Abstract
Due to climate change and the necessity of paying attention to the preservation of energy resources to deal with the impacts of climate change, the enhancement of renewable energy portions via different resources has been considered in recent years. Therefore, it is necessary to study characteristics influencing the modeling of water streams and waves to monitor the movement of sea waves as a large resource of renewable energy in the generation of electricity, desalination, and water pumping. The dominant currents in the Caspian Sea, a constituent of which is wind-induced waves, the disconnectedness of the Caspian Sea from oceans, complex topography, shoreline configuration, and considerable temperature and density differences, which make it complicated to examine ocean current patterns, are of great importance. This study investigated bottom friction, wave breaking, white capping, solution technique, and the number of directions in the MIKE-SW model and meshes, solution technique, bed resistance, and wind friction in the MIKE-FM module to model the wave height and current velocity. The effectiveness and contributions of characteristics in the simulation were found by the MIKE-SW model as the wave propagation model of sea waves toward the coastal areas and in the current model. As a result, to perform reliable and realistic simulations, it is required to investigate every component. The investigation of all the simulation indexes showed that the MIKE numerical model yielded acceptable results for the simulation of ocean currents and waves in both MIKE-SW and MIKE-FM modules.
Highlights
Climate change is not an emerging phenomenon, concerns have increased in the past two decades in this respect
The white capping had the highest effect on the modeling of the characteristic wave height, while wave breaking and bed resistance had no significant impacts on the modeling
This study evaluated characteristics influencing the simulation of wind-induced waves and ocean currents in the MIKE numerical model
Summary
Based on the computed results, the values with the highest correlations with the observation data were selected. The wave model was executed for a one-month period within 4320 time-steps every 600 seconds in order to validate the most optimal values of the effective characteristics, as shown in Figure 5 and Table 1. The MIKE21-SW module had a correlation of 90% and the minimum error and was found to be able to reliability and ideally model ocean waves in the case study. It is observed that the variable values of the characteristics were selected properly.
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