https://doi.org/10.4081/jae.2024.1589
Evaluation of microclimate in dairy farms using different model typologies in computational fluid dynamics analyses
Published: 9 July 2024
Abstract Views: 274
PDF: 167
HTML: 3
HTML: 3
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Ventilation plays a key role in the livestock buildings since it is important to guarantee a comfortable environment and adequate indoor air quality for the animals. Naturally ventilated barns are usually characterized by high variability in the ventilation conditions. Moreover, the ventilation efficiency can be very different in different areas of a barn because of the different presence of the animals. On the other hand, appropriate ventilation is an essential requirement to ensure animal welfare and efficient and sustainable production since a proper ventilation is the most efficient way to remove undesirable air pollutants and to obtain a comfortable microclimate for the welfare of the animals. In this regard, the computational fluid dynamic (CFD) simulations represent a powerful and useful tool because they can be used to assess ventilation and microclimate conditions. In this context, the present study has the object to assess whether different CFD modelling approaches (i.e. model with animals modelled as obstacles with closed volume and model enriched with cows modelled as obstacles capable of exchanging heat with the surrounding air volume) show differences in relation to the climatic conditions inside a naturally ventilated dairy barn. The comparison of the results, set in terms of indoor air temperature and air velocity contours of the two different models, arises that if a precise definition of the microclimatic features is necessary, in order to correlate them with production parameters or assess animal welfare indexes, thermal simplification is not acceptable since can lead to completely misleading conclusions and incorrect evaluations. Then, only adopting CFD models considering the animal thermal behaviour is possible to obtain effective information both for the proper barn system management and for the creation of useful tools driving the farmers' choices.
Baêta, F.D., Meador, N.F., Shanklin, Johnson, H.D. (1987). Equivalent temperature index at temperatures above the thermoneutral for lactating dairy cows. Trans. ASABE 87-4015, American Society of Agricultural Engineers.
Bartali, E.H., Jongebreur, A., Moffitt, D. (1999). Animal Production & Aquaculture Engineering. CIGR Handbook of Agricultural Engineering. American Society of Agricultural Engineers
Berman, A., Horovitz, T., Kaim, M., Gacitua, H. (2016). A comparison of THI indices leads to a sensible heat-based heat stress index for shaded cattle that aligns temperature and humidity stress. Int. J. Biometeorol. 60:1453-1462.
DOI: https://doi.org/10.1007/s00484-016-1136-9
Bernabucci, U., Biffani, S., Buggiotti, L., Vitali, A., Lacetera, N., Nardone, A. (2014). The effects of heat stress in Italian Holstein dairy cattle. J. Dairy Sci. 97:471-486.
DOI: https://doi.org/10.3168/jds.2013-6611
Bjerg, B., Svidt, K., Zhang, G., Morsing, S. (2000). The effects of pen partitions and thermal pig simulators on air flow in a livestock test room. J. Agr. Engin. Res. 77:317-326.
DOI: https://doi.org/10.1006/jaer.2000.0596
Blanes-Vidal, V., Guijarro, E., Balasch, S., Torres, A.G. (2008). Application of computational fluid dynamics to the prediction of airflow in a mechanically ventilated commercial poultry building. Biosyst. Engin. 100:105-116.
DOI: https://doi.org/10.1016/j.biosystemseng.2008.02.004
Bovo, M., Agrusti, M., Benni, S., Torreggiani, D., Tassinari, P. (2021). Random forest modelling of milk yield of dairy cows under heat stress conditions. Animals (Basel) 11:1305.
DOI: https://doi.org/10.3390/ani11051305
Bovo, M., Benni, S., Barbaresi, A., Santolini, E., Agrusti, M., Torreggiani, D., Tassinari, P. (2020). A smart monitoring system for a future smarter dairy farming. Proc. 2020 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2020, Trento. pp. 165-169.
DOI: https://doi.org/10.1109/MetroAgriFor50201.2020.9277547
Bovo, M., Santolini, E., Barbaresi, A., Tassinari, P., Torreggiani, D. (2022). Assessment of geometrical and seasonal effects on the natural ventilation of a pig barn using CFD simulations. Comput. Electron. Agr. 193:106652.
DOI: https://doi.org/10.1016/j.compag.2021.106652
Bustos-Vanegas, J.D., Hempel, S., Janke, D., Doumbia, M., Streng, J., Amon, T., (2019). Numerical simulation of airflow in animal occupied zones in a dairy cattle building. Biosyst. Engin. 186:100-105.
