The award was given for the research paper, “Predicting Added Resistance in Wind and Waves Employing Artificial Neural Nets”, Eva Herradón de Grado prepared for an international conference.
Fuel consumption in harsh weather conditions
Wind and waves force ships to slow down and cause them to use more fuel. The quantification of these effects is important in many applications, from ship design to ship operation. For example, evolving performance management solutions (such as DNV GL’s ECO Insight portal) requires corrections of varying ambient conditions (wind and waves) and operational conditions (draft, trim, speed) to make ship performance indicators comparable. For wind resistance, international standards give wind resistance coefficients in tabular form. Added resistance is often predicted using 3d simulation methods. This approach is very good, but the industry is in need of a more cost efficient and faster approach.
In her paper “Predicting Added Resistance in Wind and Waves Employing Artificial Neural Nets”, Eva Herradón de Grado's formulas to predict wind resistance convert the traditional tabular approach into a form that allows easy programming and predictions for arbitrary directions. For predicting added resistance in waves, she made use of so-called artificial neural nets, a technique that is already known for its use in pattern matching, for example in fingerprint identification.
As an Erasmus student at DTU Mechanichal Engineering, Eva Herradón de Grado followed assistant professor Ulrik Dam Nielsens course Ship Operations.
Andreas Bodmann, Director of Communications, DNV GL - Maritime (left), Eva Heradón de Grado (in the middle) and Tor E. Svensen, CEO DNV GL – Maritime (right).