بررسی اثر انحرافات هندسی بر عملکرد یک کمپرسور جریان شعاعی به کمک محاسبه ‌عدم‌قطعیت و آنالیزحساسیت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشکده مهندسی مکانیک و انرژی، دانشگاه شهید بهشتی، تهران، ایران

2 استادیار، دانشکده مهندسی مکانیک و انرژی، دانشگاه شهید بهشتی، تهران، ایران

3 استادیار، دانشکده مهندسی مکانیک، دانشگاه زنجان، زنجان، ایران

چکیده

عملکرد پروانه شعاعی یک کمپرسور گریز از مرکز شدیدا تحت تأثیر عدم قطعیت‌های هندسی آن است. مشخصه‌های هندسی پروانه ممکن است در طول فرایند تولید یا بر اثر کارکرد بلندمدت، دچار تغییرات اندکی شود و در نتیجه منحنی‌های عملکردی کمپرسور را دچار انحراف نسبت به شرایط طراحی نماید. در این پژوهش، با استفاده از آنالیز حساسیت کلی، میزان حساسیت عملکرد نسبت به برخی مشخصه­ های هندسی تعیین می­شود و به کمک محاسبات ‌عدم‌قطعیت به روش بازخورد‌ ‌سطح کریگینگ، بازه انحراف عملکرد استخراج می­گردد. بدین منظور در ابتدا، سه دسته بانک‌‌اطلاعاتی با تعداد پروانه‌های متفاوت، با مشخصات هندسی منحرف از نمونه‌اصلی ایجاد شده‌است. برای هندسه ­های مختلف، شبیه ­سازی جریان در پروانه، به صورت سه­بعدی و با استفاده از دینامیک سیالات محاسباتی انجام‌شده­است. یک مدل جایگزین با استفاده از روش کریگینگ برای هر بانک‌داده، طراحی شده تا بتوان ‌عدم‌قطعیت را با هزینه محاسباتی کمتری پیش­بینی نمود. نتایج نشان می­دهد که در 5,95 درصد مواقع، نسبت‌فشار و بازدهی کل به کل و بازدهی کل به استاتیک، به ترتیب به اندازه 88,1، 03,1 و 56,1 درصد حول مقدار پیش‌بینی‌شده نوسان خواهند‌ داشت و شعاع خروجی، حساس‌ترین پارامتر هندسی در پروانه مورد بررسی می‌باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Investigation of the Effect of the Geometric Deviations on the Performance of a Radial Flow Compressor Employing Uncertainty Quantification (UQ) and Sensitivity Analysis

نویسندگان [English]

  • Davood Altafi 1
  • Mohammad Mojaddam 2
  • Behnam Ghadimi 3
1 M.Sc. student, Faculty of Mechanical & Energy Engineering, Shahid Beheshti University, Tehran, Iran
2 Assistant Professor, Faculty of Mechanical & Energy Engineering, Shahid Beheshti University, Tehran, Iran
3 Assistant Professor, Faculty of Mechanical Engineering, Zanjan University, Zanjan, Iran
چکیده [English]

The performance of the impeller of a radial flow compressor is highly affected by its geometric uncertainties. The geometrical characteristics of the impeller may change slightly during the manufacturing process or as a result of long-term operation, resulting in deviations from design performance curves. In this research, the performance sensitivity to each geometric parameter is determined using global sensitivity analysis, and the performance deviation interval is extracted using uncertainty quantification by the Kriging surface response method. For this purpose, initially, three categories of databases with a different number of impellers are created using geometric characteristics deviating from the original sample. For different geometries, the flow simulation in the impeller is performed using computational fluid dynamics in a viscous three-dimensional turbulent solver. A surrogate model which uses the Kriging method for each database to quantify the uncertainty more quickly is utilized. The results show that in 95.5% of cases, the pressure ratio and total to total efficiency and total to static efficiency fluctuate by 1.88%, 1.03%, and 1.56% around the predicted value, respectively. Moreover, the impeller's outlet radius is the most sensitive geometric parameter. 

کلیدواژه‌ها [English]

