arXiv：cs.LG· Vinh Pham, Van Tu Nguyen, Warn-Gyu Park·· 6 小时前
物理引导多保真度代理模型预测轴对称锥形空化器自然超空泡几何
Physics-Guided Multi-Fidelity Surrogate Modeling for Natural Supercavity Geometry of an Axisymmetric Conical Cavitator
AI 导读
研究提出基于扩展分层 Kriging 的物理引导多保真度代理框架（PG-EHK），将多个解析空泡公式作为非层级低保真趋势函数，用少量高保真 CFD 数据校正，仅需不到 20 次高保真 CFD 评估即可映射工况到空泡几何。在五个独立留出 CFD 案例上的交叉验证显示，PG-EHK 的预测误差比表现最好的单一解析方法降低 61%。
正文
Abstract:Natural supercavitation behind conical cavitators generates a vapor-filled envelope that, when sustained over the full body of a high-speed underwater vehicles, reduces hydrodynamic skin-friction drag and enables performance levels unattainable in fully wetted flow. Accurate prediction of maximum cavity length and maximum cavity radius and local cavity profile as functions of cavitation number and cone half-angle is essential for both the static design and the real-time shape management of super-cavitating torpedo cavitators. Classical analytical models provide computationally inexpensive predictions but exhibit systematic disagreements in the practically important regime of moderate cavitation number and large angle, where slender-body assumptions begin to break down. Existing data-driven machine learning approaches offer higher accuracy but are constrained by the prohibitive cost of multiphase computational fluid dynamics (CFD) sweeps over the full design space. This study proposes a physics-guided multi-fidelity surrogate framework based on Extended Hierarchical Kriging (PG-EHK) that addresses this challenge by fusing the multiple competing analytical cavity formulas as non-level low-fidelity (LF) trend functions corrected by a sparse set of high-fidelity (HF) CFD data. It simultaneously incorporates the analytical models regardless of their local fidelity ordering, using Bayesian-estimated scaling factors to construct the trend function as physical baseline. The resulting surrogate maps operational condition inputs to cavity geometry responses using fewer than 20 high-fidelity CFD evaluations. Cross-validation on five independent hold-out CFD cases demonstrates that the proposed PG-EHK surrogate achieves 61% reduction in prediction error compared to the best-performing individual analytical approach.
| Subjects: | Computational Physics (physics.comp-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.18886 [physics.comp-ph] |
| (or arXiv:2610.18886v1 [physics.comp-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18886 arXiv-issued DOI via DataCite |
Submission history
From: Vinh Pham [view email]
[v1]
Tue, 6 Oct 2026 15:52:46 UTC (1,796 KB)
来源：arXiv：cs.LG · arxiv.org