پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 419 |
| تعداد مقالات | 5,514 |
| تعداد مشاهده مقاله | 6,262,060 |
| تعداد دریافت فایل اصل مقاله | 5,425,589 |
شبیهسازی باد در یک منطقه کوهستانی فوق پیچیده به روش پیشبینی عددی هوا با دقت بسیار بالا و با استفاده از روش شبیهسازی گردابه‌‌های بزرگ | ||
| نشریه مهندسی مکانیک امیرکبیر | ||
| مقاله 7، دوره 53، شماره 7، مهر 1400، صفحه 4221-4240 اصل مقاله (2.72 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/mej.2021.18863.6905 | ||
| نویسندگان | ||
| شکوفه ملک محمدی1؛ پویان هاشمی طاری* 2 | ||
| 1دانشگاه شهید بهشتی، دانشکده مکانیک و انرژی، دانشجوی بین دانشگاهی با دانشگاه ای پی اف ال. | ||
| 2دانشکده مهندسی مکانیک و انرژی، دانشگاه شهید بهشتی، تهران، ایران | ||
| چکیده | ||
| پتانسیلسنجی انرژی باد در مناطق کوهستانی با استفاده از روش پیشبینی هوای عددی مورد توجه محققان قرار گرفته‌است. در این پژوهش به کمک این روش، باد در مارتینی در کشور سوئیس شبیهسازی شده‌‌است. این شبیهسازی دارای دقت افقی بسیار بالا (100متر) بوده که امکان استفاده از روش شبیهسازی گردابه‌‌های بزرگ را فراهم می‌کند. هدف از انجام این پژوهش بررسی میزان موفقیت مدل در شبیهسازی باد و اثر افزایش دقت عمودی و استفاده از دو مدل توربولانسی مقیاس زیر شبکه اسماگورینسکی و مدل انرژی جنبشی توربولانسی 1/5 بوده‌است. نتایج نشان می‌‌دهد که مدل به خوبی قادر به شبیهسازی باد در مقایسه با داده‌‌های دریافتی از ایستگاه‌‌های اندازه‌‌گیری بوده و الگوی روزانه باد را به خوبی تولید کرده‌است. در منطقه‌‌ای که درون دره‌‌ای عریض و هموار قرار گرفته، دقت نتایج بسیار بالا بوده ولی در قله کوهها خطای اندازه سرعت باد زیاد بوده‌است. در زمان وقوع بیشینه سرعت باد در قله کوهها، با تغییر مدل توربولانسی مقیاس زیرشبکه از اسماگورینسکی سه بعدی به مدل انرژی جنبشی توربولانسی 1/5، خطا از 22 به 17 متر بر ثانیه و سپس با کاهش ارتفاع عمودی سلول‌ها از 47 متر به 37 متر، خطا از 17 به 7 متر برثانیه کاهش یافته‌است. | ||
| کلیدواژه‌ها | ||
| باد؛ پیشبینی هوای عددی؛ شبیهسازی گردابه‌‌های بزرگ؛ پتانسیلسنجی باد | ||
| عنوان مقاله [English] | ||
| Wind simulation in a complex terrain by numerical weather prediction method using large eddy simulation | ||
| نویسندگان [English] | ||
| Shokoufeh Malek Mohamadi1؛ Pooyan Hashemi Tari2 | ||
| 1Department of Mechanical and Energy Engineering, Shahid Beheshti University, Exchange student with EPFL in Switzerland. | ||
| 2Department of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran | ||
| چکیده [English] | ||
| Researchers are interested in wind resource assessment studies for mountainous terrains using numerical weather prediction methods. In present study the wind over Martigny located in Switzerland has been simulated using weather research and forecasting model. Due to high resolution of the simulation (100 m), large eddy simulation is employed to perform turbulence modeling. The objective of this study is to assess the credibility of model in wind simulation and to examine the effect resolution and two different sub-grid scale turbulence models. The results reveal that model is able to properly generate the wind in comparison with the data obtained from wind measurement stations. The results also show a promising simulation for the region, located within a wide and flat valley. However, the discrepancies between the results and those obtained from the wind station are bold for regions at mountainous peaks. At the time at which the maximum wind speed occurs, it is found that the wind error decreases from 22m/s to 17m/s by changing the sub-grid scale model from Smagronisnky3D to turbulence kinetic energy 1.5 model. Also, the predicted wind speed declines from 17m/s to 7m/s by reducing the vertical size of the grid cells. | ||
| کلیدواژه‌ها [English] | ||
| Wind, Numerical weather prediction, Weather research and forecasting, Large eddy simulation, Wind resource assessment | ||
| مراجع | ||
|
