Publications

2025

1. 

   A decade long high-resolution wave resource map for Pentland Firth and Orkney Waters - hindcast by two-way coupling of wave-current models

   Tian Tan and Vegatesan Venugopal

   Applied Ocean Research, 2025

   Abstract Link PDF

   Wave energy is a promising renewable resource globally, with the UK leading efforts in regions like Pentland Firth and Orkney Waters. These areas, known for their strong tidal currents and energetic waves, require accurate wave energy resource assessments. This study developed numerical models, including a wave-only and a coupled wave-current model, to simulate combined wave-current conditions over a decade (2014-2023), evaluating the influence of tidal currents. The models assessed interannual, seasonal, and monthly wave resource variations and the impacts of wave-current interactions. First, a North Atlantic-scale wave-only model was constructed with TOMAWAC spectra wave model to simulate wave conditions without tidal influences and generate wave boundary conditions. Then, a regional wave-current model was developed by coupling TOMAWAC and TELEMAC. This coupled model used the wave boundary conditions from the large-scale North Atlantic model to obtain wave parameters including tidal effects. The North Atlantic wave model was validated against 10 years of continuous wave buoy data across four sites, while the regional wave-current model was verified with 135 days of Acoustic Wave and Current Profiler (AWAC) and Acoustic Doppler Current Profiler (ADCP) deployments, ensuring model reliability. The results reveal significant spatial and temporal variations in wave energy resources, with pronounced tidal effects. Based on 10 years of data, including tidal currents in the model, substantially decreases mean wave height and mean wave power in the northern and southern regions of the Orkney Islands and Stroma Island in the Pentland Firth. Near Stroma Island, wave height reductions can reach up to 0.5 m (a 25% reduction compared to the wave-only scenario), and wave power decreases by 6 kW/m (over 50% reduction). Conversely, wave power increases at tidal inlets such as Pentland Firth, Hoy Mouth, and Westray Firth, with a 10-year average increase of up to 7.7 kW/m (22%) at Westray Firth. Long-term data indicate that wave-current interactions vary significantly by season, month, and year, with notable changes during winter and high-wave periods. Extreme wave conditions are also amplified by tidal currents, particularly at the tidal inlets within the regional model. The findings could benefit not just the wave energy industry, but also other fields concerned with wave-current dynamics.

2. 

   Quantifying Wave-Current Interaction Effects on Wave Energy Resources of High Energy Sites (revision submitted)

   Tian Tan and Vegatesan Venugopal

   Ocean Modelling, 2025

   Abstract Link PDF

   This paper examines the impact of wave-current interactions on wave energy resources at high energy sites of the Pentland Firth and Orkney Waters, UK, using the open-source numerical models TOMAWAC and TELEMAC for wave and tidal current simulations, respectively. Simulations are performed at two scales: a North Atlantic wave-only model and a regional wave-current coupled model. Validation of the models was carried out at various locations, using field measurements from wave buoys, Acoustic Wave and Current profiler (AWAC), and the Acoustic Doppler Current Profiler (ADCP). Eight representative sites were selected to showcase the influence of wave-current interactions. Detailed analyses of wave parameters, including significant wave height, mean wave period, mean wave direction, and directional wave spectrum were conducted. Further comprehensive maps of significant wave heights and wave power for the Pentland Firth and Orkney Waters were produced for a one-year period to, quantifying the changes in wave heights and wave power when tidal currents are included. Additionally, the annual and inter-seasonal effects of wave-current interactions were evaluated, providing valuable insights for wave energy development in this region. This study, which examines the wave-current coupling process under diverse geographic conditions, may serve as a framework for research in other regions.

2024

1. 

