carbon-free fuel sources for power generation could result in a need for EBC/CMC components with higher temperature capabilities. In this work, three tasks were outlined to improve understanding of EBC lifetimes to encourage use in IGTs with carbon-free fuel such as hydrogen: 1. Define the bond coating oxidation kinetics and EBC failure criteria, 2. Measure thermal expansion coefficients of each layered material, and 3. Perform advanced characterization and modeling to assess EBC lifetimes. Cyclic steam oxidation tests were conducted on various EBC/Si/SiC chemistries and EBC/SiC architectures to define substrate oxidation kinetics and EBC failure modes. An open-source code was developed to quantify the undulating thermally grown oxide thickness with thousands of measurements from specimen cross-section images. Bond coating oxidation kinetics were determined and used to develop a kinetic and thermodynamic model for predicting EBC lifetimes. High-temperature Raman spectroscopy was utilized for determining the SiO 2 thermally grown oxide phase transformation as the life-limiting feature for EBCs. Model efforts supported the claim that the SiO 2 phase transformation causes elevated stress during thermal cycling with associated cracking that decreases the adhesion strength of the EBC, eventually resulting in coating spallation. The finite element model subroutine will be made publicly available upon internal review. Further development of an EBC lifetime model for IGTs involves definition of a critical SiO 2 thickness for EBC spallation and must also consider both environmental (gas velocity, pressure, etc.) and specimen (EBC dopants, layer architectures, etc.) effects into predicted bond coating oxidation kinetics for long-lifetime components.
Predicting the dynamics of complex nonlinear systems remains a challenging problem both in dynamical systems theory as well as real world science and engineering applications. Data-driven methods utilizing the latest advances in machine learning (ML) provide a promising new paradigm for this task. Our work centered on Reservoir Computing (RC), which has shown itself to be capable of skillfully predicting chaotic dynamics in multiscale systems. In the first part of the work, the focus is on how to improve predictions of critical transitions in a class of slow-fast metastable systems in which the equations are known. An additional goal was to determine whether a relationship exists between RC and Koopman operator theory, to improve the efficiency and broaden the applicability of the approach. In the second part of this work, a variation on the RC model known as Reconstructive Reservoir Computing (RRC) is applied to real-world data to identify anomalies.
BACKGROUND AND PROJECT TASKS Proposed work addresses focus area 1: Waste Retrieval, Transport and Closure, with particular focus on increasing volume available for tank storage 1) Refurbish/fortify existing double shell storage tanks (Tank Refurbish – Polymer Grout) 2) Robust monitoring tools for internal corrosion (Integrity Monitoring -Reference Electrodes) 3) Mitigate external corrosion (Degradation Prevention - Cathodic Protection) 4) Determine viability of constructing new tanks, and storage room creation by way of evaporation (Cost Benefit Analysis)
This mid-year report summarizes ongoing work to identify anomalous vibration signals indicative of potential containment breaches. This work includes compiling continuous seismic datasets and testing and refining underground detection and geolocation techniques. In the first two quarters of FY25, we have completed two project work plan tasks: (1) creating a database of continuous waveforms and ground truth event data from multiple modalities and (2) refining and implementing a detection and association algorithm to create a catalog of anomalous underground activities. This report contains a summary of the seismic database including the continuous seismic data collected by a dense array of surface seismic stations above Pleasant Gap Mine, and continuous seismic data collected using subsurface distributed acoustic sensing (DAS) in the subsurface at Sanford Underground Research Facility (SURF) and the ground truth information gathered from both sites. This report also includes results from refining and applying a dynamic power spectral density detector to both continuous seismic datasets. Finally, the report provides an initial catalog of subsurface operational events from both sensing modalities.
IEA Wind Task 32 seeks to identify and mitigate the barriers to the adoption of lidar for wind energy applications. In Phase 1 of the task, a working group looked at the state of the art of wind lidar in complex flow conditions. This presentation is a short summary of that work, given at the start of Phase 2.
In October 2018, the International Energy Agency Wind Task 34 - Working Together to Resolve the Environmental Effects of Wind Energy (WREN) - organized a virtual forum to discuss the likelihood of bats experiencing barotrauma when flying near moving wind turbine blades. The forum included experts in bat biology and physiology, bat and wind turbine interactions, wind turbine technology, and atmospheric sciences. This educational brief summarizes the discussion during the forum and written comments from those who could not attend. Relevant literature was used to provide additional context when needed. Possible explanations regarding the direct cause of bat mortality at operating wind turbines are (1) blunt force trauma caused by turbine blades striking individual bats, often referred to as collisions, and (2) barotrauma, resulting from exposure to pressure changes located near the surface of moving wind turbine blades. While collision- related mortality is easily understood, the mechanism causing barotrauma is more complex. Fast-moving wind turbine blades create regions of high- and low-pressure variations along the blade surfaces. If bats fly within these regions, the rapid change in pressure may cause internal bleeding, damage to lungs or other organs, and damage to the inner ear. However, sufficient data demonstrating barotrauma as a common cause of bat mortality at wind turbines are lacking. Moreover, the pressure variation necessary to cause barotrauma in bats is so close to the surface of turbine blades that there is a higher probability of direct contact with the blades than of solely experiencing barotrauma. Regardless of the cause, bats are interacting with fast-moving wind turbine blades, and these interactions are resulting in fatalities. Resolving this issue will require a better understanding of bat behavior and cost-effective measures to reduce interactions between bats and wind turbines. This is the Spanish translation of NREL/FS-5000-84749, "The Likelihood of Bats Experiencing Barotrauma Near Moving Wind Turbine Blades."
