An autotuning approach to DOE’s earth system model
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Shand, Lyndsay↗
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Results for “mooring model”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Improvements to Particle Merge Algorithms for Sandia National Laboratories Plasma Physics Modeling Code, EMPIRE
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Watrous, Jack↗
An Open-Source Frequency-Domain Model for Floating Wind Turbine Design Optimization
A new frequency-domain dynamics model has been developed that uses open-source components to efficiently represent a complete floating wind turbine system. The model, called RAFT (Response Amplitudes of Floating Turbines), incorporates quasi-static mooring reactions, strip-theory and potential-flow hydrodynamics, blade-element-momentum aerodynamics, and linear turbine control. The formulation is compatible with a wide variety of support structure configurations and no manual or time-domain preprocessing steps are required, making RAFT very practical in design and optimization workflows. The model is applied to three reference floating wind turbine designs and its predictions are compared with results from time-domain OpenFAST simulations. There is good agreement in mean offsets as well the statistics and spectra of the dynamic response, verifying RAFT’s general suitability for floating wind analysis. Follow-on work will include verification of potential-flow and turbine-control features and application to optimization problems.
17 WIND ENERGY↗
Nature-GL: A Revolutionary Learning Paradigm Unleashing Nature’s Power in Real-World Spatial-Temporal Graph Learning
Spatial-Temporal Graph Learning (ST-GL) is a prominent research area due to its unique capability to effectively learn real-world graphs. Applications of ST-GL pose stringent and various demands on not only real-time inference with low energy cost and high ac- curacy but also fast training. Unfortunately, as Moore’s Law approaches its limits and ST-GL model complexity drastically grows, the gap between digital hardware’s computational power and ST- GL application demands is widening. In response, this paper introduces Nature-GL, a nature-powered graph learning paradigm that exploits the principle of entropy increase to advance graph learning. In particular, Nature-GL transforms both the training and inference of real-valued ST-GL into electron-speed natural anneal- ing processes of a parameterized dynamical system that represents the target graphs. Experimental results across four real-world ap- plications with six datasets demonstrate that Nature-GL achieves orders-of-magnitude speedups in both training and inference, delivering higher accuracy compared to Graph Neural Networks.
Liu, Chuan [University of Rochester]↗
Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model
In the evolving landscape of high-performance computing, especially to fight the end of Moore’s Law and Dennard’s Scaling, the ability to shift between compute-bound and memory-bound states is critical for enhancing adaptability and flexibility to diverse system and domain-specific architectures. Such capability is vital for optimizing performance across distinguished hardware configurations, such as accelerators, memory hierarchies, and cache systems. Despite that ad hoc optimization techniques, such as compressed/approximate computation, have been enabled for compute-/data-intensive computing for improved performance in distinct hardware settings, there lacks an understanding of 1) the rational behind performance improvement; 2) capability of different optimizations; 3) what optimization to respond to specific computational and memory demands. This work proposes a compression-enabled roofline model to facilitate this adaptability with data compression techniques to balance and transform between computational and memory demands. This model enables applications to adjust in response to the specific strengths and limitations of the underlying hardware and system to optimize resource utilization. The effectiveness of this approach is demonstrated with matrix multiplication kernels on different input sizes, with turning on/off various compression techniques, including 1) low-precision floating point; 2) sparse matrix formulation; and 3) compressed arrays with ZFP. By reducing memory transfer volumes and cache misses and increasing data locality and computational intensity through compression, the specific roofline model can transform between compute and memory bounds to align more efficiently with system capabilities. This advancement not only improves overall performance but also maximizes adaptability in diverse computing environments.
