  Wall-bounded turbulent flows can be challenging to measure within experiments
due to the breadth of spatial and temporal scales inherent in such flows.
Instrumentation capable of obtaining time-resolved data (e.g., Hot-Wire
Anemometers) tends to be restricted to spatially-localized point measurements;
likewise, instrumentation capable of achieving spatially-resolved field
measurements (e.g., Particle Image Velocimetry) tends to lack the sampling
rates needed to attain time-resolution in many such flows. In this study, we
propose to fuse measurements from multi-rate and multi-fidelity sensors with
predictions from a physics-based model to reconstruct the spatiotemporal
evolution of a wall-bounded turbulent flow. A "fast" filter is formulated to
assimilate high-rate point measurements with estimates from a linear model
derived from the Navier-Stokes equations. Additionally, a "slow" filter is used
to update the reconstruction every time a new field measurement becomes
available. By marching through the data both forward and backward in time, we
are able to reconstruct the turbulent flow with greater spatiotemporal
resolution than either sensing modality alone. We demonstrate the approach
using direct numerical simulations of a turbulent channel flow from the Johns
Hopkins Turbulence Database. A statistical analysis of the model-based
multi-sensor fusion approach is also conducted.
