| 研究生: |
劉智豪 Liu, Chih-Hao |
|---|---|
| 論文名稱: |
壓力瓶在不確定因素下之構形最佳化設計 Reliability-Based Shape Optimization of a Pressure Tank under Random and Stochastic Environments |
| 指導教授: |
詹魁元
Chan, Kuei-Yuan |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 機械工程學系 Department of Mechanical Engineering |
| 論文出版年: | 2008 |
| 畢業學年度: | 96 |
| 語文別: | 中文 |
| 論文頁數: | 69 |
| 中文關鍵詞: | 可靠度最佳化 、隨機變數 、馬可夫鏈 、第一次破壞時間 、反應曲面 、壓力瓶 |
| 外文關鍵詞: | Kriging, Reliability-based design, Gaussian distribution, Markov chain, Stochastic, First passage time, Crossover rate |
| 相關次數: | 點閱:114 下載:3 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
本研究將探討壓力瓶在動態及與靜態的不確定因素影響下之可靠度最佳化設計。壓力瓶在RO逆滲透系統中是一重要元件,不但可儲存過濾後之純水也利用高壓隨時提供使用端充沛水量。然而製造及使用過程中的種種不確定因素,如加工的優劣及水壓的穩定性,導致最後設計可靠度不高,因此,本研究將壓力瓶兩本體間的焊接處強度視為隨機變數,並將施加於壓力瓶內側的水壓視為不確定因素的主要來源,期望壓力瓶在面臨破壞的狀況下仍舊可以保有90%以上的可靠度。
為了進行壓力瓶的可靠度最佳化設計,壓力瓶的幾何尺寸必須先加以參數化,並將其視為壓力瓶構形最佳化設計中的設計變數。設計優劣的評估,乃藉著建立反應曲面來近似於耗費資源的有限元素分析法。研究資料中顯示,高斯分佈足以適當的代表在壓力瓶上下半部之間焊接處強度的變化,而壓力負載的不確定因素則可用馬可夫鏈來模擬。以上所提到的模型均整合成一個同時具有與動態及與靜態的可靠度最佳化問題。為了詮釋動態系統限制式的可靠度,本研究使用第一次破壞時間(first passage time)及破壞率(crossover rate)的概念,並且比較其中的差異,而最終最佳化的結果,不僅僅能滿足可靠度要求同時與現今設計相比材料用量減少了高達46%。
Reliability-based design of a pressure tank under time-independent random and time-dependent stochastic uncertainties is considered. This pressure tank is an essential element in a reverse osmosis (RO) filtration system for storing filtered water and providing a useable flow rate from the faucet outlet. In this study,we consider the randomness in the welding strength be-tween the upper and lower tanks, and the stochastic pressure applied to the inner surfaces of the tank as the main sources of uncertainty. A pressure tank with 90% reliability against fracture failure is desired.To enable the re-design of the pressure tank, the geometry is parametrized and then used as design variables in a shape optimization scheme. Kriging models are created to approximate the expensive finite element analyses in accessing the performances of each design. The uncertainty model of the welding strength between the upper and lower tanks is found to be well represented by a Gaussian distribution. The stochastic behavior of the pressure loading is modeled by a Markov-chain process.All models are integrated in a reliability-based design optimization problem formulation that has both time-independent and time-dependent reliability constraints. The first passage time and crossover rate are considered in the time-dependent reliability constraint and results of different constraint formulations are compared. The final optimal design satisfies all reliability constraints and reduces the material usage by as many as 46% comparing to the original design.
[1] 葉基光. 工業污染之成因與防治. 徐氏出版社, 1987.
[2] 顧德生. 喝乾淨的水, 您知道如何產生嗎?-RO逆滲透解析. 科技博物,1(1):116, 1999-2008.
[3] China Ningbo Cixi Imp. & Exp. Corp. January 2008.
[4] 黃雅勝. 單管中水錘現象實驗與數值分析. 碩士論文-國立成功大學水利及海洋工程研究所, 1995.
[5] 鄭榮和. 管線經年劣化之安全評估=Safety assessment of degrading pipeline,國立台灣大學機械工程學系研究所計畫研究.行政院勞工委員會勞工安全衛生研究所, IOSH 勞工安全衛生研究報告, 2002.
