Optimizing closure works

Abstract

Constructing a dam across a tidal basin has alway been a long-term integral solution to many water related problems of the surrounding area such as flooding, river control and fresh water storage. However, immense challenges are accompanied with the closure works of large basins. This research treats the closure strategy to close the Gulf of Khambhat in India. The project is known as "Kalpasar", which aims to create of a fresh water reservoir in the Gulf of Khambhat by constructing a 35 km dam across the estuary. The Kalpasar project
has been on the Indian Governments agenda since 1986. Royal Haskoning was involved in the pre-feasibility study, which was presented in 1998. However, due to an alignment change to a more northern position, earlier proposed closure work designs are now considered out of date.
To avoid irrelevance of this research through time and assist the Kalpasar development project with optimizing a new design for the closure works, this research treats the development of a fundamental parametric optimization tool to quickly perform a first-order evaluation of possible closure strategies on costs.
The tool as a product along with case results are delivered to the Kalpasar development project for further design optimization.
Closing the tidal basin involves closing a certain wet cross section along the chosen dam alignment through which large tidal currents penetrate caused by tidal differences up to 11 m. Complexity is caused by increasing tidal flow velocities due to increasing constriction of the wet cross section during the closure. The developed optimization tool can evaluate and compare six pre-programmed strategies to close a multi-sectional wet cross section in time on costs of three fundamental design requirement or "cost factors": Required dam material, bed protection and equipment. Using a multi-sectional storage model to compute the flow velocities in the gap, the channels and tidal flats can be individually modeled after which they are linked as a system. The model reacts as a system to changes in flow area by closing certain cross sections (a channel or a tidal flat). The individual cross sections can be closed strategically by defining their closure method (horizontal, vertical or sudden), execution phase and construction capacity. These are called "strategic input parameters". Defined for all sections, they determine the closure sequence of the system in time. Optimization is achieved when the strategic input parameters define a closing sequence which minimizes the combined cost of all cost factors.
Subsequent to the storage model, three computational models are introduced to quantify the required dam material, bed protection and equipment. Based on earlier research, the material model utilizes only quarried rock for gradual closures and sluice caissons for sudden closures. The equipment model utilizes large dump trucks for horizontal closures and ships or a temporary cable-way/bridge system for vertical closures. The construction capacity is linked to material and bed protection models, since both design requirements are time dependent. Increasing construction capacity can therefore decrease these requirements.
Since subsequent models largely depend on the flow velocity, an attempt to validate and calibrate the storage model was performed using results from previous research and a 2D-H Delft3D model. Deviations with respect to the Delft3D model were significantly large (factor 2-3), because storage models can only be utilized if the basin size and the remaining gap are small (usability limits). Therefore, calibration was performed by introducing an artificial contraction factor to compensate for the error in the flow velocity. An exponential relation was determined linking the error to the constriction percentage of the gap. With increasing constriction percentage, the error decreased due to increasing validity of the storage model usability limits. The artificial contraction factor can be used to optimize the closure of the Gulf of Khambhat. However, for general use, the model should be calibrated to each specific site.
Case study results show that using multiple cross sections to model the bathymetry with respect to a single cross section, the optimal strategy can change from fully vertical to a combination of horizontal and vertical with a specific capacity. Utilizing the developed model for the Kalpasar case is therefore recommendedbecause the complex bathymetry creates many possible strategies and can’t be reliably modeled with single cross-sectional models. The strategy that showed the most potential for further optimization is: First closing the tidal flats horizontally by forward dumping of rocks, while closing the channels up to 40% of their depth with dumping ships after which the remaining gap is closed vertically by a cable-way or bridge system. This strategy is commonly suggested by existing literature, thereby increasing reliability and validity of the optimization model.
A second case study showed negative effects of increasing construction capacity on the total cost. However, these case results are based on assumed costs and cost functions for equipment, which should be verified by contractors first. Bed protection requirements did decrease significantly by increasing construction capacity, showing potential for development of high capacity closure equipment to avoid these costs. Further future development should focus on vertical closure equipment to decrease both material and bed protection costs.
To conclude the recommendations, more case studies should be performed to quantify influences of parameters already included in the model, such as the permeability of the dam, the presence of a tidal power facility and the use of a sudden caisson closure to relieve the final closure. Secondly, further validation of the storage model is essential to generate more reliable results. Furthermore, research should be performed into cost functions of several existing or new high capacity equipment for vertical closures, relating costs to construction capacity to improve usability of the optimization model.