Aircraft Geometry and Meshing with Common Language Schema CPACS for Variable-Fidelity MDO Applications

Mengmeng Zhang, Aidan Jungo, Alessandro Gastaldi, Tomas Melin
2018 Aerospace (Basel)  
This paper discusses multi-fidelity aircraft geometry modeling and meshing with the common language schema CPACS. The CPACS interfaces are described, and examples of variable fidelity aerodynamic analysis results applied to the reference aircraft are presented. Finally, we discuss three control surface deflection models for Euler computation. Aerospace 2018, 5, 47 2 of 22 the modeling may require extensive ad hoc manual intervention. For example, the highest fidelity methods, RANS-based CFD. To
more » ... construct a reasonable variable fidelity CFD analysis system, one should consider the variable fidelity of the geometrical representations corresponding to the CFD tools. The level of detail in the geometry gathered from a CAD system needs to match the CFD model fidelity. The chosen high fidelity model must be as accurate as possible and can reflect all considered complex flow characteristic; the chosen low-fidelity model must reflect the basic flow characteristics and be as effective as possible. In the conceptual design stage, the usual practice, for example, in the RDS [5], the AAA [6] and the VSP [7] software systems, is to use a purpose-specific CAD that is simpler than the commercial systems, and fewer parameters need to be used for the configuration layout at this stage in the design cycle [8]. However, for some innovative configurations, different ranges of flight conditions or more detailed analyses, the simplified CAD is not sufficient for a higher fidelity CFD analysis; thus, an enriched geometry definition with more parameters is needed. The Common Parametric Aircraft Configuration Schema (CPACS) [9,10], defining the aircraft configuration parametric information in a hierarchical way, gives the opportunity to incorporate different fidelity CFD tools with one single CPACS file. For different fidelity tools to be used, the corresponding geometry information can be imported/retrieved from the common CPACS file to match the model fidelity. SUAVE, Standford University Aerospace Vehicle Environment [11] [12] [13] , which is also a multi-fidelity design framework developed at Stanford University, stores the aircraft geometry information using an inherent defined data class, which can be easily modified. The aerodynamic solutions can be generated from simple models within SUAVE or easily imported from external sources like CFD or wind tunnel results. The aircraft analysis in SUAVE is calculated with a so-called "fidelity zero" VLM to predict lift and drag, with a number of corrections such as the compressibility drag correction, parasite drag correction, etc. [12] , to adapt the VLM prediction to a wider range (transonic and supersonic flow regions). It incorporates the "multi-fidelity" aerodynamics through the provided response surface by combining the different fidelity data. However, currently, SUAVE is still working on connecting higher fidelity models directly to it; the response surfaces are only available to incorporate higher fidelity lift and drag data from the external sources [12] . At this point, one cannot guarantee that the geometry information used for different fidelity tools is consistent during data exchanging, transferring and translating. Moreover, the prediction is only limited to lift and drag, so that it might not be easy for engineers to look into the physical details for a better design, for example, the pressure isobars and distributions, the laminar flows, transitions and the shock forming, etc. Thus, a dataset that can store complete and consistent information for different fidelity tools to solve the physical flows is desired. The CPACS-based multi-fidelity aerodynamic tools show a great consistency due to the one data-centric schema, and the automation of the progressive process can thus be implemented and realized with minimum data loss. With all the computed aerodynamic data at hand, an important task is to construct surrogate models that integrate all analysis results computed by tools of different fidelity. Such data fusion applications are enabled by standardization of the data-format, syntax and semantics-of the aerodynamic simulation tools. The work in [14] describes the workflow of an automatic data fusion process for CPACS [9, 10] . The application was developed in the EU research project Aircraft 3rd Generation MDO for Innovative Collaboration of Heterogeneous Teams of Experts (AGILE) [15] , where every module (the aerodynamic module, the sampling module, the surrogate modeling module) communicates by CPACS files. This paper will address other aspects of the work in [14] , namely how the different fidelity tools in the aerodynamic module communicate and how a look-up table of the aero-dataset can be obtained automatically from the tools (L1 and L2 in this paper). Section 2 describes the CPACS file definition in more detail, especially the geometry definitions, which are important for CFD mesh generators. Section 3 details the CPACS interfaces for variable fidelity analysis. Section 4 gives an overview of the CFD flow solvers used in the work. Section 5 presents the applications to the test case using variable
doi:10.3390/aerospace5020047 fatcat:qjfi5wapmbc2fa5aftmw525g5i