Advancing a first-principles basis for the prediction and manipulation of turbulent wall-flow transport

Project description

Turbulent fluid flow adjacent to a solid surface is of pervasive importance to numerous processes and devices. Reliable prediction of these flows beyond the realm of previous empirical observation is currently not possible. This stems from the lack of a first-principles basis for predicting or controlling mean-flow behaviours. Our aim is to advance a new first-principles based theory. This is significant since it would provide the desired predictive integrity. The proposed research explores the capacity to describe the solutions of the mean temperature and velocity equations for wall-flows influenced by drag-reducing polymers. The expected outcome is an improved basis for predicting or controlling turbulent wall-flow transport.

More specifically, the purpose of the proposed research is to more deeply investigate the predictive potential of a recently developed theory that, unlike previous theories, is distinctive in its singular basis in Newton's second law. The effort will be advanced through experiments that employ Molecular Tagging Velocimetry and Molecular Tagging Thermometry (MTV and MTT, respectively). These techniques will provide very highly resolved instantaneous profile data of axial velocity and temperature simultaneously. The expected outcomes of this research are a deeper understanding of the basic mechanisms of wall-turbulence, and the advancement of prediction capabilities having reliability founded in first-prinicples.

Project team

Leader: Joseph Klewicki

Staff: Kapil Chauhan, John Elsnab

Collaborators: Chris White (University of New Hampshire) Manooch Koochesfahani (Michigan State University)

Sponsors: ARC

Other projects

Optimisation of resources and infrastructure projects

Disciplines

Mechanical Engineering

Domains

Optimisation of resources and infrastructure

Keywords

coherent structures; fluid dynamics; turbulence