Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.

Under the erosion of seawater–ice two-phase flow, seawater in pipelines of polar ships can cause the pipeline failures that threaten the safety of navigations. The discrete phase model (DPM) and erosion wear model (EWM) were established by using the computational fluid dynamics (CFD) method for numerical analysis of the 90° elbow with relatively severe erosion. This paper explores the erosion effect of pipelines under different conditions and puts forward optimal measures for pipeline protection. Compared with the existing multiphase flow research, the novelty of this study is that vibration conditions are considered and parameters such as two-phase flow velocity, ice packing factor (IPF), ice particle diameter and ice particle rotation characteristics are combined with vibration conditions. Combined with the comprehensive analysis of erosion effects of static pipelines, a general law of seawater pipeline wear under vibration is obtained. The results show that pipeline wear under vibration is more serious than under static conditions. Under static conditions, the wear of the same section in the pipeline increases with the increases of two-phase flow velocity and IPF. However, under vibration conditions, when the velocity is less than 3 m/s, the wear of the pipeline has no significant change, while when the velocity is over 3 m/s, the wear rate increases significantly. The particle diameter has little effect on the wear of static pipes, but under the vibration condition, the pipe wear rate decreases with the increase of particle diameter, and it starts to stabilize when the diameter exceeds 0.3 mm. If the rotation characteristics of ice particles are taken into account, the wear rate along the pipeline is significantly higher than that without particle rotation.

In a non-uniform electric field, the surface charge of the deformable particle is polarized, resulting in the dielectrophoretic force acting on the surface of the particle, which causes the electrophoresis. Due to dielectrophoretic force, the two deformable particles approach each other, and distort the flow field between them, which cause the hydrodynamic force correspondingly. The dielectrophoresis (DEP) force and the hydrodynamic force together form the net force acting on the particles. In this paper, based on a thin electric double layer (EDL) assumption, we developed a mathematical model under the arbitrary Lagrangian–Eulerian (ALE) numerical approach method to simulate the flow field, electric field, and deformable particles simultaneously. Simulation results show that, when two deformable particles’ distances are in a certain range, no matter the initial position of the two particles immersed in the fluid field, the particles will eventually form a particle–particle chain parallel to the direction of the electric field. In actual experiments, the biological cells used are deformable. Compared with the previous study on the DEP motion of the rigid particles, the research conclusion of this paper provides a more rigorous reference for the design of microfluidics.

In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial pattern information in time-series data by combining wind power series and related environmental features into a 2D GASF image. Firstly, the wind power data are decomposed using the ICEEMDAN algorithm optimized by the BWO (Beluga Whale Optimization) algorithm, extracting the submodal IMF (Intrinsic Mode Function) components with different frequencies. Then, modal reconstruction is performed on the basis of the permutation entropy value of the IMF components, selecting meteorological features highly correlated with reconstructed components through Spearman correlation analysis for data splicing and superposition before converting them into GASF images. Finally, the GASF images are input into the UniFormer model for wind power sequence prediction. By leveraging wind power data predictions from a coastal wind farm in East China and Sotavento in Spain, this study demonstrates the significant benefits and potential applications of this methodology for precise wind power forecasting. This research combines the advantages of image feature extraction and time-series prediction to offer novel perspectives and tools for predicting renewable energy sources such as wind power.