本研究透過應用一套具侵蝕堆積機制之二相流數值模式，模擬於崎嶇不規則地形下顆粒與流體型土石流之動態行為。模型採用深度平均二相流理論為基礎，整合地形座標系統建立控制方程，能靈活對應複雜地形變化，確保在動態侵蝕與沉積過程中，準確描繪堆積體的時空演化。侵蝕行為主要由基底剪應力所驅動，沉積則透過代表性粒徑之沉降速度加以控制；臨界條件則結合 Shields 參數與 Hjulström–Sundborg 圖，合理界定顆粒啟動門檻，進一步揭示不同地形條件與物質組成下流變行為與輸砂機制之差異性。
本研究首先針對不同粒徑大小與流體黏滯性條件進行模擬，分析其對最終堆積型態之影響。結果顯示，在固定黏滯性條件下，粒徑大小對堆積空間型態之影響有限，主要影響沉降速率與堆積歷程之時間尺度。進一步應用該模式重建 2009 年小林村大規模崩塌事件，並結合基因演算法進行沖刷係數（alpha_E）與堆積係數（alpha_D)之優化校準，使模擬結果與現地堆積型態達成高度一致，提升模型準確性與可信度。優化後之係數組合亦透過線性回歸建立粒徑與參數關聯性模型，並選用不同粒徑進行驗證，結果顯示面積重疊率均達良好表現，證明回歸模型具備良好之泛化能力。
此外，模型亦成功應用於實驗室土石流案例，精確再現堤岸形成與層狀堆積體之演化過程，展現對顆粒與流體耦合動態行為的細緻解析能力。不同入流條件模擬亦顯示出堆積型態與質心位置之顯著變化，進一步驗證本模式於動態條件控制下之靈敏性與穩健性。整體而言，本研究所建構之二相流沖刷堆積數值模式，不僅具備高度物理一致性與參數適應性，亦可廣泛應用於實際災害模擬、地形演變分析及防災工程規劃，具有重要學術價值與應用潛力。

This study employs a two-phase flow numerical model incorporating erosion and deposition mechanisms to simulate the dynamic behavior of particle-fluid debris flows over rugged and irregular terrain. The model is based on depth-averaged two-phase flow theory and formulated using a terrain-conforming coordinate system, enabling flexible adaptation to complex topographies and accurate tracking of the spatiotemporal evolution of deposited materials under dynamic conditions. Erosion is primarily triggered when basal shear stress exceeds a critical threshold, quantitatively defined by the Shields parameter, while deposition is governed by the relationship between the settling velocity of representative particle sizes and the local flow velocity. The critical conditions for particle entrainment are further constrained using the Hjulström–Sundborg diagram, allowing a comprehensive representation of sediment transport behavior across different terrain and material configurations. Numerical simulations were performed to examine the effects of particle size and fluid viscosity on final depositional patterns. The results indicate that, under fixed viscosity, particle size has limited influence on spatial morphology but significantly affects particle settling rates and the timescale of deposition. The model was subsequently applied to reconstruct the 2009 Hsiaolin Village landslide. A genetic algorithm was used to optimize the erosion coefficient (alpha_E) and deposition coefficient (alpha_D), leading to a strong match between simulation outputs and field observations. A linear regression model was then established to relate particle size to the calibrated parameters, and validation with additional particle sizes confirmed the model's generalizability. The model was also successfully applied to laboratory-scale experiments, accurately reproducing levee formation and layered deposit structures. Furthermore, simulations under varied inflow conditions revealed notable differences in depositional morphology and centroid position, confirming the model's sensitivity and robustness. Overall, the proposed model offers high physical consistency and adaptability, with promising applications in debris flow simulation, terrain evolution analysis, and disaster mitigation planning.

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