Coordination | Prof. Dr.-Ing. habil. Nils Goseberg |
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Dr.-Ing. Clemens Krautwald | |
Team | Dr.-Ing. Clemens Krautwald |
Aileen Brendel, M.Sc. | |
Funding | European Union under project number 101123732 |
Duration | 07/2024 - 06/2029 |
Many Earth system processes involving multi-physics, multi-phase conditions extend over several orders of magnitude in length- and time-scales. Engineering science, in pursuit of deeper process understanding and solution-oriented design, has used scaling theories to address scale-afflicted, complex processes through experimental work in laboratory environment at reduced scale. The standard scaling approach, the Buckingham π-theorem, is especially deficient when multi-physics and multi-phase processes require the choice of more than a single non-dimensional number, resulting in severe scale effects and typically meaning that accuracies at reduced scale are inadequately quantified. Hence, we choose a demonstrably complex multi-physics, multi-phase process for the investigation of scaling accuracies – the progressive collapsing of residential buildings and the associate debris transport, evolving from extreme flow events from natural hazards, such as flash floods or tsunami.
ANGRYWATERS seeks to achieve a breakthrough in modelling these complex processes by deriving novel scaling laws that will be developed in the framework of the Lie group of point scaling transformations. Scaling requirements will be applied to the combined fluid-structure interaction at various scales, developing sophisticated building specimens; here, we employ 3D-printing and appropriately engineered materials to match the scaling requirements. We conduct a comprehensive experimental campaign, using medium- and large-scale facilities, subjecting the specimens to extreme flow conditions in the form of dam-break waves. We consider sub-assemblages, single and multiple buildings, enhancing the understanding of energy losses and debris production upon collapse, elaborating reduced scale accuracies. High-fidelity numerical modelling will complement our experiments, deepening our process understanding; a depth-averaged model with novel debris advection model crucially enhances predictive capabilities.