Simulation Of Shoreline Changes Along Muthalapozhy Harbour, India

A study of Muthalapozhy fishing harbour, located in south India, was conducted for simulating shoreline changes using LITPACK modelling tool. The analysis shows that the estimated advancement in shoreline is of the order of 45 m/year initially, which gradually reduces to 25 m/year. It was also found that the coastline advances more during the south-west monsoon (i.e. June to September) season. Simulation of breakwaters shows that the length of the breakwater should be increased by 200 m for south breakwater and 70 m for north breakwater to keep the channel operational without dredging till 2016. The results of the simulated shoreline will help the port managers for maintaining the channel round the year.

Parameterization Of Turbulence Models Using 3DVAR Data Assimilation

In this research the 3DVAR data assimilation scheme is implemented in the numerical model DIVAST in order to optimize the performance of the numerical model by selecting an appropriate turbulence scheme and tuning its parameters. Two turbulence closure schemes: the Prandtl mixing length model and the two-equation k-ε model were incorporated into DIVAST and examined with respect to their universality of application, complexity of solutions, computational efficiency and numerical stability. A square harbour with one symmetrical entrance subject to tide-induced flows was selected to investigate the structure of turbulent flows. The experimental part of the research was conducted in a tidal basin. A significant advantage of such laboratory experiment is a fully controlled environment where domain setup and forcing are user-defined. The research shows that the Prandtl mixing length model and the two-equation k-ε model, with default parameterization predefined according to literature recommendations, overestimate eddy viscosity which in turn results in a significant underestimation of velocity magnitudes in the harbour. The data assimilation of the model-predicted velocity and laboratory observations significantly improves model predictions for both turbulence models by adjusting modelled flows in the harbour to match de-errored observations. 3DVAR allows also to identify and quantify shortcomings of the numerical model. Such comprehensive analysis gives an optimal solution based on which numerical model parameters can be estimated. The process of turbulence model optimization by reparameterization and tuning towards optimal state led to new constants that may be potentially applied to complex turbulent flows, such as rapidly developing flows or recirculating flows.