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© United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Slide 1 of.

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Presentation on theme: "© United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Slide 1 of."— Presentation transcript:

1 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Slide 1 of 26 Design For Variation UCM 2012 Sheffield, UK July 2-4, 2012 Grant Reinman, Senior Fellow, Statistics and Design For Variation Pratt & Whitney, East Hartford, CT

2 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Slide 2 of 26 Pratt & Whitney Engineering A Passion for Innovation PurePower® PW1000G Engine

3 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Slide 3 of 26 Deterministic Design, Uncertain World Traditional Approach: Empirical Design Margins, Factors of Safety ▲Manufacturing ▲Usage ▲Materials▲Computational Models

4 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 To Help Prevent Design Iterations due to a Model’s  Meanline Miss, by using Bayesian model calibration process  Margin Miss, by replacing legacy margins with a probabilistic model of uncertainty and variability To Reduce Cost  Focus on important features  Relax requirements on unimportant features  Use Robust Design to reduce sensitivity To Maximize Stage Life (Time on Wing)  Rotor life depends on max distress / min life airfoil  Weakest-link structure pervasive in gas turbines  Reducing variation increases rotor life Probabilistic Design, Uncertain World Why? Remove cost from low-impact features Model Inputs Slide 4 of 26

5 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 To increase the speed of design parametric studies and optimization using engineering model emulators iSight-FD, etc Computer Model Structural FEM CFD model Matlab code Fortran code Other models Inputs Design Space Geometric dimensions Loads Temperatures Material properties Heat transfer coefficients Etc. Drive the DOE through the model Emulator Sensitivity Output in Design Space Stress Deflection Temperature Life Performance Etc. Maximin Latin Hypercube DOE GEMSA, GPMSA, etc Hours / Run Seconds / Run Probabilistic Design, Uncertain World Why? Slide 5 of 26

6 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 DFV Estimated Benefits ▲Component-level Design For Variation has yielded an estimated 64%-88% return on internal investment. The savings resulted from: Optimized inspection procedures and tolerances Reduced quality-related analysis and investigation time Reduced design iterations Improved reliability Improved on-time engine deliveries Improved root cause investigation process ▲Based on Six Sigma history and internal trends, the return is expected to increase rapidly in subsequent years ▲System-level Design For Variation is predicted to yield 40x return on investment due to Achieving system-level performance and reliability goals earlier in the development cycle Shorter development programs Slide 6 of 26

7 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Slide 7 of 26 Design For Variation (DFV) Strategic Plan ▲Strategy ☑ Identify Key Processes ☑ Define elements of a DFV-enabled modeling process ☑ Provide Resources under Strategic Initiative Fan & Compressor HFB Producibility Parametric Airfoil Compressor Aero Design Compressor Tip Clearances Structures Probabilistic Rotor Lifing Probabilistic Fracture Mechanics Probabilistic HCF Parametric Geometry Simulation Model Engine Dynamics and Loads Combustor and Augmentor Combustor pattern factor Combustor Liner TMF Augmentor Ignition Margin Audit Mid Turbine Frame Robust Design Mechanical Systems and Externals Carbon Seal Performance Ball & Roller Bearing Design FDGS Durability Externals: Forced Response Analysis Turbine Turbine Blade Durability Turbine Vanes and BOAS Durability Rotor Thermal Model Airfoil LCF Lifing HSE Combustor / Turbine DFV Air Systems Thermal Management Model Internal Air System Model Engine Data Matching Performance Analysis Performance Monte Carlo Risk Assessment Engine Test Confidence, Uncertainty Uncertainty in Engine System Predictions Production Test Data Trending and Analysis Statistical Data-match System-Level Risk Communication and Decision Making Validation Testing Engine Validation Planning DFV Infrastructure (Statistics & Partners) Emulation, Calibration Software High Intensity Computing Parametric Modeling Optimization Training ESW Communications Input Data Tech Support Vehicle Systems Probabilistic Ambient Temp Distribution Vision: All Key Modeling Processes will be DFV-enabled

