Structural optimization of multistage depressurization sleeve of axial flow control valve based on Stacking integrated learning.

Due to the requirements of the working environment, the marine axial flow control valve needs to reduce the noise as much as possible while ensuring the flow capacity to meet the requirements. To improve the noise reduction effect of the axial flow control valve, this paper proposes a Stacking integrated learning combined with particle swarm optimization (PSO) method to optimize a multi-stage step-down sleeve of the axial flow control valve. The liquid dynamic noise and flow value of the axial flow control valve are predicted by computational fluid dynamics. Based on the preliminary evaluation of its performance, the structural parameters of the multi-stage pressure-reducing sleeve are parameterized by three-dimensional modeling software. The range of design variables is constrained to form the design space, and the design space is sampled by the optimal Latin hypercube method to form the sample space. An automated solution platform is built to solve noise and flow values under different structural parameters. The Stacking method is used to fuse the three base learners of decision tree regression, Kriging, and support vector regression to obtain a structural optimization fusion model with better prediction accuracy, and the accuracy of the fusion model is evaluated by three different error metrics of coefficient of determination (R2), Root Mean Squared Error, and Mean Absolute Error. Then the PSO particle swarm optimization algorithm is used to optimize the fusion model to obtain the optimal structural parameter combination. The optimized multi-stage depressurization structure parameters are as follows: hole diameter t1 = 3.8 mm, hole spacing t2 = 1 mm, hole drawing angle t3 = 6.4°, hole depth t4 = 3.4 mm, and two-layer throttling sleeve spacing t5 = 4 mm. The results show that the peak sound pressure level of the noise before and after optimization is 91.32 dB(A) and 78.2 dB(A), respectively, which is about 14.4% lower than that before optimization. The optimized flow characteristic curve still maintains the percentage flow characteristic and meets the requirement of flow capacity Kv ≥ 60 at the maximum opening. The optimization method provides a reference for the structural optimization of the axial flow control valve.

Preparing Ambient-Processed Perovskite Solar Cells with Better Electronic Properties via Preheating Assisted One-Step Deposition Method.

Although the power conversion efficiency (PCE) of perovskite solar cells (PSCs) increases rapidly, there are still some issues that limit their commercialization. The perovskite is sensitive to the water molecules, increasing the difficulty in the preparation of perovskite films in ambient condition. Most high-performance PSCs based on conventional method are required to be prepared in inert atmosphere condition, which increase the fabrication cost. To fabricate the high-quality perovskite in ambient condition, we preheated the substrates and selected the proper anti-solvent. As a result, the target perovskite films show a better crystallinity compared with perovskite film prepared via the conventional one-step deposition method in ambient condition. The PSCs prepared in ambient condition yield the improved PCE of 16.89% from a PCE of 11.59%. Compared with the reference devices, the performance stability of target PSCs is much better than that of reference PSCs.