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Objective Driven Design Teams, Think Outside-the-box!

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Oil production depends on many factors; e.g. Supply, Demand, present inventory, etc. An oil company may have many refineries with many distillation units. How can a company simulate extracting products 'a', 'b', and 'c' from its crude oil? Assume the company wants product 'a' on the west coast, 'b' in the middle of US, and 'c' on the east coast. Assume the company has refineries 'x' on west coast, 'y' in middle US, and 'z' on east coast. How does one model such a company's oil production so as to produce/refine the 'right' amounts of each product at each refinery site in order to meet the company's goal of maximizing profits?

Partial Differential Equations (PDEs) will be used to model the crude oil distillation for each distillation unit at each site; i.e. many PDEs must be solved at once! Are there computers large enough to handle such problems today? Are there plans for some super computer that will be able to handle many (1,000s) PDEs at once?

With maintenance of distillation units being continual, e.g. fix one, stop another, this will be a constant problem when trying to simulate the next day's crude oil work load. For example, assume a company has 600 distillation units overall. That means a computer program would be required to solve 600 PDEs ASAP; i.e. 10 hours of PDEs. My past experience with modeling in FortranCalculus™ language/compiler, I was taught that a modeling requiring 'Tmod' time to execute the model, would require around 2'Tmod' time for the optimal solution. That would then get us into the 20 hr. time range for 600 PDEs. Too long! Need faster computers and solvers to get into reasonable solution times. Ideas how this could be done today? http://goal-driven.net/apps/fc-compiler.html

Many people thought that the Wright Brother's idea of an 'airplane' would never fly. But, what if it did? What if Oil sales income doubled or more? Would crude oil prices increase? (Everyone is going to want more for their piece of the pie, right?) How would this effect your company?

John D Rockefeller was quoted saying, "If you want to succeed you should strike out on new paths, rather than travel the worn paths of accepted success."

Any future John D Rockefeller's reading this proposal? Are you interested in increasing your company profits by several orders of magnitude? Does your company have a company goal or objective that all employees know about and follow? If so, continue reading on this proposal by reading my article "Company Goal: Increase Productivity?" (a dozen pages). Go to web page http://goal-driven.net/textbooks/index.html and click on the 'download' link, its free!

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Objective Driven Design Teams, Think Outside-the-box!

