Skip to main navigationSkip to main content
The University of Southampton
NEXUSS - Next Generation Unmanned Systems Science

Jan-Torben Witte

Project

Ocean turbulence is sporadic, chaotic and, therefore, difficult to understand and predict. Yet it is critical to understand and simulate turbulence to predict ocean dynamics and the complex interactions that control the global biogeochemical cycle and the earth’s climate.

The lack of a consistent data set has been always a challenge for oceanographic research and hampers the development, correct calibration and validation of more refined models for the prediction of momentum, heat and nutrient transfer in the ocean. Microstructure-enabled ocean gliders provide an autonomous and reliable method to measure turbulence and have already been extensively used within the research community at different locations throughout the ocean. Benefits are an easy deployment, remote adaptation of the mission, long endurance and the possibility of sampling the water column horizontally and vertically at the same time.

Uncertainty still remains, however, on how the motion of buoyancy-controlled platforms impacts the flow around the attached sensors, which measure millimetre-scale turbulence. To further examine the suitability of the glider platform for turbulence measurements, the improvement in speed, performance and availability of numerical simulations is utilised using a program for solving partial differential equations on adaptive Cartesian meshes (Basilisk) to model the flow around the geometry of a glider. Initial sensitivity studies to improve algorithms for calculating glider motion and oceanic turbulence could solidify the robustness of the LES modelling approach and possibly confirm experimental studies and the hydrodynamic force assumptions.

The newly acquired skills and knowledge can be employed to validate and to analyse observational data, collect an even broader range of field data and thus enabling reliable applications to glider-based measurements.

 

View student profile

Privacy Settings