Mechanism-Separated Closed-Form Transition Modeling via Field Inversion and Symbolic Regression
Abstract
Next-generation aircraft, rotorcraft, and wind turbines demand improved aerodynamic efficiency, making drag reduction a central design objective; in transition-sensitive configurations, the extent of laminar flow strongly affects viscous drag and performance. Accurate prediction of laminar-turbulent transition is therefore essential. Transport-equation-based transition models, however, increase computational cost and implementation complexity. Neural-network-based closures can be difficult to interpret and integrate into independent flow solvers, while a single compact correction trained on heterogeneous transition data may fail to preserve mechanism-specific behavior. This study develops a mechanism-separated transition model using field inversion and symbolic regression. The framework treats natural, crossflow, and separation-induced transition with separate correction branches and yields explicit, closed-form corrections to the Spalart-Allmaras production term, without additional transport equations or runtime neural-network inference. The closed-form model is implemented in an independent flow solver to assess implementation portability and is evaluated on canonical cases and complex three-dimensional configurations, including a natural-laminar-flow transport wing and a hovering rotor. Across the tested cases, the model captures the principal transition-front trends and associated aerodynamic-performance changes. For the hovering-rotor case, it requires about 55% of the wall-clock time of the comparable transport-equation transition model.
Related
- Mechanism-informed dynamic surrogate modeling for rapid thermal-aberration prediction in bonded optical systems
- CFD-driven symbolic regression of RANS transition models
- Light-Induced Transient Polarization Reversal in Rhombohedrally Stacked Bilayer Transition Metal Dichalcogenides via an Electronic Mechanism
- Two-Component Dark Matter and Strongly First-Order Electroweak Phase Transition via a Two-Step Mechanism in the Inert Doublet plus Real Singlet Model
- Modeling Quantum Error Detection with Transition Matrices
BibTeX
@misc{joo2026mechanismseparated,
title = {{Mechanism-Separated Closed-Form Transition Modeling via Field Inversion and Symbolic Regression}},
author = {Seunghyun Joo and Younghyo Kim and Kwanjung Yee},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.24162},
eprint = {2609.24162},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.24162},
}
RIS
TY - UNPB
TI - Mechanism-Separated Closed-Form Transition Modeling via Field Inversion and Symbolic Regression
AU - Seunghyun Joo
AU - Younghyo Kim
AU - Kwanjung Yee
PY - 2026
DA - 2026/09/21
PB - arXiv
DO - 10.48550/arxiv.2609.24162
UR - https://arxiv.org/abs/2609.24162
AB - Next-generation aircraft, rotorcraft, and wind turbines demand improved aerodynamic efficiency, making drag reduction a central design objective; in transition-sensitive configurations, the extent of laminar flow strongly affects viscous drag and performance. Accurate prediction of laminar-turbulent transition is therefore essential. Transport-equation-based transition models, however, increase computational cost and implementation complexity. Neural-network-based closures can be difficult to interpret and integrate into independent flow solvers, while a single compact correction trained on heterogeneous transition data may fail to preserve mechanism-specific behavior. This study develops a mechanism-separated transition model using field inversion and symbolic regression. The framework treats natural, crossflow, and separation-induced transition with separate correction branches and yields explicit, closed-form corrections to the Spalart-Allmaras production term, without additional transport equations or runtime neural-network inference. The closed-form model is implemented in an independent flow solver to assess implementation portability and is evaluated on canonical cases and complex three-dimensional configurations, including a natural-laminar-flow transport wing and a hovering rotor. Across the tested cases, the model captures the principal transition-front trends and associated aerodynamic-performance changes. For the hovering-rotor case, it requires about 55% of the wall-clock time of the comparable transport-equation transition model.
ER -