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Durham University

Research & business

Research lectures, seminars and events

The events listed in this area are research seminars, workshops and lectures hosted by Durham University departments and research institutes. If you are not a member of the University, but  wish to enquire about attending one of the events please contact the organiser or host department.


Stats4Grads: Bayes goes to Space: inferring chemical model parameters for tomorrow’s Space journeys

Presented by Anabel del Val, von Karman Institute for Fluid Dynamics, Belgium
6 November 2019 13:00 in CM105

Venturing into Space requires large amounts of energy to reach orbital and interplanetary velocities. The bulk of this energy is exchanged during the entry phase by converting the kinetic energy of the vehicle into thermal energy in the surrounding atmosphere through the formation of a strong bow shock ahead of the vehicle. The way engineers protect spacecraft from the intense heat of atmospheric entry is by designing two kinds of protection systems: reusable and ablative. Reusable systems are characterized by re-radiating a significant amount of energy from the hot surface back into the atmosphere. Ablative materials, on the other hand, transform the thermal energy into decomposition and removal of the material.

The resulting aerothermal environment surrounding a vehicle during atmospheric entry is consequently extremely complex, as such, we often need efficient uncertainty quantification techniques to extract knowledge from experimental data that can appropriately inform the proposed models. We develop robust Bayesian frameworks that aim at characterizing chemical models parameters for re-entry plasma flows in the presence of both types of protection systems. Special care is devoted to the treatment of nuisance parameters which are unavoidable when performing flow simulations in need of proper boundary conditions beyond the interest of the specific inference. Our formulation involves a particular treatment of these nuisance parameters by solving an auxiliary maximum likelihood problem. Results will be shown for real-world cases.

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