   Characterisation of turbulence at sites with coexisting waves and currents: An analysis by empirical mode decomposition

   Tian Tan and Vegatesan Venugopal

   Ocean Engineering, 2024

   Abstract Link PDF

   This paper presents a novel side information assisted Empirical Mode Decomposition (EMD) method for separating wave orbital velocity from a combined wave-tidal current velocity data, measured by three different Acoustic Doppler Current Profilers (ADCPs) deployed in the Pentland Firth, Orkney Waters, UK. The effectiveness of this technique is confirmed through two methods: (1) the spectra of the wave-removed velocity align with the Kolmogorov −5/3 power law, and (2) the extracted wave orbital velocities were used to derive significant wave heights, which were validated against wave heights measured by the same ADCPs and through numerical modelling carried out with a coupled wave-current tool (TOMAWAC-TELEMAC 3D). The decomposed data was further used to calculate three-dimensional turbulence intensities (TI) for combined wave-current, wave-only, and current-only conditions, across different flow speeds and wave heights. The results demonstrate the robustness of the decomposition method in various scenarios and quantify the individual TI contributions from waves, currents, and their combined effects. This work provides valuable insights into the dynamics of areas where waves and currents coexist.

2. 

   Machine Learning and Deep Learning for Enhanced Spatio-Temporal Wave Parameters Prediction

   Tian Tan and Vegatesan Venugopal

   Proceedings of the ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2024, Singapore), 2024

   Best Paper Award Abstract Link

   This paper was selected as one of the 13 Best Papers out of around 800 papers at the 43rd OMAE Conference (Singapore, 2024). It was also the sole Best Paper in the ’CFD, FSI, and AI’ symposium (comprising around 100 papers). The award was officially announced at the opening ceremony of the 44th OMAE Conference in Vancouver, Canada, in June 2025.

   Traditional methods of wave prediction, which are mainly reliant on extensive numerical simulations, such as the utilization of spectral wave models SWAN, WaveWatch III, or TOMAWAC, have prompted the question: Can faster wave prediction be achieved? The answer, as demonstrated by this study, lies in the advancements of machine learning and deep neural networks. In this research, the spatio-temporal relationship between wind and wave conditions is established using the XGBoost machine learning method and Informer deep neural networks. This approach enables effective predictions of wave height and wave period within the waters of the North Atlantic and northern Scotland. Ten years of hourly wind data from ECMWF ERA5 (2012–2021) is used as training data, while field measured wave parameters from CEFAS WaveNet buoys are employed for model training and verification. The final output enable a comparison that ultimately leads to wave predictions for the year 2022. Building upon this foundation, a versatile model for typical weather conditions and a specialized model for extreme weather scenarios are devised, facilitating more precise predictions. The data-driven model, rooted in wind data, proves adept at predicting wave characteristics across different times and locations. Notably, the trained machine learning and deep learning model delivers significant efficiency gains compared to traditional numerical models. One year’s worth of data can be predicted within a few seconds by machine learning, whereas over 24 hours (on 16 logical CPUs) are required for the same prediction by TOMAWAC spectra wave model. This leap in training efficiency is a crucial development in the realm of wave prediction.

3. 

   Hydrodynamic assessment of the CorPower C4 point absorber wave energy converter in extreme wave conditions

   Vegatesan Venugopal and Tian Tan

   Proceedings of the ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2024, Singapore), 2024

   Abstract Link

   This study focussed on the evaluation of the motion responses exhibited by the CorPower C4 wave energy converter (WEC) when subjected to extreme wave conditions. The hydrodynamic performance of the device is examined through experiments conducted with a 1:25 scale model of the CorPower WEC at the FloWave Ocean Energy Research Facility, University of Edinburgh. The investigation encompasses a range of sea conditions, employing both regular and random waves to comprehensively understand the hydrodynamics of the CorPower WEC, with a particular focus on scenarios representative of the WEC’s survivability. The experimental setup involves the utilization of a Qualisys camera system to measure the six-component motion responses of the WEC at its waterline. Additionally, a 6-component load cell is employed to measure forces and moments acting on the entire model, including its mooring system. This article provides a detailed overview of the model setup, experimental procedures, data analysis techniques, and thorough discussions of the results obtained. Drawing insights from the Response Amplitude Operators (RAOs) derived through the experiments, this work asserts that the CorPower WEC exhibits remarkable stability, demonstrating its capability to safely endure extreme wave conditions similar to those simulated in this study. The findings contribute valuable knowledge to the understanding of the CorPower WEC’s performance and resilience in challenging marine environments.