  • Radial Flow Compressor
  • impeller
  • Uncertainty
  • Sensitivity analysis
  • numerical simulation
  • Kriging method
[1] N. Watson and M.  Janota, Turbocharging the Internal Combustion Engine, Macmillan International Higher Education, 1982.
[2] C. Silva, M. Ross, and T. Farias, Analysis and Simulation of Low-Cost Strategies to Reduce Fuel Consumption and Emissions in Conventional Gasoline Light-Duty Vehicles, Energy Conversion and Management, Vol. 50, No. 2, pp. 215–222, 2009.
[3] M. Momeni Movahed, H. Basirat Tabrizi, and M. Mirsalim, Experimental Investigation of the Concomitant Injection of Gasoline and CNG in a Turbocharged Spark Ignition Engine, Energy Conversion and Management, Vol. 80, pp. 126-136, 2014.
[4] F. Zhang and R. Baar, 3D-CFD-Study of Aerodynamic Losses in Compressor Impellers, SAE International Journal of Commercial Vehicles, Vol. 11, No. 3, pp. 2–11, 2018.
[5] A.A. Doustmohammadi, A. Hajilouybenisi, and M. Mojaddam, Experimental & Numerical Investigation of Losses in Centrifugal Compressor Components, ASME Turbo Expo: Power for Land, Sea, and Air, San Antonio, TX, USA, June 3–7, 2013.
[6] S.L Dixon and C. Hall, Fluid mechanics and thermodynamics of turbomachinery, Butterworth-Heinemann, 2013.
[7] M. Kulak, F. Grapow and G. Liśkiewicz, Numerical Analysis of Centrifugal Compressor Operating in Near-Surge Conditions, Journal of Physics: Conference Series, Vol. 1101, No. 1, p. 012017, 2018.
[8] R. McMullen and Y. Pino, Conditioning Turbocharger Compressor Map Data for Use in Engine Performance Simulation, SAE International Journal of Engines, Vol. 11, No. 4, pp. 491–507, 2018.
[9] A. Javed, R. Pecnik, M. Olivero and J.P. Van Buijtenen, Effects of Manufacturing Noise on Microturbine Centrifugal Impeller Performance, Journal of engineering for gas turbines and power, Vol. 134, No. 10, 2012.
[10] S. Sankaran, G. Sassanelli, G. Iurisci, and A. Panizza, Performance Uncertainty Quantification for Centrifugal Compressors, Part 2: Flange to Flange Variability, ASME Turbo Expo 2012: Turbine Technical Conference and Exposition, Copenhagen, Denmark, June 11–15, 2012.
[11] R. Qin, Y. Ju, Y.Wang, and C. Zhang, Flow Analysis and Uncertainty Quantification of a 2D Compressor Cascade with Dirty Blades, ASME Turbo Expo: Power for Land, Sea, and Air, June 13–17, Seoul, South Korea, pp. 1–11, 2016,.
[12] A. Javed, R. Pecnik and J. P. Van Buijtenen, Optimization of a Centrifugal Compressor Impeller for Robustness to Manufacturing Uncertainties, Journal of engineering for gas turbines and power, Vol. 138, No. 11, pp. 1–12, 2016.
[13] X. Tang, Z. Wang, P. Xiao, R. Peng, and X. Liu, Uncertainty Quantification Based Optimization of Centrifugal Compressor Impeller for Aerodynamic Robustness under Stochastic Operational Conditions, Energy, Vol. 195, p. 116930, 2020.
[14] S. Hong, S. Lee, S. Jun, D. H. Lee, H. Kang, Y. S. Kang and S. S. Yang, Reliability-Based Design Optimization of Axial Compressor Using Uncertainty Model for Stall Margin, Journal of Mechanical Science and Technology, Vol. 25, No. 3, pp. 731–740, 2011.
[15] C. Ma, L. Gao, Y. Cai and R. Li, Robust Optimization Design of Compressor Blade Considering Machining Error, ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition, Charlotte, NC, USA ,June 26–30 , 2017.
[16] H. G. Beyer and B. Sendhoff, Robust Optimization - A Comprehensive Survey, Computer Methods in Applied Mechanics and Engineering, Vol. 196, No. 33–34, pp. 3190–3218, 2007.
[17] R. Ghanem, D. Higdon  H. Owhadi, Introduction to Uncertainty Quantification, Springer, 2017.
[18] M. J. Duncan, S. E. Hodge and M. L. Seed, Statistics and Probability, Cambridge University Press, 1973.
[19] S. Reh,  J. D. Beley, S. Mukherjee and E. H. Khor, Probabilistic Finite Element Analysis Using ANSYS, Structural Safety, Vol. 28 , No. 1–2, pp. 17–43, 2006.
[20] A. M. Johansen, Monte Carlo Methods, International Encyclopedia of Education, Elsevier Science, 2010.
[21] J. Menčík, Latin Hypercube Sampling,  Concise Reliability for Engineers, IntechOpen , 2016.
[22] J. Zhang, J. Yin, and R. Wang, Basic Framework and Main Methods of Uncertainty Quantification, Mathematical Problems in Engineering, 2020.