[6] J. Arora. Introduction to optimum design. Elsevier, IOWA, USA, 2004.
[7] D. Wehrwein and Z. Mouraelatos. Reliability based design optimization of dynamic vehicle performance using bond graphs and time dependent metamodels.SAE Paper, 2006-01-0109, 2006.
[8] A. Bowling, J. Renaud, J. Newkirk, and N. Patel. Reliability-based design optimization of robotic system dynamic performance. Journal of Mechanical Design, 129(4):449-454, 2007.
[9] M. Ba-abbad, E. Nikolaidis, and R. Kapania. New approach for system reliability-based design optimization. AIAA, 44(5):1087-1096, 1999-2008.
[10] N. Kuschel and R. Rackeitz. Optiml design under time-variant reliability constraints. Structural Safety, 22:113-127, 2000.
[11] D. Wehrwein and Z. Mourelatos. Optimal engine torque management for reducing driveline clunk using time-depent metamodels. SAE Paper, 2007-01-2236,2007.
[12] B. Minasny and A. McBratney. A conditioned latin hypercube mmethod for sampling in the presence of ancillary information. Computers and Geosciences,32(9):378-388, 2006.
[13] R. Stocki. A method to improve design reliability using optimal latin hypercube sampling. Computer Assisted Mechanics and Engineering Sciences, 12(4):393-411, 2005.
[14]潘浙楠. 反應曲面方法在改善產品及製程品質上之研究. 成功大學統計學系暨研究所, 2002.
[15]陳信吉. ANSYS入門. 全華出版社, 2007.
[16]褚晴暉 劉晉奇. 有限元素分析與ANSYS的工程應用. 滄海書局, 2006.
[17]蔡國忠. ANSYS7.0拉伸式入門. 全華出版社, 2007..
[18] W. Chen, J. Allen, D. Schrage, and F. Mistree. Statistical experimentation methods for achieving a_ordable concurrent system design. AIAA, 35(5):893-900, 1997.
[19] G. Wang and S. Shan. Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design, 129:370-379, 2007.
[20] D. Montgomery. Design and Analysis of Experiments. John Wiley and Sons,New Jersey,U.S.A., 2005.
[21] S. Haykin. Neural Networks, a Comprehensive Foundation. Prentice Hall, NewJersey,U.S.A., 1998.
[22] T. Simpson, A. Booker, D. Ghosh, A. Ginuta, P. Koch, and R. Yang. Approximation method in multidisciplinary analysis and optimization- a panel discussion. Struct Multidisc Optim, 27:302-313, 2004.
[23] J. Sondergaard, S. Lophaven, and H. Nielsen. Dace, a matlab kriging toolbox.Technical University of Denmark Technical report, IMM-TR-2002-12, 2002.
[24] T. Simpson, J. Peplinski, P. Koch, and J. Allen. Metamodels for computer-based engineering design: Survey and recommendations. Engineering with Computers, 17:129-150, 2001.
[25] S. Gano, J. Renaud, J. Martin, and T. Simpson. Update strategies for kriging models used in variable _delity optimization. Struct Multidisc Optim, 32:287-298, 2006.
[26] J. Park, P. Oh, and H. Lim. The application of the cfd and kriging method to an optimization of heat sink. International Journal of Heat and Mass Transfer,49:3439_3447, 2006.
[27] S. Sakata, F. Ashida, and M. Zako. Eigenfrequency optimization of stiffened plate using kriging estimation. Computational Mechanics, 31:409-418, 2003.
[28] K. Lee and G. Park. A global robust optimization using kriging based approximation model. JSME International Journal, 49(3):779-788, 2006.
[29] S. Schiller, J. Sacks, and W. Welch. Design for computer eperiments. American Statistical Association and the American Society for Quality Control, 31(1):41-47, 1989.
[30] P. Papalambros and D. Wilde. Principles of Optimal Design. Cambridge University Press, New York,U.S.A., 2000.
[31] T. Simpson, T. Mauery, J. Korte, and F. Mistree. Kriging models for global approximation in simulation based multidisciplinary design optimization. AIAA,39(12):2233-2241, 2001.
