To achieve a concept that satisfies the design requirement, usually a mathematical optimization process is followed.With the design initial layout as the baseline, we can formulate an optimization problem using data computed by the multi-disciplinary analysis, to get the best performance w.r.t. the design requirements. This process is a typical MDO process for conceptual design. The recent advances in computer performance and simulation capabilities provide access to sophisticated codes and efficient analysis modules, in all aeronautical disciplines.
MDO can be described as a collection of mathematical techniques for multivariable optimization in which the optimization clearly crosses disciplinary boundaries. This optimization problem can be posed to be very complicated. Therefore, it must be approached by decomposition. Traditional decomposition leads to sub-problems of aerodynamic shape optimization coupled to structural design only by simplified constraints such as on wing thickness, limits on wing root bending moment, etc. Even wing shape optimization is complex enough, and is in practice carried out with a combination of mathematical tools and engineer know-how. Figure 1 shows the MDO group maybe be broken down into a number of weakly-interconnected sub-groups, allowing the engineer to perform separate optimizations within these sub-groups, coordinated and linked such that the entire system is optimized when the separate optimizations are brought together.
The physics based analysis in MDAO (Multidisciplinary Analysis and Optimization) applications requires not only disciplinary expertise, but also the management of cross-disciplinary model consistency. The need for a unified model supporting multiple analysis modules has been widely recognized in the aircraft design community. Many successful integrated design systems have been developed, to automate the design process from top level aircraft requirement
(TLAR) to a design solution (Torenbeek (1982) and Raymer (2006)). Nevertheless, the state of the art in aircraft predesign environment is often still based on automated, but monolithic design codes which cannot easily be adapted to cope with new configurations, or replaced when improved disciplinary analysis modules become available (Kroo et.al., 2005). The challenge is even greater if analysis modules developed by different parties are to be integrated in the
same design process. On the other hand distributed design approaches offer the desired flexibility, but need to guarantee consistency among the disciplinary abstractions generated within the design process.
This paper focuses on the aerodynamic shape optimization (ASO) technology for wing design, which is a sub-task for MDO. The optimization is carried out based on the computational design framework CEASIOMa . It requires analysis in different modules within the design process including geometry modeling, parameterization, meshing and simulation. The interaction between several disciplines is achieved by the common language CPACSb (Common Parametric Aircraft Configuration Schema), adopted by CEASIOM in its latest version, dubbed CPACScreator (Ciampa et.al., 2013). CEASIOM Aerodynamic Shape Optimization, or CEASIOM-ASO approach is reviewed here and two test cases are shown to prove that this approach is promising, especially for highly-nonlinear complex aerodynamic optimization problems.
Collaborative Design Environment for Wing Shape Design The collaborative design environment used for wing shape design is CPACS-adopted CEASIOM. This section CPACS and CEASIOM framework are briefly described. Common Parametric Aircraft Configuration Schema MDO conceptual design is carried out by teams with different fields of expertise, requiring different analysis modules. To communicate with each other for integrated design, n(n − )1 bi-lateral interfaces are needed. In a data-centric framework, each analysis module communicates with all other via a common namespace, thus the cost for data collaboration is reduced to 2n , and this common namespace is preferable to be adopted into a data-centric framework. The German Aerospace Center (DLR) has been developing a de-centralized collaborative design environment within the 7th EU CRESCENDO Project, to foster collaboration among disciplinary specialists, and integrate disciplinary expertise into a collaborative overall aircraft design process (Zill, 2011 and Zill, 2012). The design environment is built on the central data model CPACS an arbitrary number of analysis modules, and on the open source design framework RCEc (Remote Component Environment), enabling the orchestration of the design workflows. CPACS is a data format based on XML technologies, and used for the interdisciplinary exchange of product and process data between heterogeneous analysis codes and name space.
MDO framework in conceptual design
CEASIOM, the Computer-based Environment for Aircraft Synthesis and Integrated Optimisation Methods, developed within the European 6th Framework Programme SimSAC (Simulating Aircraft Stability And Control Characteristics for Use in Conceptual Design) (Rizzi, 2011), is a framework for conceptual aircraft design that integrates disciplinespecific tools like: CAD & mesh generation, CFD, stability & control analysis, etc., all for the purpose of early preliminary design. The CEASIOM framework offers possible ways to increase the concurrency and agility of the classical conceptual-preliminary process by its four core functions: geometry & meshing (Tomac and Eller, 2011), CFD (Da Ronch et. al., 2011), aeroelasticity (Cavagna et. al., 2011), and flight dynamics (Goetzendorf-Grabowski et. al., 2011), the desired attributes for MDO in conceptual design.
The New CEASIOM, or CPACScreator, connects CEASIOIM to CPACS universe. The effort was made to create a flexible, extensible, and comprehensive data centric framework for analysis, simulation, design and optimization tasks. The new features are:
• Adopting the CPACS XML data formats; and
• Graphical tools for editing the aircraft design data.
The CEASIOM-ASO approach is developed on top of New CEASIOM (Zhang, 2015). Most notably benefits are: (i) higher fidelity geometry and meshable model from CPACS; (ii) higher-fidelity CFD via sumo-Edge module in CEASIOM.
Parameterization and Geometry Modeling for Wings
There are many ways to parameterize a wing, to produce either the lofted wing surface, or the set of surface mesh points. For example, the wing surface can be lofted through airfoil stacks, or the geometry can be represented by modelling the perturbations of a “baseline” shape (Amoignon, 2014). The latter technique can also perturb off-surface mesh points, by so-called “mesh-deformation” (Jakobsson and Amoignon, 2005). CEASIOM-ASO uses the former one, followed the default wing shape definition in CPACS language. Although the CAD-free parameterization techniques such as mesh deformation have been more frequently proposed (Mohammadi et.al. 2000 and Kenway et.al. 2010), the re-meshing is easy and robust if a smooth geometry is given and a reliable and fast meshing tool is provided. In CEASIOM-ASO the mesh is thus updated by re-meshing using sumo, a tool for rapid automatic Euler and RANS meshing (Tomac and Eller, 2011). This section shows the geometry modelling and parameterization techniques used for CEASIOM-ASO, including the modelling of trailing edge movable surfaces using morphing technique...