Naraparaju, Ramasoumya [University of Washington]↗
Multispectral UAV imagery of experimental freshwater wetlands under 5 ppt saltwater intrusion, Louisiana, 2023 and 2024
Multispectral imagery was collected using an unmanned aerial vehicle (UAV) to evaluate how freshwater vegetation responds to short-term simulated saltwater intrusion events. The purpose of this data collection was to understand how plant health changes in response to acute salinity exposure, which is increasingly relevant in coastal wetland ecosystems facing sea level rise and storm surge events, such as in coastal Louisiana. Three experimental saltwater intrusions were conducted at a salinity of approximately 5 parts per thousand (ppt) for durations of 6-days, 10-days, and 17-days. UAV flights occurred both before and after each treatment. The resulting imagery was processed using Pix4DMapper software to georeference the images and generate orthomosaics. The multispectral sensor used in this study captures reflectance in five bands: blue, green, red, red-edge, and near-infrared. The uploaded data consist of georeferenced .tif orthomosaics for each spectral band, which are compatible with GIS software for vegetation analysis. This imagery can be utilized in investigations into vegetation stress, remote sensing of freshwater wetland ecosystems, and modeling of plant response to environmental changes.
EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS↗
BETO 2021 Peer Review - Catalytic Upgrading of Biochemical Intermediates (CUBI) 2.3.1.101-104
The Catalytic Upgrading of Biochemical Intermediates (CUBI) project is a multi-lab effort within ChemCatBio (CCB) that is focused on improving the catalytic upgrading of intermediates from biochemical deconstruction and/or biological conversion (i.e. fermentation) to fuels and chemical co-products. This is accomplished by developing catalysts with improved performance and durability, mitigating the impact of biogenically-derived inhibitors (including water), and evaluating intensified processes to reduce separations requirements and improve carbon utilization. Specific catalytic upgrading routes being developed are: 1) fermentation-derived 2,3-butanediol (BDO); 2) fermentation-derived carboxylic acids; and 3) lignocellulosic sugar-derived furfurals. These routes represent key technology pathways being investigated within BETO's biochemical conversion portfolio. By utilizing experimental and characterization capabilities and modeling tools across the 4 CUBI labs and collaborations with CCB Enabling Projects, the CUBI project is advancing catalytic upgrading process performance and robustness. Its impact is already well-established by several high-impact journal articles and patent publications, along with industrial engagement in related, competitively-awarded collaborations. The CUBI project will result in >25% MFSP cost reduction in the catalytic upgrading section of integrated biochemical conversion routes, as quantified by ongoing TEA modeling.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Multibody for Everybody (M4E): A Symbolic Dynamics Modeling Tool with Applications in Simulation, Control, and Optimization
Developing the analytical model of a multibody system is often the initial step in control and optimization. The analytical model (equations of motion) describes a system’s time evolution under specified forcing conditions. Although developing these equations is easy for simple systems, this process becomes more complex for systems composed of multiple bodies. Deriving equations of motion for complex multibody systems requires specialized expertise in multibody dynamics, is time-consuming, and is susceptible to error. To address this issue, this paper presents an open-source, easy-to-use, systematic framework to derive symbolic equations of motion in both Python and MATLAB using the joint coordinate formulation. This formulation results in a set of ordinary differential equations that use the minimum set of coordinates needed to model a system. The symbolic representation provides better insight into the influence of design parameters on system performance, facilitates sensitivity analysis and parameter studies, and supports direct implementation of control and optimization routines. The tool enables numerical simulation for specified parameter sets, is modular for straightforward integration with other tools and libraries, and allows incorporation of hydrodynamics, mooring, and other external forces. The result is a reproducible, extensible pipeline for modeling, simulation, and design of complex multibody systems. The proposed tool is versatile and can be applied to domains such as robotics, control, and design. In addition, we integrated external libraries that provide capabilities for modeling offshore systems such as underwater robots and marine energy converters.
