We present a Lagrangian dynamic importance Monte Carlo method without non-trivial random walks for solving the Velocity-Vorticity Poisson Equation (VVPE) in Smoothed Particle Hydrodynamics (SPH) for vortical flows. Key to our approach is the use of the Kinematic Vorticity Number (KVN) to detect vortex cores and to compute the KVN-based importance of each particle when solving the VVPE. We use Adaptive Kernel Density Estimation (AKDE) to extract a probability density distribution from the KVN for the the Monte Carlo calculations. Even though the distribution of the KVN can be non-trivial, AKDE yields a smooth and normalized result which we dynamically update at each time step. As we sample actual particles directly, the Lagrangian attributes of particle samples ensure that the continuously evolved KVN-based importance, modeled by the probability density distribution extracted from the KVN by AKDE, can be closely followed. Our approach enables effective vortical flow simulations with significantly reduced computational overhead and comparable quality to the classic Biot-Savart law that in contrast requires expensive global particle querying.

Abstract For vortex particle methods relying on SPH‐based simulations, the direct approach of iterating all fluid particles to capture velocity from vorticity can lead to a significant computational overhead during the Biot‐Savart summation process. To address this challenge, we present a Monte Carlo vortical smoothed particle hydrodynamics (MCVSPH) method for efficiently simulating turbulent flows within an SPH framework. Our approach harnesses a Monte Carlo estimator and operates exclusively within a pre‐sampled particle subset, thus eliminating the need for costly global iterations over all fluid particles. Our algorithm is decoupled from various projection loops which enforce incompressibility, independently handles the recovery of turbulent details, and seamlessly integrates with state‐of‐the‐art SPH‐based incompressibility solvers. Our approach rectifies the velocity of all fluid particles based on vorticity loss to respect the evolution of vorticity, effectively enforcing vortex motions. We demonstrate, by several experiments, that our MCVSPH method effectively preserves vorticity and creates visually prominent vortical motions.

Unmanned Aerial Vehicles (UAVs) offer a solution for remote inspection of wind turbines. However, in stormy weather conditions, the visual quality of UAV-taken images is affected by contaminated suspended atmospheric particles. To address this problem, a double-patch lightweight convolutional dehazing neural network (DPLDN) is proposed to reconstruct hazy images and enhance the image quality. Unlike other learning-based methods that measure transmission map and atmospheric light separately, the proposed DPLDN uses a transformed atmospheric scattering model to jointly transmission map and atmospheric light, employs depth-separable convolution instead of conventional convolution, and splits the image into double patches. In addition, a super-resolution reconstruction method is proposed to transform the processed low-resolution images into higher-quality images. Extensive experiments shows that our proposed method has better dehazing performance compared to other state-of-the-art image dehazing techniques. Meanwhile, the applicability of the method in wind turbine blade image segmentation is experimentally verified.

Self-adaptive system (SAS) used in the field needs to comply with strict quality requirements. In order to study the adaptation of multi-agent system to dynamic changes at runtime, an adaptive runtime verification method based on multi-agent system is proposed. The characteristic of this method is to convert the environmental impact factors in the multi-agent system into a probabilistic form, and perform runtime verification in a formal way to enable the system to regulate itself. The use of the method is discussed through a real case. The experimental results show that the method is suitable for an adaptive system with multiple agents.