STUDY OF FLOW RESISTANCE IN COMPOUND CHANNEL WITH CONVERGING FLOODPLAINS

Abstract

Accurately estimating the flow resistance in open channel flow is crucial for resolving numerous pressing engineering issues. In situations where there is excessive water flow on both banks of a river, the primary channel may become compromised, leading to the outpouring of water into the adjacent floodplain. The flow configuration within compound channels can become intricate due to the exchange of momentum between the main channel and the adjacent floodplains. This phenomenon exerts a noteworthy influence on the flow resistance within distinct segments of the floodplain as well as the primary channel. Furthermore, human activities such as agriculture and construction have been conducted within the floodplain regions of a river system. The floodplain's geometry undergoes modifications along the flow's length, leading to the creation of a compound channel that can be converging, diverging, or skewed. The reliance on empirical methods in conventional formulae has resulted in a lack of success in accurately predicting flow resistance. Consequently, there exists a persistent demand for techniques that are both precise and innovative. The current investigation involves the experimental and computational determination of flow resistance in a compound channel featuring both rough and smooth floodplains. This was accomplished through the utilization of Gene Expression Programming, with consideration given to both geometric factors and flow variables. Statistical measures are employed to validate the proposed models in the experimental investigation, thereby enabling the evaluation of the performance and efficacy of said models. The Manning roughness coefficient exhibits a decline in the prismatic segment, while in the nonprismatic segment, it experiences a reduction until reaching the midpoint. Subsequently, there is a notable surge observed in the roughness metric. Similar patterns of fluctuation have been noted in relation to greater relative depths. The analysis of roughness coefficient in compound channel with both rough and smooth floodplains indicates that the values of roughness coefficient are comparatively greater for the compound channel with rough floodplains. The findings of the computational analysis indicate a robust correlation between the Manning's roughness coefficient derived from Gene Expression Programming (GEP) and both empirical data from experiments and prior research outcomes.