Yükseltilmiş D-optimal dizayn yöntemi kullanılarak mühendislik dizaynlarında etkinliğin geliştirilmesi "sentetik jet" dizayn optimizasyon çalışması

Bu çalışmanın amacı, mühendislik dizaynlarında analiz ve optimizasyon için kullanılan deney dizaynı yaklaşımlarını araştırmak ve uygulamasını yapmaktır. Kaynak araştırmasında, çeşitli deney dizaynı yöntemleri üzerinde çalışılmış, her dizaynın avantajları ve kısıtları tartışılmış ve değerlendirilmiştir. Uygulama bölümünde yapılan sentetik jet dizaynı çalışmasında, yükseltilmiş D-Optimal dizayn kullanılmıştır. Minimum nokta deney dizaynı modeli oluşturmak ve etkinliği arttırmak amacıyla, bu çalışmada bilgisayar destekli D-Optimal dizayn yöntemi tercih edilmiştir. Deneylerin dizaynını oluşturmak ve deney sonuçlarının analizlerini yapmak amacıyla, deney dizaynı yazılımını da içeren JMP programı kullanılmıştır. Hava akışını kontrol çalışmalarında, üzerinde çalışılan sistemin performansı genellikle bilgisayar destekli analiz programları yardımıyla değerlendirilmektedir. Bu çalışmada deneyler, NASA tarafından geliştirilmiş bir simülasyon programı olan CFL3D (Computational Fluids Laboratory 3-Dimensional flow solver) kullanılarak gerçekleştirilmiştir. Çalışmanın uygulama safhasında kullanılan D-Optimal dizayn yöntemine, yükseltme tekniği uygulanarak elde edilen model geliştirilmesi sağlanmıştır. Dizaynı yükseltmek amacıyla, modele istatistiksel yöntemler vasıtasıyla birtakım deneyler ilave edilmiştir. Deneylerin sonuçlarının analizi safhasında, elde edilen verilerin istatistiksel değerlerinin geliştirilerek, matematiksel modele en uygun şekilde dahil edilmesi amacıyla logaritmik transformasyon yöntemi kullanılmıştır. Elde edilen sonuçlara göre, yükseltilmiş D-Optimal dizayn yöntemi kullanılarak oluşturulan modelin, dizayn, analiz, ve optimizasyon çalışmalarındaki etkinliği belirgin bir şekilde geliştirilebildiği görülmüştür.

Improving efficiency in engineering design using augmented D-optimal designs: 'synthetic jet' design optimization study

The purpose of this study is to study the efficiency of several “design of experiments” (DOE) approaches used for the analysis and optimization of engineering designs. A literature review is conducted to study various “design of experiments” methods and the advantages and limitations of each method are discussed. As an application, Augmented D-Optimal designs are utilized for a design study of ‘synthetic jet’. With the objective of improving efficiency and providing a minimum point experimental design model, computer-aided D-optimal method is preferred for this study. For setting up the design of the experiments and for performing the analysis of results, the “DOE” software-JMP is used. In flow control studies, performance of the system is generally reached by the use of computerized analysis programs. In this study, the experiments are performed using a NASA-developed flow simulation program, CFL3D (Computational Fluids Laboratory 3-Dimensional flow solver). The D-optimal design in this study is enhanced by applying the augmentation method. For augmenting the design, additional experiments are statistically placed in the model. Results indicate that utilizing the augmented D-Optimal designs have led to improving efficiency significantly in the design, analysis and optimization studies performed in this thesis.

