A Goal-Oriented, Reduced-Order Modeling Framework of Wall-Bounded Shear Flows

Reduced-order models (ROMs) are commonly used to reduce the complexity of large-scale fluid dynamics problems. The main technique to obtain their basis is proper orthogonal decomposition (POD), where an energy-based basis is obtained. Such a basis is derived without considering the governing equations. Moreover, if the flow dynamics are not...

Derivative Computation using an Adjoint-Based Goal-Oriented Iterative Multiscale Lagrangian Framework: Application to Reservoir Simulation

Even though negative effects on the use of crude oil have surfaced over the past years, our energy matrix still largely relies on this energy source. The production of oil, therefore, plays an important role in our society. Unfortunately, the process of oil production is highly uncertain. There are uncertainties associated on the production...

Capturing the flow: Reduced-order modeling using goal-oriented optimization

The use of detailed numerical simulations for practical applications or in the study of complex flow phenomenon is limited by the computational cost. Over the last two decades, reduced order models (ROMs) have become a viable alternative on account of their low computational cost. ROMs capture only the essential dynamics of interest rather than...

Goal-Oriented, Reduced-Order Modeling of the Incompressible Navier-Stokes Equations: Application to a 2D transitional boundary layer

Research into constructing reduced-order models (ROM) to reduce computational cost or to interpret complex datasets from numerical or experimental sources has steadily been gaining popularity over the past few years. Many methods currently exist to construct ROMs. The proper orthogonal decomposition (POD) is one of the most widely adopted...

Output error estimation using super-resolution neural networks

Numerical simulations and optimisation methods, such as mesh adaptation, rely on the accurate and inexpensive use of error estimation methods. Output-error estimation is the most accurate method; however, it relies on the use of approximations in order to be implemented in practice. The proposed method in this thesis relies on the use of super...

A goal-oriented adjoint-based optimization approach to model vortex generator flows using smooth source term distributions

Vortex generators (VGs) are typically about two orders of magnitude smaller than their host component (such as an airplane wing). For this reason, conducting a fully-resolved RANS simulation to isolate their impact on the flow field is computationally expensive. This work presents a goal-oriented adjoint-based approach to model vortex generators...