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AEflow (Autoencoder fluid flow compression network) [SWR-22-29]
As the size of turbulent flow simulations continues to grow, in situ data compression is becoming increasingly important for visualization, analysis, and restart checkpointing. For these applications, single-pass compression techniques with low computational and communication overhead are crucial. In this paper we present a deep-learning approach to in situ compression using an autoencoder architecture that is customized for three-dimensional turbulent flows and is well suited for contemporary heterogeneous computing resources. The autoencoder is compared against a recently introduced randomized single-pass singular value decomposition (SVD) for three different canonical turbulent flows: decaying homogeneous isotropic turbulence, a Taylor-Green vortex, and turbulent channel flow. Our proposed fully convolutional autoencoder architecture compresses turbulent flow snapshots by a factor of 64 with a single pass, allows for arbitrarily sized input fields, is cheaper to compute than the randomized single-pass SVD for typical simulation sizes, performs well on unseen flow configurations, and has been made publicly available. The results reported here show that the autoencoder dramatically outperforms a randomized single-pass SVD with similar compression ratio and yields comparable performance to a higher-rank decomposition with an order of magnitude less compression in regard to preserving a number of important statistical quantities such as turbulent kinetic energy, enstrophy, and Reynolds stresses.
Fluid Flow Observations of The Spray Near-Field Using High-Speed X-Ray Imaging
Sprays are important to may industrial processes, but the near-field region where spray formation takes place is difficult to visualize and characterize through visible light imaging because this region is typically optically dense. X-ray imaging is one technique that can penetrate this optically dense region and provide unique observations of the spray formation process. This study uses high-speed white beam X-ray imaging conducted at the Advanced Photon Source to capture spray formation dynamics from a canonical airblast atomizer. In this work, a range of momentum flux ratios are visualized by varying the gas flow rate while the liquid flow rate is held constant. The use of X-rays show overlapping flow structures including bags, ligaments, mushrooms, and webs. Air bubbles are captured within the liquid regions, and a very dynamic crown forms at the liquid needle exit at higher momentum flux ratios. Finally, at the highest momentum flux ratios, air bubbles and liquid droplets are observed migrating upstream in the center of the crown, and this motion is attributed to a toroidal recirculation cell at the nozzle exit.
Dataset documenting reaction-induced changes to five fractured foamed wellbore cement cores during CO2 fluid flow
This dataset consists of reconstructed CT scans for four fractured foamed cement cores, generated in the lab using Portland Class H cement, and associated processed data. The cements were exposed to reactive brines, and CT scans were take at different time points through the experiment. The CT imaging allows for the tracking of dissolution-induced changes to fracture morphology, as well as alteration and dissolution in the cement matrix.
International Collaborations on Fluid Flows in Fractured Crystalline Rocks: FY14 Progress Report.
Abstract not provided.
Multiscale Simulation Framework for Coupled Fluid Flow and Mechanical Deformation
A multiscale linear-solver framework for the pressure equation associated with flow in highly heterogeneous porous formations was developed. The multiscale based approach is cast in a general algebraic form, which facilitates integration of the new scalable linear solver in existing flow simulators. The Algebraic Multiscale Solver (AMS) is employed as a preconditioner within a multi-stage strategy. The formulations investigated include the standard MultiScale Finite-Element (MSFE) andMultiScale Finite-Volume (MSFV) methods. The local-stage solvers include incomplete factorization and the so-called Correction Functions (CF) associated with the MSFV approach. Extensive testing of AMS, as an iterative linear solver, indicate excellent convergence rates and computational scalability. AMS compares favorably with advanced Algebraic MultiGrid (AMG) solvers for highly detailed three-dimensional heterogeneous models. Moreover, AMS is expected to be especially beneficial in solving time-dependent problems of coupled multiphase flow and transport in large-scale subsurface formations.
International Collaborations on Fluid Flows in Fractured Crystalline Rocks: FY15 Progress Report
Active participation in international R&D is crucial for achieving the UFD long-term goals of conducting “experiments to fill data needs and confirm advanced modeling approaches” (by 2015) and of having a “robust modeling and experimental basis for evaluation of multiple disposal system options” (by 2020). DOE’s Office of Nuclear Energy (NE) and its Office of Used Fuel Disposition Research and Development (UFD) have developed a strategic plan to advance cooperation with international partners. The international collaboration on the evaluation of crystalline disposal media at Sandia National Laboratories (SNL) in FY15 was focused on the following three activities: (1) thermal-hydrologic-mechanical-chemical modeling single fracture evolution; (2) simulations of flow and transport in Bedrichov Tunnel, Czech Republic, and (3) streaming potential testing at Korean Atomic Energy Research Institute (KAERI) Underground Research Tunnel (KURT). The first two activities are part of the Development of Coupled Models and their Validation against Experiments (DECOVALEX) project.
Multiscale Simulation Framework for Coupled Fluid Flow and Mechanical Deformation
Our work in this project is aimed at making fundamental advances in multiscale methods for flow and transport in highly heterogeneous porous media. The main thrust of this research is to develop a systematic multiscale analysis and efficient coarse-scale models that can capture global effects and extend existing multiscale approaches to problems with additional physics and uncertainties. A key emphasis is on problems without an apparent scale separation. Multiscale solution methods are currently under active investigation for the simulation of subsurface flow in heterogeneous formations. These procedures capture the effects of fine-scale permeability variations through the calculation of specialized coarse-scale basis functions. Most of the multiscale techniques presented to date employ localization approximations in the calculation of these basis functions. For some highly correlated (e.g., channelized) formations, however, global effects are important and these may need to be incorporated into the multiscale basis functions. Other challenging issues facing multiscale simulations are the extension of existing multiscale techniques to problems with additional physics, such as compressibility, capillary effects, etc. In our project, we explore the improvement of multiscale methods through the incorporation of additional (single-phase flow) information and the development of a general multiscale framework for flows in the presence of uncertainties, compressible flow and heterogeneous transport, and geomechanics. We have considered (1) adaptive local-global multiscale methods, (2) multiscale methods for the transport equation, (3) operator-based multiscale methods and solvers, (4) multiscale methods in the presence of uncertainties and applications, (5) multiscale finite element methods for high contrast porous media and their generalizations, and (6) multiscale methods for geomechanics.
Fluid flow in disrupted porous media: Volcanological and radiological waste applications
This presentation provides Technical defense supporting documents, with the following slides: (1) Introduction (2) Long boundary drainage as a source for lahars (submitted for review) (3) Development and implementation of a porosity-dependent capillary suction function in FEHM (4) Modeling in support of field testing high-level radioactive waste in salt (5) Conclusions.
Fluid Flow in Disrupted Porous Media: Volcanological and Radiological Waste Applications [Slides]
Abstract not provided.
Fluid flow in low permeability media: Nanoconfinement and nonlinear dynamics.
Abstract not provided.
Preparing an Incompressible-Flow Fluid Dynamics Code for Exascale-Class Wind Energy Simulations.
Abstract not provided.
Thermal expansion, fluid flow, and thermal shock of cement and a cement/steel interface at elevated pressure and temperature .
Abstract not provided.
Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study
Presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.