2023

1. 

   Numerical modelling of wave and tidal current interactions and their impact on wave parameters

   Tian Tan and Vegatesan Venugopal

   Vol. 15 (2023): Proceedings of the European Wave and Tidal Energy Conference (EWTEC 2023, Bilbao, Spain), 2023

   Abstract Link

   In regions where both waves and tidal currents coexist, tidal flow can significantly alter wave parameters and hence affects the estimation of wave energy resources. This study uses a coupled wave-current numerical model to evaluate the influence of wave-current interactions on wave parameters. To achieve this, the simulation was performed in 3 stages. At first, a large-scale North Atlantic wave model was constructed using the spectral wave model TOMAWAC to generate wave conditions and boundary inputs. This wave model was calibrated and validated at four sites around the UK using field measurements. Secondly, a small-scale numerical model covering Pentland Firth and Orkney Waters, Scotland, UK, was chosen, and tidal flow and current speeds were simulated by the three-dimensional flow model TELEMAC 3D. As with the wave model, the flow model was also calibrated and validated with site measurements from an ADCP; thus, both models were validated. In the third stage, a coupled TOMAWAC-TELEMAC 3D model was employed for the small-scale region, and the wave parameters generated by the large-scale model were input as boundary conditions. The TOMAWAC-TELEMAC 3D coupled model was validated with field measurements at two locations in Orkney Waters, where waves and currents coexist. Various wave and tidal currents parameters produced from the coupled model are presented in the paper. To evaluate the wave-current impact on wave parameters, a qualitative and quantitative analysis of these parameters is carried out, and the results are presented and discussed in the paper. The large-scale and small-scale numerical models developed in this study are useful tools for generating wave boundary conditions and wave energy resource assessment, helping researchers and engineers better understand the characteristics of wave-current interactions.

2. 

   Analysis of Turbulence Parameters for a Tidal Energy Site in a Wave-Current Environment

   Vegatesan Venugopal, Tian Tan, and Brian Sellar

   Proceedings of the ASME 2023 42rd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2024, Melbourne, Australia), 2023

   Abstract Link

   Tidal power converters deployed in areas where waves and tidal currents coexist face high levels of turbulences, which can lead to unstable power generation and increase the risks of turbine blade fatigue failure. In order to improve the design and safety operation of tidal turbines in a combined wave-current environment, it is necessary to understand and characterise the turbulence parameters such as turbulence intensity (TI)/turbulent kinetic energy (TKE) etc. It is also important to characterise how waves and currents separately affect these parameters. In this study, field data collected by an Acoustic Doppler Current Profiler (ADCP) at the Pentland Firth, Orkney Islands, Scotland, are analysed to evaluate the levels of TI produced separately by waves and tidal currents. The empirical mode decomposition (EMD) method has been utilised for this analysis. At first, using the EMD, the velocity components corresponding to waves and tidal current components are separated. In order to verify the separation methodology, a two step process is adopted. In step one, wave component velocities are converted to significant wave heights using linear wave transfer function. For the second step, the significant wave heights obtained from step one are compared with the same hindcast by a coupled wave-current numerical model. A very good match between wave heights is observed with a correlation coefficient of 0.8, thus validating the methodology followed. Then, the depth-varying vertical profiles of the turbulence intensity (TI) of the decomposed wave and current components are calculated. It is found that the streamwise wave-induced TI is about 5% at the height of 15m where the turbine hub is placed, while the tidal current-induced TI is 10%–17%. The wave-current combined TI is 13%–20%. This study has demonstrated a methodology to successfully separate and quantify turbulence intensities produced individually by waves and tidal currents when they coexist at a site.