  The long time behavior of Wong-Zakai approximations of 2D as well as 3D
non-autonomous stochastic convective Brinkman-Forchheimer (CBF) equations with
non-linear diffusion terms on bounded and unbounded ($\mathbb{R}^d$ for
$d=2,3$) domains is discussed in this work. To establish the existence of
random pullback attractors, the concept of asymptotic compactness (AC) is used.
In bounded domains, AC is proved via compact Sobolev embeddings. In unbounded
domains, due to the lack of compact embeddings, the ideas of energy equations
and uniform tail estimates are exploited to prove AC. In the literature, CBF
equations are also known as \emph{Navier-Stokes equations (NSE) with damping},
and it is interesting to see that the modification in NSE by linear and
nonlinear damping provides better results than that available for NSE. The
presence of linear damping term helps to establish the results in the whole
domain $\mathbb{R}^d$. The nonlinear damping term supports to obtain better
results in 3D and also for a large class of nonlinear diffusion terms.
Moreover, we prove the existence of a unique random pullback attractor for
stochastic CBF equations with additive white noise. Finally, for additive as
well as multiplicative noise case, we establish the convergence of solutions
and upper semicontinuity of random pullback attractors for Wong-Zakai
approximations of stochastic CBF equations towards the random pullback
attractors for stochastic CBF equations when correlation time of colored noise
converges to zero.
