Lin Fu

The Hong Kong University of Science and Technology, Mechanical and Aerospace Engineering, Faculty Member

Stanford University, Mechanical Engineering, Post-Doc

Technical University of Munich, Mechanical Engineering, Post-Doc

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I am a Professor in HKUST.
Phone: +852 3469 2969
Address: Assistant Professor of Mechanical and Aerospace Engineering
The Hong Kong University of Science and Technology

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Harbin Institute Of Technology

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Resolvent based prediction framework for incompressible turbulent channel flow with limited measurements

Journal of Fluid Mechanics, 2023

A new resolvent-based method is developed to predict the space–time properties of the flow field.... more A new resolvent-based method is developed to predict the space–time properties of the flow field. To overcome the deterioration of the prediction accuracy with increasing distance between the measurements and predictions in the resolvent-based estimation (RBE), the newly proposed method utilizes the RBE to estimate the relative energy distribution near the wall rather than the absolute energy directly estimated from the measurements. Using this extra information from RBE, the new method modifies the energy distribution of the spatially uniform and uncorrelated forcing that drives the flow system by minimizing the norm of the cross-spectral density tensor of the error matrix in the near-wall region in comparison with the RBE-estimated one, and therefore it is named as the resolvent-informed white-noise-based estimation (RWE) method. For validation, three time-resolved direct numerical simulation (DNS) datasets with the friction Reynolds numbers Reτ=180, 550 and 950 are generated, with various locations of measurements ranging from the near-wall region (y+=40) to the upper bound of the logarithmic region (y/h≈0.2, where h is the half-channel height) for the predictions. Besides the RWE, three existing methods, i.e. the RBE, the λ-model and the white-noise-based estimation (WBE), are also included for the validation. The performance of the RBE and scale-dependent model (λ-model) in predicting the energy spectra shows a strong dependence on the measurement locations. The newly proposed RWE shows a low sensitivity on Reτ and the measurement locations, which may range from the near-wall region to the upper bound of the logarithmic region, and has a high accuracy in predicting the energy spectra. The RWE also performs well in predicting the space–time properties in terms of the correlation magnitude and the convection velocity. We further utilize the new method to reconstruct the instantaneous large-scale structures with measurements from the logarithmic region. Both the RWE and RBE perform well in estimating the instantaneous large-scale structure, and the RWE has smaller errors in the estimations near the wall. The structural inclination angles around 15∘ are predicted by the RWE and WBE, which generally recover the DNS results.

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