In this study, the sinusoidal wavy (SW) copper microchannels with triangular cross section were fabricated with the ultrafast laser micromachining approach. The flow boiling of de-ionized water in SW microchannels were experimentally studied under mass fluxes of 43.43–217.15 kg (m2s)−1 and heat fluxes of 0–590 kW m−2. The flow patterns in the SW microchannels were visually observed and the boiling heat transfer characteristics were analyzed based on the obtained data. It shows the SW microchannel has a maximum enhancement of 127.7% in the local heat transfer coefficient with an acceptable increase of pressure loss compared to the straight one in the present work. In SW microchannels, a low degree of superheat can trigger the onset of nucleate boiling. A slow decreasing tendency of local heat transfer coefficient under a high effective heat flux or a high vapor quality was observed. The heat transfer mechanism of flow boiling in SW microchannels was analyzed based on the experimental visualization. Nucleate boiling and thin film evaporation are the dominant modes under different thermal boundary conditions. The flow boiling instability phenomenon in microchannels can be diminished using the wavy structures.
Laser thermal adjustment as an application of laser forming in microsystems attracts much attention. Previous work on laser thermal adjustment of the two-bridge actuator (TBA) shows that the deformations induced by laser forming are limited. In this paper, an actuator with three cut-outs including six heating positions is designed to enhance the deformation range. A deformation model is developed for such an actuator by introducing the factors of the in-plane and out-of-plane angles to the TBA's formula, which takes energy constraints into account to avoid the melting phenomenon and negative deformations. The deformation range of the three cut-outs actuator (TCA) is determined by using the relation between in-plane and out-of-plane angles of the TBA. The optimization of the processing parameters for the TCA is conducted to reach the designed target position based on the optimization algorithm of adaptive simulated annealing (ASA). The model prediction is validated by the finite-element analysis (FEA) simulation and experiments.
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