Ocean wave energy assessment

This repository contains some data for assessment of the potential wave power along a coastal province, Central Vietnam. The project is carried out with funding from Vietnamese goverment under a DTDLCN scheme.

The folder Wave_modelling contains spreadsheets and code to analyse the output from various simulations to present the long-term distribution of the wave power along the local shoreline:

• Domain.dxfm is the MIKE21 master file for the simulation, in which the domain is defined.
• SpecWaveModel.sw is the input data for MIKE21 Spectral Wave Module.
• January.dfsu is the simulation output for the January wave condition.
• wave_NE_monsoon.dfsu is the simulation output for the typical northeast monsoon wave condition (the predominant wind regime).
• wave_S_monsoon.dfsu is the simulation output for the typical south monsoon wave condition (the weaker wind regime).
• cntr*.xy are locations of the points along corresponding wave contours.
• *.dxfm files facilitate the extraction of wave power vector components from *.dfsu files.
• *.dfs0 files contains Px, Py extracted from the dfsu files.

The folder Analysis contains spreadsheets and code to analyse the output from various simulations to present the long-term distribution of the wave power along the local shoreline:

• Pn-along-30m-contour.xlsx computes the distribution of wave power along the 30 m contour for various wave conditions. This file also contains the coordinate points of the contour.
• Pn-various-contours.xlsx estimate the wave power for the 20-m, 30-m and 50-m contours to find their inter-relationship and prepare the results for long-term wave power analysis.
• Long-term-analysis.xlsx summarises the wave power for different months and season for the local coastal area, mainly for the 30-m depth contours, but also consider the 20-m and 50-m contours.
• Goodness-of-fit.xlsx presents the measured and computed data, along with performance indices (Nash, RMSE, BSS, etc.)
• wave_data_processing.ipynb contains Python code to analyse the wave climate data and generate wave rose plots.
• wave_typical_cond.ipynb contains Python code to read the wave climate data, partition into subsets, and estimate typical wave conditions for each month.

The folder WaveRose contains plots of wave condition for all months in a typical year.