Alec Kucala

Flow stabilization by subsurface phonons

The interaction between a fluid and a solid surface in relative motion represents a dynamical process that is central to the problem of laminar-to-turbulent transition (and consequent drag increase) for air, sea and land vehicles, as well as long-range pipelines. This problem may in principle be alleviated via a control stimulus designed to impede the generation and growth of instabilities inherent in the flow. Here, we show that phonon motion underneath a surface may be tuned to passively generate a spatio-temporal elastic deformation profile at the surface that counters these instabilities. We theoretically demonstrate this phenomenon and the underlying mechanism of frequency-dependent destructive interference of the unstable flow waves. The converse process of flow destabilization is illustrated as well. This approach provides a condensed-matter physics treatment to fluid–structure interaction and a new paradigm for flow control.

PARALLEL IMPLEMENTATION OF A NAVIER–STOKES SOLVER: TURBULENT EKMAN LAYER DIRECT SIMULATION

A massively parallel direct numerical solution procedure for the turbulent Ekman layer is presented. The simulations study the dynamics of turbulence in this flow by solving the incompressible Navier–Stokes equations with Coriolis and buoyancy terms. The governing equations are integrated via a semi-implicit time advancement algorithm which is massively parallelized using the Portable, Extensible Toolkit for Scientific Computation (PETSc) libraries. Accuracy of the numerical scheme was validated by comparisons of simulation results with the hydrodynamic linear stability theory for Poiseuille flow. Two cases are presented to demonstrate the capabilities of the code: (a) a neutrally stable case of Reynolds number, Re = 400 and (b) an unstably stratified case at Re = 1,000 requiring very high resolution in all coordinate directions. Results indicate that the scalability is not limited by the overall size of the problem, but rather by the number of mesh points per processor. Strong scaling is demonstrated for both cases with as few as 10,000 unknowns per processor.

Wake effects on turbulent transport in the convective boundary layer

The effect of the downstream propagation of a wake on the transport of momentum, energy and scalars (such as humidity) in the convective boundary layer (CBL) is studied using a direct numerical simulation. The incompressible Navier–Stokes and energy equations are integrated under neutral and unstable thermal stratification conditions in a rotating coordinate frame with the Ekman layer approximation. Wake effects are introduced by modifying the mean velocity field as an initial condition on a converged turbulent Ekman layer flow. With this initial velocity distribution, the governing equations are integrated in time to determine how turbulent transport in the CBL is affected by the wake. Through the use of Taylor’s hypothesis, temporal evolution of the flow field in a doubly periodic computational domain is transformed into a spatial evolution. The results clearly indicate an increase in the scalar flux at the surface for the neutrally stratified case. An increase in wall scalar and heat flux is also noted for the CBL under unstable stratification, though the effects are diminished given the enhanced buoyant mixing associated with the hot wall.

Parallel Implementation of a Navier-Stokes Solver: Turbulent Ekman Layer Direct Numerical Simulation

A massively parallel direct numerical solution procedure for the turbulent Ekman layer is presented. The simulation studies the dynamics of turbulence for this flow by solving the incompressible Navier-Stokes equations with Coriolis and buoyancy terms. The governing equations are integrated via a semi-implicit time advancement algorithm which is massively parallelized using the Portable, Extensible Toolkit for Scientific Computation (PETSc) libraries. Accuracy of the numerical scheme was validated by comparisons of simulation results with the hydrodynamic linear stability theory for Poiseuille flow. Two cases are presented to demonstrate the capabilities of the code: a) a neutrally stable case of Reynolds number, Re = 400 and b) an unstably stratified case at Re = 1000 requiring very high resolution in all coordinate directions. Results indicate that the scalability is not limited by the overall size of the problem, but rather by the number of mesh points per processor. Strong scaling is demonstrated for both cases with as few as 10,000 unknowns per processor.