  1. 1. EnhanceEnhance Scientific & EngineeringScientific & Engineering ProductivityProductivity How?How? Through what is calledThrough what is called Objective-Driven Engineering,Objective-Driven Engineering, Objective-Driven Research, &Objective-Driven Research, & Objective-Driven ManagementObjective-Driven Management Optimal Designs EnterpriseOptimal Designs Enterprise goal-driven.netgoal-driven.net
  2. 2. Today's EngineeringToday's Engineering Ask a dozen engineersAsk a dozen engineers what their objective is for their present projectwhat their objective is for their present project and you'll get dozen+ objectives.and you'll get dozen+ objectives. Projects need Team players to solve their tasks.Projects need Team players to solve their tasks. A team must have -one- objective andA team must have -one- objective and All members must know it.All members must know it.
  3. 3. Planning a trip?Planning a trip? Before getting behind the wheel,Before getting behind the wheel, how many have a destination?how many have a destination? Before getting onboard an airplane,Before getting onboard an airplane, how many have a destination?how many have a destination?
  4. 4. A Team Effort!A Team Effort! Objective-Driven EmployeesObjective-Driven Employees requires input fromrequires input from Engineers, Managers, & CEO.Engineers, Managers, & CEO.
  5. 5. Building an ObjectiveBuilding an Objective What are yourWhat are your Goals/ObjectivesGoals/Objectives for a today's Project?for a today's Project? Take 5 min. and write down all thingsTake 5 min. and write down all things that your today's project shouldthat your today's project should Minimize & MaximizeMinimize & Maximize
  6. 6. Building an ObjectiveBuilding an Objective Find common threads amonst teamFind common threads amonst team members; summarize your teams Minimizemembers; summarize your teams Minimize & Maximize lists.& Maximize lists. CEO thru all staff need to know your teamCEO thru all staff need to know your team objective. Keep everyone updated.objective. Keep everyone updated.
  7. 7. Building an ObjectiveBuilding an Objective Project objective createdProject objective created andand agreed upon?agreed upon? Continually tweak your objective thru-outContinually tweak your objective thru-out project's life.project's life.
  8. 8. Objective-Driven EngineeringObjective-Driven Engineering What are your company goals?What are your company goals? Are these goals well known by all employees?Are these goals well known by all employees? Past Present FuturePast Present Future BeforeBefore With Computer With OptimizationWith Computer With Optimization ComputersComputers Gained some visionGained some vision On ComputersOn Computers
  9. 9. Objective-Driven RefineryObjective-Driven Refinery Basics (R&D)Basics (R&D) Processes (Mfg)Processes (Mfg) TotalTotal
  10. 10. Chaotic Design ProcessChaotic Design Process Control BoxControl Box Bingo, a design!Bingo, a design!
  11. 11. Optimal Design ProcessOptimal Design Process Design Objective for winning team?Design Objective for winning team? Win the race!Win the race! (Other teams spent time building a team.)(Other teams spent time building a team.) A math model was derived by learningA math model was derived by learning previous years winning race times.previous years winning race times. Given: Total Time , speed, & distance.Given: Total Time , speed, & distance. Solution: Wheel Diameter > ...Solution: Wheel Diameter > ... Paper Bicycle Design, a Team ProjectPaper Bicycle Design, a Team Project
  12. 12. Objective-Driven DesignObjective-Driven Design Sawmill's operationSawmill's operation Processing Objective?Processing Objective?
  13. 13. Objective-Driven DesignObjective-Driven Design Sawmill's operationSawmill's operation Processing Objective?Processing Objective? Maximize company's profitsMaximize company's profits Minimize pollutionMinimize pollution ??????
  14. 14. Objective-Driven DesignObjective-Driven Design Sawmill's operationSawmill's operation Processing Objective?Processing Objective? Maximize company's profitsMaximize company's profits Minimize pollutionMinimize pollution ?????? Parameters to consider:Parameters to consider: Log-to-lumber Processing:Log-to-lumber Processing: Size of logSize of log: diameter, length, taper, knots, etc.: diameter, length, taper, knots, etc. Time requiredTime required to: cut log, sharpen blades,to: cut log, sharpen blades, lubricate machinery, etc.lubricate machinery, etc. Strength or flexibilityStrength or flexibility desired of various sizes.desired of various sizes. InventoryInventory Market trendsMarket trends
  15. 15. Objective-Driven DesignObjective-Driven Design Surveillance Design Objective:Surveillance Design Objective: Maximize surveillance coverageMaximize surveillance coverage Minimize number of satellitesMinimize number of satellites Solution? (Not realistic; Objective needs work.)Solution? (Not realistic; Objective needs work.)
  16. 16. Objective-Driven DesignObjective-Driven Design Thin-Film-Head (TFH) for Magnetic RecordingThin-Film-Head (TFH) for Magnetic Recording TFH with coilTFH with coil Disc platter cross-sectionDisc platter cross-section Typical Readback PulseTypical Readback Pulse Optimum pulse shapeOptimum pulse shape versusversus
  17. 17. Objective-Driven DesignObjective-Driven Design Thin-Film-Head (TFH) for Magnetic RecordingThin-Film-Head (TFH) for Magnetic Recording Design Objective?Design Objective? Maximize ProfitMaximize Profit Maximize Pulse SymmetryMaximize Pulse Symmetry Minimize Pulse WidthMinimize Pulse Width Minimize PollutionMinimize Pollution Maximize User SatisfactionMaximize User Satisfaction ?????? Solution: Determine TFH geometry parameters A, B,Solution: Determine TFH geometry parameters A, B, C, etc. to achieve objectiveC, etc. to achieve objective
  18. 18. Objective-Driven DesignObjective-Driven Design Matched Filter for Magnetic RecordingMatched Filter for Magnetic Recording Electrical FilterElectrical Filter Yin(t) ==>Yin(t) ==> TransferTransfer FunctionFunction -------------------------- H(s)H(s) ==> Yout(t)==> Yout(t) versusversus Typical Input Pulse, Yin(t)Typical Input Pulse, Yin(t) Desired Output Pulse, Yout(t)Desired Output Pulse, Yout(t)
