Reduced Location Uncertainty Models – RedLUM

To achieve these ambitious goals, the dimensionality of the problems will be considerably reduced through the use of reduced-order data and models. The errors induced by the reduction in dimension will be quantified by a stochastic, physical and multi-scale parameterisation called ‘Models under location uncertainty’. This quantification of uncertainty will enable simulation-measurement coupling via recent data assimilation algorithms.

The expected results will be scientific, methodological and software knowledge for the rapid, predictive and low-cost simulation of turbulent flows. A proof of concept will be carried out on real flows in a wind tunnel and then from the laboratory to the field on the control of micrometeorology in agriculture.

The methodology developed could have practical applications in various industries in which rapid simulation of turbulent flows is used to make decisions or control systems, such as aeronautics, wind energy, water sports, ventilation and processes.

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The objectives of the RedLUM project are the development and use of mathematical and computer tools, for real-time estimation and short-term prediction of 3D fluid flows, using limited computing resources. This will be made possible by the coupling between data, numerical simulations and sparse fluid flow measurements.
To reach these ambitious goals, the dimensionality of the problems will be considerably reduced thanks to the use of data and so-called reduced order models. The errors induced by the dimension reduction will be quantified by a stochastic, physical and multi-scale parameterization called "Models under location uncertainty". This uncertainty quantification will allow simulation-measurement coupling via the latest data assimilation algorithms.