8 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/ Elements of a DFV-Enabled Modeling Process Physics-Based Models  Model Preparation 1.A robust parametric physics-based model  Model Input Variability and Uncertainty Quantification 2.Process for retrieving data needed to quantify variability and uncertainty in model inputs 3.Process for performing statistical analysis/developing statistical model of input data a.Preserve correlations  Model Sensitivity Analysis 4.Process for generating a matrix of space-filling computer experiments (model runs) for emulator development 5.Process for running the computer code at the space-filling design points 6.Process for a.Building and validating the model emulator b.Performing a variance-based sensitivity analysis  Model Calibration 7.Process for determining what experimental/field data are required for model calibration and measurement uncertainty (amount, characteristics to be measured,..) 8.Process for performing Bayesian model calibration: calibrate and bias correct (if needed) and assess residual variation  Uncertainty Analysis 9.Process for generating a Monte-Carlo sample and driving it through Parametric model (if fast enough), Model emulator, or Bias corrected and calibrated model  Enable Practice 10.Update local ESW and local training. Put in place a process to ensure the model is capable over time. Slide 8 of 26

9 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Design For Variation DEFINE Customer requirements (probabilistic) ANALYZE Quantify model input variation / uncertainty, emulate and calibrate model, perform sensitivity and uncertainty analyses SOLVE Identify ‘optimum’ design that satisfies requirements VERIFY/VALIDATE Variability/Uncertainty model SUSTAIN Stable system of causes of performance variation ANALYZE SOLVE VERIFY VALIDATE DEFINE SUSTAIN Five Steps for Executing a DFV-Enabled Process Slide 9 of 26

10 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 ▲How do we define the allowable risk of not meeting a requirement? Requirement Risk DEFINE Design For Variation (DFV): Five Steps Define Customer Requirements Slide 10 of 26 Explicit customer requirement Safety Impact: Follow Regulatory Requirements Safety Impact: Follow Regulatory Requirements System-Level Job Ticket Metric Impact: Follow flow-down or roll-up process System-Level Job Ticket Metric Impact: Follow flow-down or roll-up process Engine Certification Test Impact None of the above Previous acceptable experience or other business considerations 6 Sigma Criteria Solve for the probability or rate that minimizes expected total cost None of the above Previous acceptable experience or other business considerations 6 Sigma Criteria Solve for the probability or rate that minimizes expected total cost

11 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Develop Model Emulator, Sensitivity Analysis Refine Distributions of Important Model Inputs Run Real World Uncertainty Analysis Perform Bayesian Model Calibration Design Space Filling Experiment Over Model Input Space ANALYZE Quantify model input variation & uncertainty, emulate & calibrate model, perform sensitivity and uncertainty analyses Design For Variation Model Output Run Experiment Through Engineering Model Accounting for uncertainty in Model input Model itself Slide 11 of 26

12 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/ Latin Hypercube Experimental Designs 3. Variance-Based Sensitivity Analysis 2. Gaussian Process Emulators 4. Bayesian Model Calibration ANALYZE : Key Technologies Design For Variation Slide 12 of 26 w yy F Y X w

13 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 ▲Performance characteristic y = f (x 1, x 2, …, x p ) depends on p inputs ▲The variance of y can be approximated by SOLVE Identify optimum design that satisfies requirements Design For Variation SOLVE ▲We can reduce by 1.Reducing : the variance in the inputs x 1, x 2, …, x p 2.Reducing : the sensitivity of y to variation in x 1, x 2,..., x p Slide 13 of 26

14 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Design for Variation SOLVE: Robust Design Strategies Noise Factors Filter Isolate Reduce at source Inoculate (anneal, heat treat) Input Signal Alter/smooth Selectively block Control Factors Robust optimization Material change Create multiple operating modes Output Response Calibrate Average System SOLVE Slide 14 of 26 Adapted from: Jugulum, R. and Frey, D. (2007). Toward a taxonomy of concept designs for improved robustness, Journal of Engineering Design, 18:2,

15 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 ▲VERIFY/VALIDATE includes – Data collection and analysis to validate model input probability distributions Manufacturing process data Material property data Temperatures, pressures, rotor speeds, airflows Flight characteristics (e.g. length, T2 at takeoff, taxi time,..) – Additional calibration of physics-based models –Trending in-service parts (wear, performance, etc) where feasible to validate models and their inputs VERIFY/VALIDATE Assumptions made in variability and uncertainty modeling Design For Variation VAL/VER Slide 15 of 26

