Many engineering tasks, such as parametric study and uncertainty quantification, require rapid and reliable solution of partial differential equations (PDEs) for many different configurations. In this talk, we consider goal-oriented model reduction of parametrized nonlinear PDEs with an emphasis on aerodynamics problems. The key ingredients are as follows: the discontinuous Galerkin (DG) method, which provides stability for convection-dominated flows; adaptive mesh refinement, which controls DG spatial error; reduced basis (RB) spaces, which provide rapidly convergent approximations of the parametric manifolds; the dual-weighted residual (DWR) method, which provides effective error estimates for quantities of interest; the empirical quadrature procedure (EQP), which provides hyperreduction of the nonlinear residual and error estimates; and adaptive greedy algorithms, which simultaneously trains the DG spaces, RB spaces, and EQP to meet the user-specified output error tolerance. We demonstrate the framework for parametrized aerodynamics problems modeled by the compressible Euler and Reynolds-averaged Navier-Stokes equations, including unsteady flows and geometry transformation problems with high-dimensional parameter spaces. In the offline stage, the adaptive greedy algorithm trains reduced models in a fully automated manner. In the online stage, the reduced models accelerate the computation by several orders of magnitude and provide the associated error estimate for the quantities of interest.