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How Value-Driven Design eliminates the cause of weight and cost growth

How Value-Driven Design eliminates the cause of weight and cost growth. Extensive Attributes. Performance, Weight , Cost, and -ilities. Composition Function. Value-Driven Design = Optimization. Value. Improve. Evaluate. Optimizer. Objective Function. Design Optimization.

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How Value-Driven Design eliminates the cause of weight and cost growth

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  1. How Value-Driven Design eliminates the cause of weight and cost growth

  2. Extensive Attributes Performance, Weight, Cost, and -ilities Composition Function

  3. Value-Driven Design = Optimization Value Improve Evaluate Optimizer Objective Function Design Optimization Attributes(Weight, Eff., Cost) Design Variables(Length, Displacement) Definition Analysis CAD System Physical Models Configuration

  4. Optimization Extensive Attributes Thrust Drag Efficiency Weight Manufacturing Cost Maint. Cost Reliability Maintainability Radar Cross Section Objective Function Value

  5. Gradient What? VDD Vision: Pervasive use of Optimizationin Engineering Design Engine Inlet Status Value Efficiency 90% 150,000 135,000 Weight 700 -130 -91,000 Reliability 1500 2.3 3,450 Maintainability -340 -2,652 7.8 Maintenance Cost 500 -0.5 -250 Support Equipment 12 -15 -180 -1200 Radar Cross-Section 0.1 -120 InfraRed Signature 1.4 -50 -70 Manufacturing Cost 700 -1 -700 Design Value $ 43,478 Technical detail on distributed optimization can be found at http://www.dfmconsulting.com/opt.pdf

  6. Staus Quo: Requirements Flowdown If each module meets its requirements, the overall system will meet its requirements Requirements Method promises Functionality Aircraft Systems Wing Design Cockpit Design Propulsion Systems Landing Gear Systems Avionics Systems Turbine Design Propulsion Control System Turbine Blade Heads-Up Servovalve Temperature FADEC Radar Design Design Design Design Sensor Design Display Design

  7. VDD Vision: Distributed Optimal Design If you design the best components, you will realize the best system If each component is optimized, the overall system will be optimized Aircraft Systems Wing Design Cockpit Design Propulsion Systems Landing Gear Systems Avionics Systems Turbine Design Propulsion Control System Turbine Blade Heads-Up Servovalve Temperature FADEC Radar Design Design Design Design Sensor Design Display Design

  8. Distributed Optimal Design • Overview • Design Attribute Spaces • Composition Function • Objective Function • Linearization and Decomposition

  9. Status Value Gradient Efficiency 90% 150,000 135,000 Weight 700 -130 -91,000 Reliability 1500 2.3 3,450 Maintainability -340 -2,652 7.8 Maintenance Cost 500 -0.5 -250 Support Equipment 12 -15 -180 -1200 Radar Cross-Section 0.1 -120 InfraRed Signature 1.4 -50 -70 Manufacturing Cost 700 -1 -700 Design Value $ 43,478 Distributed Optimal Design Overview System Value Value Model Effect of Component Attribute on System Value System Attributes Composition Function Component Attributes

  10. z r z r r x z Design Attribute Spaces Unit Profit • Coordinate Axes are Design Attributes • Different Space for • Whole Product: x1, x2, ... xm • Each Component: yk1, yk2, ... ykn (describes component k) • Super attribute space composed of all attributes of all components: = [y11, y12, ... y21, ... ypn] • describes whole product; describes all components Reliability Horsepower Intake Manifold Weight 6.0 Cost 12.0 Life 20000.0 Intake Valve Weight 0.1 Cost 2.0 Efficiency 0.9 Cylinder Head Weight 0.5 Cost 42.0 Efficiency 0.9 Life 10000.0

  11. For distributed optimization, h is the composition function Extensive attributes in affect collectively no other attributes matter for global optimization Example elements:   Weightchassis component system + Weightengine = Weighttractor + Weighttransmission . . . The Composition Function r r ( ) = x h z r r x z model 1 1   MTBF MTBF tractor component

  12. Objective Function (Value Model) r ( ) p The objective function is for the whole system x ( ) r r r r ( ) * An optimum point is where for all p ³ p * x x x x We want local objective functions, vj for components j = 1 to n such that when ( ) ( ) r r r r r r ( ) ( ) * * ³ " " Þ p ³ p " v y v y y j x x x j j That is, when the components are optimized, the product is optimized

