Meshing generation strategy for prediction of ship resistance using CFD approach

Yulianti, Serliana and Samuel, S and Nainggolan, T S and Iqbal, Muhammad (2022) Meshing generation strategy for prediction of ship resistance using CFD approach. IOP Conference Series: Earth and Environmental Science, 1081 (1). 012027. ISSN 1755-1315 (https://doi.org/10.1088/1755-1315/1081/1/012027)

[thumbnail of Yulianti-etal-MASTIC-2022-Meshing-generation-strategy-for-prediction-of-ship-resistance-using-CFD-approach]
Preview
Text. Filename: Yulianti_etal_MASTIC_2022_Meshing_generation_strategy_for_prediction_of_ship_resistance_using_CFD_approach.pdf
Final Published Version
License: Creative Commons Attribution 3.0 logo

Download (1MB)| Preview

Abstract

Abstract: CFD is a numerical approach used to solve fluid problems. In the CFD simulation process, the meshing stage is crucial to produce high accuracy. Meshing is a process where the geometric space of an object is broken down into many nodes to translate the physical components that occur while representing the object’s physical shape. The research objective was to analyze the characteristics of the mesh technique in the Finite Volume Method (FVM) using the RANS (Reynolds - Averaged Navier - Stokes) equation. The numerical simulation approach used three mesh techniques, namely overset mesh, morphing mesh, and moving mesh. The k-ε turbulent model and VOF (Volume of Fluid) were used to model the water and air phases. The mesh technique approach in CFD simulation showed a pattern under experimental testing. This research showed the difference in value to the experimental results, namely by using the moving mesh method, the difference in resistance difference was 8% at high-speed conditions, the difference in trim value at overset mesh was 11%, and the difference in heave value with the moving mesh method was 14% at low speed. The conclusion reported that overset mesh had better than other mesh methods.

ORCID iDs

Yulianti, Serliana, Samuel, S, Nainggolan, T S and Iqbal, Muhammad ORCID logoORCID: https://orcid.org/0000-0003-3762-3757;