Reduced-order models (ROMs) are a critical tool for sensitivity analysis, parameter inference, and uncertainty quantification where high-fidelity models would be computationally intractable. Galerkin POD-ROMs are one particular class of ROMs which project high-fidelity model equations onto a set of model solutions to construct ROMs retaining original model parameters and physics, enabling accurate sensitivity analysis, parameter inference, and uncertainty quantification. However, major limitations of POD-ROMs are inability to quantify boundary conditions, identifying new high-fidelity solutions to improve the POD-ROM, and efficiently updating POD modes with new high-fidelity solutions. To solve these limitations, we extend the use of boundary penalties to quantify perturbed boundary values in a POD-ROM of an incompressible Navier-Stokes flow. Next, we improve sensitivity approximation using POD-ROMs by projecting sensitivity equations onto the existing POD basis. Finally we propose a sensitivity-based error estimator on which to apply greedy sampling and an iterative mode updating approach using randomized SVD and test these advances on the adiabatic tank problem. This presentation is a practice for Harley’s upcoming preliminary exam.