Abstract
Presented is a novel framework for performing flexible computational design studies at preliminary design stage. It incorporates a workflow management device (WMD) and a number of advanced numerical treatments, including multi-objective optimization, sensitivity analysis and uncertainty management with emphasis on design robustness. The WMD enables the designer to build, understand, manipulate and share complex processes and studies. Results obtained after applying the WMD on various test cases, showed a significant reduction of the iterations required for the convergence of the computational system. The tests results also demonstrated the capabilities of the advanced treatments as follows:
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The novel procedure for global multi-objective optimization has the unique ability to generate well-distributed Pareto points on both local and global Pareto fronts simultaneously.
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The global sensitivity analysis procedure is able to identify input variables whose range of variation does not have significant effect on the objectives and constraints. It was demonstrated that fixing such variables can greatly reduce the computational time while retaining a satisfactory quality of the resulting Pareto front.
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The novel derivative-free method for uncertainty propagation, which was proposed for enabling multi-objective robust optimization, delivers a higher accuracy compared to the one based on function linearization, without altering significantly the cost of the single optimization step.
The work demonstrated for the first time that such capabilities can be used in a coordinated way to enhance the efficiency of the computational process and the effectiveness of the decision making at preliminary design stage.
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References
Balachandran, L.K., Fantini, P.F., Guenov, M.D.: Computational process management for aircraft conceptual design. In: 7th AIAA Aviation Technology, Integration and Operations Conference (ATIO). Belfast (2007)
Balachandran, L.K.: Computational Workflow Management for Conceptual Design of Complex Systems: An Air-Vehicle Design Perspective. PhD Thesis, Cranfield University, Cranfield, UK (2007)
Chen, W., Allen, J.: A procedure for robust design: minimizing variations caused by noise factors and control factors. J. Mech. Des. 118(4), 478–493 (1996). doi:10.1115/1.2826915
Cukier, R.I., Levine, H.B., Shuler, K.E.: Non-linear sensitivity analysis of multi-parameter model systems. J. Comput. Phys. 26(1), 1–42 (1978). doi:10.1016/0021-9991(78)90097-9
Das, I., Dennis, J.E.: Normal-boundary intersection: a new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. 8, 631–657 (1998). doi:10.1137/S1052623496307510
Das, I.: An improved technique for choosing parameters for pareto surface generation using normal-boundary intersection. In: Proceedings of the Third World Congress of Structural and Multidisciplinary Optimization WCSMO-3. Buffalo, NY (1999)
Fantini, P.: Effective Multiobjective MDO for Conceptual Design - An Aircraft Design Perspective. PhD Thesis, Cranfield University, Cranfield, UK (2007)
Fantini, P., Balachandran, L.K., Guenov, M.D.: Computational intelligence in multi disciplinary optimization at conceptual design stage. In: Proceedings of the First International Conference on Multidisciplinary Design Optimization and Applications. Besancon, France (2007)
Guenov, M.D., Libish, T.D., Lockett, H.: Computational design process modelling. In: Proceedings of 25th International Council of the Aeronautical Sciences. Hamburg, Germany (2006)
Guenov, M.D., Utyuzhnikov, S.V., Fantini, P.: Application of the modified physical programming method to generating the entire pareto frontier in multiobjective optimization. In: Proceedings of EUROGEN 2005. Munich, Germany (2005)
Maginot, J.: Sensitivity Analysis for Multidisciplinary Design Optimization. PhD Thesis, Cranfield University, Cranfield, UK (2007)
Maginot, J., Guenov, M.D., Fantini, P., Padulo, M.: A method for assisting the study of Pareto solutions in multi-objective optimization. In: Proceedings of the 7th AIAA/ATIO Conference. Belfast, Northern Ireland (2007)
Mattison, C.A., Mullur, A.A., Messac, A.: Minimal representation of multiobjective design space using smart pareto filter. In: Proceeding of the 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Atlanta, GA (2002)
Messac, A., Mattson, C.A.: Generating well-distributed sets of pareto points for engineering design using physical programming. Optim. Eng. 3(4), 431–450 (2002)
Messac, A., Ismail-Yahaya, A., Mattson, C.A.: The normalized normal constraint method for generating the pareto frontier. Struct. Multidiscipl. Optim. 25(2), 86–98 (2003)
Messac, A., Mattson, C.: Normal constraint method with guarantee of even representation of complete pareto frontier. AIAA J. 42(10), 2101–2111 (2004). doi:10.2514/1.8977
Messac, A.: Physical programming: effective optimization for computational design. AIAA J. 34(1), 149–158 (1996). doi:10.2514/3.13035
Miettinen, K.M.: Nonlinear Multiobjective Optimization. Kluwer Academic, Boston (1999)
Murphy, T.E., Tsui, K.L., Allen, J.K.: A review of robust design methods for multiple responses. Res. Eng. Des. 16, 118–132 (2005). doi:10.1007/s00163-005-0004-0
Padulo, M., Campobasso, M.S., Guenov, M.D.: Comparative analysis of uncertainty propagation methods for robust engineering design. In: Proceedings of the International Conference on Engineering Design ICED07. Paris (2007)
Park, G.J., Lee, T.H., Hwang, K.H.: Robust design: an overview. AIAA J. 44(1), 181–191 (2006). doi:10.2514/1.13639
Saltelli, A., Chan, K., Scott, M.: Sensitivity Analysis. Wiley, New York (2000)
Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.: Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Wiley, Chichester (2004)
Saltelli, A., Bolado, R.: An alternative way to compute Fourier Amplitude Sensitivity Test (FAST). Comput. Stat. Data Anal. 26(4), 445–460 (1998). doi:10.1016/S0167-9473(97)00043-1
Sobol’, I.M.: Sensitivity estimates for non-linear mathematical models. Math. Model. Comput. Exper. 1(4), 407–414 (1993)
Sobol’, I.M.: Global sensitivity indices for non-linear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 55, 271–280 (2001). doi:10.1016/S0378-4754(00)00270-6
Utyuzhnikov, S.V., Fantini, P., Guenov, M.D.: Numerical method for generating the entire Pareto frontier in multiobjective optimization. In: Proceedings of EUROGEN 2005, pp. 12–14. Munich, Germany (2005)
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Guenov, M., Fantini, P., Balachandran, L. et al. Multidisciplinary Design Optimization Framework for the Pre Design Stage. J Intell Robot Syst 59, 223–240 (2010). https://doi.org/10.1007/s10846-010-9397-8
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DOI: https://doi.org/10.1007/s10846-010-9397-8