[1] A.C. Kruyt, Potential and uncertainty of wind energy in the Swiss Alps, EPFL, 2019. [2] B. Blocken, 50 years of computational wind engineering: past, present and future, Journal of Wind Engineering and Industrial Aerodynamics, 129 (2014) 69-102. [3] K. Murthy, O. Rahi, A comprehensive review of wind resource assessment, Renewable and Sustainable Energy Reviews, 72 (2017) 1320-1342. [4] B. Blocken, A. van der Hout, J. Dekker, O. Weiler, CFD simulation of wind flow over natural complex terrain: case study with validation by field measurements for Ria de Ferrol, Galicia, Spain, Journal of Wind Engineering and Industrial Aerodynamics, 147 (2015) 43-57. [5] B. Conan, A. Chaudhari, S. Aubrun, J. van Beeck, J. Hämäläinen, A. Hellsten, Experimental and numerical modelling of flow over complex terrain: the Bolund hill, Boundary-layer meteorology, 158(2) (2016) 183-208. [6] L. Li, P. Chan, Numerical simulation study of the effect of buildings and complex terrain on the low-level winds at an airport in typhoon situation, Meteorologische Zeitschrift, 21(2) (2012) 183. [7] J. Smargorinsky, General circulation experiment with the primitive equations, Monthly Weather Review, 91(3) (1963) 99-164. [8] T. Uchida, Y. Ohya, Numerical simulation of atmospheric flow over complex terrain, Journal of Wind Engineering and Industrial Aerodynamics, 81(1-3) (1999) 283-293. [9] T. Uchida, Y. Ohya, Large-eddy simulation of turbulent airflow over complex terrain, Journal of wind engineering and industrial aerodynamics, 91(1-2) (2003) 219-229. [10] O. Temel, L. Bricteux, J. van Beeck, Coupled WRF-OpenFOAM study of wind flow over complex terrain, Journal of Wind Engineering and Industrial Aerodynamics, 174 (2018) 152-169. [11] I. Staffell, S. Pfenninger, Using bias-corrected reanalysis to simulate current and future wind power output, Energy, 114 (2016) 1224-1239. [12] S. Jafari, T. Sommer, N. Chokani, R.S. Abhari, Wind resource assessment using a mesoscale model: the effect of horizontal resolution, in: Turbo Expo: Power for Land, Sea, and Air, American Society of Mechanical Engineers, 2012, pp. 987-995. [13] B. Kruyt, J. Dujardin, M. Lehning, Improvement of wind power assessment in complex terrain: the case of COSMO-1 in the Swiss Alps, Frontiers in Energy Research, 6 (2018) 102. [14] B. Pickering, C.M. Grams, S. Pfenninger, Sub-national variability of wind power generation in complex terrain and its correlation with large-scale meteorology, Environmental Research Letters, 15(4) (2020) 044025. [15] L.J. Wicker, W.C. Skamarock, Time-splitting methods for elastic models using forward time schemes, Monthly weather review, 130(8) (2002) 2088-2097. [16] P.A. Jiménez, J. Dudhia, On the ability of the WRF model to reproduce the surface wind direction over complex terrain, Journal of Applied Meteorology and Climatology, 52(7) (2013) 1610-1617. [17] P.A. Jiménez, J. Dudhia, J.F. González‐Rouco, J. Montávez, E. García‐Bustamante, J. Navarro, J. Vilà‐Guerau de Arellano, A. Muñoz‐Roldán, An evaluation of WRF's ability to reproduce the surface wind over complex terrain based on typical circulation patterns, Journal of Geophysical Research: Atmospheres, 118(14) (2013) 7651-7669. [18] P.A. Jiménez, J.F. González-Rouco, E. García-Bustamante, J. Navarro, J.P. Montávez, J.V.-G. De Arellano, J. Dudhia, A. Muñoz-Roldan, Surface wind regionalization over complex terrain: Evaluation and analysis of a high-resolution WRF simulation, Journal of Applied Meteorology and Climatology, 49(2) (2010) 268-287. [19] P.A. Jiménez, J.F. González-Rouco, J.P. Montávez, E. García-Bustamante, J. Navarro, J. Dudhia, Analysis of the long-term surface wind variability over complex terrain using a high spatial resolution WRF simulation, Climate dynamics, 40(7-8) (2013) 1643-1656. [20] E.A. Aligo, W.A. Gallus, M. Segal, On the impact of WRF model vertical grid resolution on Midwest summer rainfall forecasts, Weather and forecasting, 24(2) (2009) 575-594. [21] R. Borge, V. Alexandrov, J.J. Del Vas, J. Lumbreras, E. Rodríguez, A comprehensive sensitivity analysis of the WRF model for air quality applications over the Iberian Peninsula, Atmospheric Environment, 42(37) (2008) 8560-8574. [22] M.O. Mughal, M. Lynch, F. Yu, B. McGann, F. Jeanneret, J. Sutton, Wind modelling, validation and sensitivity study using Weather Research and Forecasting model in complex terrain, Environmental Modelling & Software, 90 (2017) 107-125. [23] B. Sandeepan, V.G. Panchang, S. Nayak, K.K. Kumar, J.M. Kaihatu, Performance of the WRF model for surface wind