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This dataset contains the files (scripts and data) required to produce the figures of the above reference.
Parameter estimation for complex physical problems often suffers from finding 'solutions' that are not physically realistic. The CheKiPEUQ software provides tools for finding physically realistic parameter estimates.and CheKiPEUQ provide tools for making graphs of the parameter positions within parameter space as well as plots of the final simulation results. The primary purpose of the CheKiPEUQ software is to enable more physically realistic parameter estimation from comparing simulations to experiments. Specifically, when prior knowledge is available about the region of parameter space which is physically realistic and when the level of uncertainty from experiment can be estimated. The software is intentionally general and can be used for almost any type of simulation. The software is made in a user friendly manner so that users can simply enter the required data and then run the program, following guidelines provided by the authors along with some trial and error. Examples are provided. Users do not need to understand the methodology that will be namedi in the following sentences. While CheKiPEUQ can be used for conventional parameter estimation and other uses, the primary uses for CheKiPEUQ are: 1) Bayesian Parameter Estimation, 2) Bayesian Model Discrimination, 3) Bayesian Design of Experiments. For more information see the project website, documentation, examples, and related publications.
To achieve the desired particle size of biomass feedstocks during preprocessing for trouble-free handling and conversion to produce biofuels and bioproducts, the raw materials must undergo a crucial milling process. The particle size of biomass plays a critical role in subsequent biofuel manufacturing, where a larger area-to-volume ratio facilitates efficient synthesis while balancing the impact of moisture on biomass storage. To optimize biofuel production efficiency and overcome these challenges, it is imperative to accurately predict the particle size distribution (PSD) of the biomass in the design of efficient preprocessing systems. The population balance model (PBM), upon empirical calibration and validation, can provide rapid prediction of post-milling PSD of granular biomass. However, PSD has limitations related to mass conservation and the absence of moisture considerations. To overcome these drawbacks, a deep learning model called the enhanced deep neural operator (DNO+) is implemented in the code. This model not only retains the capabilities of the PBM in handling complex mapping functions but also incorporates additional factors influencing the system. By considering various experimental conditions such as sieve size and moisture content, the trained DNO+ model can effectively predict the PSD after milling for any given feed PSD. To further reduce the reliance on experimental data, the PBM is integrated into the DNO+ model, resulting in a physics-informed DNO+ (PIDNO+). The PIDNO+ model addresses the non-conservation of quality exhibited by the PBM while inheriting the advantages of the DNO+ model in considering multiple influencing factors. Moreover, the PIDNO+ model significantly reduces the amount of data required for model training. Both deep learning models, i.e., DNO+ and PIDNO+, are excellent in predictive performance, offering swift and accurate machine learning-based predictions. The use of this code that contains these models will assist in guiding the proper milling equipment selection and operational conditions to achieve the desired biomass particle sizes, ensuring the efficiency of subsequent biofuel and bioproduct production processes.
Rotational symmetries are a fundamental and inherent property of many sources of data. Ensuring NNs embody rotation invariance will enhance their accuracy and radically lower their data requirements across a transformatively wide spectrum of applications.
Here, we study an element agglomeration coarsening strategy that requires data redistribution at coarse levels when the number of coarse elements becomes smaller than the number of MPI processes used on the finest level. The overall procedure generates coarse elements (general unstructured unions of fine grid elements) within the framework of element-based algebraic multigrid methods (or AMGe) studied previously. The AMGe-generated coarse spaces have the ability to exhibit approximation properties of the same order as the fine-level spaces since by construction they contain the piecewise polynomials of the same order as on the fine level. These approximation properties are key for the successful use of AMGe in multilevel solvers for nonlinear partial differential equations as well as for multilevel Monte Carlo (MLMC) simulations. The ability to coarsen without being constrained by the number of MPI processes, as described in the present paper, allows to improve the scalability of these solvers as well as the overall MLMC method. The paper illustrates this latter fact with detailed scalability study of MLMC simulations applied to model Darcy equations with a stochastic log-normal permeability field.
The diversity of living cells, in both size and internal complexity, calls for imaging methods with adaptable spatial resolution. Soft x-ray tomography (SXT) is a three-dimensional imaging technique ideally suited to visualizing and quantifying the internal organization of single cells of varying sizes in a near-native state. The achievable resolution of the soft x-ray microscope is largely determined by the objective lens, but switching between objectives is extremely time-consuming and typically undertaken only during microscope maintenance procedures. Since the resolution of the optic is inversely proportional to the depth of focus, an optic capable of imaging the thickest cells is routinely selected. This unnecessarily limits the achievable resolution in smaller cells and eliminates the ability to obtain high-resolution images of regions of interest in larger cells. Here, we describe developments to overcome this shortfall and allow selection of microscope optics best suited to the specimen characteristics and data requirements. We demonstrate that switchable objective capability advances the flexibility of SXT to enable imaging cells ranging in size from bacteria to yeast and mammalian cells without physically modifying the microscope, and we demonstrate the use of this technology to image the same specimen with both optics.