[32] J. Martin. Using maximum likelihood estimation to estimate kriging model parameters. In Proceedings of the ASME International Design Engineering TechnicalConferences, September 2007. DETC2007-34662.
[33] G. Jost, G. Heuvelink, and A. Papritz. Analysis the space-time distribution of soil water storage of a forest ecosystem using saptio- temporal kriging. Geoderma, 28:258-273, 2005.
[34] K. Irfan. Application of kriging method to structural reliability problems. Structural Safety, 27(2):133-151, 2005.
[35] D. Montgomery. Design and Analysis of Experiments. John Wiley and Sons,Inc., New York,U.S.A., 2005.
[36] S. Carlos, S. Martorell, and A. Sanchez. A tolerance interval based approach to address uncertainty for rams+c optimization. Reliability Engineering and System Safety, 92:408-422, 2007.
[37] W. Meeker and L. Escobar. Statistical Methods for Reliability Data. John Wiley and Sons,Inc., New Jersey,U.S.A., 1998.
[38] C. Reimann and P. Filzmoser. Normal and lognormal data distribution in geochemistry: Death of a myth. consequences for the statistical treatment ofgeochemical and environmental data. Environmental Geology, 39(9):1001-1014,2000.
[39] D. Fabiani and L. Simoni. Discussion on application of the weibull distribution to electrical breakdown of insulating materials. IEEE Transactions on Dielectrics and Electrical Insulation, 12(1):11-16, 2005.
[40] G. Gu and W. Zhou. Statistical properties of daily ensemble variables in the chinese stock markets. Statistical Mechanics and its Applications, 383(2):497-506, 2007.
[41] L. Colangelo and J. Patel. Prediction intervals based on ranges and waiting times for an exponential distribution. IEEE Transactions on Reliability,41(3):469-472, 1992.
[42] R. Melchers. Structural,reliability analysis and prediction. J Ellis Horwood Limited, Chichester, England,1987.
[43] P. Reimann and P. Hanggi. Introduction to the physics of brownian motions.Materials Science and Processing, 75(2):169-178, 2002.
[44] F. Postali and P. Picchetti. Geometric brownian motion and structural breaks in oil prices: A quantitative analysis. Energy Economics, 28(4):506-522, 2006.
[45] K. Alam. Peak rate of occurrence of a poisson process. Naval Research Logistics Quarterly, 20(2):269-275, 1973.
[46] J. Tsay and C. Tsao. Statistical gambler's ruin problem. Communications in Statistics - Theory and Methods, 32(7):1337-1359, 2003.
[47] D. Mukhopadmyay, P. Sarkar, and A. Sarna. Application of markov chain in traffic folw forecasting. Indian Highways, 15(3):14-28, 1987.
[48] B. Berg. Markov Chain Monte Carlo Simulations and Their Statistical Analysis.World Scienti_c Publishing Inc., New Jersey,U.S.A., 2004.
[49] R. Karandikar. On the markov chain monte carlo (mcmc) method. Sadhana -Academy Proceedings in Engineering Sciences, 31(2):81-104, 2006.
[50] S. Sheu and Y. Chien. Optimal burn-in time to minimize the cost for general repairable products sold under warranty. European Journal of Operational Research, 163(2):445-461, 2005.
[51] D. Jones. A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, 21:345-383, 2001.
[52] J. Li and Z. Mourelatos. Relability estimation for time-dependent problems using a niching genetic algorithm. In Proceedings of the ASME International Design Engineering Technical Conferences, September 2007.DETC2007-34865.
[53] A. Halar and S. Mahadevan. Probability, Reliability and Statistical Method in Engineering Design. John Wiley and Sons, New York,U.S.A., 2000.
[54] S. Gupta and C. Manohar. S improved response surface method for time-variant reliability analysis of nonlinear random structure under non-stationary excitations. Nonlinear Dynamics, 36:267-280, 2004.
[55] V. Joseph, Y. Hung, and A.Sudjianto. Blind kriging:a new method for developing metamodels. Journal of Mechanical Design, 130(3-01102), 2008.