Performance Evaluation of a Python-Based Model for Computing Normal and Critical Depths in Open Channel Flows

Abstract

This paper presents the performance evaluation of a python-based model, a computer program developed for efficiently computing normal and critical depths in trapezoidal, circular, rectangular, and triangular open channel cross-sections. The model utilizes improved explicit equations to provide fast and highly accurate solutions, addressing the limitations of existing methods that are restricted to certain cross-sections or limited applicability conditions. The performance of the model was evaluated in terms of usability, stability, and accuracy. Accuracy was assessed by comparing the model's results with solutions from trial-and-error for a range of channel geometries in terms of maximum relative error and coefficient of determination. Usability was evaluated based on the model's user-friendly interface and ease of use. Stability was assessed by examining the model's ability to provide consistent results across different input conditions. The results demonstrate the model's high precision, with the maximum relative error of 1-2% and coefficient of determination (R²) exceeding 0.99 for both normal and critical depth computations across the cross-sections. The model proved to be stable, giving solutions even for extreme channel geometries. The use of Python programming language ensures a user-friendly interface, allowing hydraulic designers and engineers to efficiently determine critical and normal depths without the need for manual calculations or complex software. Overall, the model provides a practical and versatile tool for open channel flow analysis, contributing to improved hydraulic design and management practices. The model's accuracy, efficiency, and broad applicability make it a valuable resource for engineers and researchers working in the field of water resources and hydraulic engineering.