  19. 19. Objective-Driven DesignObjective-Driven Design Matched Filter for Magnetic RecordingMatched Filter for Magnetic Recording Results:Results: Textbook problem solved in 4 hoursTextbook problem solved in 4 hours Design required 2 years to acquire a trueDesign required 2 years to acquire a true practical objective functionpractical objective function Development time droppedDevelopment time dropped from 12 to 1 weekfrom 12 to 1 week Design was mathematically optimalDesign was mathematically optimal
  20. 20. Design Process HistoryDesign Process History Mr. Arithmetic Mr. Algebra Mr. CalculusMr. Arithmetic Mr. Algebra Mr. Calculus BeforeBefore With Computer With OptimizationWith Computer With Optimization ComputersComputers Gained some visionGained some vision On ComputersOn Computers Process MethodologyProcess Methodology One Step at a Simulate Problem Find Optimal Solution.One Step at a Simulate Problem Find Optimal Solution. Time on Computer Must 'See' EntireTime on Computer Must 'See' Entire Problem & ObjectivesProblem & Objectives
  21. 21. Design Process HistoryDesign Process History Mr. Arithmetic Mr. Algebra Mr. CalculusMr. Arithmetic Mr. Algebra Mr. Calculus Cut log so Cut to meet Cut to Maximize ProfitCut log so Cut to meet Cut to Maximize Profit boards areboards are Inventory while MinimizingInventory while Minimizing as straight as Demand Waste, and on eachas straight as Demand Waste, and on each possible individual log;possible individual log; MaximizeMaximize Strength whileStrength while Minimizing WeightMinimizing Weight Example Problem, A Sawmill Goals:Goals:
  22. 22. Design Process HistoryDesign Process History Mr. Arithmetic Mr. Algebra Mr. CalculusMr. Arithmetic Mr. Algebra Mr. Calculus Goal:Goal: 'Build it!' 'Organize a Win contest!'Build it!' 'Organize a Win contest! team'team' Develop-Develop- Chaos Organized Logical Steps 4 WinChaos Organized Logical Steps 4 Win mentment Chaos - Solved ProblemChaos - Solved Problem Process:Process: QuicklyQuickly - Identified- Identified parameters thatparameters that affected solutionaffected solution Design & Build a Paper Bicycle ContestDesign & Build a Paper Bicycle Contest
  23. 23. "One Step from First Principles to Solutions""One Step from First Principles to Solutions" EnhancingEnhancing Scientific & EngineeringScientific & Engineering ProductivityProductivity FortranCalculus IntroFortranCalculus Intro Optimal Designs EnterpriseOptimal Designs Enterprise goal-driven.netgoal-driven.net
  24. 24. Industry IssueIndustry Issue A Proven ApproachA Proven Approach SummSummaryary FortranCalculusFortranCalculus LanguageLanguage AgendaAgenda
  25. 25. Scientific & EngineeringScientific & Engineering ProductivityProductivity Industry IssueIndustry Issue Costly Problem/Solution Cycle ...Costly Problem/Solution Cycle ... Model Married to AlgorithmModel Married to Algorithm Validation DelayedValidation Delayed Long Problem/Solution CycleLong Problem/Solution Cycle Problem "Understanding" DelayedProblem "Understanding" Delayed
  26. 26. Formulate Problem Approximations,Approximations, Methods, etc.Methods, etc. Programming ofProgramming of Reduced ProblemReduced Problem Debug Problem,Debug Problem, Math, & ProgramMath, & Program EngineerEngineer MathematicianMathematician ProgrammerProgrammer AllAll Present Engineering SimulationsPresent Engineering Simulations Basic, Fortran, MACSYMA, etc. LanguagesBasic, Fortran, MACSYMA, etc. Languages Engineering:Engineering: Quickly FrozenQuickly Frozen Commitment:Commitment: LargeLarge
  27. 27. Present Engineering SimulationsPresent Engineering Simulations Basic, Fortran, MACSYMA, etc. LanguagesBasic, Fortran, MACSYMA, etc. Languages (Cont.)(Cont.) Engineering:Engineering: Quickly FrozenQuickly Frozen Commitment:Commitment: LargeLarge Cost:Cost: HighHigh Delay:Delay: LongLong Algebra Level SummaryAlgebra Level Summary
  28. 28. FC Technology HistoryFC Technology History Late 1960's - Pioneered - TRW / NASALate 1960's - Pioneered - TRW / NASA Mid 1970's - Validated - PROSE, Inc.Mid 1970's - Validated - PROSE, Inc. Late 1980's - Migrated - Du PontLate 1980's - Migrated - Du Pont Today -Today - Fortran CalculusFortran Calculus PioneeredPioneered ValidatedValidated MigratedMigrated OperationalOperational
  29. 29. Rapid PrototypingRapid Prototyping for Adaptive Engineeringfor Adaptive Engineering Fortran Calculus LanguageFortran Calculus Language FormulateFormulate ProblemProblem Debug ProblemDebug Problem EngineerEngineer EngineerEngineer
  30. 30. Rapid PrototypingRapid Prototyping for Adaptive Engineeringfor Adaptive Engineering FortranFortran Calculus Language (Cont.)Calculus Language (Cont.) Engineering:Engineering: AdaptiveAdaptive Commitment:Commitment: SmallSmall Cost:Cost: LowLow Delay:Delay: ShortShort Calculus Level SummaryCalculus Level Summary
  31. 31. Enabling TechnologyEnabling Technology Fortran CalculusFortran Calculus Symbolic Differentiation Evaluated at a PointSymbolic Differentiation Evaluated at a Point GeneratesGenerates Gradient VectorsGradient Vectors Jacobian MatricesJacobian Matrices Hessian MatricesHessian Matrices ... of Any Programmed Model... of Any Programmed Model Automatic DifferentiationAutomatic Differentiation
  32. 32. Enabling TechnologyEnabling Technology Fortran CalculusFortran Calculus EnablesEnables Inverse Problem SolvingInverse Problem Solving Nonlinear OptimizationNonlinear Optimization Optimization of Differential EquationsOptimization of Differential Equations Structured Nesting of Optimization AlgorithmStructured Nesting of Optimization Algorithmss Automatic Differentiation (Cont.)Automatic Differentiation (Cont.)
  33. 33. Fortran CalculusFortran Calculus,, a calculus level languagea calculus level language Increases Science/Engineering ProductivityIncreases Science/Engineering Productivity Allows Rapid Model PrototypingAllows Rapid Model Prototyping Reduces Costly Problem/Solution CycleReduces Costly Problem/Solution Cycle Accelerates Problem "Understanding"Accelerates Problem "Understanding" Proven Concept Since 1968Proven Concept Since 1968 Provides a Competitive Technical EdgeProvides a Competitive Technical Edge SummarySummary Optimal Designs EnterpriseOptimal Designs Enterprise goal-driven.netgoal-driven.net

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