Direct measurement of hairpin-like vortices in the bottom boundary layer of the coastal ocean

Laboratory measurements and numerical modeling at low Reynolds numbers (Reθ<7700) indicate the energy-containing turbulence of boundary layer flows comprises coherent packets of hairpin vortices. Here direct measurements in the bottom boundary layer of the coastal ocean at higher Reynolds numbers (Reθ = 266,150) show tidal flows also contain packets of large vortices separated by periods of more quiescent conditions. The 1452 vortices recorded within a 20 min period are typically aligned along stream (∼8.0° from the mean flow direction) and inclined to the horizontal (∼27.0° from the seabed), with a mean period of occurrence of 4.3 s. These results lend three-dimensional, in situ support to an interpretation of the coastal ocean bottom boundary layer as comprising coherent packets of hairpin vortices. This demonstrates a direct linkage from low Reynolds number experiments to higher Reynolds number flows, permitting fine-scale details of particle transport and pollutant dispersion to be inferred from lower Reynolds number data

Corrigendum: A physics-enabled flow restoration algorithm for sparse PIV and PTV measurements (2015 Meas. Sci. Technol . 26 065301)

journal_title: Measurement Science and Technology article_type: error article_title: Corrigendum: A physics-enabled flow restoration algorithm for sparse PIV and PTV measurements (2015 . 065301) copyright_information: © 2016 IOP Publishing Ltd date_received: 2016-03-17 date_accepted: 2016-04-21 date_epub: 2016-05-1

A real-Time spatio-temporal machine learning framework for the prediction of nearshore wave conditions

This is the author accepted manuscriptData Availability Statement: In-situ wave data collected using Datawell Directional Wave Rider Mk III buoys and operated by the Channel Coastal Observatory (Channel Coastal Observatory, 2021) was used in the model development described in this manuscript. The spatial surrogate model that the present work builds on is attributed to Chen et al. (2021) for which the underlying physics based numerical model is attributed to van Nieuwkoop et al. (2013). The benchmark UKMO regional wave forecast is an instance of the WAVEWATCH-III model, whose domain covers the seas on the North-West European continental shelf, forced by 10 m winds from the UKMO atmospheric global Unified Model (Walters et al., 2011), with lateral wave boundary conditions and surface current inputs from the UKMO global wave forecast (Saulter et al., 2016) and UKMO Atlantic Margin Model ocean physics forecast (Tonani et al., 2019), respectively. The open-source ML library Scikit-learn (Pedregosa et al., 2011) and deep learning framework TensorFlow (Abadi et al., 2016) in Python were used to implement the models.The safe and successful operation of offshore infrastructure relies on a detailed awareness of ocean wave conditions. Ongoing growth in offshore wind energy is focused on very large scale projects, deployed in ever-more challenging environments. This inherently increases both cost and complexity, and therefore the requirement for efficient operational planning. To support this, we propose a new machine learning framework for the short-term forecasting of ocean wave conditions, to support critical decision-making associated with marine operations. Here, an attention-based Long Short-Term Memory (LSTM) neural network approach is used to learn the short-term temporal patterns from in-situ observations. This is then integrated with an existing, low-computational cost spatial nowcasting model to develop a complete framework for spatio-temporal forecasting. The framework addresses the challenge of filling gaps in the in-situ observations, and undertakes feature selection, with seasonal training datasets embedded. The full spatio-temporal forecasting system is demonstrated using a case study based on independent observation locations near the southwest coast of the United Kingdom. Results are validated against in-situ data from two wave buoy locations within the domain and compared to operational physics-based wave forecasts from the Met Office (the UK’s national weather service). For these two example locations, the spatio-temporal forecast is found to have the accuracy of R2 0.9083 and 0.7409 in forecasting 1 hour ahead significant wave height, and R2 0.8581 and 0.6978 in 12 hour ahead forecasts, respectively. Importantly, this represents respectable levels of accuracy, comparable to traditional physics-based forecast products, but requires only a fraction of the computational resources.Royal Academy of Engineering (RAE)Engineering and Physical Sciences Research Council (EPSRC

Deriving spatial wave data from a network of buoys and ships

This is the author accepted manuscript.The real-time provision of high-quality estimates of the ocean wave parameters at appropriate spatial resolutions are essential for the sustainable operations of marine structures. Machine learning affords considerable opportunity for providing additional value from sensor networks, fusing metocean data collected by various platforms. Exploiting the ship as-a-wave-buoy concept, this article proposes the integration of vessel-based observations into a wave-nowcasting framework. Surrogate models are trained using a high-fidelity physics-based nearshore wave model to learn the spatial correlations between grid points within a computational domain. The performance of these different models are evaluated in a case study to assess how well wave parameters estimated through the spectral analysis of ship motions can perform as inputs to the surrogate system, to replace or complement traditional wave buoy measurements. The benchmark study identifies the advantages and limitations inherent in the methodology incorporating ship-based wave estimates to improve the reliability and availability of regional sea state informationResearch Council of NorwayEngineering and Physical Sciences Research Council (EPSRC)Royal Academy of Engineering (RAE