DOI: https://doi.org/10.1016/j.biosystemseng.2019.07.002
Cao, M., Yi, Q., Wang, K., Li, J., Wang, X. (2023). Predicting ventilation rate in a naturally ventilated dairy barn in wind-forced conditions using machine learning techniques. Agriculture-Basel, 13:837.
DOI: https://doi.org/10.3390/agriculture13040837
Carabaño, M.J., Logar, B., Bormann, J., Minet, J., Vanrobays, M.-L., Díaz, C., Tychon, B., Gengler, N., Hammami, H. (2016). Modeling heat stress under different environmental conditions. J. Dairy Sci. 99:3798-3814.
DOI: https://doi.org/10.3168/jds.2015-10212
Choi, H.L., Han, S.H., Albright, L.D., Chang, W.K. (2011). The correlation between thermal and noxious gas environments, pig productivity and behavioral responses of growing pigs. Int. J. Environm. Res. Public Health 8:3514-3527.
DOI: https://doi.org/10.3390/ijerph8093514
De la Torre-Gea, G., Soto-Zarazúa, G.M., López-Crúz, I., Torres-Pacheco, I., Rico-García, E. (2011). Computational fluid dynamics in greenhouses: A review. Afr. J. Biotechnol. 10:17651-17662.
DOI: https://doi.org/10.5897/AJB10.2488
Ding, W., Hasemi, Y., Yamada, T. (2005). Natural ventilation performance of a double-skin facade with a solar chimney. Energ. Buildings 37:411-418.
DOI: https://doi.org/10.1016/j.enbuild.2004.08.002
Doumbia, E.M., Janke, D., Yi, Q., Amon, T., Kriegel, M., Hempel, S. (2021). CFD modelling of an animal occupied zone using an anisotropic porous medium model with velocity depended resistance. Comput. Electron. Agr. 181:105950.
DOI: https://doi.org/10.1016/j.compag.2020.105950
Garcia, C.A.P., Bovo, M., Torreggiani, D., Tassinari, P., Benni, S. (2023). 3D numerical modelling of temperature and humidity index distribution in livestock structures: a cattle-barn case study. J. Agric. Eng. 54:1522.
Giannone, C., Bovo, M., Ceccarelli, M., Torreggiani, D., Tassinari, P. (2023). Review of the heat stress-induced responses in dairy cattle. Animals (Basel) 13:3451.
DOI: https://doi.org/10.3390/ani13223451
Gonçalves de Oliveira, D.C., Selaysim Di Campos, M., Passé-Coutrin, N., Onésippe Potiron, C., Bilba, K., Arsène, M.-A., Savastano Junior, H. (2021). Modeling of the thermal performance of piglet house with non-conventional floor system. J. Build. Eng. 35:102071.
DOI: https://doi.org/10.1016/j.jobe.2020.102071
Heinicke, J., Ibscher, S., Belik, V., Amon, T. (2019). Cow individual activity response to the accumulation of heat load duration. J. Therm. Biol. 82:23-32.
DOI: https://doi.org/10.1016/j.jtherbio.2019.03.011
Heinicke, J., Ott, A., Ammon, C., Amon, T. (2021). Heat load-induced changes in lying behavior and lying cubicle occupancy of lactating dairy cows in a naturally ventilated barn. Ann. Anim. Sci. 21:1543-1553.
DOI: https://doi.org/10.2478/aoas-2020-0113
Huang, W.-X., Tian, F.-B. (2019). Recent trends and progress in the immersed boundary method. P. I. Mech. Eng. C-J. Mec. 233:7617-7636.
DOI: https://doi.org/10.1177/0954406219842606
Jackson, P., Nasirahmadi, A., Guy, J.H., Bull, S., Avery, P.J., Edwards, S.A., Sturm, B. (2020). Using CFD modelling to relate pig lying locations to environmental variability in finishing pens. Sustainability (Basel) 12:1928.
DOI: https://doi.org/10.3390/su12051928
Ji, B., Banhazi, T., Perano, K., Ghahramani, A., Bowtell, L., Wang, C., Li, B. (2020). A review of measuring, assessing and mitigating heat stress in dairy. Biosyst. Eng. 199: 4-26.
DOI: https://doi.org/10.1016/j.biosystemseng.2020.07.009
Ji, B., Banhazi, T., Phillips, C.J.C., Wang, C., Li, B. (2022). A machine learning framework to predict the next month’s daily milk yield, milk composition and milking frequency for cows in a robotic dairy farm. Biosyst. Eng. 216:186-197.
