This paper proposes an intelligent optimization method based on Gaussian process regression (GPR) surrogate model and particle swarm optimization (PSO) algorithm for the heat dissipation optimization of finned liquid cooling plates. First, a sample dataset of fin angles, temperatures, and pressures is constructed through COMSOL simulations, and the GPR surrogate model is trained to establish a high-precision response surface. On this basis, the PSO algorithm is used to perform global optimization within the feasible range of fin angles, with the optimization objectives of simultaneously reducing the bottom wall temperature of the liquid cooling plate and the internal flow pressure. This method can efficiently determine the optimal fin angle. The entire process uses only coarse mesh data to train the model, and the average relative errors of the peak temperature and pressure are 0.07% and 5.36%, respectively. At a flow rate of 500 mL/min, the peak temperature of the liquid cooling plate after fin angle optimization is reduced by approximately 8.94 K. This method significantly reduces the dependence of the optimization process on computing resources and provides an effective approach for the rapid design optimization of finned liquid cooling plates.

An analytical method for the sound absorption performance of the acoustic coating with cavities has been proposed to investigate the effect of the hydrostatic pressure on the performance of the acoustic coating with the consideration of the deformation and prestress of the cavities. By using the finite element method, the sound absorption performance of the acoustic coating that only considers the deformation of the cavities is compared with that of the acoustic coating that considers the deformation and prestress of the cavities under the same hydrostatic pressure. The differences of the maximum deformation, average absorption coefficient and absorption coefficient variation are then studied for the acoustic coating with cylinder, conical and horn cavities under the same porosity and different hydrostatic pressure. The results show that there are significant differences between the sound absorption performance of the acoustic coating that only considers the deformation of the cavities and that of the acoustic coating that considers the deformation and prestress of the cavities. The hydrostatic pressure causes the largest deformation of the coating with cylinder cavities, followed by the coating with horn cavities, and then the coating with conical cavities. The effect of the hydrostatic pressure on the acoustic coating can be neglected below 2000 Hz. The absorption coefficient curves of the acoustic coating with cylinder cavities and conical cavities gradually get closer with the increase of the hydrostatic pressure, and almost match to each other under 10 MPa hydrostatic pressure.