Scaling fluid-driven processes: Building Collapse in Extreme Flow Conditions

Introduction

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 environments at reduced scale.

Scaling Challenges

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. This results in severe scale effects and typically means that accuracies at reduced scale are inadequately quantified.

Research Focus

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 associated debris transport, evolving from extreme flow events from natural hazards, such as flash floods or tsunamis.

Objectives of ANGRYWATERS

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

Scaling requirements will be applied to the combined fluid-structure interaction at various scales, developing sophisticated building specimens. Here, we employ:

• 3D-printing
• Appropriately engineered materials to match the scaling requirements.

Experimental Campaign

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.

Focus Areas

We consider:

1. Sub-assemblages
2. Single and multiple buildings

This enhances the understanding of energy losses and debris production upon collapse, elaborating reduced scale accuracies.

Numerical Modelling

High-fidelity numerical modelling will complement our experiments, deepening our process understanding. A depth-averaged model with a novel debris advection model crucially enhances predictive capabilities.