1. Department of Aeronautics & Astronautics
Multidisciplinary Design of Complex Engineering Systems With Implications for Manufacturing
Juan J. Alonso
Department of Aeronautics & Astronautics
Stanford University
AIM Meeting
April 6, 2004

2. Outline Introduction Sample Results from Current Work
The design process
Design goals and challenges
Our approach
Sample Results from Current Work
Aerodynamic shape optimization
Aero-structural optimization
Outlook and future work
Treatment of the boom problem
Manufacturing and cost observations

3. The Design Process Three major phases
Conceptual: market determination, rough “outline” of the design
Preliminary: “detailed” aero shape and structure, mission, S&C
Detailed: actual drawings of every part in the aircraft ready to cut
Key elements of preliminary design
Comprehensive set of requirements / constraints (including manufacturing)
Inputs
Goals and objectives
Outcome

4. Design Requirements
Carefully formulated set of needs for the proposed aircraft platform stated by the customer
Range, payload, speed, fuel efficiency, performance
Weight, cost, noise, emissions
Mission, ultimate loads / maneuver
“The more/less, the better...” - Objective functions to be maximized / minimized
“Must have no less/more than...” - Inequality constraints
“Must exactly satisfy...” - Equality constraints

5. We Do Not Design, We “Tweak”…
Most transonic transports designed today are evolutions of existing aircraft. Why?
We know how to do tube-and-wing aircraft
Large experience database, lower risk

6. … But Some Aircraft Are Not “Tweaks”
Bae/Aerospatiale Concorde
Lockheed SR-71 Blackbird

7. Current Preliminary Design Practices
Conceptual design (maybe misguided) ties the process together
Major disciplines (aerodynamics, structures) are designed while the other one is “frozen”
Implicit constraints (maybe not optimal) appear as part of the procedure (including poorly formulated manufacturing const.)
Important trades between weight, performance, cost are somewhat “ad-hoc”
This approach is not sufficient for new designs

8. Our Goals
Simultaneously change the aerodynamic shape and material thicknesses of the structure to achieve a design that is “best”
Use sophisticated mathematical methods to achieve reasonable turnaround
Include all relevant disciplines and constraints to produce realistic designs
Where are we at?

9. Inputs for Preliminary Design
Detailed list of design requirements
“Rough” description of the aerodynamic shape (Outer Mold Line - OML)
“Rough” description of the internal structural layout (no details of the actual material distribution required - just topology information)

10. Objectives for Preliminary Design
Detailed aerodynamic shape of the configuration
Detailed material thickness distribution throughout the structure
Satisfy all constraints AND maximize our measure of efficiency

11. Aircraft Design Optimization Problem

12. Optimization Approaches

13. Why Is This Challenging?
Curse of dimensionality: to properly describe the detailed aerodynamic shape and structure of an aircraft, hundreds of design variables are needed.
Highly constrained problem: many disciplines impose limits on the allowed variations of the design variables. These limits may be hard to compute.
Brute-force methods will not work: a single high-fidelity aero- structural analysis (NS + FEM) may take several hours on multiple processors.

14. Available Approaches
Efficient methods to obtain sensitivities of many functions with respect to a few variables - Direct method
Efficient methods to obtain sensitivities of a few functions with respect to many variables - Adjoint method
No known methods to obtain sensitivities of many functions with respect to many design variables
This is the aircraft design problem!!!

15. What Makes Our Approach Feasible?
Target: Overnight turnaround with “reasonable” large-scale computing resources ~ 128 processors
Formulation of the adjoint problem for multiple disciplines
Simply a sophisticated way of computing gradient information
Two system analyses (of the aero-structural type) provide all necessary information to compute the full gradient vector

16. Quiet Supersonic Platform (QSP) Program
Range = 5,000 nmi
Cruise Mach No. =
TOGW = 100,000 lbs
Initial Overpressure < 0.3 psf
Payload = 20,000 lbs
Swing-wing concept
Gulfstream Aerospace Corporation QSJ Configuration

17. Low Boom Supersonic Designs
Is this combination of requirements achievable? Can we actually do this? This is a set of conflicting requirements: the airplane may not “close”
Classical sonic boom minimization theory says that
What is the necessary aircraft length? Can we achieve this with our target TOGW?
At Stanford we have decided to focus on:
Using aerodynamic shape optimization to take advantage of the nonlinear interactions between shock waves and expansions to produce shaped booms
Using Multidisciplinary Design Optimization (MDO) methods to minimize the weight of the airframe

18. Aero-Structural Aircraft Design Optimization
Simultaneously change aero shape and structural thicknesses (high- fidelity) to maximize aircraft performance (aero and structure) while satisfying all constraints
Compute gradients and use with gradient-based optimizers
Achieve overnight turnaround with the use of parallel computing

19. Outline Introduction Sample Results from Current Work
The design process
Design goals and challenges
Our approach
Sample Results from Current Work
Aerodynamic shape optimization
Aero-structural optimization
Outlook and future work
Treatment of the boom problem
Manufacturing and cost observations

