发表机构
Thapar Institute of Engineering and Technology（塔帕尔工程与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种非侵入式降阶框架，通过压缩运动学交叉矩替代标量磨损率，实现多经验侵蚀模型快速预测，无需重新训练。
AI 中文摘要
高保真欧拉-拉格朗日模拟在弯管中的固体颗粒侵蚀需要每个工况点数小时的计算时间，阻碍了快速参数扫描和实时磨损评估。现有的降阶模型（ROMs）加速了这些评估，但通常针对单一固定的经验侵蚀公式（如Oka或Finnie）进行训练。改变材料定律或目标硬度则需要完全重新训练代理模型。在此，我们提出了一种非侵入式降阶框架，通过近似底层颗粒碰撞运动学而非标量磨损率来避免这种模型锁定。具体而言，我们在管道表面投影并压缩23个欧拉边界交叉矩（$\mathbb{E}[V_p^u \sin^v\alpha_p \cos^w\alpha_p]$）。利用372个高保真CFD-DPM案例，涵盖三个弯曲比（$R/D \in \{1.5, 2.0, 5.0\}$）、五个雷诺数、五个密度比和六个惯性区（$St > 1$）的颗粒直径，我们评估了一种混合压缩方案。线性本征正交分解（POD）和模式1张量展开SVD与分块卷积自编码器（CNN-AE）相结合，以处理广泛的对流输运和局部冲击坑。各向异性高斯过程回归（GPR）代理将四个无量纲$\Pi$组映射到压缩潜空间，在约$2\\,\mathrm{ms}$内评估完整二维磨损形貌（主要运动学场上$R^2 > 0.99$）。由于运动学与材料损伤定律解耦，所得代理可在事后评估多个经验模型，无需重新训练即可精确匹配Finnie并紧密逼近Oka、McLaury和Arabnejad。
英文摘要
High-fidelity Eulerian--Lagrangian simulations of solid particle erosion in curved pipes require hours of compute per operating point, preventing rapid parameter sweeps and real-time wear assessment. Existing reduced-order models (ROMs) speed up these evaluations, yet they are typically trained on a single, fixed empirical erosion formula (e.g., Oka or Finnie). Changing the material law or target hardness then requires a complete retrain of the surrogate. Here, we present a non-intrusive reduced-order framework that avoids this model-locking by approximating the underlying particle collision kinematics instead of scalar wear rates. Specifically, we project and compress 23 Eulerian boundary cross-moments ($\mathbb{E}[V_p^u \sin^vα_p \cos^wα_p]$) across the pipe surface. Using 372 high-fidelity CFD-DPM cases of $90^\circ$ elbows over three bend ratios ($R/D \in \{1.5, 2.0, 5.0\}$), five Reynolds numbers, five density ratios, and six particle diameters in the inertial regime ($St > 1$), we evaluate a hybrid compression scheme. Linear Proper Orthogonal Decomposition (POD) and Mode-1 tensor unfolding SVD are combined with block-wise Convolutional Autoencoders (CNN-AE) to handle both broad convective transport and localized impact craters. An anisotropic Gaussian Process Regression (GPR) surrogate maps four dimensionless $Π$-groups to the compressed latent space, evaluating full 2D wear topographies in roughly $2\,\mathrm{ms}$ ($R^2 > 0.99$ on primary kinematic fields). Because kinematics are decoupled from material damage laws, the resulting surrogate evaluates multiple empirical models post-hoc exactly matching Finnie and closely approximating Oka, McLaury, and Arabnejad without retraining.
