The collapse of a polymer dome intended for carbon sequestration during a storm raises critical questions about the interaction between internal pressure and wind loads. The digital reconstruction of the pre-failure state using photogrammetry with Pix4Dmapper, combined with simulations in Ansys CFX, allows isolating pressure error as the root cause. This case demonstrates how the fusion of metric data and computational fluid dynamics (CFD) surpasses traditional visual inspection methods, offering a quantitative diagnosis of structural failure.
Forensic reconstruction: from digital model to pressure analysis 🔍
The forensic process begins with capturing images of the collapsed dome and its surroundings. Pix4Dmapper processes these shots to generate a dense point cloud and a three-dimensional mesh model reflecting the post-collapse deformed geometry. Based on this, the ideal pre-failure shape is extrapolated using parametric tools in Rhino and Grasshopper, adjusting the curvature and surface tension of the polymer. This ideal model is imported into Ansys CFX, where the storm scenario with lateral winds is simulated. By comparing the calculated internal pressure distribution with design values, a localized underpressure is identified in the windward zone, which caused structural instability and collapse. Unlike a visual inspection, which would only detect visible damage, this workflow reveals the exact mechanical cause.
Lessons for inflatable carbon capture systems 💨
This case underscores the need to integrate real-time pressure sensors into inflatable structures for CO2 sequestration. Photogrammetry and CFD not only validate collapse hypotheses but also allow redesigning anchoring systems and pressurization protocols for storms. Traditional methods, such as periodic drone inspections, fail to quantify fluid-structure interaction. Adopting this digital forensic approach is key to preventing future failures and ensuring the viability of these installations in adverse climatic environments.
Could the integration of real-time wind data into CFD models have anticipated the structural fatigue of the polymer dome and prevented its collapse during the storm?
(PS: Simulating a collapse is easy. The hard part is keeping the program from crashing.)