16 TIDAL AND WAVE POWER↗
2024 Incubator Final Report: Active Hybrid Mooring
This Incubator project has defined and developed a representation of an active hybrid mooring (AHM) system in Simulink to assess whether the speed and accuracy of AHM is sufficient to warrant further investigation in a larger project. We first built a representative mooring system for the VolturnUS-S 15MW floating offshore wind turbine (FOWT) in 1,000-meter water depth to provide baseline loads and displacements needed in the AHM system, with the results showing off-the-shelf actuators would be practical. We then defined model configurations for the numerical and physical substructures, using standalone MoorDyn v2 with a novel input strategy and chained hydraulic actuators represented in Simulink Simscape, respectively. The remainder of the project has focused on verifying the existing models for speed and accuracy against OpenFAST simulations, increasing their complexity to enable six degree of freedom (6DOF) operation, and connecting the numerical and physical substructures into a single holistic model. The verification phase of the project has shown promising results, though further refinement is needed to resolve accuracy discrepancies and for robust performance across a wider range of metocean conditions. The numerical model we developed is computationally stable and operates faster than real time using a time step of 0.01 seconds or shorter, indicating the model is likely fast enough for our AHM design to operate successfully. Additionally, the Simscape model of a hydraulic actuator operates with less than 0.3% relative error when actuating mooring loads from an OpenFAST simulation of the VolturnUS-S 1,000-meter mooring system. The aforementined refinements will be performed in the next year as a Continuing Incubator, with key remaining tasks include programming the predictor-corrector loop to minimize movement error, testing against more complex nonlinear wave cases, and expanding to enable loads from connected shared mooring lines.
17 WIND ENERGY↗
Scalable Geometric Modeler for Overlap Detection and Resolution (ASC IC L2 Milestone 7181 FY2020 Final Review)
The final review for the FY20 Advanced Simulation and Computing (ASC) Integrated Codes (IC) L2 Milestone #7181 was conducted on August 31, 2020 at Sandia National Laboratories in Albuquerque, New Mexico. The review panel unanimously agreed that the milestone has been successfully completed. Roshan Quadros (1543) led the milestone team and various members from the team presented the results. The review panel was comprised of staff from Sandia National Laboratories Albuquerque and California that are involved with computational engineering modeling and analysis. The panel consisted of experts in the fields of solid modeling, discretization, meshing, simulation workflows, and computational analysis including personnel Brett Clark (1543, Chair); Jay Foulk (8363); Jackie Moore (1553); Ron Kensek (1341); Ed Hoffman (8753); Dan Ibanez (1443). The presentation documented the technical approach of the team and summarized the results with sufficient detail to demonstrate both the value and the completion of the milestone. A separate SAND report was also generated with more detail to supplement the presentation. The purpose of the milestone was to advance capabilities for automatically finding, displaying, and resolving geometric overlaps in CAD models.
97 MATHEMATICS AND COMPUTING↗
Dynamics of fractionalized mean-field theories: Consequences for Kitaev materials
There have been substantial recent efforts, both experimentally and theoretically, to find a material realization of the Kitaev spin liquid—the ground state of the exactly solvable Kitaev model on the honeycomb lattice. Candidate materials are now plentiful, but the presence of non-Kitaev terms makes comparison between theory and experiment challenging. Here, we rederive time-dependent Majorana mean-field theory and extend it to include quantum phase information, allowing the direct computation of the experimentally relevant dynamical spin-spin correlator, which reproduces exact results for the unperturbed model. In contrast to previous work, we find that small perturbations do not substantially alter the exact result, implying that α-RuCl 3 is perhaps farther from the Kitaev phase than originally thought. Our approach generalizes to any correlator and to any model where Majorana mean-field theory is a valid starting point.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Exploring Dependencies of Work Function on Topography and the Application to Micron-scale Field Emission Model for PIC-DSMC Simulations .
Abstract not provided.
