Asif, ShaikShaikAsif2026-07-312026-07-312026-07-31https://hdl.handle.net/11420/61857This 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.enhttps://creativecommons.org/publicdomain/zero/1.0/Recurrence CFDrCFDstirred tank reactor (STR)mixing timelattice Boltzmann method (LBM)M-Starcomputational speedupexperimental validationparametric optimizationtracer injectionprocess intensificationTechnology::660: Chemistry; Chemical Engineering::660.2: Chemical EngineeringDataset10.15480/882.16811Pietsch-Braune, SwantjeSwantjePietsch-Braune10.18419/DARUS-5523