Integrating multi-body simulation and CFD: toward complex multidisciplinary design optimisation

Transcription

1 Integrating multi-body simulation and CFD: toward complex multidisciplinary design optimisation Federico Urban ESTECO Italy Martin Mühlmeier AUDI Germany Stefano Pieri Department of Energetics University of Trieste Italy

2 Presentation Outline This work show-cases how to carry out a multi-disciplinary design process coupling all the tools generally involved in a complete multi-body analysis. The integration will be managed by modefrontier MDO package The race-car: Audi R8 The track: Le Mans

3 Presentation Outline Optimisation Goals; The physics behind the problem: Aerodynamics Multi-body Dynamics; The numerical analyses for simulating the real-life complexity: Tools; How to put together the numerical tools for achieving the result: Methods; Optimisation Results.

4 Goals INCREASE VEHICLE PERFOMANCES Objective: Input Variables: Minimization of Lap-time Geometrical Entities

5 Aerodynamics Aerodynamics is a crucial issue in the design of a high-speed vehicle. It is useful and safer to simulate in advance the Aerodynamics at different race scenarios Porsche911-GTO (McNish) Le Mans '98

6 Numerical Analyses For achieving a complete and reliable numerical simulation we should consider the impact of the aerodynamic forces on the mechanical behaviour of the vehicle In practice, carry out the coupled numerical solution with CFD tool (CFX) and Multi-Body tool (Adams) using a three-dimensional model.

7 Tools The Numerical Tools SCENARIO: 3-D Analysis CAE Tools: Catia V5 : Parametrization. Icem CFD 4.3: Mesh Generation. CFX 5.6: Fluid-Dynamics Simulation. MSC-Adams: Multi-Body Dynamics Simulation. modefrontier: the wrapper Multiple CFD analyses at various positions of the car

8 Tools: Catia V5 The parametric model: Coordinates of the fundamental points of the diffuser (Xpar, Zpar). Inclination and the Height of the rear wing profile (H2, Alpha).

9 Tools: Icem CFD 4.3 The finite-element model: Hybrid mesh (tetra+prisms) - 3 millions cells

10 Tools: : CFX Boundary conditions INLET v=44 m/s Wall no slip v=0 Wall no slip (v=44 m/s) OUTLET Rotating wheels Simmetry

11 Tools: : CFX Results Velocity Pressure

12 Tools: : MSC-ADAMS CarMotorsport. Simulating vehicle dynamics The global model is represented by means of a group of mechanical elements with specific characteristics. The structure of the complete car model has a pyramidal layout.

13 Methods: : The Idea! Original Geometry CFD Simulations Aerodynamic Matrices New Geometry Dynamic Simulation Optimization Algorithm LAP time

14 Methods: : The Design Flow Parmacro.CATScript AUDI_box.model cfx5macro CATIA ICEM CFD CFX ADAMS LAP time H2, Alfa, Xpar, Zpar

15 Methods: : Design Process Requirements In order to achieve the most reliable map of aerodynamic forces to be used as boundary conditions along the multi-body analyses, each design is evaluated in 12 different vehicle body positions

16 Methods: : The Design Flow H1, BetaBody D.A.C.E. (Design and analysis of computer experiment), external Response Surface Modeller Parmacro.CATScript AUDI_box.model n < 12 CATIA ICEM CFD CFX if DACE ADAMS LAP time H2, Alfa, Xpar, Zpar Matrix 3x4 Matrix 6x8 With 12 simulations, it is possible to extrapolate the complete 6x8 matrix of the aerodynamic forces required by ADAMS.

17 Methods: : The Design Flow H1, BetaBody Parmacro.CATScript AUDI_box.model n < 12 CATIA ICEM CFD CFX if DACE ADAMS LAP time H2, Alfa, Xpar, Zpar Matrix 3x4 Matrix 6x8 modefrontier

18 Methods: Process Itegration with modefrontier

19 Methods: Process Itegration with modefrontier

20 Methods: : Complete Process Flow

21 Methods: : Input Parameters Ranges Parameter Lower Bound Upper Bound Xpar (mm) Zpar (mm) -244,124-94,124 Alfa (degrees) -2 2 H2 (mm) mm

22 Results: History Chart Lap Time vs. Design ID RESULT Design 17 improves 2,36 sec. the lap time Simplex Algorithm

23 Results: : CFD Plots Original vs Optimal Minimum height Maximum inclination Cd coefficient: -15 % Cl coefficient: +6.0 % Cd coefficient: -11 % Cl coefficient: -12 %

24 Results: Dynamics Diagrams Original vs Optimal VELOCITY Optimal Original FUEL CONSUMPTION

25 Conclusive remarks modefrontier managed a real-life multi-disciplinary optimization problem in a easy-to-use environment. CATIA, ICEM, CFX, ADAMS have been integrated in a process integration framework. The design chain worked successfully achieving virtually 2 seconds reduction of the lap time. Each the Process Integration issue and the Design Optimization problem have been reliably solved