In January 2022, the International Energy Agency Wind Task 34 - Working Together to Resolve the Environmental Effects of Wind Energy (WREN) - organized a forum to discuss aspects of raptor collision risk with wind turbines. The forum included experts in raptor biology and physiology, collision risk modeling, wind energy development, and atmospheric scientists from seven countries. They represented a range of international stakeholder groups including academia, government agencies, national laboratories, and wildlife consultants. This educational brief summarizes the discussion during the forum and written comments from those who could not attend. Relevant literature was used to provide additional context when needed. For several species of raptors, such as golden eagles (Aquila chrysaetos), griffon vultures (Gyps fulvus) and white-tailed eagles (Haliaatus albicilla), collision risk with wind turbines continues to be a concern among stakeholders. These concerns include the potential population-level impact related to collisions, compliance with regulatory mechanisms for protected species, and the ability to generate renewable energy. To make siting and operational decisions, stakeholders require some level of certainty of the risk associated with a proposed project. Understanding this risk, in part, requires species-specific data on raptors and how they perceive and interact with wind farms or individual wind turbines. Collision risk models (CRMs) are a tool, often used in environmental impact assessments, that can provide estimates of risk relative to specific turbines or an entire wind farm. However, questions associated with the uncertainty in CRM estimates remain. This is the Spanish translation of NREL/FS-5000-84747, "Collision Risk Modeling - A Tool for Assessing Risks to Raptors at Wind Energy Facilities."
In June 2023, the International Energy Agency Wind Task 34 - Working Together to Resolve the Environmental Effects of Wind Energy (WREN) - organized a forum to discuss monitoring and minimization strategies used to study raptor interactions with wind energy facilities. The forum included experts in raptor movement and behavior, minimization measures, technology validation, and wind energy development from four countries. The experts represented a range of international stakeholder groups including private industry, financial institutions, government agencies, nonprofit organizations, and wildlife consultants. This educational brief summarizes the discussion during the forum and written comments from additional participants who could not attend the live event. Relevant literature was used to provide additional context when needed.
We pursue the use of deep learning methods to improve state-of-the-art computations in theoretical high-energy physics. Planar N = 4 Super Yang-Mills theory is a close cousin to the theory that describes Higgs boson production at the Large Hadron Collider; its scattering amplitudes are large mathematical expressions containing integer coefficients. In this paper, we apply Transformers to predict these coefficients. The problem can be formulated in a language-like representation amenable to standard cross-entropy training objectives. We design two related experiments and show that the model achieves high accuracy (> 98%) on both tasks. Our work shows that Transformers can be applied successfully to problems in theoretical physics that require exact solutions.
Recent work under two previous phases of the NNSA NA-22 Source Physics Experiment (SPE) have shown that underground chemical high-explosive experiments can produce detectable surface changes that differ in spatial extent and vertical magnitude depending on the geologic media at the site (Schultz-Fellenz et al., 2018; 2020; Crawford et al., 2021). Neither of these two prior phases of SPE identified natural earthquake-related surface effects in the same region that occurred at a similar depth as the explosive experiments for direct comparison. While earthquakes can also produce surface changes that are detectable using remote sensing data analyses, it is expected that the pattern and spatial extent of surface changes would vary between earthquakes and explosions. However, no direct-observed surface-change signature comparison between earthquakes and explosions has ever been performed. The SPE Phase 3 Rock Valley/Direct Comparison (RV/DC) program presents a unique opportunity to investigate and characterize co-occurrence of both earthquakes and explosions. In this project, we worked to address five tasks in a workflow, as follows: 1. Identify and obtain existing high-resolution legacy satellite and aerial imagery as close in time before and after the 1993 Rock Valley earthquake sequence to temporally constrain the analyses. 2. Transform these pre-earthquake and post-earthquake datasets into digital elevation models (DEMs) using geospatial analysis software packages (e.g., ArcGIS, Agisoft Metashape, and Google Earth Engine). 3. Perform DEM differencing analyses to assess and quantify earthquake-related changes from the 1993 sequence, and develop map products that visualize these analyses. 4. Use the analyses from (3) to: (a) assess spatial distribution and magnitude of surface changes due to the 1993 Rock Valley earthquake sequence, and (b) determine parameters of forthcoming, planned explosion-related surface change data collection from sensors mounted on unmanned aerial vehicles (UAVs) (e.g., spatial extent of collection, design and density of survey control, sensors to deploy, forward speed and line spacing of UAV flight lines, flight altitude). 5. Develop a summary report on the analyses, including how the analyses define parameters and identify focus areas for any future surface change analytical field campaigns related to the explosive experiment. Analyzing these legacy data and identifying whether they can detect any surface changes related to the earthquake sequence facilitates opportunities for direct signature comparison of surface change from explosions at one location, which has never previously been performed. Comparing the surface change signatures from a co-located and depth-equivalent earthquake and an explosion could help to advance remote sensing event discrimination techniques. This report summarizes the work completed toward this ambitious goal.