[23] B. Ghadimi, A. Nejat, S. A. Nourbakhsh, and N. Naderi,  Multi-Objective Genetic Algorithm Assisted by an Artificial Neural Network Metamodel for Shape Optimization of a Centrifugal Blood Pump,  Artificial Organs, Vol. 43, No. 5, pp. E76–E93, 2019.
[24] R. L. Iman, Latin Hypercube Sampling,  The Encyclopedia of Quantitative Risk Analysis and Assessment, Wiley Online Library, 2008.
[25] M. Mojaddam  and K.R. Pullen,  Optimization of a Centrifugal Compressor Using the Design of Experiment Technique,  Applied Sciences, Vol. 9, No. 2, pp. 1–19, 2019.
[26] D. R. De Cock, Kriging as an Alternative to Polynomial Regression in Response Surface Analysis, Iowa State University, 2003.
[27] J. Zhang, H. W. Huang and K. K. Phoon,  Application of the Kriging-Based Response Surface Method to the System Reliability of Soil Slopes, Journal of Geotechnical and Geoenvironmental Engineering, Vol. 139, No. 4, pp. 651–655, 2013.
[28] A. A.Giunta, M. S. Eldred and J. P.Castro,  Uncertainty Quantification Using Response Surface Approximations, 9th ASCE Joint Specialty Conference on Probabilistic Mechanics and Structural Reliability, July 2004.
[29] H. Lee, D. J. Lee and H. Kwon,  Development of an Optimized Trend Kriging Model Using Regression Analysis and Selection Process for Optimal Subset of Basis Functions,  Aerospace Science and Technology, Vol. 77, pp. 273–285, 2018.
[30] S. Tennøe, G. Halnes and G. T. Einevoll,  Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience, Frontiers in Neuroinformatics, Vol. 12, pp. 1–29, 2018.
[31] H. El-Ramly, N. R. Morgenstern and D. M. Cruden,  Probabilistic Slope Stability Analysis for Practice, Canadian Geotechnical Journal, Vol. 39, No. 3, pp. 665–683, 2002.
[32] P. Schober and L. A. Schwarte,  Correlation Coefficients: Appropriate Use and Interpretation,  Anesthesia & Analgesia, Vol. 126, No. 5, pp. 1763–1768, 2018.
[33] M. Stephanou and M. Varughese,  Sequential Estimation of Spearman Rank Correlation Using Hermite Series Estimators, The Journal of Multivariate Analysis, Vol. 186, 2021.
[34] H. K. Versteeg and W. Malalasekera, An Introduction to Computational Fluid Dynamics, Pearson education, 2005.
[35] S. Hosder, B. Grossmany, R. T. Haftkaz, W. H. Masonx and L. T. Watson,  Observations on CFD Simulation Uncertainties, 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, April 2002.
[36] D. Altafi, M. Mojaddam, S. Javadi, and M. Mohammadi,  Entropy Generation Analysis of a Turbocharger Centrifugal Compressor in the Range Surge to Choke, 12th Annual International Conference on IC Engines & Oil (ICICE&O), Tehran, Iran, Feb. 22-24,  2022.
[37] N. Khoshkalam, M. Mojaddam and K. R. Pullen,  Characterization of the Performance of a Turbocharger,  Energies, pp. 2–21, 2019.
[38] P. V. Rao, G. V. R. Murty and G. V. Rao,  CFD Analysis of Impeller-Diffuser Interaction in a Centrifugal Compressor with Twisted Vaned Diffuser,  Lecture Notes in Mechanical Engineering, pp. 911–922, 2017.
[39] I. Shahin,  M. Alqaradawi, M. Gadala and O. Badr,  Large Eddy Simulation of Surge Inception and Active Surge Control in a High Speed Centrifugal Compressor with a Vaned Diffuser,  Applied Mathematical Modelling, Vol. 40, No. 23–24, pp. 04–18, 2016.
[40] O. Dumitrescu, B. Gherman and A. Alcea,  Tip Clearance Influence in CFD Calculations and Optimization of a Centrifugal Compressor Stage through CFD Methods,  IOP Conference Series: Materials Science and Engineering, Vol. 400, No. 4, 2018.
[41] W. Ju, S.  Xu, X. Wang, X. Chen, S. Yang and J. Meng,  Numerical Study on the Influence of Tip Clearance on Rotating Stall in an Unshrouded Centrifugal Compressor,  ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition, Charlotte, NC, USA ,June 26–30  , pp. 1–8, 2017.
[42] D. Büche, Robust Compressor Optimization by Evolutionary Algorithms, Springer International Publishing, 2019.
[43] I. Martin, L. Hartwig and D. Bestle,  A Multi-Objective Optimization Framework for Robust Axial Compressor Airfoil Design, Structural and Multidisciplinary Optimization, Vol. 59 , No. 6, pp. 35–47, 2019.
[44] S. Shankaran and A. Marta,  Robust Optimization for Aerodynamic Problems Using Polynomial Chaos and Adjoints,  ASME Turbo Expo 2012: Turbine Technical Conference and Exposition, Vol. 8, pp. 17–27, Copenhagen, Denmark, June 11–15, 2012.
[45] J. R. Taylor, An Introduction to Error Analysis, Univ. Science Sausalito, CA, USA, 1997.