DOI: https://doi.org/10.1016/j.biosystemseng.2022.02.013
Kim, M., Won, W, Kim, J. (2017). Integration of carbon capture and sequestration and renewable resource technologies for sustainable energy supply in the transportation sector. Energ. Convers. Manage. 143:227-240.
DOI: https://doi.org/10.1016/j.enconman.2017.04.010
King, M.-F., Gough, H.L., Halios, C., Barlow, J.F., Robertson, A., Hoxey, R., Noakes, C.J. (2017). Investigating the influence of neighbouring structures on natural ventilation potential of a full-scale cubical building using time-dependent CFD. J. Wind Eng. Ind. Aerod. 169:265-279.
DOI: https://doi.org/10.1016/j.jweia.2017.07.020
Launder, B.E., Spalding, D.B. (1983). The numerical computation of turbulent flows.In: S.V. Patankar, A. Pollard, A.K. Singhal, S. Pratap Vanka (eds.), Numerical prediction of flow, heat transfer, turbulence and combustion. Oxford, Pergamon. pp. 96-116.
DOI: https://doi.org/10.1016/B978-0-08-030937-8.50016-7
Li, H., Rong, L., Zhang, G. (2016). Study on convective heat transfer from pig models by CFD in a virtual wind tunnel. Comput. Electron. Agr. 123:203-210.
DOI: https://doi.org/10.1016/j.compag.2016.02.027
Liu, X., Yang, L., Niu, S. (2021). Research on the effect of different position on classroom ventilation in a “L” type teaching building. J. Build. Eng. 33:101852.
DOI: https://doi.org/10.1016/j.jobe.2020.101852
Marumo, J.L., Lusseau, D., Speakman, J.R., Mackie, M., Hambly, C. (2022). Influence of environmental factors and parity on milk yield dynamics in barn-housed dairy cattle. J. Dairy Sci. 105:1225-1241.
DOI: https://doi.org/10.3168/jds.2021-20698
Molina-Aiz, F.D., Norton, T., López, A., Reyes-Rosas, A., Moreno, M.A., Marín, P., Espinoza, K. Valera, D.L. (2017). Using Computational Fluid Dynamics to analyse the CO2 transfer in naturally ventilated greenhouses. Acta Hortic. 1182:283-292.
DOI: https://doi.org/10.17660/ActaHortic.2017.1182.34
Mondaca, M., Choi, C.Y. (2016). An evaluation of simplifying assumptions in dairy cow computational fluid dynamics models. Trans. ASABE 59:1575-1584.
DOI: https://doi.org/10.13031/trans.59.11908
Moretti, R., Biffani, S., Chessa, S., Bozz, R. (2017). Heat stress effects on Holstein dairy cows’ rumination. Animal 11:2320-2325.
DOI: https://doi.org/10.1017/S1751731117001173
Mossad, R.R. (2005). Optimization of the ventilation system for a forced ventilation piggery. J. Green Build. 4:113-133.
DOI: https://doi.org/10.3992/jgb.4.4.113
Müschner-Siemens, T., Hoffmann, G., Ammon, C., Amon, T. (2020). Daily rumination time of lactating dairy cows under heat stress conditions. J. Therm. Biol. 88:102484.
DOI: https://doi.org/10.1016/j.jtherbio.2019.102484
National Research Council U.S., Committee on Physiological Effects of Environmental Factors on Animals. (1971). A guide to environmental research on animals. Washington, National Academies Press.
Norton, T., Grant, J., Fallon, R., Sun, D.-W. (2009). Assessing the ventilation effectiveness of naturally ventilated livestock buildings under wind dominated conditions using computational fluid dynamics. Biosyst. Eng. 103:78-99.
DOI: https://doi.org/10.1016/j.biosystemseng.2009.02.007
Norton, T., Grant, J., Fallon, R., Sun, D.-W. (2010). Optimising the ventilation configuration of naturally ventilated livestock buildings for improved indoor environmental homogeneity. Build. Environ. 45:983-995.
DOI: https://doi.org/10.1016/j.buildenv.2009.10.005
Oliveira, C.E.A., de Fátima Ferreira Tinôco, I., Campos de Sousa, F., Damasceno, F.A., Andrade, R., de Fátima Maciel, F., Barbari, M., Arêdes Martins, M. (2023). Analysis of heat and mass transfer in compost-bedded pack barns for dairy cows using computational fluid dynamics: a review. Appl. Sci. (Basel) 13:9331.
