Analysis of Temperature-Field Error of Computational Fluid Dynamics (CFD) in a New Type of Cigar-Curing Shed
1. Sichuan Institute of Tobacco Science, Chengdu 610000, China
2. College of Agriculture, Sichuan Agricultural University, Chengdu 611130, China
3. Sichuan Tobacco Corporation Deyang Branch, Deyang 618400, China
| Abstract: | Computational fluid dynamics (CFD) offers a powerful tool for simulating the temperature field within an object and it is widely being used in studies related to tobacco curing. However, there remains a discrepancy between the simulated temperature values and the actual monitoring data. To quantitatively understand temperature simulation errors for a CFD model of a novel cigar-curing shed, the temperature within the cigar-curing area was simulated and errors were calculated. This model produced a relative error between −5% and 4%. The upper monitoring surface of the cigar-curing rack exhibited insignificant errors between 0:00–9:00 and 18:00–24:00 (maximum at 10:00–17:00). The distribution area was relatively disordered, and the negative-error area increased sharply. Furthermore, the simulation performance for the upper monitoring surface was superior to that for the lower surface, particularly at night. The worst results were realized in the lower monitoring surface at 11:00–18:00. The ambient temperature influences the temperature uniformity inside the shed, and a greater surface temperature dispersion led to larger simulation errors. CFD modeling errors are mainly responsible for these differences and offer a primary optimization direction. These errors can be reduced by using correction values. This study offers valuable insights for improving the effectiveness of CFD simulations of drying temperatures, thereby achieving a comprehensive and accurate understanding of the novel drying shed's performance. |
| Keywords: | Cigar; Curing Shed; Curing Temperature; CFD Simulation; Error |
| DOI: | 10.57237/j.jaf.2024.03.002 |
View:
Downloads:
收稿日期: 2024-05-30
接受日期: 2024-07-16
在线出版日期: 2024-07-18
基金
| 1. | China Tobacco Corporation Sichuan Provincial Company regarding research on the Development of High-Quality Cigar Core Curing Facilities and Supporting Modulation Techniques for the Science and Technology Project (Sichuan Tobacco Science, 2021, SCYC202123) |
参考文献
| [1] | Hu, W. R.; Zhou, Q. W.; Cai, W.; Liu, J.; Li, P.; Hu, D.; Luo, C.; Li, D. Effects of coffee and cocoa as fermentation additives on sensory quality and chemical compositions of cigar tobacco leaves. Food Sci Technol 2023, 43. https://doi.org/10.1590/fst.96122 |
| [2] | Jia, J. X.; Zhang, M. J.; Zhao, J. C.; Wang, J.; He, F.; Wang, L. The effects of increasing the dry-bulb temperature during the stem-drying stage on the quality of upper leaves of flue-cured tobacco. Processes 2023, 11(3), 726. https://doi.org/10.3390/pr11030726 |
| [3] | Liu, H.; Duan, S.; Luo, H. Design and temperature modeling simulation of the full closed hot air circulation tobacco bulk curing barn. Symmetry 2022, 14(7), 1300. https://doi.org/10.3390/sym14071300 |
| [4] | Zhang, Q.; Kong, G. H.; Zhao, G. K.; Liu, J.; Jin, H.; Li, Z.; Zhang, G.; Liu, T. Microbial and enzymatic changes in cigar tobacco leaves during air-curing and fermentation. Appl Microbiol Biotechnol 2023, 107, 5789–5801. https://doi.org/10.1007/s00253-023-12663-5 |
| [5] | Gao, L. Studies on dynamic changes of physiological and biochemical indexes and its relationship with quality in burley tobacco 2005. |
| [6] | Yuhang, Z.; Yizhi, T.; Huashu, Z.; et al. Preliminary study on the technology of cigar clothing and cigarette blending. China Agric Inf 2015, 01, 83–84. |
| [7] | Chao, Y.; Jian, Y.; Jinglei, X.; et al. Development achievements and prospects of CFD simulation methods. Prog Mech 2011, 41, 562–589. |