16 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 ▲The SUSTAIN phase requires process control to ensure stable and consistent distributions over time – Manufacturing – Assembly – Acceptance Testing ▲Process Certification is vitally important – Sustaining capabilities to meet design requirements – Identifying production & design improvement opportunities ▲Design Sensitivity and Uncertainty Analyses indicate where process control resources should be focused SUSTAIN Stable system of causes of performance variation Design For Variation SUSTAIN Slide 16 of 26

17 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Design For Variation ▲Establish probabilistic design requirements ▲Emulate, calibrate engineering models ▲Solve for design that meets probabilistic requirements –Look for opportunities for making design less sensitive to variation ▲Validate and sustain model ▲Write Engineering Standard Work, develop local training Systematic Process for Designing for and Managing Uncertainty and Variability Slide 17 of 26

18 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Design For Variation ▲Additional Training Courses Developed ▲Automated Multi-physics Workflow ▲System-Level Design What’s New in 2012? Slide 18 of 26

19 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 ▲Software – Emulation, Sensitivity Analysis, Model Calibration – Statistical Analysis, Monte Carlo Simulation, Optimization ▲High Performance Computing Resources ▲Training – INTRODUCTION – PRACTITIONERS I: SENSITIVITY ANALYSIS, EMULATION, AND DOE – PRACTITIONERS II: ISIGHT-FD FOR SENSITIVITY AND UNCERTAINTY ANALYSIS – PRACTITIONERS III: MODEL CALIBRATION AND UNCERTAINTY ANALYSIS – MANAGERS: INTRODUCTION, REVIEW CHECKLIST ▲Communication – Wiki, Website, Meetings ▲Input Data Quality and Availability – Process Capability, Material Properties – Systems Performance, Mission Analysis ▲Engineering Standard Work Infrastructure: Enabling Design For Variation Design For Variation – What’s New? Slide 19 of 26

20 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Design For Variation - What’s New? Multi-discipline Automated Workflows Link disciplines: Aero, Thermal, Structures, Materials, Design Link components Enable probabilistic analyses, optimization 20 Slide 20 of 26

21 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 What’s New - PADME Program System Level Probabilistic Design & Validation of Engines PADME is a System-Level Extension of Design For Variation Quantify uncertainty/risk in system-level metrics Determine design drivers Determine optimum path to reduce risk Design changes Test changes PADME Goals Improve Mature vs. EIS Performance Gap by 33% Improve Mature vs. EIS Reliability Gap by 33% Reduce EVP Time by up to 50% PADME: Probabilistic Analysis and Design of Materials and Engines 21 Slide 21 of 26

22 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 PADME Vision Entire Engine Life Cycle Governed By Uncertainty Quantification and Management Rigorously Manage Uncertainty Throughout Life Cycle, Target Validation Testing to Address Largest Sources of Uncertainty 22 Slide 22 of 26 Fuel Consumption Delay/Cancellation Rate Weight Cost

23 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 PADME: Manage Uncertainty Throughout Engine Life Cycle Quantification of Uncertainty Enables Optimized Trades on System Level Metrics 23 PADME Governed By System-Level Networks Populated By Calibrated Component-Level Emulators Slide 23 of 26

24 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 ▲ Uncertainty-Based Design Approach Relies on Calibration of Multivariate Aero-Thermal-Structural Models Using Highly Instrumented Engine Deterministic Design Engine Test Deterministic Redesign Engine Test Deterministic Redesign Engine Test Probabilistic Design R&D Rig/Engine Test Engine Endurance Test crack oxidation Robust Design Legacy Approach DFV- PADME Approach EAR Export Classification: EAR 99 PADME Strategy Slide 24 of 26

25 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 Design For Variation – For More Information ▲Statistical Engineering Issue Slide 25 of 26

26 © United Technologies Corporation (2012) This document contains no technical data subject to the EAR or the ITAR. Reinman, Rev Date 6/19/2012 ▲Goal: quantify, understand, and control the risk of not meeting design criteria or exceeding thresholds ▲“The revolutionary idea that defines the boundary between modern times and the past is the mastery of risk: the notion that the future is more than a whim of the gods and that men and women are not passive before nature.” –Peter Bernstein, “Against the Gods: The remarkable story of risk” Model Prediction Design Criteria True Process Value Design For Variation Slide 26 of 26


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