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Structural and Multidisciplinary Optimization

, Volume 58, Issue 3, pp 919–933 | Cite as

Optimization via multimodel simulation

A new approach to optimization of cyclone separator geometries
  • Thomas Bartz-Beielstein
  • Martin Zaefferer
  • Quoc Cuong Pham
RESEARCH PAPER
First Online:
Received:
Revised:
Accepted:

Abstract

Increasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation. Several simulation tools can be run independently and in parallel, e.g., long running computational fluid dynamics simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. However, there are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes. At the same time, data-driven models require input and output data, but analytical models do not. The optimization via multimodel simulation (OMMS) approach, which is able to combine results from these different models, is introduced in this paper. We believe that OMMS improves the robustness of the optimization, accelerates the optimization-via-simulation process, and provides a unified approach. Using cyclonic dust separators as a real-world simulation problem, the feasibility of this approach is demonstrated and a proof-of-concept is presented. Cyclones are popular devices used to filter dust from the emitted flue gasses. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities. Pros and cons of this multimodel optimization approach are discussed and experiences from experiments are presented.

Keywords

Combined simulation Multimodeling Simulation-based optimization Metamodel Multi-fidelity optimization Stacking Response surface methodology 3D printing Computational fluid dynamics 
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Notes

Acknowledgements

This work is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 692286. We would like to thank Horst Stenzel, Beate Breiderhoff, Dimitri Gusew, Aylin Mengi, Baris Kabacali, Jerome Tünte, Lukas Büscher, Sascha Wüstlich, and Thomas Friesen for their support.

