Two models based on convolutional neural networks are trained to predict the two-dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a turbulent open-channel flow, using the wall-shear-stress components and the wall pressure as inputs. The first model is a fully convolutional neural network (FCN) which directly predicts the fluctuations, while the second one reconstructs the flow fields using a linear combination of orthonormal basis functions, obtained through proper orthogonal decomposition (POD), and is hence named FCN-POD. Both models are trained using data from direct numerical simulations at friction Reynolds numbers $Re_{\tau } = 180$ and 550. Being able to predict the nonlinear interactions in the flow, both models show better predictions than the extended proper orthogonal decomposition (EPOD), which establishes a linear relation between the input and output fields. The performance of the models is compared based on predictions of the instantaneous fluctuation fields, turbulence statistics and power-spectral densities. FCN exhibits the best predictions closer to the wall, whereas FCN-POD provides better predictions at larger wall-normal distances. We also assessed the feasibility of transfer learning for the FCN model, using the model parameters learned from the $Re_{\tau }=180$ dataset to initialize those of the model that is trained on the $Re_{\tau }=550$ dataset. After training the initialized model at the new $Re_{\tau }$ , our results indicate the possibility of matching the reference-model performance up to $y^{+}=50$ , with $50\,\%$ and $25\,\%$ of the original training data. We expect that these non-intrusive sensing models will play an important role in applications related to closed-loop control of wall-bounded turbulence.

State-of-art preprocessing methods for Particle Image Velocimetry (PIV) are severely challenged by time-dependent light reflections and strongly non-uniform background. In this work, a novel image preprocessing method is proposed. The method is based on the Proper Orthogonal Decomposition (POD) of the image recording sequence and exploits the different spatial and temporal coherence of background and particles. After describing the theoretical framework, the method is tested on synthetic and experimental images, and compared with well-known pre-processing techniques in terms of image quality enhancement, improvements in the PIV interrogation and computational cost. The results show that, unlike existing techniques, the proposed method is robust in the presence of significant background noise intensity, gradients, and temporal oscillations. Moreover, the computational cost is one to two orders of magnitude lower than conventional image normalization methods. A downloadable version of the preprocessing toolbox has been made available at http://seis.bris.ac.uk/~aexrt/PIVPODPreprocessing/.

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This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse wall measurements. The method is applied both for the resolution enhancement of wall fields and the estimation of wall-parallel velocity fields from coarse wall measurements of shear stress and pressure. The analysis has been carried out with a database of a turbulent open-channel flow with a friction Reynolds number [Formula: see text] generated through direct numerical simulation. Coarse wall measurements have been generated with three different downsampling factors [Formula: see text] from the high-resolution fields, and wall-parallel velocity fields have been reconstructed at four inner-scaled wall-normal distances [Formula: see text]. We first show that SRGAN can be used to enhance the resolution of coarse wall measurements. If compared with the direct reconstruction from the sole coarse wall measurements, SRGAN provides better instantaneous reconstructions, in terms of both mean-squared error and spectral-fractional error. Even though lower resolutions in the input wall data make it more challenging to achieve highly accurate predictions, the proposed SRGAN-based network yields very good reconstruction results. Furthermore, it is shown that even for the most challenging cases, the SRGAN is capable of capturing the large-scale structures that populate the flow. The proposed novel methodology has a great potential for closed-loop control applications relying on non-intrusive sensing.

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The use of the infrared camera as a temperature transducer in wind tunnel applications is convenient and widespread. Nevertheless, the infrared data are available in the form of 2D images while the observed surfaces are often not planar and the reconstruction of temperature maps over them is a critical task. In this work, after recalling the principles of IR thermography, a methodology to rebuild temperature maps on the surfaces of 3D object is proposed. In particular, an optical calibration is applied to the IR camera by means of a novel target plate with control points. The proposed procedure takes also into account the directional emissivity by estimating the viewing angle. All the needed steps are described and analyzed. The advan- tages given by the proposed method are shown with an experiment in a hypersonic wind tunnel

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