prediction around Qatar, Journal of Atmospheric and Oceanic Technology, 35(3) (2018) 575-592. [24] G. Kirkil, J. Mirocha, E. Bou-Zeid, F.K. Chow, B. Kosović, Implementation and evaluation of dynamic subfilter-scale stress models for large-eddy simulation using WRF, Monthly Weather Review, 140(1) (2012) 266-284. [25] J. Mirocha, J. Lundquist, B. Kosović, Implementation of a nonlinear subfilter turbulence stress model for large-eddy simulation in the Advanced Research WRF model, Monthly Weather Review, 138(11) (2010) 4212-4228. [26] C. Moeng, J. Dudhia, J. Klemp, P. Sullivan, Examining two-way grid nesting for large eddy simulation of the PBL using the WRF model, Monthly weather review, 135(6) (2007) 2295-2311. [27] D. Muñoz-Esparza, B. Kosović, J. Mirocha, J. van Beeck, Bridging the transition from mesoscale to microscale turbulence in numerical weather prediction models, Boundary-layer meteorology, 153(3) (2014) 409-440. [28] D. Muñoz-Esparza, B. Kosović, J. Van Beeck, J. Mirocha, A stochastic perturbation method to generate inflow turbulence in large-eddy simulation models: Application to neutrally stratified atmospheric boundary layers, Physics of Fluids, 27(3) (2015) 035102. [29] W.C. Skamarock, Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., … Huang, X. -yu., A Description of the Advanced Research WRF Model Version 4 2019. [30] Y. Liu, T. Warner, Y. Liu, C. Vincent, W. Wu, B. Mahoney, S. Swerdlin, K. Parks, J. Boehnert, Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications, Journal of Wind Engineering and Industrial Aerodynamics, 99(4) (2011) 308-319. [31] R.K. Rai, L.K. Berg, M. Pekour, W.J. Shaw, B. Kosovic, J.D. Mirocha, B.L. Ennis, Spatiotemporal variability of turbulence kinetic energy budgets in the convective boundary layer over both simple and complex terrain, Journal of Applied Meteorology and Climatology, 56(12) (2017) 3285-3302. [32] C. Talbot, E. Bou-Zeid, J. Smith, Nested mesoscale large-eddy simulations with WRF: Performance in real test cases, Journal of Hydrometeorology, 13(5) (2012) 1421-1441. [33] F.K. Chow, C. Schär, N. Ban, K.A. Lundquist, L. Schlemmer, X. Shi, Crossing multiple gray zones in the transition from mesoscale to microscale simulation over complex terrain, Atmosphere, 10(5) (2019) 274. [34] C. Hald, M. Zeeman, P. Laux, M. Mauder, H. Kunstmann, Large-eddy simulations of real-world episodes in complex terrain based on era-reanalysis and validated by ground-based remote sensing data, Monthly Weather Review, 147(12) (2019) 4325-4343. [35] M.H. Daniels, K.A. Lundquist, J.D. Mirocha, D.J. Wiersema, F.K. Chow, A new vertical grid nesting capability in the Weather Research and Forecasting (WRF) Model, Monthly Weather Review, 144(10) (2016) 3725-3747. [36] F. Gerber, V. Sharma, Running COSMO-WRF on very-high resolution over complex terrain (2018), Laboratory of Cryospheric Sciences CRYOS, École Polytechnique Fédérale de Lausanne EPFL, Lausanne, Switzerland, doi: 10.16904/envidat. 35, Cite along with: Gerber, F., N. Besic, V. Sharma, R. Mott, M. Daniels, M. Gabella, A. Berne, U. Germann, and M. Lehning (2018): Spatial variability of snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain, The Cryosphere, submitted, (2018) 1-20. [37] T.T. Warner, Numerical weather and climate prediction, cambridge university press, 2010. [38] N. Pineda, O. Jorba, J. Jorge, J. Baldasano, Using NOAA AVHRR and SPOT VGT data to estimate surface parameters: application to a mesoscale meteorological model, International journal of remote sensing, 25(1) (2004) 129-143. [39] P.A. Jiménez, J. Dudhia, Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model, Journal of Applied Meteorology and Climatology, 51(2) (2012) 300-316. [40] F. Ngan, H. Kim, P. Lee, K. Al-Wali, B. Dornblaser, A study of nocturnal surface wind speed overprediction by the WRF-ARW model in southeastern Texas, Journal of applied meteorology and climatology, 52(12) (2013) 2638-2653. [41] L. van Veen, The Perdigão field campaign: evaluation of the Cell Perturbation Method in atmospheric simulations, University of Twente, 2018. | ||
|
آمار تعداد مشاهده مقاله: 611 تعداد دریافت فایل اصل مقاله: 933 |
||