Multidisciplinary Design of Complex Engineering Systems With Implications for

Manufacturing

Juan J. Alonso

Department of Aeronautics & Astronautics

Stanford University

jjalonso@stanford.edu

AIM MeetingApril 6, 2004

Outline

• Introduction– 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

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

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

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

… But Some Aircraft Are Not “Tweaks”

Bae/Aerospatiale Concorde Lockheed SR-71 Blackbird

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

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?

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)

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

Aircraft Design Optimization Problem

Optimization Approaches

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.

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!!!

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

Quiet Supersonic Platform (QSP) Program

Gulfstream Aerospace Corporation QSJ Configuration

• Range = 5,000 nmi

• Cruise Mach No. = 1.6-1.8

• TOGW = 100,000 lbs

• Initial Overpressure < 0.3 psf

• Payload = 20,000 lbs

• Swing-wing concept

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

€

Δp =W

L3

2

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

Outline

• Introduction– 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

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

Baseline Design

• M = 1.5• C_L = 0.1• H = 55,000 ft• Axisymmetric

fuselage• Inviscid C_D =

0.00858

Optimized Design

• M = 1.5• C_L = 0.1• H = 55,000 ft• Axisymmetric

fuselage• Inviscid C_D =

0.00785• 15 Design

Iterations

Sample Design Problem

Sample Design Problem (2)

Sample Design Problem (3)

Sample Design Problem (4)

Sample Design Problem (5)

Comparison with Sequential Optimization

Outline

• Introduction– 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

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

Aircraft Parametric Model

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

Aircraft Parametric Model Range

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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.)

Pyre Distributed services

WorkstationWorkstation Front endFront end Compute nodesCompute nodes

launcher

journal

monitorsolid

fluid

Michael Aivazis, Caltech

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?

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

Initial mesh generationCentaur

CAD parametric modelAEROSURF

Mesh perturbation and regeneration

Movegrid

Parallel flow solution (CFD)AirplanePlus

Near field pressure extractionBoom prediction

PCBOOM

Fully nonlinear 3D boom predictionDriven by parametric CAD model

Unstructured mesh size ~ 2.4 / 3.5 million Solution from 7 min on 16 procs ( Athlon 2100xp )

Fully Automated Sonic Boom Prediction Procedure

Initial surface triangular mesh Initial pressure distribution

Initial Unstructured Mesh Generation

Initial mesh with 562,057 nodes

Mesh after 3 adaptation cycleswith 2,397,938 nodes

Near Field Pressure Distribution after 3 adaptation cycles Corresponding to different R/L

Near field pressure distributions at R/L = 1.5 after different adaptation cycles

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

Solution Adapted Meshes (3 Cycles)

High-Fidelity Multi-Disciplinary OptimizationBase Configuration

Best CD Configuration

Best Boom Configuration

• GA needed for simultaneous boom and Cd optimization

• Non-dominated solutions form Pareto front

• Different compromises achieved

• Additional disciplines needed to constrain change

in aero shape

• High-fidelity trade-offs important for low-boom

supersonic aircraft

Design Space Exploration

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Inside a Hard Disk Drive

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

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