Mechanical Engineering - Theses
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ItemExploring the Similarity between Velocity and Temperature Fluctuations in Turbulent Boundary Layers with Forced Convection( 2023-10)Improvements in understanding turbulent boundary layers with forced convection enable enhanced heat transfer control solutions in practical engineering applications. However, systematic investigations on how energy-containing eddies, especially large-scale structures, relate to heat transport and how this corresponds to notions of Reynolds analogy remain to be explored further. This study delves into the role of large-scale turbulence on heat-transfer processes and sheds light on the applicability of Reynolds analogy in smooth wall-bounded turbulence with forced convection. A custom dual-wire sensor has been utilised, permitting simultaneous measurements of streamwise velocity and temperature, facilitating a comprehensive exploration of thermal boundary layer dynamics. To explore scenarios with different streamwise origins for the thermal boundary layer, the ratio of the momentum to the thermal boundary layer thickness ($\delta_\theta/\delta$) varies from 0.26 to 1.1 while maintaining a constant momentum boundary layer thickness. Statistical analysis reveals a strong correlation between velocity and temperature. The mean temperature and velocity profiles exhibit similar patterns when the thermal and momentum boundary layers have matched edges, while discrepancies arise with mismatched edges. Using a two-state TNTI-associated model, the mean temperature profiles are predicted. Near the edge of the thermal boundary layer for the case of $\delta_\theta\approx \delta$, one-sided temperature fluctuations are observed, while $u$-fluctuations remain two-sided, highlighting dissimilarity between $u$ and $\theta$. Furthermore, the one-sided bias phenomenon is evident even closer to the wall, as $\delta_\theta/\delta$ decreases, corroborated by the observed skewed temperature signals even within the logarithmic region. Concerning energy spectra, large scales exhibit a greater degree of (anti) correlation between temperature and velocity fluctuations than smaller scales. As the thermal boundary layer becomes thinner, coherence between $u$ and $\theta$ at larger scales appears to diminish. This observation can be interpreted through a simple model where large-scale motions that span the edge of the thermal boundary layer will produce increasingly one-sided temperature fluctuations while velocity fluctuations will maintain a comparatively Gaussian distribution. Two-point measurements with the application of two customised dual-wire sensors entail the estimation of the streamwise/wall-normal aspect ratio, $\mathcal{AR}$. Here, ${\mathcal{AR} = \lambda^\mathcal{H}_x/\mathcal{H}}$, where $\lambda^\mathcal{H}_x$ is the streamwise length of a wall coherent structure, and $\mathcal{H}$ is the wall-normal extent). The coherence results highlight the prominent role of wall coherent motions in both momentum and heat transport. Diminished coherence when $\delta_\theta/\delta \ll 1$ is attributed to temperature fluctuations produced by large-scale motions that extend vertically beyond the edge of the thermal boundary layer (e.g., $\lambda^+_x\gtrsim \mathcal{AR}\delta^+_\theta$). Temperature fluctuations associated with these structures and impacted by the one-sidedness effect bring about a diminished coherence, reinforcing the proposed model and challenging notions of Reynolds analogy for these scales.
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ItemRobotic Haptic Object Identification through Grasping( 2023-05)Robotic haptic object identification is the process of identifying objects out of a given object set using a robotic hand equipped with tactile and finger-joint displacement sensors. Efficiency and accuracy, two essential evaluation metrics in haptic object identification, are the focus of this thesis. In terms of identification efficiency, when the robotic hand is smaller than an object, multiple grasps are required to capture the whole information of the object. However, from the practical consideration for robotic haptic object identification, it is always preferred to have the least number of grasps to identify an object. Regarding identification accuracy, when taking measurements by grasping the object, the uncertainties in the pose of the object relative to the hand will affect the identification accuracy. Each tactile sensor can capture contact within their specific local areas, thus, any change in the positions where objects make contact in relation to the robotic hand will significantly affect the tactile measurements. This thesis, therefore, aims to address the issues proposed above. The contributions of the thesis are: 1) An information gain-based method is proposed to improve the efficiency of haptic object identification by determining where to grasp to obtain the most distinguishing information about the object, thereby minimising the number of grasps needed; 2) A statistical method based on the Beta mixture model is proposed to improve the accuracy of haptic object identification by systematically characterising the uncertainties in the haptic measurements caused by the deviation in the relative pose between the object and the end-effector.