Version 1
: Received: 13 October 2021 / Approved: 18 October 2021 / Online: 18 October 2021 (15:17:20 CEST)
How to cite:
Schmitt, P.; Gillan, C.; Finnegan, C. On the Use of Artificial Intelligence to Define Tank Transfer Functions. Preprints2021, 2021100252. https://doi.org/10.20944/preprints202110.0252.v1
Schmitt, P.; Gillan, C.; Finnegan, C. On the Use of Artificial Intelligence to Define Tank Transfer Functions. Preprints 2021, 2021100252. https://doi.org/10.20944/preprints202110.0252.v1
Schmitt, P.; Gillan, C.; Finnegan, C. On the Use of Artificial Intelligence to Define Tank Transfer Functions. Preprints2021, 2021100252. https://doi.org/10.20944/preprints202110.0252.v1
APA Style
Schmitt, P., Gillan, C., & Finnegan, C. (2021). On the Use of Artificial Intelligence to Define Tank Transfer Functions. Preprints. https://doi.org/10.20944/preprints202110.0252.v1
Chicago/Turabian Style
Schmitt, P., Charles Gillan and Ciaran Finnegan. 2021 "On the Use of Artificial Intelligence to Define Tank Transfer Functions" Preprints. https://doi.org/10.20944/preprints202110.0252.v1
Abstract
Experimental test facilities are generally characterised using linear transfer functions to relate the wavemaker forcing amplitude to wave elevation at a probe located in the wavetank. Second and third order correction methods are becoming available but are limited to certain ranges of waves in their applicability. Artificial intelligence has been shown to be a suitable tool to find even highly nonlinear functional relationships. This paper reports on a numerical wavetank implemented using the OpenFOAM software package which is characterised using artificial intelligence. The aim of the research is to train neural networks to represent non-linear transfer functions mapping a desired surface-elevation time-trace at a probe to the wavemaker input required to create it. These first results already demonstrate the viability of the approach and the suitability of a single setup to find solutions over a wide range of sea states and wave characteristics.
Keywords
tank transfer function; neural networks; machine learning; OpenFOAM; computational fluid dynamics
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
