Small-scale model studies are a crucial step in the development of wave energy converters (WECs) prior to deployment in rough ocean conditions. They serve not only to assess performance across various sea states but also to fine-tune design parameters, thereby mitigating risks and curtailing costs associated with full-scale deployment. To accurately assess WEC performance in model scale experiments, confidence in the various measurements is essential, best quantified by the experimental uncertainty and its propagation into performance outputs. Illustrating the expected degree of uncertainty in results helps evaluate performance and de-risk the transition to full-scale deployment. Despite its importance, current literature reveals that uncertainty analysis (UA) of WEC model studies is yet to reach full maturity, necessitating further research across diverse WEC configurations.

The Blue Economy Cooperative Research Centre is planning to deploy a Moored Multi-Mode Multibody (commonly known as M4) WEC at the coast of Albany as part of the research program ‘‘Seeding marine innovation in SW WA with a WEC deployment in Albany’. A 1:15 scaled model of this device was tested in the model test basin facility of Australian Maritime College (AMC). This project intends to develop a detailed uncertainty analysis of this model study using Monte-Carlo method (MCM) of error propagation. MCM render how error propagates to influence the results and the sensitivity of each measurement and variables to uncertainties. This analysis will help in identifying the important parameters, factors and causes of uncertainty which need to be considered and methodologies will be developed to mitigate their influence in the results for subsequent experiment.

Secondary set of M4 model testing will be carried out aiming to reproduce the results with minimum uncertainties which will be preceded by a pre-testing UA. Design stage UA or pre-testing UA provides an estimate of the expected minimum uncertainty and if these values are too large, alternative approach of testing, for example changing instruments, can be considered. Conclusion from both these UA can be used to design the best approach for the secondary model study with minimum uncertainties. Reproducibility of the model study in conjunction with the MCM model will help in further quantify the presence of random error in experiments. The outcomes of this project will help future experimenters to perform model studies with more certainty, mitigating the risk of failures or performance uncertainties, reduce the economic expenditure for confidence in results and increase the confidence of developers and investors in WEC technologies.