Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in the academic community. We look forward to seeing you there!
PO288: Fast modelling of long term hindcast wave conditions and extremes
Pierre Swiegers, Senior Metocean Engineer, DHI A/S
Abstract
We introduce the Fast Wave Emulator (FWE) which is meant for modelling high resolution nearshore and offshore hindcast wave conditions at a fraction of the time of state-of-the-art spectral wave modelling methods. Fast and cost-efficient modelling of wave conditions is useful for rapid assessment of operational and extreme conditions for pre-FEED design and risk assessments during the installation, operating and maintenance phases. The FWE is a web-based interface, which makes simplified modelling accessible to engineers without wave modelling expertise and reduces the model set-up time significantly for experienced engineers. This is achieved by the following: * The FWE guides the user to set up an automated mesh for modelling of the conditions. * FWE automatically applies relevant and high-quality data from the MetOcean On Demand (MOOD) data portal as boundary conditions to the wave model. * FWE leverages the cloud to remove hardware investment, increasing cost-effectiveness. The modelling is powered by recognized MIKE software (MIKE 21 SW). The FWE shares the generated hindcast data with the MOOD data portal. In the MOOD data portal, the data can be validated against existing in-situ data or satellite altimeter data. The MOOD data portal can also be used to generate analytics from the hindcast data, including extreme value analysis (EVA). An example case is presented which compares the FWE and MOOD EVA against a state-of-the-art numerical spectral wave model and manual EVA in terms of computational cost, engineering resources and reliability of models and analysis.
