Investigation of Reynolds number effects on aerodynamic characteristics of generic aircraft and estimation of Reynolds-dependent aerodynamic database using artificial neural network models

Abstract

This paper examines an Artificial Neural Network (ANN) model that was constructed to create a comprehensive aerodynamic database, including the effects of Reynolds number on the aerodynamic features of a generic aircraft model, with a particular emphasis on the impacts of altitude and scale. Using Computational Fluid Dynamics (CFD) simulations and an ANN model, a comprehensive aerodynamic database that accounts for the wide-ranging impacts of Reynolds number is generated. Using a generic aircraft model, a comprehensive set of CFD calculations were performed to evaluate the effect of Reynolds number on key aerodynamic properties. The simulations included different Mach numbers (0.2 to 0.95), angles of attack (AoA) ranging from -12 to 40 degrees, sideslip angles (Beta) ranging from 0 to 20 degrees, different altitudes, and different scales. The purpose of these simulations was to investigate the effect of Reynolds number on a variety of aerodynamic parameters, including as shock location translation, flow separation point, control surface efficiency, wing stall region change, and drag exchange. By carefully scrutinizing the CFD results, the effects of altitude and scale on the aerodynamic database were uncovered. The findings demonstrated that the Reynolds number, which was affected by both altitude and scale, significantly affected the aerodynamic behavior of the aircraft. It has been noted that the translation of shock location, flow separation point, wing stall region, and drag change are all sensitive to variations in Reynolds number. Moreover, the Reynolds number was found to influence the effectiveness of control surfaces such as flaps, ailerons, and rudder, which could have significant ramifications for the aircraft's overall performance, stability, and maneuverability. Using a subset of CFD results, an ANN model was created in order to construct a comprehensive aerodynamic database that accounts for Reynolds number effects. The ANN model displayed encouraging results in forecasting various aerodynamic characteristics based on Reynolds number, giving a valuable tool for comprehending and enhancing the performance of an aircraft under varying flying situations. This unique method enabled the effective identification of correlations between Reynolds number and aerodynamic properties, so contributing to a deeper comprehension of aircraft performance. This study illuminates the major influence of Reynolds number on the aerodynamic performance of a generic airplane model. Using CFD analyses and an ANN model, a comprehensive aerodynamic database accounting for the impacts of altitude, scale, and control surface efficiency was developed. This study's findings can be utilized to enhance the design and performance of aircraft, particularly in respect to the influence of Reynolds number on various aerodynamic parameters. Future study directions could include expanding the database to encompass a broader range of flight situations, examining the impacts of Reynolds number in supersonic and hypersonic flight regimes, and improving control surfaces further for improved efficiency and performance. In addition, the creation of new ANN models for certain flight zones and the incorporation of control surface-deflected CFD findings may provide more insights into the design and optimization of control surfaces. This study contributes to the growing body of knowledge regarding the critical function of Reynolds number in determining aerodynamic performance, with possible implications in the design and development of sophisticated aircraft and the broader area of aerodynamics.