## 4 Discussion
### Discussion on Influencing Factors of Adhesive Clogging in Underground Coal Bunkers  
This section systematically discusses the effects of coal bunker convergence angle (\(\alpha\)), raw coal moisture content (\(w\)), and coal storage height (\(h\)) on the adhesive clogging characteristics of underground coal bunkers. The results demonstrate that the convergence angle (\(\alpha\)) significantly affects coal flow patterns, the degree of adhesive clogging, and pressure distribution on the bunker walls. As \(\alpha\) increases, coal flow velocity improves significantly, with the flow pattern transitioning from central flow to mass flow. This finding aligns with [PERSON] et al. (25), who reported the relationship between flow patterns and the geometric characteristics of bunker outlets. At lower angles (e.g., \(\alpha\) = 50\({}^{\circ}\)), coal exhibits central flow characteristics with slow velocities and significant retention near the bunker walls. In contrast, at \(\alpha\) = 70\({}^{\circ}\), coal achieves mass flow, with minimal retention and a marked reduction in adhesive clogging. However, excessively high angles result in overly rapid coal discharge, complicating process control. Moreover, changes in \(\alpha\) significantly influence wall pressure distribution. As \(\alpha\) increases, wall pressure decreases, indicating reduced contact force between coal and the bunker wall, thereby mitigating adhesion. Unlike prior studies, which primarily focus on static pressure distributions [37, 38, 39], this study incorporates dynamic discharge analysis, revealing the impact of angle variation on pressure fluctuations. Based on operational efficiency and discharge safety, an angle of \(\alpha=60^{\circ}\) is recommended as it effectively reduces adhesive clogging risk while maintaining flow stability. Raw coal moisture content (\(w\)) also significantly impacts adhesive clogging behavior. Under low moisture content (e.g., \(w=0\%\)), coal exhibits good overall flowability, characterized by mass flow. As \(w\) increases, adhesion intensifies, transitioning the flow pattern to central flow. This trend is consistent with [PERSON] et al. [16, 40], who highlighted the effect of liquid bridge forces on particle flowability. The proportion of adhesive coal increases markedly with \(w\). At \(w=3\%\), significant coal agglomeration emerges, substantially elevating clogging risk. At \(w=5\%\), coal loses flowability entirely. Higher moisture content enhances inter-particle aggregation. Wall pressure peaks increase significantly under high-moisture conditions, reflecting stronger adhesion to the walls, exacerbating clogging. By systematically simulating wet coal behavior, this study elucidates the mechanisms underlying adhesion and flow failure, providing quantitative guidance for controlling raw coal moisture levels. Coal storage height (\(h\)) critically influences adhesive clogging as it directly impacts compaction forces and flow distribution. The findings indicate that as h increases, the flow pattern transitions from mass flow to central flow, with a significant velocity reduction. The proportion of adhesive coal exhibits nonlinear growth with \(h\). At \(h\leq 7\) m, adhesive coal distribution aligns with the flow pattern. At \(h\geq 10\) m, the proportion exceeds 90%, indicating severe clogging. Furthermore, higher storage heights increase static wall pressure, intensify inter-particle contact forces, and amplify adhesion, consistent with the findings of [PERSON] et al. [41] regarding the relationship between compaction forces and adhesive behavior. This study quantifies adhesive clogging under varying storage heights using numerical simulations, providing refined insights into bunker behavior. The analysis of coal bunker convergence angle, raw coal moisture content, and storage height reveals that coal flow patterns and adhesion characteristics are governed by the coupling effects of these factors. Through numerical simulation, this study identifies the critical conditions and mechanisms of these influencing factors.