{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.09997",
  "author_total": 3,
  "authors": [
    {
      "name": "Animesh Yadav"
    },
    {
      "name": "Rajesh Kumar Shukla"
    },
    {
      "name": "Ravinder Kumar Duvedi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fd4fheqhkd9hv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.09997",
  "fulltext_url": null,
  "id": "fd4fheqhkd9hv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.09997",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.app-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.09997",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fd4fheqhkd9hv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:12Z",
      "native_id": "2609.09997",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.09997"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.app-ph",
      "subfield": "applied-instrumentation"
    }
  ],
  "title": "A Multi-Model Non-Intrusive Reduced-Order Framework for Parametric Erosion Prediction via Kinematic Cross-Moment Compression",
  "title_text": "A Multi-Model Non-Intrusive Reduced-Order Framework for Parametric Erosion Prediction via Kinematic Cross-Moment Compression",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:12Z",
  "url": "https://research.draconitic.com/paper/fd4fheqhkd9hv",
  "venue": "arXiv",
  "versions": []
}