This document provides general instructions on performing a walk-through water survey to collect the required data for a comprehensive water evaluation and entering data into the online Water Balance Tool.
Thermal properties of molten salts, both with and without fissile and fertile material, are fundamental to modeling and simulation that supports design, construction, operation, and accident progression evaluation of molten salt reactors. This roadmap provides guidance to the research community on the types and amounts of required data for base, or carrier, salt systems, recommends simulated burnup fuel salt compositions to evaluate, and provides a technical foundation for a focused salt property evaluation matrix. The property values generated from both computation and experimental methods are used to develop models that constitute the Molten Salt Thermal Properties Database (MSTDB). While the building blocks of MSTDB are fundamental in nature, its data facilitates constructing engineering models that allow stakeholders to investigate the behavior of specific compositions of interest and supports broader modeling and simulation of MSRs through coupling to multi-physics, multi-scale mass accountancy tools.
The Aeroacoustic Assessment project aims to quantify changes in aeroacoustic noise generation by a utility scale wind turbine operating under imposed yaw offsets common for wake steering and wind plant control strategies. Active plant control utilizing wake deflection control strategies have been shown experimentally and computationally to reliably produce 1-2% of additional annual energy production (AEP) without significant changes in turbine structural loads, the potential impact on aeroacoustic emissions has yet to be quantified or completely understood. Yawed operation of a wind turbine changes the three-dimensional aerodynamic interaction between the rotor blades and the incoming atmospheric flow, leading to changes in noise generation. This work quantifies the extent to which active control induces additional aeroacoustic emissions from additional separation and other flow interaction dynamic effects. Given public concerns about wind turbine noise and the need for observational data required for regulators to establish noise restrictions, we must understand potential acoustic emissions resulting from active control prior to commercial deployment and the development of practical noise reduction methods and technologies. The work outlined in this report details the new aeroacoustic measurement capability developed at NREL and its application to making full-field observations of noise from a utility-scale wind turbine. Preliminary noise modeling with the aeroacoustics module in OpenFAST helped to establish the design of the experiment, specifically targeting the noise observed at observers distributed around the DOE-owned GE 1.5 MW wind turbine. Low-frequency and infrasound measurement capability implemented in this project establish an operational baseline for the wind turbine, setting the stage for future control, rotor blade, and turbine design research.
Over 85,000 non-powered dams (NPDs) exist in the United States that provide services such as flood control, navigation, and water storage for irrigation/domestic water use (USACE 2019). The existing infrastructure of NPDs poses an opportunity for improving economic and environmental performance and generating electricity, as well as a challenge for maintaining aging structures and remediating fragmented river systems. NPD stakeholders interested in the rehabilitation, retrofit, or removal of NPDs must have the relevant information about the population of dams to support decision-making. Each NPD has unique characteristics describing its design, operation, environmental impacts, social impacts, and economic potential. The large number of dams, the diversity of interests related to dams, the variety of dam characteristics, and the types of data required to describe dams all pose major challenges to an analysis of the entire dam population.
The mission of the Nuclear Criticality Safety Program is to provide sustainable expert leadership, direction and the technical infrastructure necessary to develop, maintain, and disseminate the essential technical tools, training, and data required to support safe, efficient fissionable material operations within DOE; the vision of the program is a Continually improving, adaptable, and transparent program that communicates and collaborates globally to incorporate technology, practices, and programs to be responsive to the essential technical needs of those responsible for developing, implementing, and maintaining nuclear criticality safety. Training and education topics for the program include a 1-week Manager/Criticality Safety Officer Course and a 2-week NCS Engineer Hands-on Training Course.
The mission of the NCSP is to provide sustainable expert leadership, direction, and the technical infrastructure necessary to develop, maintain, and disseminate the essential technical tools, training, and data required to support safe, efficient fissionable material operations within the Department of Energy. The vision of the NCSP is a continually improving, adaptable, and transparent program that communicates and collaborates globally to incorporate technology, practices, and programs to be responsive to the essential technical needs of those responsible for developing, implementing, and maintaining nuclear criticality safety.
The Source Physics Experiment (SPE) series is a long-term NNSA research and development effort designed to improve U.S. arms control and nuclear nonproliferation verification and monitoring capabilities. The findings from the SPE will advance the United States’ nuclear explosion monitoring capabilities, particularly with respect to detection, discrimination and determination of yields associated with small nuclear explosions that can be lost amid the noisy seismo-acoustic background from other sources. The data generated from the SPE, a series of well-designed and recorded chemical explosions, will contribute to the development and validation of first-principles explosive source generated seismo-acoustic modeling codes. These codes will then facilitate the update of semi-empirical methods, currently based on historic test site data, such that key explosion observables can be reproduced, thus improving confidence in nuclear test monitoring in new areas and/or under novel emplacement conditions. The overall SPE project is comprised of both the development of the new explosion simulation codes and the chemical explosion test series. The chemical explosion test series will generate the empirical data required to both develop and validate the new simulation codes.