Moore, Christopher↗
Expanding The Envelope For Spall Modeling Using Porosity Mechanics Incorporating Microinertia
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Materials science, Physics - Condensed matter phys↗
CalWave - Open Water Demonstration - LCOE Content Model
Data for the CalWave - Open Water Demonstration, a submerged pressure differential Wave Energy Converter (WEC) Device. Device is moored to the seabed, and the motion of the waves causes the sea level to rise and fall above the device, inducing a pressure differential in the device. The alternating pressure pumps fluid through a system to generate electricity, which is transmitted to shore via bidirectional cables. Documentation and data here includes: Levelized Cost of Energy (LCOE) Content Model
16 TIDAL AND WAVE POWER↗
Information theory optimization of signals from small-angle scattering measurements
Small-angle X-ray scattering (SAXS) of particles in solution informs on the conformational states and assemblies of biological macromolecules (bioSAXS) outside of cryo- and solid-state conditions. In bioSAXS, the SAXS measurement under dilute conditions is resolution limited, and through an inverse Fourier transform, the measured SAXS intensities directly relate to the physical space occupied by the particles via the P (r)-distribution. Yet, this inverse transform of SAXS data has been historically cast as an ill-posed, ill-conditioned problem requiring an indirect approach. Here, we show that through the applications of matrix and information theories, the inverse transform of SAXS intensity data is a well-conditioned problem. The so-called ill-conditioning of the inverse problem is directly related to the Shannon number. By exploiting the oversampling enabled by modern detectors, a direct inverse Fourier transform of the SAXS data is possible, provided the recovered information does not exceed the Shannon number. The Shannon limit corresponds to the maximum number of significant singular values that can be recovered in a SAXS experiment, suggesting this relationship is a fundamental property of band-limited inverse integral transform problems. This correspondence reduces the complexity of the inverse problem to the Shannon limit and maximum dimension. We propose a hybrid scoring function using an information theory framework that assesses both the quality of the model-data fit as well as the quality of the recovered P (r)-distribution. The hybrid score utilizes the Akaike information criteria and Durbin-Watson statistic that considers parameter-model complexity, i.e., degrees of freedom, and the randomness of the model-data residuals. The described tests and findings extend the boundaries for bioSAXS by completing the information theory formalism initiated by Peter B. Moore to enable a quantitative measure of resolution in SAXS, robustly determine maximum dimension, and more precisely define the best parameter model appropriately representing the observed scattering data.
Rambo, Robert P. [Science and Technology Facilitie↗
Graph Neural Networks for Surrogate Modeling of Offshore Floating Platforms
Floating offshore wind turbines (FOWTs) present an significant opportunity to increase renewable energy generation. However, significant challenges remain before FOWTs can be widely commercialized and deployed. In particular, hydrodynamic loading on the platforms can stress the overall structure, damage the mooring systems, and impact power generation. Studying these loads is difficult and often relies on computationally expensive models or experiments. In this work, we explore the use of graph neural networks (GNNs) to construct flexible, data-driven surrogates for hydrodynamic loads on platforms. We leverage the natural graph-like structure of offshore wind platform designs to enable the GNN model to learn to approximate the loads for different wave conditions and structural designs. We demonstrate potential uses for the surrogate by performing parameter sweeps and ridge analysis on the trained model to identify the impacts of different wave and structural features on the loads.
floating offshore wind turbines↗
CalWave - Open Water Demonstration - System and Component Overview Content Models and Drawings
Data for the CalWave - Open Water Demonstration, a submerged pressure differential Wave Energy Converter (WEC) Device. Device is moored to the seabed, and the motion of the waves causes the sea level to rise and fall above the device, inducing a pressure differential in the device. The alternating pressure pumps fluid through a system to generate electricity, which is transmitted to shore via bidirectional cables. Documentation and data here includes: System Overview Content Model and Drawings as well as Component Overview Content Model and Drawings.
16 TIDAL AND WAVE POWER↗
Utah FORGE 5-2565: Evolution of Permeability and Strength Recovery of Shear Fractures Under Hydrothermal Conditions - 2024 Annual Workshop Presentation
This is a presentation on the Evolution of Permeability and Strength Recovery of Shear Fractures Under Hydrothermal Conditions by United States Geological Survey, presented by Tamara Jeppson. This video slide presentation, by the USGS, discusses the determination of how thermal, hydrologic, mechanical, and chemical (THMC) processes affect the sustainability of fracture networks in geothermal reservoirs. This includes (1) the qualification of rates of change of fracture properties, (2) the parameterization of modes of reaction, (3) the development of micromechanical and empirical fracture models, and (4) extended THMC models for laboratory- and reservoir-scale models. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.
15 GEOTHERMAL ENERGY↗