DOI: https://doi.org/10.3390/app13169331
Pinto, S., Hoffmann, G., Ammon, C., Amon, T. (2020). Critical THI thresholds based on the physiological parameters of lactating dairy cows. J. Therm. Biol. 88:102523.
DOI: https://doi.org/10.1016/j.jtherbio.2020.102523
Richards, P.J., Hoxey, R.P. (1993). Appropriate boundary conditions for computational wind engineering models using the k-ϵ turbulence model. J. Wind Eng. Ind. Aerod. 46-47:145-153.
DOI: https://doi.org/10.1016/0167-6105(93)90124-7
Rong, L., Liu, D., Pedersen, E.F., Zhang, G. (2015). The effect of wind speed and direction and surrounding maize on hybrid ventilation in a dairy cow building in Denmark. Energ. Build. 86:25-34.
DOI: https://doi.org/10.1016/j.enbuild.2014.10.016
Rong, L., Nielsen, P.V., Bjerg, B., Zhang, G. (2016). Summary of best guidelines and validation of CFD modeling in livestock buildings to ensure prediction quality. Comput. Electron. Agr. 121:180-190.
DOI: https://doi.org/10.1016/j.compag.2015.12.005
Saha, C.K., Yi, Q., Janke, D., Hempel, S., Amon, B., Amon, T. (2020). Opening size effects on airflow pattern and airflow rate of a naturally ventilated dairy building - A CFD study. Appl. Sci. 10:6054.
DOI: https://doi.org/10.3390/app10176054
Teitel, M., Wenger, E. (2014). Air exchange and ventilation efficiencies of a monospan greenhouse with one inflow and one outflow through longitudinal side openings. Biosyst. Eng. 119:98-107.
DOI: https://doi.org/10.1016/j.biosystemseng.2013.11.001
Tomasello, N., Valenti, F., Cascone, G. (2019). Development of a CFD model to simulate natural ventilation in a semi-open free-stall barn for dairy cows. Buildings 9:183.
DOI: https://doi.org/10.3390/buildings9080183
Tominaga, Y., Mochida, A., Yoshie, R., Kataoka, H., Nozu, T., Yoshikawa, M., Shirasawa, T. (2008). AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings. J. Wind Eng. Ind. Aerod. 96:1749-1761.
DOI: https://doi.org/10.1016/j.jweia.2008.02.058
Tresoldi, G., Schütz, K.E., Tucker, C.B. (2019).Cooling cows with sprinklers: Effects of soaker flow rate and timing on behavioral and physiological responses to heat load and production. J. Dairy Sci. 102:528-538.
DOI: https://doi.org/10.3168/jds.2018-14962
Tu, J., Yeoh, G.H., Liu, C. (2012). Computational fluid dynamics: a practical approach. Oxford, Butterworth-Heinemann.
Vitali, M., Santolini, E., Bovo, M., Tassinari, P., Torreggiani, D., Trevisi, P. (2021). Behavior and welfare of undocked heavy pigs raised in buildings with different ventilation systems. Animals (Basel) 11:2338.
DOI: https://doi.org/10.3390/ani11082338
Wang, K., Pan, Q., Li, K. (2017). Computational fluid dynamics simulation of the hygrothermal conditions in a weaner house in eastern China. Trans. ASABE 60:195-205.
DOI: https://doi.org/10.13031/trans.11655
Wang, X, Wu, J., Yi, Q., Zhang, G., Amon, T., Janke, D., Li, X., Chen, B., He, Y., Wang, K. (2021). Numerical evaluation on ventilation rates of a novel multi-floor pig building using computational fluid dynamics. Comput. Electron. Agr. 182:106050.
DOI: https://doi.org/10.1016/j.compag.2021.106050
Wang, X., Zhang, G., Choi, C.Y. (2018a). Effect of airflow speed and direction on convective heat transfer of standing and reclining cows. Biosyst. Eng. 167:87-98.
DOI: https://doi.org/10.1016/j.biosystemseng.2017.12.011
Wang, X., Zhang, G., Choi, C.Y. (2018b). Evaluation of a precision air-supply system in naturally ventilated freestall dairy barns. Biosyst. Eng 175:1-15.
DOI: https://doi.org/10.1016/j.biosystemseng.2018.08.005
Willits, D. H. (2002). The effect of shade cloth temperature on the cooling efficiency of shade cloths in greenhouses. Proc. 2002 ASAE Annual Meet., Paper 024113. St. Joseph, American Society of Agricultural and Biological Engineers.
