# A Machine Learning Approach to Predict Fluid Viscosity Based on Droplet Dynamics Features
## 1 Introduction  
The study of the rupture dynamics of Newtonian fluids was first started in 1870 by [PERSON] and [PERSON] [1; 2]. It has been extensively developed over the last hundred years. The phenomenon of fluid frequently flows out from capillary tubes in various industrial and natural environments [3; 4]. Due to the universality and importance of the free interfacial evolution process of liquid droplets, it has attracted the attention of many researchers. With the rapid development of computer technology, machine learning has demonstrated powerful data analysis capabilities, which have made great contributions to the development of many industries. Droplets of different sizes, physical properties, and concentrations are produced in processes involving droplet generation, such as micro-fluid [5; 6], ink-jet printing [7], and spraying [8]. The study of droplet dynamics will generate a large number of valuable but complex droplet datasets. In the field of micro-fluid generation, engineers can optimise the composition and concentration of micro-fluid by accurately predicting the viscosity of the micro-fluid to maximise the accuracy of droplet generation [9]. Analyzing these complex data is beneficial for detecting and quantifying droplet generation, and machine learning tools have successfully demonstrated their capabilities in automating the most complex systems [10]. Despite the currently rich results at the intersection of droplet generation and machine learning, the generalisation of methods is still problematic because different models require different sets of raw data [11]. Flow rate, viscosity, surface tension, as well as geometrical parameters of droplet generation (width, length, height, angle), and kinetic parameters (pressure, frequency) all affect droplet generation. The multitude of reasons affecting droplet generation is the main reason for the inconsistency of the inputs to the datasets currentlyused in viscosity studies. [PERSON] et al. [12] used temperature, micro-particle, size and concentration to train a viscosity prediction model and used these data as raw inputs to predict the viscosity of a fluid containing micro-particles. [PERSON] et al. [13] built on [PERSON]'s [12] study by adding micro-particle density and base fluid viscosity as inputs to the original dataset. [PERSON] et al. found in the results that micro-particle concentration and base fluid viscosity had the greatest impact on the predicted results. [PERSON] et al. [14] used the water content and density of the fluid as input data when predicting fuel viscosity. These studies have used factors affecting mobility as the original inputs to the model, which is the main reason that limits the ability of the model to generalise. When the experimental equipment or fluid materials are different, the raw datasets are naturally different. In fact, when the viscosity of the fluid is different, each viscosity will exhibit different flow states due to different resistance to flow. In many problems where the viscosity has to be tested, there will be differences in the flow conditions due to the different viscosities. The ability to generalise the model would be greatly improved if the flow had a direct correlation with the viscosity based on the differences in flow characteristics. Therefore, it becomes important to understand droplet dynamics and to use more correlated datasets of flow characteristics. The flow regimes during droplet generation are usually classified into three modes based on the size of the Weber number: three periodic dripping (PD), dripping faucet (DF), and jetting (J). In PD mode, droplets suspended at the nozzle outlet will fall due to gravity when the force of gravity exceeds the capillary force of the droplet. This mode is commonly used by researchers to study the evolution of the drop process and the contraction of the liquid neck due to the stable and reproducible results of the droplet falling and contraction process [15; 16].