Ship path planning is one of the key technologies for ship automation. Establishing a cooperative collision avoidance (CA) path for multi-ship encounters is of great value to maritime intelligent transportation. This study aims to solve the problem of multi-ship collaborative collision avoidance based on the algorithm of Conflict Search (CS) and Space-Time Hybrid A-star (STHA). First, a static CA path is searched for each ship by using the space-time Hybrid A-star algorithm, and the conflict risk area is determined according to the ship safety distance constraint and fuzzy Collision Risk Index (CRI). Secondly, the space-time conflict constraint is introduced into the multi-ship cooperative CA scheme, and the binary tree is used to search for an optimal navigation path with no conflict and low cost. In addition, the optimal path is smoothed by using cubic interpolation to make the path consistent with actual navigation practice and ship maneuvering characteristics. Finally, considering the constraints of the International Regulations for Preventing Collisions at Sea (COLREGs), the typical two-ship and multi-ship encounter scenarios are designed and simulated to verify the effectiveness of the proposed method. Furthermore, a comparative analysis of actual encounters and encounters based on CS-STHA is also carried out. The results indicate that the proposed algorithm in the study can obtain an optimal CA path effectively and provide a reference of CA decision-making for autonomous ships.

Aerosol pollutants composed of suspended droplets significantly impact environmental quality and human health. Predicting the spatiotemporal distribution of cough droplets remains a challenge due to their complex multiphase dynamics, involving intricate interactions between droplet motion and turbulent airflow. This study presents a three-dimensional Gaussian parameter model integrating computational fluid dynamics (CFD) with machine learning to efficiently simulate and predict the transport and dispersion of indoor cough droplets. The Gaussian model derived from CFD flow field dynamics and droplet kinematics adheres to conservation principles and hyperbolicity, ensuring physical consistency. An adaptive polynomial feature random forest algorithm predicts model parameters, enabling rapid reconstruction of droplet trajectories and spatial distribution patterns. The approach achieves a 76.4% reduction in computational cost compared to traditional CFD simulations while maintaining high accuracy, with a mean absolute error below 0.07 and a mean squared error below 0.014. This robust and versatile framework advances the understanding of aerosol transport dynamics, offering critical insight and practical tools for indoor air quality management and aerosol pollution control.

Current airway models for simulating human expiratory cloud diffusion face challenges due to numerous difficult-to-define entry boundaries and unverified simplifications, potentially leading to inaccurate simulations of dynamic characteristics of exhaled clouds. To address this challenge, a nozzle geometry boundary structure is designed with inclined channels and a main channel containing an internal obstacle. The inclined channels primarily affect the vertical velocity of the cloud, while the obstacle in the main channel primarily influences the internal vortices, thereby impacting the diffusion of the exhalation cloud. The effects of the angle of inclined channels, obstacle length, and obstacle width on four key parameters characterizing cloud dispersion: penetration distance, area, upper angle, and lower angle, are assessed in this study. Bayesian optimization was employed based on the results of simulations involving various nozzle structures. Optimization results indicated that an inclined channel angle of 63.3 degrees, obstacle dimensions of 2.8 mm width, and 5.2 mm length yielded minimal deviation. Numerical simulations using these optimized parameters closely matched the human results captured by Schlieren, with an average deviation of within 8%, effectively simulating the dynamic characteristics of exhaled clouds. The nozzle model offers reliable geometry boundary conditions for numerical simulations of human exhalation, thereby minimizing discrepancies between simulations and experimental results.