Knowledge Extraction from Aerodynamic Design Data and its Application to 3D Turbine Blade Geometries Lars Graening 03 July 2009 International Workshop on Machine Learning for Aerospace Marseille, France

Outline Validation of the extracted knowledge Universal representation of design modifications Knowledge extraction from unstructured surface meshes

3D Turbine Blade Knowledge extraction from a 3D turbine blade design data set Ultra-low aspect ratio transonic turbine stator blade of a small Honda turbofan engine Honda Business Jet Part of the stator section3D Blade Design Pressure side Suction side Leading edge Trailing edge

Knowledge Extraction Design Database Knowledge Extraction Design data (Design, Performance) Knowledge (e.g. Design Rules) Pre-Processing Optimizer Design Generation & Modification Evaluation How can we extract knowledge from the design data resulting from various optimization runs where different shape representations are used? CAD software Rapid Prototyping Splines, FFD / DMFFD … Aerod. Engineer Computer: Evo. Opt. Response Surface … CFD simulation Wind tunnel Real physical environment … Usually huge amount of design data is generated during the design and optimization process.

Universal Design Representation & Pre-Processing Reference Surface Mesh Modified Surface Mesh Vertex A Vertex A’ Displacement Displacement: Measure of the local deformation along the normal vector of a reference design Using performance difference instead of performance values An identification of corresponding vertices is needed The calculation of the displacement leads to a reduction in the number of parameters under consideration Universal representation: Use unstructured surface mesh as a universal geometric representation Vertices sample the design surface, triangles define the neighborhood, normal vectors define local curvature

Knowledge Extraction from Aerodynamic Design Data Where are unchanged and most frequently changed design regions? What are the mean differences of multiple designs relative to a base design? Which design regions are sensitive to performance changes? How similar are two designs? log

Knowledge Extraction from Aerodynamic Design Data A reduction of the parameters is needed Assuming neighboring vertices with similar sensitivity belong to the same design region Clustering vertices to sensitive design regions Cluster centers are used to analyze interrelations How are distant design areas interrelated? Blade geometries Design Rules

A: A reduction of the blade thickness is expected to increase the performance B: Surprisingly, reducing the thickness at the suction side but increasing the thickness at the pressure side is expected to decrease the performance Pressure Side: Suction Side: Investigate the consequences of an interrelated displacement of distant vertices to the performance How reliability is the extracted information?

Knowledge Validation 1.Select vertex and calculate its new position based on the given displacement 2.Adapt the control points of the control volume concerning the new vertex coordinates 3.Deform the surface mesh and the CFD grid based on the modifications of the control volume 4.Perform CFD calculation to calculate the performance of the new blade design Algorithm for Knowledge Validation: (using Direct Manipulation of Free Form Deformation) Object Point Control Point Deform the design using Direct Manipulation of Free-Form Deformation Generate CFD mesh for the reference blade Apply deformations to the CFD mesh Simulate the flow using CFD (HSTAR3D) Displacement Performance Knowledge (e.g. design rule) Menzel, S., Olhofer, M., Sendhoff B.: Direct Manipulation of Free Form Deformation in Evolutionary Design Optimization, PPSN 2006

Knowledge Validation Reference blade Surface Deformation CFD Grid Deformation Resulting Blade Performance after CFD (Pressure Loss) worse better CC7 Sensitivity The displacement of vertex CC7 is positive correlated with the aerodynamic pressure loss of the turbine blade! 1.The hypothesis of the direct relation between performance and displacement of CC7 is true 2.Using DMFFD for validation and utilization works out well

Knowledge Validation A: B: Decrease in pressure loss of about % Increase in pressure loss of about 5.86 % The performed experiments confirm the expected outcome of the extracted hypothesis

Summary & Outlook framework for extracting knowledge from aerodynamic design data technique based on DMFFD for validating the extracted knowledge validation experiments provide evidence for the reliability of the knowledge extraction techniques outlook measurements are needed that allow to extract rules which are potentially interesting for the aero engineer

Thank You!