## 2 Dataset
### Dataset Characteristics and Analysis  
Table 3 and Figure 2 provide an intuitive display of the experimental data points for spherical and non-spherical particles in both Newtonian and non-Newtonian fluids. The data points are well balanced in this study, with 378 and 553 data points in Newtonian and non-Newtonian fluids, respectively. Notably, this dataset includes non-spherical particles in power-law fluids, which is a significant contribution to the literature. In contrast, [PERSON] et al. [40] utilized 3328 data points, but the majority are in Newtonian fluids (2732 data  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Particle Properties**} & \multicolumn{2}{c}{**Fluid Properties**} \\ \hline \multirow{2}{*}{**Shape (Sphericity)**} & \(\rho_{p}\) (kg/m3) & \multirow{2}{*}{**Fluid Type**} & \multicolumn{2}{c}{**Rheology**} \\ \cline{3-3}  & & & \(K\) (Pa-s**n**) & \(n\) \\ \hline \multirow{6}{*}{Sphere (1)} & \multirow{6}{*}{Newtonian Cylinder (0.697, 0.779, 0.640)} & \multirow{2}{*}{
\begin{tabular}{c} \multirow{2}{*}{7960} \\ \end{tabular} } & 0.066 & 1 \\  & & 0.124 & 1 \\  & & 0.135 & 1 \\  & & 0.289 & 1 \\  & & 0.6685 & 1 \\ \cline{1-1}  & & 0.260 & 0.755 \\ \cline{1-1}  & & 1.202 & 0.650 \\ \cline{1-1}  & & Power-law & 3.233 & 0.576 \\ \cline{1-1}  & & 5.028 & 0.548 \\ \cline{1-1}  & & 9.608 & 0.505 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental matrix of particle settling.  
Figure 1: Successive settling process of non-spherical particles with varying shapes and orientations (based on our previous experiments. **(a)**: cylinder-shaped particle with 20 mm diameter and 5 mm height, **(b)**: cylinder-shaped particle with 18 mm diameter and 6 mm height, **(c)**: cube-shaped particle with 8 mm length, **(d)**: cylinder-shaped particle settling with 30 mm diameter and 3 mm height).  
points, 82%). Consequently, the AI model's features are primarily extracted from Newtonian fluids in that study. Furthermore, [PERSON] et al. [40] did not provide settling velocity data for non-spherical particles in non-Newtonian fluids, indicating that all data for non-spherical particles are in Newtonian fluids.  
It could be found from Table 3 and Figure 2 that there is an imbalance in these data towards non-spherical samples in non-Newtonian fluids. This was primarily due to the following three reasons. (1) Experimental constraints: All data used were obtained from our experiments. Non-spherical particles, due to their varied shapes and sizes, inherently require more experimental observations compared to spherical ones. Similarly, characterizing non-Newtonian fluids, represented by multiple parameters like consistency index and flow behavior index, necessitate more extensive experimental setups. (2) Focus on underrepresented areas: This study particularly aimed to address the less-explored domain of non-spherical particles in non-Newtonian fluids, prevalent in petroleum engineering. Although spherical particles and Newtonian fluids were included for comparative analysis and to broaden the model's applicability, our primary focus was on more complex scenarios. (3) Existing literature: There are abundant research and highly accurate predictive formulas available for spherical particles in Newtonian fluids, which guided our decision to focus on areas with less available data.