An Excel VBA‐based educational module for bed roughness predictors

Abstract

Estimation of flow resistance coefficients plays an inevitable role in open‐channel flows in hydraulic modeling and water resources engineering. Despite many equations and methods recommended for estimating roughness coefficients, only two standard resistance equations (original Manning's and Keulegan's equations) have been widely taught and utilized in practice. There can be various reasons for not using new bed roughness predictors (BRPs), one of which is the absence of teaching BRPs. A rigorous analysis of available BRPs for estimating grain and form roughness coefficients delineates that most of them are comprised of a scheme rather than a single equation. Also, each BRP is tailored to solve a distinct type of problem; however, a proper definition of a problem statement helps shedding light on this application. This study presents an Excel VBA‐based educational estimator to address the gap. For this purpose, the problem statement is first revisited to accomplish a better understanding in light of proper problem classification. On the basis of different groups of input and output data, each BRP is implemented into an Excel spreadsheet and VBA codes are presented for BRPs incorporating both grain and form roughness. This computer application can help students in open‐channel engineering courses not only to become familiar with modified BRPs but also to acknowledge the merit of the Excel spreadsheet for estimating flow resistance coefficients. Finally, as the VBA codes are presented in detail, they can be modified by users in accordance with their priorities for both educational and practical purposes.

A new formula for predicting movable bed roughness coefficient in the Middle Yangtze River

“…Because the magnitude of roughness coefficient cannot be measured directly, empirical or semi-empirical roughness coefficient predictors, in the form of Manning or Darcy–Weisbach roughness coefficient, are widely applied to determine the stage-discharge relations at a given hydrometric section (Ferro and Porto, 2018). However, most of them incorporate the energy slope, which is difficult to be accurately measured in natural alluvial rivers (Brownlie, 1983; Ferro and Porto, 2018; Niazkar, 2020; Peterson and Peterson, 1988). In the MYR, the energy slope is not included in the measurement items at a hydrometric station, and the application of these formulas is somewhat limited consequently.…”

“…Movable bed roughness formulas were also proposed through theoretical or empirical methods using flume or field measurements, which can be divided into two types of formulas. The first kind of formula develops empirical or semi-empirical relationships based on the hypotheses that hydraulic radius or energy gradient can be linearly divided into two components covering grain roughness and bedform drag (Einstein and Barbarosssan, 1952; Engelund, 1966; Niazkar, 2020; Saghebian et al, 2020; Yang et al, 2005). It is difficult to investigate the bedform configurations in natural rivers, with the data of bedform geometry and motion being limited.…”

“…The second type of formula directly establishes the equations between the total roughness coefficient and representative flow-sediment or bedform-related parameters (Andharia et al, 2013; Kumar, 2011; Li and Liu, 1963; Wu and Wang, 1999; Yu and Lim, 2003). However, most of the formulas proposed in the literature have a complex structure and require some hydraulic parameters that are difficult to obtain in field observations (Niazkar, 2020). Besides, various resistance estimator models were developed using the artificial intelligence method, while these models usually have an iterative solution scheme, requiring initial values without considering the mechanism for revising the first initial guess (Azamathulla, 2013; Kumar and Rao, 2010; Kumar et al, 2021; Roushangar and Mouaze, 2014; Roushangar et al, 2017; Niazkar et al, 2019c).…”

“…Another is about the prediction of bed roughness coefficients. Most of the previous studies back-calculate the roughness coefficients under different discharges according to the observed hydrographs of discharge and water level at hydrometric stations of the study reach due to the lack of available predictors, which is basically a cumbersome trial and error process (Niazkar, 2020; Xia et al, 2018).…”

Application of Third-Order Schemes to Improve the Convergence of the Hardy Cross Method in Pipe Network Analysis

“…Each and every algorithm was coded in MATLAB and MS Excel to solve the sample pipe networks for four different scenarios (92 cases overall), while applications of these programs were previously recommended for implementing numerical modeling [31][32][33] and in particular for pipe network analysis [2,7,9]. The detail of the four different scenarios is presented in the following: The detail results achieved for solving the sample pipe network using 23 methods for four scenarios are presented in the following:…”