The special surface appearance of complex surfaces restricts the coating film quality of spraying. The study of the atomization and film formation characteristics of typical complex surfaces, as well as the spraying mechanism, is essential for planning the spraying robotic trajectory and improving the spraying efficiency. In this paper, modeling and characteristics of the atomization and film formation process, based on CFD numerical simulations in previous studies, are systematically reviewed, focusing especially on airless spraying. In addition, the advantages and disadvantages of the existing research from the perspective of numerical models and methods are discussed. Finally, a further research direction for spraying on complex surface is prospected. Overall, a comprehensive and up-to-date review of spray atomization and film formation characteristics is considered valuable to practitioners and researchers in these fields, and will facilitate the further application of robotic spraying in the mechanical, automotive, marine, aerospace, petrochemical and other industries.

A model of wax deposition based on molecular diffusion mechanism, for oil-gas two-phase stratified pipe flow is developed. In the model, unidirectional fully developed flow analyses of momentum, heat and mass transfer are presented. And, a cube cage model is used to describe the wax deposit structure considering the effect of oil flow shear on the deposit. Calculation of wax deposit is compared well with a flow loop experiment. In particular, the model could give the wax deposit forming a crescent shape at the cross section of pipe, which is observed in different experiments. Furthermore, the cause of forming a crescent shape is revealed, which is indicated by the non-uniform circumferential distribution of mass flux for wax deposition along the pipe wall wetted by the oil. The mass flux from oil bulk flow to the oil-deposit interface is closely related to three parameters, diffusivity at oil-deposit interface, the temperature gradient at the oil-deposit interface at different time, and the slope of the wax solubility curve at oil-deposit interface temperature.

Gas–liquid flow in a pipeline is a very common. Slug two-phase flow is dominated in the case of slightly upward flow (+0.25°) and considered to be the comprehensive flow configuration, and can be in close contact with all the other flow patterns. The models of different flow patterns can be unified. Precise prediction of the slug flow is crucial for proper design and operation. In this paper, we develop hydrodynamics unified modeling for gas–liquid two-phase slug flow, and the bubble and droplet entrainment is optimized. For the important parameters (wall and interfacial friction factors, slug translational velocity and average slug length), the correlations of these parameters are optimized. Furthermore, the related parameters for liquid droplet and gas bubble entrainment are given. Accounting for the gas–liquid interface shape, hydrodynamics models, i.e., the flat interface model (FIM) and the double interface model (DIM), of liquid film in the slug body are applied and compared with the experimental data. The calculated results show that the predictions for the liquid holdup and pressure gradient of the DIM agree with experimental data better than those of the FIM. A comparison between the available experimental results and Zhang’s model calculations shows that the DIM model correctly describes the slug dynamics in gas–liquid pipe flow.

The mobile pipeline is the most effective and reliable means for the emergency oil transfer task, which is temporarily laid on the field. After completing the task, the oil in the pipeline should be emptied before the mobile pipeline is removed. The oil in pipeline gradually displaced by the air is a main method of pipeline evacuation, which is the only choice of jet fuel pipeline. Due to the convection between gas and oil phases, the length of the oil–gas mixing section and the evolution of the oil–gas interface change with time. The evolution of the gas–liquid interface directly determines the different flow patterns of oil–gas two phases, which are very important for the mobile pipeline evacuation. In this paper, the characteristics of the gas–liquid interface evolution during the water displaced by air are studied, using the multi-phase pipeline experiment. Through the change of the gas–liquid interface in the initial stage of gas cap evacuation, it is found that the gas–liquid interface can be divided into smooth, wavy, and dispersed forms under different initial gas flow rates and different inclination angles. Slug phenomenon may occur in the process of gas-carrying liquid flow in the pipeline. The emergence of slug is mainly affected by hydraulic conditions and pipeline operating conditions. The liquid plug body is complex and unstable, and there will be a mutual transition between different liquid slug shapes. The increase of the inclination angle and gas phase velocity will accelerate the occurrence of liquid slug frequency.

Water displacing oil is one of the main emptying methods for mobile pipelines. It has the advantages of being a simple process and highly safe. At present, the determination of a water displacing oil scheme of mobile pipelines is based on the oil–oil alternating transport theory of product oil pipelines. However, the insolubility of the oil phase and the water phase results in a great difference between the flow characteristics of water displacing oil and the oil–oil alternating transport of a product oil pipeline. In addition, due to the effect of buoyancy, the oil phase gathers at the high point of the pipeline and forms a liquid accumulation, which is difficult to carry away by water flow, resulting in the low emptying efficiency of the mobile pipeline. The essence of water displacing oil in a mobile pipeline is an oil–water two-phase unsteady displacement flow, involving liquid–liquid displacement flow, oil–water two-phase flow and water carrying oil. Aiming at such problems, domestic and foreign scholars have carried out a large number of theoretical and experimental studies, established the oil–water mixing model of water displacing oil and the relationship between macroscopic quantity (flow pattern, pressure drop and water content) and microscopic quantity (local flow field and droplet dispersion pattern, etc.) under each flow type, and explored the influence of pipeline diameter, oil phase velocity, pipeline inclination angle and other parameters on the capacity of carrying liquid accumulation. On this basis, this paper analyzes the shortcomings of the current research on the oil–water flow characteristics of water displacing oil in a mobile pipeline from three aspects: the formation mechanism of the oil–water mixture, displacing flow characteristics of immiscible fluids and flow characteristics of water carrying oil. Five future research directions are proposed, including the interface morphology and flow field characteristics of oil–water two-phase layered flow, local mixing characteristics of an oil–water two-phase dual continuous flow interface, droplet distribution and flow characteristics of oil–water two-phase dispersed flow, unsteady flow characteristics of the oil–water mixture of water displacing oil and oil accumulation and flow characteristics in topographic relief pipes.

Pipelines are the most common way to transport crude oil. The crude oil developed from different fields is mixed first and then transported. The pour point of mixed crude oil is very important for pipeline schemes and ensuring the safe, efficient, and flexible operation of the pipeline. An integrated machine learning model based on XGBoost is identified as optimal to predict the pour point of mixed crude oil by comprehensive comparison among six different types of machine learning models: multiple linear regression, random forest, support vector machine, LightGBM, backpropagation neural network, and XGBoost. A mixed crude oil pour point prediction model with strong engineering adaptability is proposed, focusing on enhancing the flexibility of machine learning model inputs (using density and viscosity instead of component crude oil pour points) and addressing challenges such as data volume and input missing in engineering scenarios. With the inputs of pour point T_g, density ρ, viscosity μ, and ratio X_i in component oils, the mean absolute error of the model prediction estimations after training with 8912 data is 1.12 °C, when the pour point T_g of the component crude oil is missing, the mean absolute error is 1.93 °C and the percentage of the predicted absolute error within 2 °C is 88.0%. This study can provide support for the intelligent control of flow properties of pipeline transport mixed oil.