Application of evolutionary computation to open channel flow modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{Sharifi:thesis,
  author =       "Soroosh Sharifi",
  title =        "Application of evolutionary computation to open
                 channel flow modelling",
  school =       "Dept. of Civil Engineering, University of Birmingham",
  year =         "2009",
  address =      "UK",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://etheses.bham.ac.uk/478/1/Sharifi09PhD.pdf",
  size =         "330 pages",
  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.",
  notes =        "ID Code: 478

                 Supervisor(s): Mark Sterling and Donald W. Knight

                 NSGA II, matlab, GPlab",
}

Genetic Programming entries for Soroosh Sharifi

Citations