Data-Driven Model Reduction and Real-Time Estimation and Control of Coherent Structures in Turbulent Flows

This grant will support research that can promote a paradigm shift in the way we study, simulate and control turbulent flows whose potential benefits can have a significant economic and environmental effect. The active flow control methods are envisioned to play a key role in many real-world aerospace and transportation engineering applications by having a positive impact on efforts to reduce drag on, for example, airplanes, trains, and trucks (and thus increase performance and reduce fuel consumption and greenhouse gas emissions).

The researched methods can also find application in the technology of extracting renewable energy by large arrays of wind turbines. In addition, this research project will promote nationwide efforts to enable synergies between the rapidly emerging data sciences and traditional engineering fields such as control theory and fluid dynamics. To promote research dissemination and reproducibility, the algorithms will be made available to the public by means of relevant online platforms and repositories.

This research will also help recruit graduate students from underrepresented and minority groups, help undergraduate research and K-12 outreach and create a package of material for a course on turbulent flow control.

In this research project, we will create novel active flow control methods which incorporate mechanisms for the detection and manipulation of the so-called large-scale coherent structures that characterize wall-bounded turbulent flows. The long-term goal of this effort is to develop a holistic framework for modeling, estimation and control of turbulent flows which can induce a paradigm shift in the way one can manipulate and exploit wall bounded turbulence characterized by large-scale coherent structures.

The key idea of our approach lies in the direct detection and control of isolated structures viewed as targets of opportunity. The specific objectives of the work are (i) Generate data through high-fidelity direct numerical simulations of a laminar and a turbulent boundary layer with force-field inputs. These data together with sparsity promoting optimization tools will form the basis for reduced order descriptions of the flow to control inputs. (ii) Create robust real-time algorithms for flow field estimation and control based on respectively, multi-model estimation and stochastic control techniques, and (iii) Validate the algorithms for detection and selective manipulation (e.g. steering toward or away from a target region) of large-scale coherent structures using direct numerical simulations.

The algorithms developed in the work will be demonstrated in an abstracted version of a wind turbine array performance optimization by selective steering of large-scale motions.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.