The mission of the NCSP is to provide sustainable expert leadership, direction and the technical infrastructure necessary to develop, maintain and disseminate the essential technical tools, training and data required to support safe, efficient fissionable material operations within the Department of Energy. The vision of the NCSP is to create a continually improving, adaptable and transparent program that communicates and collaborates globally to incorporate technology, practices and programs to be responsive to the essential technical needs of those responsible for developing, implementing and maintaining nuclear criticality safety.
Algorithms for machine learning and data analysis for the 3013 Surveillance Program are being developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). To detect the presence of corrosion and cracking, data is collected from large binary files generated by a Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS). Software is being developed to use the physical attributes in the data files (e.g., height, color, and grayscale values; all as functions of a location in a plane projection) to detect the presence of surface corrosion and cracking. A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data for training ML algorithms, flag significant features, execute Machine Learning (ML) algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Surface defects can be called out by setting user-specified thresholds, feature based analysis or machine learning algorithms. Enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the very large volume of data required to train ML algorithms.
The PNNL Annual Site Environmental Report is prepared to meet the requirements of DOE Order 231.1B, Environmental, Safety and Health Reporting, and DOE Order 458.1, Radiation Protection of the Public and the Environment, assuring that the public is informed of any PNNL Richland Campus or PNNL Sequim Campus event that could adversely affect the health and safety of the public, site staff, or the environment. The report provides a synopsis of ongoing environmental management performance and compliance activities for operations that occur on the PNNL Richland Campus in Richland, Washington, and at the PNNL Sequim Campus near Sequim, Washington. It describes the location of and background for each facility; addresses compliance with applicable DOE, federal, state, and local regulations and site-specific permits; documents environmental monitoring efforts and their status; presents potential radiation doses to staff and the public in the surrounding areas; and describes DOE-required data quality assurance methods used for data verification.
There is a strong need to develop the knowledge base that will enable the rational design of improved crop species for sustainable agriculture and biomass production. To attain this goal, researchers must learn how plant cells integrate metabolism and cellular dynamics during growth and in response to environmental challenges. Meeting these goals require data sharing and the development and training of faculty, post-doctoral fellows, graduate and undergraduate students in state-of-the-art quantitative integrative cell biology methods. The inaugural Plant Cell Biology International (PCBI) meeting capitalized on the interaction between two well-established plant cell biology communities across the Atlantic, the Midwest Plant Cell Dynamics (PCD) and the European Network for Plant Endomembrane Research (ENPER) and provided a framework to meet these goals. The conference took place in person successfully during August 1-5th 2022 in Crete, Greece. The conference offered opportunities for undergraduate and graduate students, postdocs, young investigators across the globe to present their results and interact across different disciplines. The conference provided an open and inclusive forum for oral presentations that allowed all labs to share research results. It was attended over 80 % by students, postdocs and young investigators. Workshops on best practices for image acquisition and processing pipelines, publication guidelines by scientific journal editors, professional development and well-being benefited the participants at large. Overall, this was the inaugural joint Plant Cell Biology International conference that for the first time officially brought together plant cell biologists to discuss opportunities and challenges in the field to gain a mechanistic understanding of complex biological processes and advance sustainable agriculture. Due to its success, it was rescheduled for 2025 in Crete, to continue strengthening the plant cell biology community and exchanging knowledge and expertise across the globe.
Pacific Northwest National Laboratory (PNNL), one of the U.S. Department of Energy (DOE) Office of Science’s 10 national laboratories, provides innovative science and technology development in the areas of energy and the environment, fundamental and computational science, and national security. There are three DOE offices within the Richland area. Two are responsible for the Hanford Site, whereas the Pacific Northwest Site Office (PNSO) oversees PNNL. PNNL prepares this Annual Site Environmental Report to meet the requirements of DOE Order 231.1B, Environmental, Safety and Health Reporting, and DOE Order 458.1, Radiation Protection of the Public and the Environment, assuring that the public is informed of any PNNL-Richland Campus or PNNL-Sequim Campus event that could adversely affect the health and safety of the public, site staff, or the environment. The report provides a synopsis of ongoing environmental management performance and compliance activities for operations that occur on the PNNL-Richland Campus in Richland, Washington, and at the PNNL-Sequim Campus near Sequim, Washington. It describes the location of and background for each facility; addresses compliance with applicable DOE, federal, state, and local regulations, and site-specific permits; documents environmental monitoring efforts and their status; presents potential radiation doses to staff and the public in the surrounding areas; and describes DOE-required data quality assurance methods used for data verification. The ASER summarizes site compliance with federal, state, and local environmental laws, regulations, policies, directives, permits, and Orders, and provides environmental management performance benchmarks and their status to the public, regulatory agencies, community officials, Native American tribes, and public interest groups.