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    Item type:Research Data, Access status: with files ,
    Data-driven Transport Modeling for Tracer Injection and Mixing Time Screening in Stirred Tank Reactors
    (2026-07-31)
    Asif, Shaik
    This repository contains a **complete research data package** for the first experimental validation of transport-based recurrence CFD (rCFD) methodology applied to liquid mixing in stirred tank reactors. The dataset includes M-Star lattice Boltzmann simulations providing velocity field databases, rCFD implementation with calibrated diffusion parameters, experimental conductivity probe measurements, systematic parametric injection location optimization (90 cases), post-processing workflows, and comprehensive validation results demonstrating 3,400× computational speedup versus ANSYS Fluent while achieving closer agreement with experimental data than conventional high-fidelity CFD methods. **Research Context**: This dataset addresses a critical computational bottleneck in stirred tank reactor (STR) design and optimization. Conventional CFD methods (ANSYS Fluent, M-Star) require days to weeks to simulate mere seconds of mixing time, rendering parametric studies and real-time process optimization impractical for industrial applications. Recurrence CFD (rCFD) exploits pseudo-periodic flow patterns in turbulent stirred tank flows to achieve orders-of-magnitude computational acceleration while preserving predictive accuracy. **Key Innovation**: First rigorous experimental validation of rCFD for liquid mixing, demonstrating that calibrated rCFD (operating on coarse 4.7M lattice point M-Star flow fields) predicts mixing time closer to experimental measurements (τ₉₅=24.5±1.0 s vs 24.9±2.7 s experimental) than high-resolution M-Star (29.1±1.4 s, 90M nodes) or ANSYS Fluent (29.2±2.9 s, 3.4M cells). The resulting computational efficiency enables a 90-case parametric design space exploration in 2.25 hours—a task requiring 320 days with Fluent.
    Data Type: Dataset
      53  49
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    MRI-Validated CFD-DEM Simulation of Bubbling Fluidized Beds: Drag Model Selection and Computational Speedup via Recurrence-Based CFD
    (2025-10-06)
    Asif, Shaik
    MRI-Validated CFD-DEM and rCFD Dataset for Gas-Solid Fluidized Beds This comprehensive research dataset provides complete computational and experimental data for validating coupled CFD-DEM (Computational Fluid Dynamics - Discrete Element Method) simulations and recurrence-based CFD (rCFD) predictions of gas-solid fluidized beds. The repository contains CFD-DEM simulation cases for four drag models (Beetstra, DiFelice, Gidaspow, Koch-Hill), rCFD time-extrapolation implementations achieving 100-1000× computational speedup, MRI experimental measurements of bubble dynamics in poppy seed fluidization, automated post-processing workflows, and quantitative validation results. This dataset enables researchers to reproduce published results, validate their own CFD-DEM models against MRI experimental data, develop and benchmark rCFD methodologies for real-time multiphase flow predictions, optimize drag model selection for fluidized bed applications, and apply advanced image processing and bubble detection algorithms. The data follows FAIR principles with open formats (CSV, OpenFOAM, Python scripts), comprehensive documentation in eight README files, complete provenance from CAD geometry to validation results, and example workflows for researchers, students, and engineers working with fluidized bed systems, particle technology, and multiphase flow simulations.
    Data Type: Experimental Data ; Data Type: Simulation Data ; Data Type: Source Code ; Data Type: Video ; Data Type: Measurement and Test Data ; Data Type: Compiled Data
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