Recent studies show the significant effect of the third dimension and flow unsteadiness of laterally confined Rayleigh-Bénard convection (RBC). However, there are limited studies investigating 4D flow properties. The application of 4D two-colour laser-induced fluorescence to explore a laterally confined RBC at high Rayleigh number of 𝑅𝑎 = 9.9 × 10 7 and Prandtl number of 𝑃𝑟 = 6.1 is investigated using a scanning laser system. A two-colour, two dyes approach was employed to resolve the laser sheet intensity variations due to refractive index variations caused mainly by the generation of thermal plumes. Two temperature-sensitive fluorescent dyes with opposite sensitivities were used to enhance the overall temperature sensitivity to 7.3 % /℃. Details of the experimental procedure and optical system employed to reach such a high sensitivity is demonstrated. From the whole field temperature distribution, the dimensionless heat transfer coefficient, the Nusselt number, and its evolution was calculated for both hot and cold boundaries.Temperature field and Nusselt number obtained from 3D and 2D fields are reported to compare the results for these two scenarios. Thermal plumes were found to have a conical shape in the laterally confined RBC compared to the conventional mushroom shape. From visualization of the time-resolved 3D temperature field and 2D distribution of the Nusselt number, it was also found that only by volumetric measurement, temporal and spatial variations of the temperature and heat transfer can be evaluated. This shows that to evaluate the classic and ultimate theories, volumetric measurement is required for a coherent understanding of the physics.

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Particulate two-phase flows normally manifest high dimensional and complex nonlinear flow phenomena. To capture the intrinsic characteristics of the fluid flows, dimension reduction and feature capturing are of fundamental importance. In this work, we focused on the flow reconstruction and prediction based on dimension reduction and feature capturing with small noised datasets obtained by particle image velocimetry (PIV) experiments by use of a skip-connection convolutional neural network based on autoencoder (scCAE). We evaluated the performances of scCAE in reconstructing and predicting the high dimensional and nonlinear flows around a single particle for moderate Reynolds numbers (Re) of 400− 1400. It is shown that scCAE with the latent vector size of four can well reconstruct and predict the fluid flow fields around either a sphere or cube based on the small noised PIV datasets with the data size of several hundreds, which suggests the robustness and generality of scCAE in dimension reduction and feature capturing. This may be extended to wider applications in extracting dimension-reduction latent vectors from limited ground truth experimental PIV data and disclosing the inherent physics.

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To study the flow topology and temperature distribution of Rayleigh-Benard convection in a highly slender cell, measurement of the simultaneous velocity and temperature in the 3-D domain is required. For this aim, implementing a simultaneous time-resolved 3-D PTV and two-colour PLIF is planned. As a part of this development, for both PTV and two-colour PLIF techniques, the experimental setup has been implemented separately to measure time-resolved 2-D velocity and temperature and is presented in this paper. For PTV, a scanning system is also utilized to scan the flow field to capture the planar velocity in different depths of the flow domain. Progress on calculation of the out-of-plane velocity component including the theory is discussed. Finally, results of the time-resolved 2-D PTV and PLIF systems are presented.