ABSTRACT
The present study aims to the improvement of the accuracy of the empirical drag coefficient correlations using global optimization. Sixteen popular models are considered in two groups based on the range of applicability. The first group covers subcritical region while the second covers Reynolds numbers up to 106. Shuffled Complex Evolution (SCE) algorithm is used to improve the parameters of the equations through a direct fit to the 486 reliable experimental data points. Furthermore, some parameters are added to drag equations for giving more degrees of freedom in fitting experimental data. By using this procedure, ten new equations which were improved both in range and in accuracy were developed with the same or different forms of the existing drag correlations. The proposed equations in comparison with the existing correlations substantially (up to almost 96% ) improve the fit to experimental data in terms of the Sum of Squared of Logarithmic Deviations (SSLD).
