Abstract
Analytical solutions for numerical wavetanks are limited presently to a simple bathymetry and third
order accuracy. Furthermore, tanks are generally characterised using linear transfer functions to relate
the wavemaker forcing amplitude to wave elevation at a probe located in the wavetank. This paper
reports on a numerical wavetank implemented using the OpenFOAM software package. The aim of
the research is to train neural networks to represent non-linear transfer functions mapping a desired
wave surface-elevation time-trace at a probe to the wavemaker input required to create it.
order accuracy. Furthermore, tanks are generally characterised using linear transfer functions to relate
the wavemaker forcing amplitude to wave elevation at a probe located in the wavetank. This paper
reports on a numerical wavetank implemented using the OpenFOAM software package. The aim of
the research is to train neural networks to represent non-linear transfer functions mapping a desired
wave surface-elevation time-trace at a probe to the wavemaker input required to create it.
| Original language | English |
|---|---|
| Publication status | Published - 03 Jun 2021 |
| Event | 9th Conference on Computational Methods in Marine Engineering - Edinburgh, Edinburgh, United Kingdom Duration: 02 Jun 2021 → 04 Jun 2021 Conference number: 9th https://congress.cimne.com/marine2021/frontal/Program.asp |
Conference
| Conference | 9th Conference on Computational Methods in Marine Engineering |
|---|---|
| Abbreviated title | MARINE2021 |
| Country/Territory | United Kingdom |
| City | Edinburgh |
| Period | 02/06/2021 → 04/06/2021 |
| Internet address |
Keywords
- computational fluid dynamics; neural networks; machine learning, OpenFOAM; tank transfer function