20. Aerodynamic Shape Optimization
Minimize drag coefficient and fixed lift, M=1.5
100,000 lbs vehicle
136 design variables:
Wing twist, camber and detailed shape (Hicks-Henne) bumps
Fuselage camber modifications
Wing and fuselage volumes are constrained not to decrease
Wing curvature may not exceed manufacturing constraints (provided by Raytheon aircraft)
Typically 20 design iterations (using NPSOL) arrive at an optimum design

21. Baseline Design M = 1.5 C_L = 0.1 H = 55,000 ft Axisymmetric fuselage
Inviscid C_D =

22. Optimized Design M = 1.5 C_L = 0.1 H = 55,000 ft Axisymmetric fuselage
Inviscid C_D =
15 Design Iterations

23. Sample Design Problem

24. Sample Design Problem (2)

25. Sample Design Problem (3)

26. Sample Design Problem (4)

27. Sample Design Problem (5)

28. Comparison with Sequential Optimization

29. Outline Introduction Sample Results from Current Work
The design process
Design goals and challenges
Our approach
Sample Results from Current Work
Aerodynamic shape optimization
Aero-structural optimization
Outlook and future work
Treatment of the boom problem
Manufacturing and cost observations

30. Parametric CAD Geometry Descriptions
Complex geometry is difficult to handle during automated design, particularly if
Complex intersections need to be computed
Geometric level of detail is high
Manufacturing constraints are imposed
CAD-based design system overcomes these limitations
Simulation directly interfaced to CAD via CAPRI
Parametric/Master-Model concept
Parallel/Distributed AEROSURF module for performance

31. Aircraft Parametric Model

32. Aircraft Parametric Model
100 scalar parameters and 36 sections can be controlled
Wing, fuselage, vertical and horizontal tail, nacelles
Wing components (main wing, v- and h-tail)
Reference area, aspect ratio, taper ratio, sweep angle, leading and trailing edge extensions, twist distribution (among others)
Detailed airfoil shape design at a number of sections
Fuselage
15 sections with modifiable shape, area, camber
Nacelles
10 parameterizations, fixed aifoil, solid of revolution
Information returned to the simulation using quad-patch surface grids

33. Aircraft Parametric Model Range

34. Software Integration Environments
Neglecting the software integration challenge will lead to failure
Aero-structural adjoint optimization approach has over 30 well-defined modules that interact with each other
In our ASCI project, we have chosen to explore the use of Python to “wrap” Fortran 90/95, C, and C++ so that everything that is available to Python can be used by these languages
Rely on open source frameworks that add functionality to existing code (distributed computing, visualization, journaling, unit conversion, etc.)

35. Pyre Distributed services
Workstation
Front end
Compute nodes
launcher
monitor
solid
fluid
journal
Michael Aivazis, Caltech

36. How About Sonic Boom? We have only discussed improvements to
Aerodynamic performance
Structural weight
Indirect improvements in sonic boom
How about direct impact of shaping on sonic boom?

37. How About Sonic Boom?
Sonic boom optimization presents particularly challenging problems because
Large mesh sizes required for accurate boom prediction
Design space is not smooth
Design space contains discontinuities
Gradient-based methods do not work well in general
Developed Genetic Algorithm based optimizations with
Kriging and Co-Kriging response surfaces
Gradient-enhanced Pareto front search

38. Fully Automated Sonic Boom Prediction Procedure
Fully nonlinear 3D boom prediction
Driven by parametric CAD model
Unstructured mesh size ~ 2.4 / 3.5 million
Solution from 7 min on 16 procs ( Athlon 2100xp )
Initial mesh generation
Centaur
CAD parametric model
AEROSURF
Mesh perturbation
and regeneration
Movegrid
Parallel flow solution (CFD)
AirplanePlus
Near field pressure extraction
Boom prediction
PCBOOM

39. Initial Unstructured Mesh Generation
Initial surface triangular mesh
Initial pressure distribution

40. Solution Adapted Meshes (3 Cycles)
Mesh after 3 adaptation cycles
with 2,397,938 nodes
Near field pressure distributions
at R/L = 1.5
after different adaptation cycles
Initial mesh
with 562,057 nodes
Near Field Pressure Distribution
after 3 adaptation cycles
Corresponding to different R/L
R/L = 0.4
R/L = 0.8
R/L = 1.2
R/L = 1.6
R/L = 2.0
Pressure extraction at different R/L

41. High-Fidelity Multi-Disciplinary Optimization
Base Configuration
Best CD Configuration
Best Boom Configuration
Additional disciplines needed to constrain change in aero shape
High-fidelity trade-offs important for low-boom supersonic aircraft
GA needed for simultaneous boom and Cd optimization
Non-dominated solutions form Pareto front
Different compromises achieved

42. Design Space Exploration

43. Inside a Hard Disk Drive

44. MDO for Launch Vehicles
Why MDO for launch vehicles?
Cost: Current U.S. LV’s ~ $40,000 / kg to LEO; small improvements yield big rewards.
Performance: Payload mass to orbit depends exponentially on many vehicle parameters
Coupled Environments: Wide-ranging aerodynamic, thermal, and structural loading tightly coupled

45. Conclusions & Future Work
High-fidelity design becoming a reality
Much work remains in making it truly useful
Manufacturing constraints and cost modeling are not a major part of the process
Plenty of opportunities to streamline / optimize the complete process, not just the performance of the vehicles