Modelling the impact of the foundation shadow effect and flow misalignment disparity on tidal stream turbine performance - Bryn Townley; Qing Xiao; Athanasios Angeloudis; Ian Ashton; George Dadd (Ocean Engineering, 2026)

Distinguishing regional spatio-temporal hydrological regimes - Nicholas Wray; Athanasios Angeloudis; Ian Pattison; Lindsay Beevers (Hydrological Sciences Journal, 2026)

Hybrid methods in flood inundation modeling: a systematic review - Manimeldura Devki Devanga Perera; Athanasios Angeloudis; Adil Siripatana; Lindsay Beevers (Natural Hazards, 2026)
Flooding is a destructive natural hazard that is exacerbating due to climate change, causing significant socio-economic losses. Flood inundation models are critical tools in the mitigation and management of such hazards. Conventional standalone process-based models can be limited in real-time applications due to their computational intensity and the trade-off between accuracy, resolution and speed. Data-driven (machine learning) models offer an alternative route as surrogate models, promising computational efficiency, however, are noted to have limitations of high data-dependency and a black-box nature. Hybridization, implemented in machine learning models to enhance strengths and limit tradeoffs has been shown to increase physics awareness, real-time applicability, and adaptability. This paper conducts a systematic review of state-of-the-art research on hybrid flood models, and presents a definition of hybridization. A classification of hybridization techniques into enhancing inputs, structure and processing is proposed. Sequentially, common metrics used to evaluate flood models are classified into accuracy and speed-based metrics, and the usefulness of hybridization is discussed. An extensive benchmarking framework is proposed for the comparison of models before application, based on quantitative classification of the model and standardized datasets and tests. Finally, a way forward is proposed in the context of physics informed machine learning for flood inundation modeling.

Economics-constrained tidal turbine array layout optimisation at the Putuoshan–Hulu island waterway - Can Zhang; Xiaoming Cheng; Athanasios Angeloudis; Stephan C. Kramer; Chengsheng Wu; Ying Chen; Matthew D. Piggott (Ocean Engineering, 2024)

Disentangling climate change & land use change effects on river flows: A probabilistic approach - Nicholas Wray; Duncan Bowie; Ian Pattison; Athanasios Angeloudis; Lindsay Beevers (Journal of Hydrology, 2024)

Thetis-SWAN: A Python-interfaced wave–current interactions coupled system - Anastasia K. Fragkou; Christopher Old; Vengatesan Venugopal; Athanasios Angeloudis (Environmental Modelling & Software, 2024)

Drivers of Laptev Sea interannual variability in salinity and temperature - Phoebe A. Hudson; Adrien C. H. Martin; Simon A. Josey; Alice Marzocchi; Athanasios Angeloudis (Ocean Science, 2024)
Eurasian rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice concentration. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. Previous in situ and modelling studies suggest that local wind forcing is a driver of variability in Laptev sea surface salinity (SSS) but there is no consensus on the roles of Lena River discharge and sea ice cover in contributing to this variability or on the dominant driver of variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. In this region, satellite-based SSS is found to agree well with in situ data (r>0.8) and provides notable improvements compared to the reanalysis product used in this study (r>0.7) in capturing patterns and variability observed in in situ data. This study demonstrates a novel method of identifying the dominant drivers of interannual variability in Laptev Sea dynamics within reanalysis products and testing if these relationships appear to hold in satellite-based SSS, sea surface temperature (SST) data, and in situ observations. The satellite SSS data firmly establish what is suggested by reanalysis products and what has previously been subject to debate due to the limited years and locations analysed with in situ data; the zonal wind is the dominant driver of offshore or onshore Lena River plume transport. The eastward wind confines the plume to the southern Laptev Sea and drives alongshore transport into the East Siberian Sea, and westward wind drives offshore plume transport into the northern Laptev Sea. This finding is affirmed by the strong agreement in SSS pattern under eastward and westward wind regimes in all reanalyses and satellite products used in this study, as well as with in situ data. The pattern of SST also varies with the zonal wind component and drives spatial variability in sea ice concentration.

Benchmarking a two-way coupled coastal wave–current hydrodynamics model - Anastasia K. Fragkou; Christopher Old; Vengatesan Venugopal; Athanasios Angeloudis (Ocean Modelling, 2023)

Sedimentological data-driven bottom friction parameter estimation in modelling Bristol Channel tidal dynamics - Simon C. Warder; Athanasios Angeloudis; Matthew D. Piggott (Ocean Dynamics, 2022)
Accurately representing the bottom friction effect is a significant challenge in numerical tidal models. Bottom friction effects are commonly defined via parameter estimation techniques. However, the bottom friction coefficient (BFC) can be related to the roughness of the sea bed. Therefore, sedimentological data can be beneficial in estimating BFCs. Taking the Bristol Channel and Severn Estuary as a case study, we perform a number of BFC parameter estimation experiments, utilising sedimentological data in a variety of ways. Model performance is explored through the results of each parameter estimation experiment, including applications to tidal range and tidal stream resource assessment. We find that theoretically derived sediment-based BFCs are in most cases detrimental to model performance. However, good performance is obtained by retaining the spatial information provided by the sedimentological data in the formulation of the parameter estimation experiment; the spatially varying BFC can be represented as a piecewise-constant field following the spatial distribution of the observed sediment types. By solving the resulting low-dimensional parameter estimation problem, we obtain good model performance as measured against tide gauge data. This approach appears well suited to modelling tidal range energy resource, which is of particular interest in the case study region. However, the applicability of this approach for tidal stream resource assessment is limited, since modelled tidal currents exhibit a strong localised response to the BFC; the use of piecewise-constant (and therefore discontinuous) BFCs is found to be detrimental to model performance for tidal currents.

The Potential for Tidal Range Energy Systems to Provide Continuous Power: A UK Case Study - Lucas Mackie; Daniel Coles; Matthew Piggott; Athanasios Angeloudis (Journal of Marine Science and Engineering, 2020)
The extraction of tidal energy from head differences represents a predictable and flexible option for generating electricity. Here, we investigate the generation potential of prospective tidal power plants in the UK. Originally conceived as separate projects, operating these schemes as a cooperative system could prove beneficial. Combined with the inherent operational flexibility of tidal range-based schemes, a notable tidal phase difference in selected sites allows for the system to spread power generation over a larger proportion of the day. Using depth-averaged modelling and gradient-based optimisation techniques, we explore how a flexible cumulative operation schedule could be applied to provide a degree of continuous supply if desirable. While fully continuous operation is not achieved, a number of different optimisation schedules deliver cumulative continuous supply for over half of the year. The average minimum cumulative power output on these days is consistently over 500 MW out of a total installed capacity of 6195.3 MW. Furthermore, by introducing financial incentives associated with reliable, baseload supply, we provide an economic assessment of the tidal power plant system. The daily minimum cumulative power output determines income in the modelled idealised baseload market, while excess supply is traded in an hourly variable wholesale energy market. Results indicate that subsidies would be required in order to make a pursuit of continuous generation financially advantageous over energy maximisation strategies.

Fate of large-scale vortices in idealized tidal lagoons - Carolanne V. M. Vouriot; Athanasios Angeloudis; Stephan C. Kramer; Matthew D. Piggott (Environmental Fluid Mechanics, 2019)

Contact Tank Design Impact on Process Performance - Athanasios Angeloudis; Thorsten Stoesser; Carlo Gualtieri; Roger A. Falconer (Environmental Modeling & Assessment, 2016)