###### Abstract
[PERSON] et al. [14] conducted CFD simulations on 90\({}^{\circ}\) elbows with various curvature radii and a 45\({}^{\circ}\) bend configuration. Comparison with experimental data indicated consistent erosion patterns and maximum erosion locations. [PERSON] et al. [15] employed a Lagrangian-based CFD model to investigate  
Figure 1: Coiled tubing operations site. Figure 2: Erosion leads to thinning of coiled tubing wall. erosion wear on coiled tubing, revealing that severe erosion on curved pipes, particularly at the pipe-casing gap, intensified with higher mass flow rates and sensitivity to sand size, was expedited by increased injection rates, and moderated by higher fluid viscosity. [PERSON] et al. [16] investigated erosion on the inner walls of spiral curved coiled tubing using experimental and numerical simulation methods. The study revealed that higher flow rates worsened erosion, larger sand diameters and pitches decreased erosion rates, and smaller pitches led to a more even erosion distribution. [PERSON] et al. [17] used a two-way coupled Eulerian-Lagrangian approach, merging [PERSON]'s reflection model with the erosion model from the Erosion/Corrosion Research Center, to predict erosion rates and patterns in solid particle erosion within a centrifugal pump for liquid-solid flow. [PERSON] et al. [18] analyze pipeline erosion in liquid-solid flow through coiled tubing using CFD, emphasizing the impact of factors like flow rate, slurry concentration, proppant characteristics, and fluid viscosity. [PERSON] et al. [19] used an Eulerian-Lagrangian approach to examine the erosion of four elbows in series, finding that erosion behavior was affected by connection length, particle diameter, curvature radius, and varying sensitivities to particle size among the elbows. [PERSON] et al. [20] used the Euler-Lagrange method to analyze gas-solid two-phase flow, comparing one-way coupled DPM with four-way coupled DDPM. Their study revealed that DDPM accurately predicts erosion distribution, showing that 50 um particles experience erosion rates 8.32 times higher than 10 um particles at similar gas transmission rates. [PERSON] et al. [21] used the CFD-DPM model in the Lagrange coordinate system to investigate the erosion behavior of different pipeline structures under various flow conditions. [PERSON] et al. [22] utilized the CFD-DPM model in the Lagrange coordinate system to design an optimized pipeline structure, achieving a significant improvement with an 80% reduction in turbulent dissipation rate and a 99.2% decrease in erosion wear rate. The rapid development of machine learning and intelligent algorithms has brought great impetus to the study of solid-liquid erosion. Intelligent algorithms such as neural networks, extreme learning machine, and their optimization methods have achieved remarkable results in this field. [PERSON] et al. [23] developed an improved erosion prediction model combining CFD and machine learning. Through experiments and optimization, the prediction error of the erosion rate was reduced from more than 40% to less than 10%. [PERSON] et al. [24] have shown that the adaptive neuro-fuzzy inference system (ANFIS) model has high accuracy and reliability in predicting erosion. [PERSON] et al. [25] used CFD to analyze the erosion of slot screen and established a life prediction model based on the BP neural network. The prediction results of the model applied to the actual conditions of Bohai Bay are consistent with the actual situation. [PERSON] et al. [26] proposed a genetic algorithm-optimized extreme learning machine (GA-ELM) method to predict the erosion rate and residual life of defective elbows. [PERSON] et al. [27] have shown that the ANN model is accurate and robust in predicting the remaining useful life of the water pipe network. [PERSON] et al. [28] developed a feedforward back-propagation neural network (FFBPN) to predict the status of crude oil pipelines.