DOI: https://doi.org/10.13031/2013.9540
Wu, W., Zhai, J., Zhang, G., Nielsen, P.V. (2012). Evaluation of methods for determining air exchange rate in a naturally ventilated dairy cattle building with large openings using computational fluid dynamics (CFD). Atmos. Environ. 63:179-188.
DOI: https://doi.org/10.1016/j.atmosenv.2012.09.042
Xin, Y., Rong, L., Wang, C., Li, B., Liu, D. (2022). CFD study on the impacts of geometric models of lying pigs on resistance coefficients for porous media modelling of the animal occupied zone. Biosyst. Eng. 222:93-105.
DOI: https://doi.org/10.1016/j.biosystemseng.2022.07.015
Yee, H.C. (1987). Construction of explicit and implicit symmetric TVD schemes and their applications. J. Comput. Phys. 68:151-179.
DOI: https://doi.org/10.1016/0021-9991(87)90049-0
Zhang, Z., Zhang, W., Zai, J.Z., Chen, Q.Y. (2007). Evaluation of various turbulence models in predicting airflow and turbulence in enclosed environments by CFD: Part 2 - Comparison with experimental data from literature. HVAC&R Res. 13:871-886.
DOI: https://doi.org/10.1080/10789669.2007.10391460
How to Cite
Santolini, E. (2024) “Evaluation of microclimate in dairy farms using different model typologies in computational fluid dynamics analyses”, Journal of Agricultural Engineering, 55(3). doi: 10.4081/jae.2024.1589.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Saddam Hussain, Decong Zheng , Haiyan Song, Muhammad Usman Farid, Abdul Ghafoor, Xinyi Ba, Hao Wang, Wenjun Wang, Alam Sher, Salah Jumaa Alshamali, Computational fluid dynamics simulation and optimisation of the threshing unit of buckwheat thresher for effective cleaning of the cleaning chamber , Journal of Agricultural Engineering: Vol. 53 No. 1 (2022)
- Meng Junjie, Cao Ziang, Guo Dandan, Wang Yuwei, Zhang Dashan, Liu Bingyou, Hou Wenhui, Grape detection in natural environment based on improved YOLOv8 network , Journal of Agricultural Engineering: Vol. 55 No. 4 (2024)
- Senanur Durgut Malçok, Azime Özkan Karabacak, Ertürk Bekar, Cüneyt Tunçkal, Canan Ece Tamer, Influence of a hybrid drying combined with infrared and heat pump dryer on drying characteristics, colour, thermal imaging and bioaccessibility of phenolics and antioxidant capacity of mushroom slices , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- H. Kursat Celik, Ibrahim Akinci, Nuri Caglayan, Allan E.W. Rennie, Structural strength analysis of a rotary drum mower during harvesting , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Hossein Khaledian, Homayoun Faghih, Ata Amini, Classifications of runoff and sediment data to improve the rating curve method , Journal of Agricultural Engineering: Vol. 48 No. 3 (2017)
- Xiwang Du, Xia Li, Fangtao Duan, Jiawei Hua, Mengchao Hu, Static laser weeding system based on improved YOLOv8 and image fusion , Journal of Agricultural Engineering: Vol. 55 No. 4 (2024)
- Roberto Beghi, Simone V. Marai, Valentina Giovenzana, Enrico Ferrari, Riccardo Guidetti, Testing and design of a passive container for the optimisation of highbush blueberries (Vaccinium corymbosum L.) cold chain , Journal of Agricultural Engineering: Vol. 47 No. 2 (2016)
- Caiqi Liao, Jin Chen, Fanzhao Geng, Xueming Tang, Airflow basin structure numerical optimisation analysis and suction nozzle characteristics experimental study of vacuum-vibration tray precision seeder , Journal of Agricultural Engineering: Vol. 53 No. 4 (2022)
- Giovanni Francesco Ricci, Faouzi Zahi, Ersilia D'Ambrosio, Anna Maria De Girolamo, Giuseppe Parete, Taha-Hocine Debieche, Francesco Gentile, Evaluating flow regime alterations due to point sources in intermittent rivers: A modelling approach , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
- Yun Zhu, Shuwen Liu, Xiaojun Wu, Lianfeng Gao, Youyun Xu, Multi-class segmentation of navel orange surface defects based on improved DeepLabv3+ , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
