机制分离的闭式转捩建模：基于场反演与符号回归
Mechanism-Separated Closed-Form Transition Modeling via Field Inversion and Symbolic Regression
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- Seoul National University（首尔大学）
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中文总结 AI 辅助
本研究利用场反演与符号回归构建机制分离的闭式转捩模型，为自然、横流和分离诱导转捩提供显式修正，无需额外输运方程，在悬停旋翼算例中耗时约为输运方程模型的55%。
中文摘要 AI 辅助
下一代飞机、旋翼机和风力涡轮机对气动效率提出了更高要求，使得减阻成为核心设计目标；在转捩敏感构型中，层流范围对粘性阻力和性能有显著影响。因此，准确预测层流-湍流转捩至关重要。然而，基于输运方程的转捩模型增加了计算成本和实现复杂性。基于神经网络的封闭模型难以解释且难以集成到独立的流场求解器中，而在异构转捩数据上训练的单一紧凑修正可能无法保持机制特定的行为。本研究利用场反演和符号回归开发了一种机制分离的转捩模型。该框架对自然转捩、横流转捩和分离诱导转捩采用独立的修正分支，并生成对Spalart-Allmaras生成项的显式闭式修正，无需额外的输运方程或运行时神经网络推理。该闭式模型已在独立流场求解器中实现以评估实现可移植性，并在典型算例和复杂三维构型上进行了评估，包括自然层流运输机翼和悬停旋翼。在所有测试案例中，该模型捕捉了主要转捩前沿趋势及相关的气动性能变化。对于悬停旋翼案例，其墙钟时间约为可比输运方程转捩模型的55%。
英文摘要
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.
