Abstract:
Thermo-fluid convection is a phenomenon observed across various spatial scales, from
large-scale ocean currents to small, enclosed cavities, such as those found in insulating glazing
units (IGUs). In these systems, convective flows undergo a transition from laminar to turbulent
regimes as the Rayleigh number varies, which has significant implications for heat transfer. In
high-aspect ratio cavities, such as IGUs, this transition manifests as a bifurcation from a
unicellular to a multicellular flow pattern within the laminar regime, thereby increasing the
overall heat transfer.
The Rayleigh number is a dimensionless quantity that measures the influence of buoyancy
forces relative to viscous forces in a fluid. In IGUs, as the Rayleigh number increases, the
convective flow changes from a simple unicellular pattern to more complex multicellular
patterns. This bifurcation is important because it marks a transition that affects the efficiency
of the heat transfer process.
To study this complex phenomenon, traditional computational methods like Computational
Fluid Dynamics (CFD) are commonly used. However, CFD simulations can be
computationally expensive and time-consuming, especially when trying to model a range of
parameters like different Rayleigh numbers.
This thesis investigates the bifurcation response of convective flows in high-aspect ratio
cavities using a Physics-Informed Neural Network (PINN) framework. The PINN leverages
the Navier-Stokes equations to model the flow dynamics, while its loss function integrates both
data loss, derived from limited Computational Fluid Dynamics (CFD) snapshot data, and
physics loss, which includes governing equations and boundary conditions. Despite the
constraint of limited data, the surrogate PINN model demonstrates the capability to accurately
predict the multicellular bifurcation behavior.
This work highlights the potential of PINNs in developing computationally efficient surrogate
models for exploring the bifurcation regime in thermo-fluid convection as key control
parameters, such as the Rayleigh number, are varied. The findings provide valuable insights
into optimizing heat transfer in IGUs and other high-aspect ratio cavities, contributing to
advancements in energy-efficient building technologies
