abstract = "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 (lambda) and a sink term
representing the effects of secondary flow (Gamma). 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
\(f\), lambda and Gamma 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 \(f\),
lambda and Gamma 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 lambda and Gamma are highly affected by
the panel structure but lambda 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.",