The design of hydraulic structures like breakwaters and crest walls is often based on empirical formulations, physical models test, numerical models and a fair amount of expert judgement. Each technique has its own pros and cons. The main limitation of the empirical formulas is that often they have to be applied outside their range of validity. Physical modelling also has its own shortcomings. When breaking waves hit the structure, the location of the maximum pressures is still not well known due to the high spatial variability. By using an array with low spatial resolution, the forces estimated in the physical ume will usually underestimate to some extent the actual forces experienced by the wall (Ramachandran et al., 2013). In the last decades, numerical modelling has become an attractive alternative in simulating wave-structure interactions. Nevertheless, estimating loads on crest walls in the numerical ume is still at its early stages. On that account, the present work validates the prediction of wave induced forces on the front face of crest walls on top of rubble mound breakwaters in CoastalFOAM. A scale model of the Holyhead breakwater, located in Wales, is used. The key validation topics are the reproduction of wave-structure interaction when heavily breaking waves reached the wall, the evaluation of the ventilated boundary condition implemented by Jacobsen et al. (2018), the porous ow inside the armour layer formed by Tetrapods units and whether the simplication of not using a turbulence model, as done by Jensen et al. (2014) and Jacobsen et al. (2018), is also valid under heavily breaking waves. Four validation cases were used to test the capabilities of the numerical ume. The results conrm that it is possible to accurately reproduce the wave conditions and the wave induced forces from a physical modelling campaign. Overall, the CoastalFOAM model is able to capture the shape and the order of magnitude of the force events. A calibrated model predicts the highest wave induced forces (forces with an exceedance probability of 0.4% and 0.1%) with errors lower than 20%. Moreover, the results indicate that for practical applications it is not essential to include a turbulence model in the numerical ume to obtain reliable forces on the front face of crest walls for dierent wave conditions. Another outcome of this study is that the implementation of the ventilated boundary condition is required in the interface between water surface and structural elements to mimic accordingly the air-water mixture when the structure is subjected to a heavy wave attack. Nonetheless, there is still room for improvement in this area, where a better understanding of this boundary condition and of the air entrapment during wave-structure interaction needs further research. Despite the large computational time required by the numerical ume when large wave trains must be simulated, a CFD model during design stages of breakwaters and crest walls provides higher spatial and temporal resolution of the wave induced pressures exerted on the wall than a physical test. Therefore, a better picture of the forces and pressure distribution in the front face can be obtained.

Punchiná reservoir is part of the San Carlos Hydroelectric Power Plant, situated in the Guatapé watershed. The Guatapé river is an affluent of the Samaná Norte river, which in turn is an affluent of the Magdalena river. San Carlos Hydroelectric Project uses the waters from the rivers San Carlos and Guatapé and discharges the turbined flow directly into the Samaná Norte river by a tunnel. Currently, there is flow downstream of the Punchiná dam only on the days where the spillway operates, significantly impacting the riverine ecosystem. Additionally, claims have been made about how hydropeaking causes floods in villages downstream, particularly in La Pesca village. This town is located in the confluence of Samaná Norte and Magdalena river, on the left bank of Samaná Norte river mouth. The present report deals with the multi-objective optimization of the Punchiná reservoir of San Carlos Hydroelectric Project in Colombia by considering the objectives of maximizing hydropower revenues, maximizing the ecological discharge at Guatapé river, downstream of the dam, and reducing the flood risk at La Pesca village. Four numerical methods were coded in Python to solve the reservoir routing. To solve the multi-objective optimization, the non-dominated sorting genetic algorithm II (NSGA-II) using the Pymoo framework in Python was set up, along with the use of an Explicit Euler numerical method for modelling the river routing. The simulation was performed for 3 periods (high, average and low flow conditions) within the years 2010-2017. After multiple optimization scenarios, it can be concluded that the hydropower and environmental flows are competing objectives, i.e., allocating water for environmental flow purposes from the Punchiná reservoir will always result in a reduction of the hydropower revenues. Hence, it is recommended that an incentive system is developed so that the ecosystemic services are compensated to persuade the generating companies into including ecological objectives into their optimal operation curves. In addition, suggestions on considering a bypass tunnel to let the discharge flow into Guatapé river dry trajectory while adding a turbine to take advantage of this flow are also given. The results also show that the flood mitigation objective does not result in a competing objective against the hydropower and environmental flow objectives when there are average flow conditions in the Magdalena river. Floods commonly occur during extreme weather periods whereas the optimization of the Punchiná reservoir is performed for monthly average flow conditions at Magdalena river. Thus, to assess the hydropeaking effect in the water levels at La Pesca site, it is recommended that the reservoir optimization should also include extreme flow conditions at Magdalena river when experienced.