## 1 Introduction
To overcome some limitations of the RANS approach, a novel generalized turbulence modeling procedure was recently introduced in the framework of the CFD code Ansys Fluent 2023 R2 [11]. This commercial software package is based on the finite volume (FV) approach, where the conservation principles are applied over each control volume, representing a commonly used tool for building virtual wind tunnels in the aerospace industry [12]. The new procedure is referred to as a generalized \(k\)-\(\omega\) (GEKO) model, since it constitutes a reformulation of this well-established two-equation eddy-viscosity model. The proposed formulation employs six free coefficients that can be tuned by the user without compromising the built-in model calibration for fundamental flows. As a result, CFD analysts can optimize the turbulence closure procedure for the particular application in a safe parameter space. Furthermore, with the advancement of machine learning (ML) algorithms, which are able to make predictions based on huge amounts of available flow data, data-driven RANS turbulence modeling has been recently developing [13]. The data extracted from either experiments or high-fidelity CFD simulations are infused into the learning procedure, while ML techniques are used to link the turbulence modeling behavior to geometric and mean flow variables [14]. In the future, the GEKO model could evolve into an adaptive data-driven turbulence modeling procedure, where the primary objective of achieving high prediction capability is pursued by training the free coefficients using available data and artificial intelligence algorithms rather than exploiting the various traditional turbulence models [15]. The very first step toward the development of an innovative data-driven RANS approach supplied with the GEKO scheme will be taken here by assessing the capability of the generalized model to predict aeronautical flows of practical engineering interest, which is the main aim of this work. For this purpose, the computational evaluation of the mean turbulent flow around the scaled model of a modern regional turboprop aircraft is performed. A series of different RANS calculations will be conducted, where the free coefficients fine-tuning methodology will be proven by comparing the various solutions with each other as well as to both wind tunnel (WT) and higher-fidelity numerical data. Drawing particular attention to the stall regime of the cruise wing, it will be demonstrated that two free parameters of the generalized model, namely, the separation and the near wall coefficients, play the main role in adjusting the prediction of the separated boundary layer (BL) flow on the main wing of the aircraft model. This way, the current investigation provides a clear preliminary idea of the effectiveness of the new GEKO concept, paving the way for future further developments for complex full aircraft configurations. The remainder of this paper is organized as follows. In Section 2, the generalized two-equation turbulence model is briefly presented together with the reference higher-fidelity SRS method. The particular aeronautical application under consideration is described in Section 3, where the main settings of the numerical calculations are also provided. The results of the model calibration are made known and discussed in Section 4, while some concluding remarks are drawn in Section 5.