Subaqueous bed forms are ubiquitous on the seafloor, along coastlines, in estuaries, and on riverbeds, affecting a wide range of phenomena including sediment transport, granular sorting, waves and circulation, benthic activity, and acoustic penetration into sediments. Since many of the processes driving sediment transport occur over small spatial and temporal scales, we believe small-scale bed form dynamics are directly linked to large-scale morphodynamics. However, the ephemeral and turbulent nature of bed forms inhibits our understanding of their impact on large-scale sedimentary and hydrodynamic processes. Consequently, bed forms are of interest to the coastal engineering, underwater acoustics, oceanography, riverine, and environmental communities.
 Of the many different types of bed forms (e.g., ripples, dunes, anti-dunes, megaripples), ripples can occur in areas seaward of the breaker zone and where waves have reformed after breaking on a sandbar [Nielsen, 1981; Hanes et al., 2001] and have been observed in situ when the Shields parameter is less than 1.0 [Fredsøe and Deigaard, 1992]. Ripples are classified as rolling grain ripples if the flow does not separate behind the ripple crests, and vortex ripples if vortices are generated as the flow reverses directions [Bagnold, 1946]. The process of vortex formation can entrain sediment and dissipate wave energy [Tunstall and Inman, 1975; Nakato et al., 1977; Ardhuin et al., 2002].
 Obtaining measurements at the spatial and temporal resolution necessary for the examination of small-scale turbulent fluctuations over vortex ripples is difficult. For example, an oscillating tray of sediment has been used to study three-dimensional bed forms generated by combined steady currents and oscillations induced by the tray motion [Lacy et al., 2007]. However, steady streaming effects are not produced by the device, and the tray motion induces a mean flow and an inertial force on the bed forms not found in the field [Scherer et al., 1999; Lacy et al., 2007]. Two-dimensional bed forms have been studied in detail in oscillatory flow tunnels that simulate horizontal flows [Foti and Blondeaux, 1995; O'Donoghue and Clubb, 2001; O'Donoghue et al., 2006; Ribberink et al., 2008], but three-dimensional bed form geometries are less common in one-dimensional flows. Recently, detailed information on bed form dynamics under surface gravity waves has been studied in both laboratory flumes [Landry et al., 2007; van der Werf et al., 2007, 2009; Nichols and Foster, 2009; Hurther and Thorne, 2011] and in the field [Traykovski et al., 1999; Hanes et al., 2001; Doucette, 2002a, 2002b]. However, limitations exist in laboratory flumes such as the inability to produce multi-directional waves and combined wave-current flows. In situ measurements of vector and scalar quantities of a three-dimensional domain are even more sparse, as the technology to take such measurements has only recently been developed and is still being refined [Doucette et al., 2002; Elsinga et al., 2006; Fouras et al., 2009].
 Previous numerical studies of the flow structure over static (or fixed) bed forms [Blondeaux and Vittori, 1991; Scandura et al., 2000; Barr et al., 2004; Chang and Scotti, 2003; Zedler and Street, 2006; Shimizu et al., 2001; Bhaganagar and Hsu, 2009] have broadened our knowledge of vorticity dynamics in oscillatory flow; however, the dynamic coupling of the fluid and sediment, affecting turbulence and resulting morphodynamics has been lacking. Because strong correlations exist between the sediment transport, flow field, and bed morphology, coupling these processes is necessary to fully understand bottom boundary layer dynamics. We hypothesize it is the three-dimensional small-scale turbulent fluctuations and the small-scale fluid-sediment interactions that govern much of the morphologic evolution of the seafloor. However, the disparate spatial and temporal scales of interest for vortex entrainment of sediment over a sand ripple compared with the transition of a natural ripple field in response to changing wave conditions, for example, limit our present ability to perform three-dimensional high-fidelity numerical simulations for the latter. Existing two-dimensional numerical models that solve the Reynolds-Averaged Navier-Stokes (RANS) equations have been used to simulate coupled bed form dynamics under unidirectional [Giri and Shimizu, 2006] and oscillatory flow [Marieu et al., 2008]. However, these formulations must parameterize the three-dimensional temporal and spatial fluctuations that drive boundary layer turbulence and ultimately, morphodynamics. While these types of models may be useful for engineering applications, a three-dimensional Navier-Stokes solver is necessary to resolve the turbulent structures and the coupled fluid-sediment interactions over an evolving bed.
 We use a three-dimensional mixture theory model, SedMix3D, that solves the unfiltered Navier-Stokes equations for a fluid-sediment mixture to simulate bottom boundary layer oscillatory flow over ripples [Penko et al., 2011]. Mixture theory treats the fluid and sediment phases as a single continuum with the inter- and intra-phase interactions (e.g., effective viscosity, hindered settling, diffusion) parameterized through closure relations. Results from a three-dimensional simulation performed with SedMix3D are compared with planar particle image velocimetry (PIV) data obtained in the laboratory under scaled forcing conditions. Model validation is performed using both time domain flow velocities and bulk statistics.
 Although the comparisons made here utilize velocities measured in a two-dimensional vertical plane, it is important to note that the results obtained from SedMix3D are three-dimensional. In the following section, the model formulation and the experimental conditions are described. Direct comparisons between the laboratory data and the simulation results are made in section 3. The three-dimensionality of the vorticity and resulting sediment suspension that is unobtainable from the data but simulated by the model is also discussed in section 3. The discussion focuses on addressing discrepancies between laboratory data and simulation results where they exist and the limitations of the PIV technique.