Robust och Multidisciplinär Optimering av Fordonsstrukturer 2009-00314

1. Robust och Multidisciplinär Optimering av Fordonsstrukturer 2009-00314 Fordons- och Trafiksäkerhet Resultatkonferens - 2014

2. Project Partners Principal applicant: Volvo Car Corporation Project partners: Combitech AB Altair Engineering EnginSoftNordic AB Dynamore Nordic AB Academic partner: LinköpingsTekniska Högskola

3. Overall Project Objective • Find suitable methods for implementing robust and multidisciplinary design optimization in automotive product development process Sandeeep Shetty

4. Robust design optimization • Scope • Develop efficient methodologies to perform multiobjective robust and reliability-based design optimization of large-scale vehicle structures • Investigation of approximate modelling techniques to reduce the computational effort of the optimization process • Implementation of developed methodologies into the existing product development process Sandeeep Shetty

5. Overall accomplishments • Different approaches to evaluate robustness and to perform non-deterministic optimisation have been studied • An approach to perform multiobjective reliability-based optimization and robust design optimization is presented and verified using a vehicle side impact crashworthiness application • An efficient reliability-based optimization using a combined metamodel and FE-based strategy is proposed and illustrated using industrial examples • Comparison between FE-based and metamodel-based robustness analysis has been performed • An approach to handle the discrete responses using metamodels is also presented • PhD courses – 60hp Sandeeep Shetty

6. Robust design procedure • Define problem • Inputs and outputs • Select Objectives • Uncertainties quantification • DOE strategy • Design of experiments • Optimisation strategy • Estimation of the mean and • standard deviation • Meta model • ‘ • Design evaluation • Select a optimum design • Verification Verification Sandeeep Shetty

7. Articles • Article -1 • Robustness-analysis • Comparison between FE-based and metamodel-based robustness analysis • Validation of metamodels • New metamodelling approach to handle discrete responses is proposed • Conclusion • Computational effort is minimised significantly by using meta models • Meta-model approach had acceptable accuracy compared to FE-based approach. • Article -2 • Non-deterministic optimization • Comparative study of deterministic and non- deterministic optimization • An approach to perform • optimization of large- scale vehicle structural application is presented • Conclusion • Presented metamodel-based approach was found to be suitable for large-scale deterministic optimization • Further improvement in the presented approach is required in the case of non-deterministic optimization • Article -3 • Efficient Reliability-based optimizationapproach • An efficient reliability-based optimization method is proposed and validated using industrial examples • Conclusion • Proposed method has better accuracy and the method is computationally efficient Sandeeep Shetty

8. Documented Results Licentiate thesis S.shetty: Optimization of Vehicle Structures under Uncertainties, Licentiate thesis, Linköping university, Thesis No. 1643 Journal Papers S. Shetty and L. Nilsson: Multiobjective reliability-based and robust design optimisation for crashworthiness of a vehicle side impact, accepted for publication in the international journal of vehicle design. S. Shetty and L. Nilsson: Robustness study of a hat profile beam made of boron steel subjected to three point bending, Submitted for publication. • Conference Paper • S.shetty: Efficient reliability-based optimization using a combined metamodel and FE-based strategy. published in proceedings of 4th International Conference on engineering optimization (EngOpt2014) Sandeeep Shetty

9. Multidisciplinary design optimization of automotive structures Scope Find an efficient MDO process • for large-scale applications • that takes the special characteristics of automotive structural applications into account • considers aspects related to implementation within an organization and product development process Outcome • Description and demonstration of an MDO process that is • simpler than multi-level methods • fits existing organizations better than sequential response surface methods (SRSM) and direct optimization • often more computationally efficient than direct optimization, SRSM and multi-level methods Ann-Britt Ryberg

10. Work performed • Literature survey • MDO methods • metamodel-based optimization •  Technical report • Comparison ofMDO methods • single-level methods • multi-level methods • Conclusion: • A single-level method + metamodels is often the best choice •  Article 1 • MDO process • description • demonstration on asimple example • Conclusion: • The process is efficient,flexible, and suitable forcommon automotivestructural MDO applications. • The process fits existingorganizations and productdevelopment processes. • etc. •  Article 2 • PhD courses • optimization courses • solid mechanics courses • etc •  75.5 hp Licentiate thesis • MDO studies • different software • different sizes • different methods •  Experience Ann-Britt Ryberg

11. Initiation Step 1 Define problem (load cases, objectives, constraints, and design variables). Setup Setup load case 1 load case n MDO process Application example Step 2 Find important design variables. Variable screening Variable screening Step 3 Define DOE, run simulations, and extract results. Design of experiments Design of experiments … SetupMinimize mass without degrading thedisciplinary performances. Screening25  15, 7, 11, 12 variables DOEAcceptable accuracy 90, 42, 55, 48 simulations MetamodelsRBF neural networks +Feedforward neural networks OptimizationAdaptive simulatedannealing VerificationRBFNN: 8% mass red. (1 constr. viol.)FFNN: 12% mass red. Optimization Step 4 Build, check, and compare metamodels. Metamodel creation Metamodel creation Step 5 Find optimum solutions. Decision Front impact Side impact v Step 6 Check results with detailed model. Verification Verification v tx05_mid_front intr_mid_front intr_upper_side intr_lower_side Roof crush Modal analysis d forc_3_roof forc_max_roof freq_m1_modal freq_m2_modal Ann-Britt Ryberg

12. Publications Licentiatethesis LIU-TEK-LIC-2013:1Metamodel-based design optimization – A multidisciplinary approach for automotive structuresby A-B Ryberghttp://liu.diva-portal.org/smash/record.jsf;jsessionid=d0d8422fc5bf97e6f729a89c0b32?searchId=1&pid=diva2:601789 TechnicalreportLIU-IEI-R-12/003Metamodel-based multi-disciplinary design optimization for automotive applicationsby A-B Ryberg, R D Bäckryd, L Nilssonhttp://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84701 Article 1Multidisciplinary design optimization methods for automotive structuresby R D Bäckryd, A-B Ryberg, L NilssonSubmitted Article 2A metamodel-based multi-disciplinary design optimization process for automotive structuresby A-B Ryberg, R D Bäckryd, L NilssonUnder revision Ann-Britt Ryberg

13. Futuredwork Phase II accepted and started Project number: 2014-01340 Aim: Take researcher from licentiate to PhD. Continue development of models for industrial problems. Industrial implementation of the result from earlier project. Couple the two areas in a combined study and paper..