Application of evolutionary computation to open channel flow modelling

[摘要] This thesis examines the application of two evolutionary computation techniques to two different aspects of open channel flow. The first part of the work is concerned with evaluating the ability of an evolutionary algorithm to provide insight and guidance into the correct magnitude and trend of the three parameters required in order to successfully apply a quasi 2D depth averaged Reynolds Averaged Navier Stokes (RANS) model to the flow in prismatic open channels. The RANS modeled adopted is the Shiono Knight Method (SKM) which requires three input parameters in order to provide closure, i.e. the friction factor (\(f\)), dimensionless eddy viscosity (λ) and a sink term representing the effects of secondary flow (Γ). A non-dominated sorting genetic algorithm II (NSGA-II) is used to construct a multiobjective evolutionary based calibration framework for the SKM from which conclusions relating to the appropriate values of \(f\), λ and Γ are made. The framework is applied to flows in homogenous and heterogeneous trapezoidal channels, homogenous rectangular channels and a number of natural rivers. The variation of \(f\), λ and Γ with the wetted parameter ratio (\(P_b\)/\(P_w\)) and panel structure for a variety of situations is investigated in detail. The situation is complex: \(f\) is relatively independent of the panel structure but is shown to vary with P\(_b\)/P\(_w\), the values of λ and Γ are highly affected by the panel structure but λ is shown to be relatively insensitive to changes in \(P_b\)/\(P_w\). Appropriate guidance in the form of empirical equations are provided. Comparing the results to previous calibration attempts highlights the effectiveness of the proposed semi-automated framework developed in this thesis. The latter part of the thesis examines the possibility of using genetic programming as an effective data mining tool in order to build a model induction methodology. To this end the flow over a free overfall is exampled for a variety of cross section shapes. In total, 18 datasets representing 1373 experiments were interrogated. It was found that an expression of form \(h_c\)=A\(h_e\)\(^{B\sqrt S_o}\), where \(h_c\) is the critical depth, \(h_e\) is the depth at the brink, \(S_o\) is the bed slope and A and B are two cross section dependant constants, was valid regardless of cross sectional shape and Froude number. In all of the cases examined this expression fitted the data to within a coefficient of determination (CoD) larger than 0.975. The discovery of this single expression for all datasets represents a significant step forward and highlights the power and potential of genetic programming.

[发布机构] University:University of Birmingham;Department:School of Engineering, Department of Civil Engineering

[关键词] T Technology;TC Hydraulic engineering. Ocean engineering