Title:	Roughness modelling with RANS
DNr:	NAISS 2025/22-599
Project Type:	NAISS Small Compute
Principal Investigator:	Kadir Burak Korkmaz <Burak.Korkmaz@ri.se>
Affiliation:	RISE Research Institutes of Sweden
Duration:	2025-04-09 – 2026-05-01
Classification:	20705
Keywords:

Abstract

Accurately predicting how ships perform in real-world conditions is essential for both shipbuilders and regulatory compliance. Traditionally, physical towing tank tests have played a central role in this process. However, advances in computing have opened the door to powerful new methods that combine physical testing with Computational Fluid Dynamics (CFD). These hybrid methods are now being adopted in international standards, and SSPA/RISE has been a key contributor to this development. This project focuses on one important piece of the puzzle: how to model the effects of surface roughness on a ship’s resistance through water. In simple terms, even minor imperfections on a ship’s hull—caused by paint texture, biofouling, or wear—can significantly influence how much power is needed to propel it. Currently, these effects are estimated using legacy formulas that were developed decades ago. But to fully benefit from the capabilities of modern simulation tools, we need updated, more accurate models that reflect today’s understanding and computational possibilities. The aim of this work is to develop and test improved roughness allowance models. Specifically, we plan to: Develop a new friction line that includes surface roughness effects Calibrate this model using CFD, validated against experimental data from previous towing tank tests with controlled roughness conditions Investigate whether existing scaling methods still hold for different ship types and sizes Explore a more refined approach for applying correlation factors, moving from traditional simplified corrections to more precise, customized adjustments This project marks the start of a larger research campaign that will be carried out in several phases. In this first stage, the focus is on roughness modelling, which is expected to make a tangible impact on the accuracy of ship performance predictions. The results will form the basis for future investigations into other components of the prediction process. Ultimately, this research will contribute to better tools for both designers and regulators, supporting the development of cleaner, more efficient ships. By leveraging high-performance computing resources, we aim to advance methods that are both scientifically sound and practically applicable, aligning with ongoing international efforts to modernize maritime performance assessment.