References

  1. Barth W (1956) Berechnung und Auslegung von Zyklonabscheidern aufgrund neuerer Untersuchungen. Brennstoff-Wärme-Kraft 8(1):1–9Google Scholar
  2. Bartz-Beielstein T (2016) Stacked generalization of surrogate models - a practical approach. Technical Report 5/2016, TH Köln, Köln. https://cos.bibl.th-koeln.de/frontdoor/index/index/docId/375. Accessed 31 Oct 2017
  3. Bartz-Beielstein T, Zaefferer M (2017) Model-based methods for continuous and discrete global optimization. Appl Soft Comput 55:154–167CrossRefGoogle Scholar
  4. Bartz-Beielstein T, Lasarczyk C, Preuss M (2005) Sequential parameter optimization. In: McKay B et al (eds) Proceedings 2005 congress on evolutionary computation (CEC’05), Edinburgh, Scotland. IEEE Press, Piscataway, pp 773–780Google Scholar
  5. Barzier MK, Perry CJ (1991) An approach to the construction and usage of simulation modeling in the shipbuilding industry. In: Nelson BL, Kelton WD, Clark GM (eds) 1991 winter simulation conference proceedings. IEEE, pp 455–464Google Scholar
  6. Breiman L (1996) Stacked regression. Mach Learn 24:49–64MathSciNetMATHGoogle Scholar
  7. Bucila C, Caruana R, Niculescu-Mizil A (2006) Model compression: making big, slow models practical. In: Proceedings of the 12th international conference on knowledge discovery and data Mining (KDD’06)Google Scholar
  8. Chaudhuri A, Haftka RT, Ifju P, Chang K, Tyler C, Schmitz T (2015) Experimental flapping wing optimization and uncertainty quantification using limited samples. Struct Multidiscip Optim 51(4):957–970CrossRefGoogle Scholar
  9. Dempster AP (1968) A generalization of bayesian inference. J R Stat Soc Ser B Methodol 30(2):205–247MathSciNetMATHGoogle Scholar
  10. Elsayed K, Lacor C (2010) Optimization of the cyclone separator geometry for minimum pressure drop using mathematical models and CFD simulations. Chem Eng Sci 65(22):6048–6058CrossRefGoogle Scholar
  11. Elsayed K, Lacor C (2012) CFD modeling and multi-objective optimization of cyclone geometry using desirability function, artificial neural networks and genetic algorithms. Appl Math Model 37(8):5680–5704CrossRefGoogle Scholar
  12. Fishwick PA, Zeigler BP (1992) A multimodel methodology for qualitative model engineering. ACM Trans Model Comput Simul 2(1):52–81CrossRefMATHGoogle Scholar
  13. Forrester A, Sóbester A, Keane A (2007) Multi-fidelity optimization via surrogate modelling. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science 463(2088):3251–3269MathSciNetCrossRefMATHGoogle Scholar
  14. Fu MC (1994) Optimization via simulation: a review. Ann Oper Res 53(1):199–247MathSciNetCrossRefMATHGoogle Scholar
  15. Goel T, Haftka RT, Shyy W, Queipo NV (2007) Ensemble of surrogates. Struct Multidiscip Optim 33(3):199–216CrossRefGoogle Scholar
  16. Haftka RT (2016) Requirements for papers focusing on new or improved global optimization algorithms. Struct Multidiscip Optim 54(1):1–1CrossRefGoogle Scholar
  17. Haftka RT, Villanueva D, Chaudhuri A (2016) Parallel surrogate-assisted global optimization with expensive functions—a survey. Struct Multidiscip Optim 54(1):3–13MathSciNetCrossRefGoogle Scholar
  18. Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural Network. arXiv:1503.02531
  19. Hoekstra AJ, Derksen JJ, Van Den Akker HEA (1999) An experimental and numerical study of turbulent swirling flow in gas cyclones. Chem Eng Sci 54(13-14):2055–2065CrossRefGoogle Scholar
  20. Hoffmann AC, Stein LE (2007) Gas cyclones and swirl tubes. Springer, BerlinGoogle Scholar
  21. Jin Y (2003) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9 (1):3–12CrossRefGoogle Scholar
  22. Jin R, Chen W, Simpson TW (2001) Comparative studies of metamodelling techniques under multiple modelling criteria. Struct Multidiscip Optim 23(1):1–13CrossRefGoogle Scholar
  23. Kazemi P, Khalid MH, Szlek J, Mirtič A, Reynolds GK, Jachowicz R, Mendyk A (2016) Computational intelligence modeling of granule size distribution for oscillating milling. Powder Technol 301 (Supplement C):1252–1258CrossRefGoogle Scholar
  24. Kleijnen JPC (2008) Design and analysis of simulation experiments. Springer, New YorkMATHGoogle Scholar
  25. Kleijnen JPC (2014) Simulation-optimization via Kriging and bootstrapping: a survey. Journal of Simulation 8(4):241–250CrossRefGoogle Scholar
  26. Konan A, Huckaby D (2015) Modeling and simulation of a gas-solid cyclone during an upset event (presentation). OpenFOAM WorkshopGoogle Scholar
  27. Law AM (2007) Simulation modeling and analysis, 4th edn. McGraw-Hill, New YorkGoogle Scholar
  28. LeBlanc M, Tibshirani R (1996) Combining estiamates in regression and classification. J Am Stat Assoc 91(436):1641MATHGoogle Scholar
  29. Löffler F (1988) Staubabscheiden. Thieme, StuttgartGoogle Scholar
  30. Meerschaert MM (2013) Mathematical modeling (fourth edition), 4th edn. Elsevier, AmsterdamGoogle Scholar
  31. Mothes H, Loeffler F (1984) Bewegung und Abscheidung der Partikel im Zyklon. Chem-Tech-Ing 56:714–715CrossRefGoogle Scholar
  32. Müller J, Shoemaker CA (2014) Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems. J Glob Optim 60(2):123–144MathSciNetCrossRefMATHGoogle Scholar
  33. Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, CambridgeMATHGoogle Scholar
  34. Muschelknautz E (1972) Die Berechnung von Zyklonabscheidern für Gase. Chemie Ingenieur Technik 44(1-2):63–71CrossRefGoogle Scholar
  35. Nelson BL (1995) Stochastic modeling: analysis and simulation. Dover, New YorkMATHGoogle Scholar
  36. OpenFOAM Foundation (2016) OpenFOAM tutorials lagrangian MPPICFoam cyclone. Official OpenFOAM repository. https://github.com/OpenFOAM/. Accessed 16 Nov 2016
  37. Overcamp TJ, Mantha SV (1998) A simple method for estimating cyclone efficiency. Environ Prog 17(2):77–79CrossRefGoogle Scholar
  38. Preen R, Bull L (2014) Towards the coevolution of novel vertical-axis wind turbines. IEEE Trans Evol Comput PP(99):284–294Google Scholar
  39. Santner TJ, Williams BJ, Notz WI (2003) The design and analysis of computer experiments. Springer, BerlinCrossRefMATHGoogle Scholar
  40. Simpson T, Toropov V, Balabanov V, Viana F (2012) Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come - or not. In: 12th AIAA/ISSMO multidisciplinary analysis and optimization conference. American Institute of Aeronautics and Astronautics, Reston, pp 1–22Google Scholar
  41. Turner AJ, Balestrini-Robinson S, Mavris D (2013) Heuristics for the regression of stochastic simulations. Journal of Simulation 7(4):229–239CrossRefGoogle Scholar
  42. Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241–259CrossRefGoogle Scholar
  43. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRefGoogle Scholar
  44. Yang Y (2003) Regression with multiple candidate models: selecting or mixing?. Stat Sin 13(3):783–809MathSciNetMATHGoogle Scholar
  45. Zaefferer M, Breiderhoff B, Naujoks B, Friese M, Stork J, Fischbach A, Flasch O, Bartz-Beielstein T (2014) Tuning multi-objective optimization algorithms for cyclone dust separators. In: Proceedings of the 2014 conference on genetic and evolutionary computation, GECCO ’14. ACM, New York, pp 1223–1230Google Scholar
  46. Zeigler BP, Oren TI (1986) Multifaceted, multiparadigm modeling perspectives: tools for the 90’s. In: Proceedings of the 18th conference on winter simulation. ACM, New York, pp 708–712Google Scholar
  47. Zerpa LE, Queipo NV, Pintos S, Salager J-L (2005) An optimization methodology of alkaline–surfactant–polymer flooding processes using field scale numerical simulation and multiple surrogates. J Pet Sci Eng 47(3):197–208CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Technische Hochschule KölnGummersbachGermany

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