Hierarchical Flexible Toolset for Generating Airfoil Performance Tables for Aircraft Design

Despite the capabilities and usability improvements of modern computational fluid dynamics software, rotary-wing aircraft design and analysis often uses lower fidelity methods that require pre-computed 2D airfoil data tables because these methods can provide adequate accuracy in a fraction of the setup and computation time. The C81 data format has long been used by rotorcraft sizing, conceptual, and preliminary design tools to define airfoil lift, drag and moment as a function of angle of attack, Mach number, and Reynolds number. Even though C81 tables have a long legacy of use, their generation, especially for new airfoil sections typical of modern Urban Air Mobility configurations, is far from routine given the need to cover 360o angle of attack over a range of Mach and Reynolds numbers – with manual tuning often used to fill-in missing data or remove questionable points. The proposed effort by Continuum Dynamics, Inc., (CDI), seeks to build upon our decades of experience developing and using C81 tables for rotorcraft design and analysis to develop a software toolset tailored to automatically and robustly generating accurate and reliable C81 tables. The toolset will feature a range of methods for automatically predicting single and multi-element airfoil section performance, and assembling the resulting C81 tables. In Phase I, a prototype tool with automatic grid generation, will be developed that launches, monitors, and post-processes airfoil performance predictions. The tool will then assemble the desired C81 table from the database of predictions and experimental data, and provide uncertainty metrics to the user associated with the source of the data used. The tool will be built around CDI’s in-house 2D CFD solver that we use to generate 2D look-up tables, but within a framework flexible enough to support alternate CFD solvers. Phase II will see software enhancement and generalization to support a variety of data and prediction generation sources.