The work was created to provide a tool for the purpose of improving the management of tasks associated with Agile projects. Agile projects are typically completed in an iterative manner with many short duration tasks being performed as part of iterations. These iterations are generally referred to as sprints. The objective of this work is to create a single tool that enables sprint teams to manage all of their tasks in multiple sprints and automatically produce all standard sprint performance charts with minimum effort. The format of the printed work is designed to mimic a standard Kanban board. The work is developed as a single Excel file with worksheets capable of managing up to five concurrent sprints and up to one hundred tasks. It also includes a summary worksheet providing performance information from all active sprints. There are many commercial project management systems typically designed with features desired by larger organizations with many resources managing multiple programs and projects. The audience for this work is the small organizations and Agile project teams desiring an inexpensive, simple, user-friendly, task management tool. This work uses standard readily available software, Excel, requiring minimum data entry and automatically creating summary charts and performance data. It is formatted to print out and resemble standard flip charts and provide the visuals associated with this type of work.
At the core of the predictive analytics applied to High Performance Computing (HPC), the most prominent tasks are the prediction of job runtimes and the prediction of job queue times, both of which have the potential for informing HPC users during their every-day decision making. Accurate runtime predictions can help users better choose so-called wallclock times at job submission, decreasing the odds of their jobs waiting in queues longer than necessary. The accurate and timely queue time predictions offered for the available partitions can inform the favorable selection of partitions for running jobs. This potential is well understood as we see in the abundance of research studies that propose solutions for these tasks, including the work published in the last several years. These tasks are seemingly receptive to the Machine Learning (ML) solutions, considering that there is no shortage of training data where HPC centers over time run millions and millions of jobs. However, we study the existing research literature, as well as look for examples in the toolchains supported on the exemplar HPC facilities, and, surprisingly, do not find any practical solutions that are ready to be adopted. We interpret this as a manifestation of the shortage of UX/UI efforts that support HPC analytics and also as a sign that the research has not come to the consensus on solving these tasks. In this study, we aim to shed new light on the long-running task of job queue time prediction by exploring the utility of runtime predictions in improving prediction accuracy and, actually, predicting these two metrics together, in tandem. In other words, we show how runtime predictions become valuable input in the queue time modeling. We challenge the existing approaches to feature engineering for the queue time prediction and describe promising results we obtained for a large dataset of HPC jobs from a supercomputer at the National Renewable Energy Laboratory.
This report is a resume of the activities in which I was involved during my internship at Fermilab Particle Physics and Accelerator Laboratory in Batavia. The experiment at whom my work is connected is the Mu2e experiment, in particular I worked in the Tracker’s group. The main task of my training program was to design a gas service connection that take place in a small volume located between the Tracker and the Calorimeter (two main elements that constitute the detector). During the initial phase I was lightly trained about the experiment and its general design, then I was supervised on my specific task. All the work aimed to reach a design solution that could meet all the BCs, in this view, a reasonable solution was prototyped and tested. The tests performed at Lab 3 were another fundamental aspect of my experience at Fermilab closing the circle of studying, modelling, machining and testing. I want to thank primarily Aseet Mukherjee and Mete Yucel that were my supervisors, they constantly spent their time attempting to create a favourable workplace, suggesting, listening and meeting all the requests in short time, working for them was absolutely like "working with them", without their help achieve the result would been greatly harder. Giuseppe Gallo deserve a special thanks too for revising mostly of my work and being always available for support. I had also the chance to share my work during the "Weekly Tracker’s Meetings" dealing with other bright engineers and physicists, between them I really appreciate George Ginter, Russel Rucinski, Yujing Sun and Waqar Ahmed. I want also to express my gratitude to Simone Donati, Giorgio Bellettini and Emanuela Barzi that make this unique experience possible organizing this program for decades. Thanks to my Mother, my Father, my Brother, my Nephew, my uncle Damiano and my friend Simona always near, never stopping to dream with me.
This report summarizes the research outcomes for the work performed in fiscal year 2020. The report is divided into two main sections addressing the radiation and electromagnetic sensing components. Both sections present an introduction with theory to lay the foundation of how the systems work. The report is the key deliverable for Task 1: Radiation and Electromagnetic Detector Development - Laboratory Work. This task involves all work related to initial laboratory research associated with the radiation and electromagnetic detector development to reach TRL-3.