Beyond incompressible phenomenology: mixing in compressible turbulent flows Grant uri icon


  • PI: Donzis, DiegoProposal Number: 1605914Mixing of substances or species (commonly called "scalars" in the literature) in turbulent flows is a topic of paramount importance from both fundamental and practical standpoints. The focus of this proposal is on the investigation of compressible turbulent flows and mixing. In order to achieve this goal, it is proposed to use simulations of unprecedented high fidelity, pushing the boundaries of the class of simulations known as Direct Numerical Simulations (DNS).While substantial work has been accumulated over decades on mixing in incompressible flows, there is no similar level of exploration or data on mixing in compressible turbulence in spite of its critical importance in diverse areas such as air transportation and aerodynamics, physics of solar wind, flows in stars and supernovae, chemically reacting flows in engines or the atmosphere, among many others. In applications, compressible mixing is commonly modeled using incompressible results based mainly on classical phenomenology. There is, thus, a gap in knowledge which this proposal aims to close by providing groundwork for understanding the fundamental physical processes through controlled and systematic studies in new canonical configurations and the resulting databases of high-fidelity DNS data for the community to explore. The proposed research will comprehensively investigate the effects of Reynolds number, Mach number and Schmidt number. The work proposed includes the definition of a class of canonical flows that can be used to obtain high fidelity results that could lead to advances in compressible scalar mixing. Results for these canonical flows would provide reliable data that can be used as benchmark for theories and models. Furthermore, it is proposed to develop a so-called Langley curve for compressible mixing. The high performance computing techniques to be developed as the side effect of this project and the data will be available to the community of interested scientists and engineers.

date/time interval

  • 2016 - 2020