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[1] Unal, R., R. Braun, A. Moore and R. Lepsch, “Response Surface Model Building Using Orthogonal Arrays for Computer Experiments," Proceedings of the 19th Annual Conference of the International Society of Parametric Analysts, May, pp. 469-481 (1997). [2] Unal, R.,”development of response surface models for rapid analysis & multidisciplinary optimization of launch vehicle design concepts,” NASA Grant No: NAG-1-2157 [3] JMP ® Design User’s Guide, SAS Institute Inc, Cary, NC. 1992. [4] Phadke S.M., “quality engineering using robust design,” Englewood Cliffs, Prentice Hall, 1989. [5] American Society For Quality (ASQ). Six Sigma Forum. Retrieved Dec 19, 2005, from http://www.asq.org/sixsigma/terms/a.html [6] Umertics. Design of Experiments- Principles and Applications. Retrieved Dec 25,2005, from http://www.umetrics.com/pdfs/books/DOEBook.pdf [7] Unal, R., ENMA 763 Class Notes, Old Dominion University. Spring 2006. [8] Della Role, P.G., “Design of Experiments - Basic concepts”. July 2003. [9] The Math Works. Statistical Toolbox. Retrieved Dec 26, 2005 from http://www.mathworks.com/access/helpdesk/help/toolbox/stats/f56635.html [10] LyeA L.M., “Design of Experiments in Civil Engineering: “Are We Still in The 1920’S?” Annual Conference of the Canadian Society for Civil Engineering, Montréal, Québec, Canada. June 5-8, 2002. [11] Oxford Journals. Journal of Tropical Pediatrics. Research Method II-Multivariate Analysis. Chapter 9. Retrieved Dec 30, 2005 from http://www.oxfordjournals.org /tropej/online/ma_chap9.pdf [12] NIST/SEMATECH e-Handbook of Statistical Methods, [13] http://www.itl.nist.gov/div898/handbook. Retrieved Dec 12,2005. [14] iSixSigma. Design_of_Experiments_-_DOE. Retrieved Dec 19, 2005 from http://www.isixsigma.com/dictionary/Design_of_Experiments_-_DOE-41.htm [15] Fahner,G., “Learning Strategies and Experimental Design”, A Fair Isaac White Paper. Dec 2002. [16] American Society for Quality (ASQ). Statistics Division-glossory and tables. Retrieved Feb 19, 2006 from http://www.asqstatdiv.org/glossary-tables.htm [17] Frey, D.D. and Greitzer,E.M.,“One Step at a Time”, The American Society of Mechanical Engineers. 2004. [18] Anderson, M.J., and Whitcomb, P.J., “Response Surface Methods for Process Optimization”. Nov 2004. [19] Goos, P.and Leemans, H., “Teaching Optimal Design of Experiments Using a Spreadsheet”, Journal of Statistics Education Volume 12, Number 3.(2004). [20] Canadian Forest Service. Data Transformation. Retrieved Feb 12, 2006 from http://www.pfc.forestry. ca/profiles/wulder/mvstats/transform_e.html [21] Wikipedia. Outlier. Retrieved Feb 27, 2006 from http://en.wikipedia. org/wiki/Outlier [22] Wolfram Research. Skewness. Retrieved Feb 23, 2006 from http://mathworld.wolfram.com/ Skewness.html [23] Nettleton,D, Iowa State University. Retrieved Feb 24, 2006 from http://www.public.iastate.edu/~ dnett/S401/nlogtransform.pdf [24] Kowalski, M., “LogInt,” Retrieved Feb 24, 2006 from http://www.stat.ualberta.ca/~kowalski/LogInt.pdf [25] University Of Kentucky, Engineering Department. Computational Fluid Dynamics (CFD) Group Website. Flow Separation Control Using Synthetic Jetshttp. Retrieved Dec 29, 2005 from//www. engr.uky.edu/ ~cfd/Synthetic%20jet%20FC.htm [26] Kral L.D., ASME Fluids Engineering Division Technical Brief, ”Active Flow Control Technology” Washington University St. Louis, Missouri [27] CFL3D User’s Manual, NASA Langley Research Center, Hampton, VA http://cfl3d.larc.nasa. gov/Cfl3dv6/V5Manual/Intro.pdf [28] Unal R, Stanley D.O., Lepsch R.A., “Parametric Modeling Using Saturated Designs,” Journal Of Parametrics vol 16, no 1 (fall 1996) : 319 [29] Unal, R., Lockwood, M.K., Mcmillin, M.L., “Model Building for Rapid Multidisciplinary Integration and Optimization Using Experimental Designs,” Old Dominion University & NASA Langley Research Center [30] Wikipedia. JMP (Application Software). Retrieved Dec 25,2005 from http://en.wikipedia.org/ wiki/JMP _(application_software) [31] Montgomery D.C., Introduction to Statistical Quality Control, Fourth Edition, New York: John Wiley & Sons Inc. Jan 2000. [32] Crow Kenneth, “Robust Product Design Through Design of Experiments”, DRM Associates. 1997. [33] Objective Design of Experiments. “Teaching Practical efficent experimentation to Chemists, engineers and scientists”. Retrieved Dec,19, 2005, from http://www.objectivedoe.com/faq.php?name =#a14. [34] Shaikh, S.M..,“Design Optimization Using Statistical Techniques”, PhD Thesis, Melbourne, FL. May 2005. [35] R. Wille, "Landing Gear Weight Optimization Using Taguchi Analysis," Presented at the 49th Annual International Conference of Society of Allied Weight Engineers, Chandler, 1990. [36] D.O. Stanley, R. Unal., and R. Joyner, "Application of Taguchi Methods to Dual Mixture Ratio Propulsion System Optimization for SSTO Vehicles," AIAA-92-0213, January 1992. [37] Unal, R., R. A. Lepsch, W. Engelund and D. O. Stanley, “Approximation Model Building and MDO Using Response Surface Methods with Applications To Launch Vehicle Design”, 6th AIAA/ USAF/ NASA/ ISSMO Symposium on Multidisciplinary Analysis and Optimization, (September 1996). [38] Mitchell, T.J., “An Algorithm for the Construction of "D-Optimal" Experimental Designs”, Technometrics. (Feb, 2000). [39] Cimbala J.M., ME 82 Class Notes, Penn State University. Fall 2003. [40] Todoroki, A., “Teach Yourself Response Surface Methodology,” Tokyo Institute of Technology. 2001. [41] Unal, R., Lepscht, R.A., McMillint, M.L., “Response Surface Model Buýlding and Multidisciplinary Optimization Using D-Optimal Designs”, AIAA-98-4759, Old Dominion University Norfolk, VA & NASA Langley Research Center Hampton VA [42] Response Surface Methodology http://www.mne.psu.edu/me82/Learning/RSM/ rsm.html, Retrieved Dec,19, 2005, from http://www.objectivedoe.com/faq.php?name=#a14. [43] Zahran, A., Christine M. Anderson-Cook and Raymond H. Myers, “Fraction of Design Space to Assess Prediction Capability of Response Surface Designs”, Cairo University, Cairo, Egypt &Virginia Polytechnic Institute and State University, Blacksburg, VA