| [8] | Cheng, X.; Li, D.; Shao, L.; Ren, Z. A virtual sensor simulation system of a flower greenhouse coupled with a new temperature microclimate model using three-dimensional CFD. Comput Electron Agric 2021, 181, 105934. https://doi.org/10.1016/j.compag.2020.105934 |
| [9] | Bai, Z.; Guo, D.; Li, S.; Hu, Y. Analysis of temperature and humidity field in a new bulk tobacco curing barn based on CFD. Sensors 2017, 17(2), 279. https://doi.org/10.3390/s17020279 |
| [10] | Xinjie, C.; Yu, Z.; Huan, X.; et al. Analysis of temperature and humidity field in a new type of cigar tobacco leaf drying shed based on CFD. Chin J Tob 2023, 29, 135–144. |
| [11] | Piscia, D.; Montero, J. I.; Baeza, E.; Bailey, B. J. A CFD greenhouse night-time condensation model. Biosyst Eng 2012, 111(2), 141–154. https://doi.org/10.1016/j.biosystemseng.2011.11.006 |
| [12] | Chen, J. L.; Cai, Y. W.; Xu, F.; Hu, H.; Ai, Q. Analysis and optimization of the fan-pad evaporative cooling system for greenhouse based on CFD. Adv Mech Eng 2014, 6. https://doi.org/ 10.1155/2014/712740 |
| [13] | Thorpe, G. R. The application of computational fluid dynamics codes to simulate heat and moisture transfer in stored grains. J Stored Prod Res 2008, 44(1), 21–31. https://doi.org/10.1016/j.jspr.2007.07.001 |
| [14] | Wiser, R. A First Complete Approach to Address Model Error in Computational Turbulent Heat Transfer. Dissertation, Massachusetts Institute of Technology, Cambridge, MA, USA, 2023. |
| [15] | Hu, G. Uncertainty Assessment for CFD Using Error Transport Equation. Dissertation, West Virginia University, Morgantown, WV, USA, 2002. |
| [16] | Xu, K.; Guo, X.; He, J. M.; Yu, B.; Tan, J.; Guo, Y. A study on temperature spatial distribution of a greenhouse under solar load with considering crop transpiration and optical effects. Energy Convers Manag 2022, 254, 115277. https://doi.org/10.1016/j.enconman.2022.115277 |
| [17] | Yeo, U.-H.; Lee, S.-Y.; Park, S.-J.; Kim, J.-G.; Choi, Y.-B.; Kim, R.-W.; Shin, J. H.; Lee, I.-B. Rooftop greenhouse: (1) design and validation of a BES model for a plastic-covered greenhouse considering the tomato crop model and natural ventilation characteristics. Agriculture 2022, 12(7), 903. https://doi.org/10.3390/agriculture12070903 |
| [18] | Boulard, T.; Roy, J. C.; Pouillard, J. B.; Fatnassi, H.; Grisey, A. Modelling of micrometeorology, canopy transpiration and photosynthesis in a closed greenhouse using computational fluid dynamics. Biosyst Eng 2017, 158, 110–133. https://doi.org/10.1016/j.biosystemseng.2017.04.001 |
| [19] | Sun, J.; Chen, Y.; Liu, L.; Zhu, F.; Li, Z.; Yu, L.; Xu, S.; Yue, Y.; Ma, Y.; Li, D. Simultaneous measurement of temperature-dependent thermal conductivity and heat capacity of an individual cured tobacco leaf. Int J Thermophys 2021, 42. https://doi.org/10.1007/s10765-021-02881-2 |
| [20] | Zhao, S.; Wu, Z.; Lai, M.; Zhao, M.; Lin, B. Determination of optimum humidity for air-curing of cigar tobacco leaves during the browning period. Ind Crops Prod 2022, 183, 114939. https://doi.org/10.1016/j.indcrop.2022.114939 |
| [21] | Li, K.; Xue, W.; Mao, H.; Chen, X.; Jiang, H.; Tan, G. Optimizing the 3D distributed climate inside greenhouses using multi-objective optimization algorithms and computer fluid dynamics. Energies 2019, 12(15), 2873. https://doi.org/10.3390/en12152873 |
| [22] | Paliwal, N.; Damiano, R. J.; Varble, N. A.; Tutino, V. M.; Dou, Z.; Siddiqui, A. H.; Meng, H. Methodology for computational fluid dynamic validation for medical use: Application to intracranial aneurysm. J Biomech. Eng 2017, 139(12), 1210041. https://doi.org/10.1115/1.4037792 |
| [23] | Wüstenhagen, C.; John, K.; Langner, S.; Brede, M.; Grundmann, S.; Bruschewski, M. CFD validation using in-vitro MRI velocity data - Methods for data matching and CFD error quantification. Comput Biol Med 2021, 131, 104230. https://doi.org/ 10.1016/j.compbiomed.2021.104230 |
