  The core-collapse supernova (CCSN) mechanism is fundamentally
three-dimensional with instabilities, convection, and turbulence playing
crucial roles in aiding neutrino-driven explosions. Simulations of CCNSe
including accurate treatments of neutrino transport and sufficient resolution
to capture key instabilities remain amongst the most expensive numerical
simulations in astrophysics, prohibiting large parameter studies in 2D and 3D.
Studies spanning a large swath of the incredibly varied initial conditions of
CCSNe are possible in 1D, though such simulations must be artificially driven
to explode. We present a new method for including the most important effects of
convection and turbulence in 1D simulations of neutrino-driven CCSNe, called
Supernova Turbulence In Reduced-dimensionality, or STIR. Our new approach
includes crucial terms resulting from the turbulent and convective motions of
the flow. We estimate the strength of convection and turbulence using a
modified mixing length theory (MLT) approach introducing a few free parameters
to the model which are fit to the results of 3D simulations. For sufficiently
large values of the mixing length parameter, turbulence-aided neutrino-driven
explosions are obtained. We compare the results of STIR to high-fidelity 3D
simulations and perform a parameter study of CCSN explosion using 200
solar-metallicity progenitor models from 9 to 120 $M_\odot$. We find that STIR
is a better predictor of which models will explode in multidimensional
simulations than other methods of driving explosions in 1D. We also present a
preliminary investigation of predicted observable characteristics of the CCSN
population from STIR, such as the distributions of explosion energies and
remnant masses.
