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 EnginSoft Nordic AB Dynamore Nordic AB Academic partner:Linköpings Tekniska 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.  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 Overall accomplishments Sandeeep Shetty

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

7. 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 optimization approach  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 Articles 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. Ann-Britt Ryberg 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

10. Ann-Britt Ryberg Work performed Literature survey MDO methods metamodel-based optimization  Technical report PhD courses optimization courses solid mechanics courses etc  75.5 hp Comparison of MDO 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 a simple example Conclusion: The process is efficient, flexible, and suitable for common automotive structural MDO applications. The process fits existing organizations and product development processes. etc.  Article 2 MDO studies different software different sizes different methods  Experience Licentiate thesis

11. Ann-Britt Ryberg MDO processApplication example Decision Initiation Design of experiments Variable screening Metamodel creation Setup Verification load case 1 Design of experiments Variable screening Metamodel creation Setup Verification load case n … Optimization Step 1 Define problem (load cases, objectives, constraints, and design variables). Step 2 Find important design variables. Step 3 Define DOE, run simulations, and extract results. Step 4 Build, check, and compare metamodels. Step 5 Find optimum solutions. Step 6 Check results with detailed model. Modal analysis freq_m1_modal freq_m2_modal Front impact v tx05_mid_front intr_mid_front Roof crush d forc_3_roof forc_max_roof Side impact v intr_upper_side intr_lower_side Setup Minimize mass without degrading the disciplinary performances. Screening 25  15, 7, 11, 12 variables DOE Acceptable accuracy  90, 42, 55, 48 simulations Metamodels RBF neural networks + Feedforward neural networks Optimization Adaptive simulated annealing Verification RBFNN: 8% mass red. (1 constr. viol.) FFNN: 12% mass red.

12. Ann-Britt Ryberg Publications Licentiate thesis LIU-TEK-LIC-2013:1 Metamodel-based design optimization – A multidisciplinary approach for automotive structures by A-B Ryberg http://liu.diva-portal.org/smash/record.jsf;jsessionid=d0d8422fc5bf97e6f729a89c0b32?searchId=1&pid=diva2:601789 http://liu.diva-portal.org/smash/record.jsf;jsessionid=d0d8422fc5bf97e6f729a89c0b32?searchId=1&pid=diva2:601789 Technical report LIU-IEI-R-12/003 Metamodel-based multi-disciplinary design optimization for automotive applications by A-B Ryberg, R D Bäckryd, L Nilsson http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84701 http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84701 Article 1 Multidisciplinary design optimization methods for automotive structures by R D Bäckryd, A-B Ryberg, L Nilsson Submitted Article 2 A metamodel-based multi-disciplinary design optimization process for automotive structures by A-B Ryberg, R D Bäckryd, L Nilsson Under revision

13. 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.. Futured work