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Volume 107
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Knowledge-informed reduced-order modeling for erosion prediction in pipe bends
Ming Pan a, Lijing Mu b, Cenfan Liu b *, Xizhong Chen a *
a Department of Chemical Engineering, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
b Key Laboratory of Special Equipment Safety and Energy-saving, State Administration for Market Regulation, China Special Equipment Inspection and Research Institute, Beijing, 100029, China
10.1016/j.partic.2025.10.010
Volume 107, December 2025, Pages 204-215
Received 31 July 2025, Revised 19 September 2025, Accepted 14 October 2025, Available online 25 October 2025, Version of Record 3 November 2025.
E-mail: liucenfan@csei.org.cn; chenxizh@sjtu.edu.cn
Highlights

• CFD–DEM simulations quantify erosion behavior under single-parameter variation.

• A POD–Kriging surrogate is developed for efficient erosion field prediction.

• Knowledge-informed Kriging significantly improves erosion prediction accuracy.

• Optimized surrogate enables fast and accurate prediction in pipelines.


Abstract

Solid particle erosion in pipeline elbows poses a persistent challenge in the energy and process industries, where accurate yet efficient prediction methods are urgently needed. While computational fluid dynamics–discrete element method (CFD–DEM) simulations provide high-fidelity erosion predictions, their computational demands severely limit practical deployment. To bridge this gap, this study proposes a knowledge-informed reduced-order modeling framework that couples proper orthogonal decomposition (POD) with Kriging interpolation. The surrogate model is enhanced by numerically validated, physically motivated correlations between erosion ratios and key impact parameters, enabling improved extrapolation and interpretability. Validation against full-order CFD–DEM results demonstrates that the enhanced POD–Kriging model accurately reproduces spatial erosion fields while achieving speedups exceeding 2000 × . Compared to the conventional POD-based surrogate, the proposed approach reduces prediction errors by up to 76 %, with local error at high-risk elbow regions reduced to within 4 %. These results highlight the model's robustness and generalizability across both single- and multi-parameter operating conditions. The framework offers a computationally efficient and physically consistent alternative for erosion assessment and design optimization in industrial pipeline systems.

Graphical abstract
Keywords
CFD‒DEM; Reduced-order modeling; Erosion; POD‒Kriging
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