Powering and seakeeping forecasting for energy efficiency

Assessment of the fuel savings potential for weather routing by in-service data and ensemble prediction techniques

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Abstract

In this work an integration of high-resolution meteo-marine forecast data with detailed ship powering and seakeeping computational algorithms is investigated in order to asses the potential for the attainable fuel savings by weather routing algorithms along relatively short routes (w.r.t. oceanic ones) as those in the Mediterranean Sea. The capability of high resolution models to predict the detailed space-time evolution of meteo-marine conditions at the Mediterranean basin scale is exploited in order to reliably forecast ship performances along Mediterranean routes. Based on this a computational framework applicable to weather routing algorithms is implemented and tested with the main goals of improving operational efficiency, by fuel savings, and of reducing the navigational risks connected to heavy weather at sea. The main elements of the proposed approach are: i) the construction of a detailed numerical model of a real ship; ii) the exploitation of in-service performance data recorded during real voyages of such a ship to tune its numerical model; iii) the use of high-resolution meteo-marine forecast and hindcast data for winds and complete directional wave spectra to assess the fuel saving potentialities of the implemented computational framework cited above. This last task is performed by exploiting forecast data coming from Ensemble Prediction Systems (EPS) applied to high resolution wind and wave forecasts. The obtained results are very interesting and encouraging to further develop the presented approaches. In particular a fuel saving potential ranging from few points of percentage till nearly 10% emerged from the two studied cases. Moreover the approach based on EPS reveled itself to be very useful as an investigation tool for the assessment of the reliability and stability of the fuel minimization with respect to forecast uncertainties.