  13.      h z Objective Function with Local Attributes r r r ( ) ( ) p = • Since value = and , then value , a function of local attributes • This gives us global value in terms of local attributes, but does not give an independent objective function for each component • For independence, we must linearize • Thus each component has its own goal x x h z ( ) r ( ) = p h z

  14. Given smoothness of and h, the linear approximation is reasonable for small changes (< 10% of whole system value) near the preliminary design Validity of Linearization

  15.      h z Linearizing the Objective Function • Start with a reference design (preliminary design) with attributes x* and z* • Generate the Taylor expansion of around z* : • O2 represents second order and higher terms that we can ignore in the vicinity of z* • Without O2, the Taylor series is linear                 *   * 2    h z  h z    x  J  z  z  O   h * * z x 

  16. Solving the Taylor Expansion         , , , ,     x  x  x  x   • is the gradient of • Jh is the Jacobian Matrix of h: 1 2 3 4  x  x  x  x   1 1 1 1     z  z  z  z   1 2 3 p   x  x  x  x  2 2 2 2     z  z  z  z  1 2 3 p   x  x  x  x   3 3 3 3     z  z  z  z 1 2 3 1 p           x  x  x  x   m m m m     z  z  z  z   1 2 3 p

  17. Solving the Taylor Expansion p m        x          * i *      h z   h z    z  z j j    x  z   i j j  1 i  1 Objective functions are used for ranking—they are not changed by the addition or subtraction of a constant. Thus, the expression above can be simplified by dropping all terms that use the constant z*: p m    x         i    h z   z j    x  z   i j j  1 i  1  Linear objective functions have the property that can be maximized by maximizing each zj term or any group of zj terms independently

  18. Component Optimization For a group of zj’s that correspond to a single component, we can relable them y1 though yn and determine the component objective function (in the vicinity of the preliminary design): n m    x    i      y component k    x  y    i k * k  1 i  1 x

  19. “But you can’t DO that!” Value Evaluate Search $ Optimizer Objective Function Properties (Weight, Eff., Cost) Parameters (Length, Displ.) Definition Analysis Design Drawing Physical Models Configuration Value landscape in parameter space Value landscape in property space

  20. Gradient Implementing Distributed Optimal Design Partial Derivatives of the Objective Function Engine Inlet Status Value Efficiency 90% 150,000 135,000 Component Design Value is Commensurate with System Design Value Weight 700 -130 -91,000 Reliability 1500 2.3 3,450 Maintainability -340 -2,652 7.8 Maintenance Cost 500 -0.5 -250 Support Equipment 12 -15 -180 -1200 Radar Cross-Section 0.1 -120 InfraRed Signature 1.4 -50 -70 Manufacturing Cost 700 -1 -700 Design Value $ 43,478

  21. Other Benefits of VDD • Optimization Finds a Better Design • Requirements Cause Preference Conflicts

  22. Optimization Finds a Better Design Requirements Increasing Score < $30 M unit mfg cost Limit of Feasibility Cost Cost < 30,000 lbs. weight Best (0,0) (0,0) Weight Weight Traditional Spec Method Optimal Design

  23. Requirements Cause Preference Conflicts Brake Material+ $11,000- 90 lbs. Rudder- $10,000+ 190 lbs. Net Impact + $ 1,000 + 100 lbs. Differences in revealed values within a design team lead to choices that, taken together, are clearly lose-lose

  24. Conflicts: Folding in Attribute Space Design Potential Value A Requirements Method Distributed Optimal Design Value B

  25. Extensions of VDD beyond Detailed Design • Conceptual Design • Develop a Value Model for system optimization • Technology Development • Develop a Value Model / Composition Function for technology insertion and evaluation • Risk Management • Quantitative valuation of consequences • Test Planning • Value of Information

  26. Summary • VDD can improve system value by tens of billions of dollars for complex aerospace systems • versus flowing down requirements for extensive attributes • Value-Driven Design in a nutshell • Optimization is used to design all components • Extensive variables are incorporated into the objective function • Component objective functions are coordinated with the system objective function for distributed optimal design • We must transition VDD to practice

  27. Future Steps • What is necessary to mature the VDD concept • pilot application • University research project - simulated prototype • Small DoD application (too small means low value, but proof of concept) • evaluate scalability - to more complex systems and across life cycle • test theory formally on an existing program • derive falsifiable hypotheses, collect data, assess hypotheses • use the Abbas-Matheson model to quantify results • three programs, working from small to large • How should VDD be introduced into the Community? • Guidebook ... • What needs to be done prior to PDR to make VDD feasible? • Value model development (need a process) • Concept optimization (closer to practice) • What does the govt need to do to make this happen?

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