A comprehensive review, CFD and ML analysis of flow around tandem circular cylinders at sub-critical Reynolds numbers

The hybrid review paper meticulously examines crucial research on tandem cylinders across a broad range of Reynolds (Re) numbers, extending up to 170,000 for Strouhal (St) and 300,000 for pressure coefficients (C_P). By consolidating findings on various flow parameters, including Strouhal number, drag (C_D), lift (C_L), and pressure coefficients (C_P), the paper advocates the use of experimental and three-dimensional numerical data, exclusively omitting two-dimensional numerical data, especially at higher Re numbers. To this end, the predictive performance of different machine learning techniques-such as XGBoost, genetic optimization, ensemble modeling, and Random Forest-was evaluated using numerical simulations and data sourced from literature. The results demonstrate that, given a sufficiently large dataset, these techniques can accurately predict flow variables like Strouhal number and pressure coefficients with minimal computational cost. However, it is crucial to use only three-dimensional datasets for such analyses. The study identifies Random Forest and XGBoost models as the most accurate in forecasting flow-induced oscillations and pressure distributions around the cylinders, exhibiting the lowest mean squared errors for Strouhal number and pressure coefficient predictions.