Research on inspection route of hanging environmental robot based on computational fluid dynamics

Published: 20 February 2024
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Spatial irregularity is a common feature of a closed piggery’s environment, and as of right now, there are no established guidelines for where different environmental monitoring sensors should be installed. In order to find environmental monitoring points and guarantee a scientific monitoring point layout, the project team employed the hanging track inspection robot (HTIR) as an environmental monitoring platform. The environmental parameter change rules at 1.6 m (α plane), 0.7 m (β plane), and 0.4 m (γ plane) above the ground were examined using the Ansys-computational fluid dynamics software. The 300 monitoring points ((x1~x30) × (y1~y10)) in each plane were analyzed to determine the most suitable monitoring points and inspection routes for HTIR. The results showed that: i) all monitoring points could be arranged directly below the y3 track; ii) monitoring points (x1, y3), (x10, y3), and (x30, y3) were environmental feature points. At (x1, y3), the maximum relative humidity (RH) and NH3 concentration on the α plane could be detected, and the maximum wind speed, maximum temperature, and maximum NH3 concentration on other planes could also be detected; at (x10, y3), the minimum temperature and maximum RH of the β and γ planes could be detected; at (x30, y3), the maximum NH3 concentration in the α plane and the minimum RH in all planes could be detected. This study scientifically arranged the inspection track and monitoring points for HTIR, improved the accuracy of environmental monitoring, and put forward suggestions for reducing NH3 concentration in closed piggeries, laying the foundation for the next step.



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Babadi, K.A., Khorasanizadeh, H., Aghaei, A. 2022. CFD modeling of air flow, humidity, CO2, and NH3 distributions in a caged laying hen house with tunnel ventilation system. Comput. Electron. Agr. 193:106677.
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.
Drewry, J.L., Mondaca, M.R., Luck, B.D., Choi, C. 2018. A computational fluid dynamics model of biological heat and gas generation in a dairy holding area. T. Asabe. 61:449-60.
Fang, J., Wu, S., Wu, Z., Wenge, B. 2022. CFD simulation of vertical ventilation in nursery pig house and optimization design of windshield. J. Northeast Agric. Univ. 53:59-68.
Fu, X., Shen, W., Yin, Y., Zhang, Y., Yan, S., Kou S., Qu, T., Jacqueline, M. 2022. Remote monitoring system for livestock environmental information based on LoRa wireless ad hoc network technology. Int. J. Agric. Biol. Eng. 15:79-89.
Gao, L., Er, M., Li, L., Wen, P., Jia, Y. 2022. Microclimate environment model construction and control strategy of enclosed laying brooder house. Poultry Sci. 101:101843.
Gonçalves, J.C., Lopes, A.M., Pereira, J.L. 2023. Computational fluid dynamics modeling of ammonia concentration in a commercial broiler building. Agriculture-Basel. 13:1101.
Guzmán, C.H., Carrera, J.L., Durán, H.A., Berumen, J., Ortiz, A.A., Guirette O.A., Arroyo, A., Brizuela, J.A., Gómez, F., Blanco, A., Azcaray, H.R., Hernández, M. 2018. Implementation of virtual sensors for monitoring temperature in greenhouses using CFD and control. Sensors-Basel. 19:60.
Hou, F., Shen, C., Cheng, Q. 2022. Research on a new optimization method for air flow organization in breeding air conditioning with perforated ceiling ventilation. Energy. 254:124279.
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.
Jung, S., Chung, H., Mondaca, M.R., Nordlund, K.V., Choi, C.Y. 2023. Using computational fluid dynamics to develop positivepressure precision ventilation systems for large-scale dairy houses. Biosyst. Eng. 227:182-94.
Kibwika, A.K., Seo, H.J., Seo, I.H. 2023. CFD model verification and aerodynamic analysis in large-scaled venlo greenhouse for tomato cultivation. AgriEngineering. 5:1395-414.
Küçüktopcu, E., Cemek, B., Simsek, H., Ni, J.Q. 2022. Computational fluid dynamics modeling of a broiler house microclimate in summer and winter. Animals-Basel. 12:867.
Li, H., Li, Y., Yue, X., Liu, X., Tian, S., Li, T. 2020. Evaluation of airflow pattern and thermal behavior of the arched greenhouses with designed roof ventilation scenarios using CFD simulation. PloS One. 15:e0239851.
Li, M., Zou, X., Feng, B., Qiu, X. 2023. Use of computational fluid dynamics to study ammonia concentrations at pedestrian height in smart broiler chamber clusters. Agriculture-Basel. 13:656.
Li, Y., Fu, C., Yang, H., Li, H. 2023. Design of a closed piggery environmental monitoring and control system based on a track inspection robot. Agriculture-Basel. 13:1501.
Limtrakarn, W., Boonmongkol, P., Chompupoung, A., Rungprateepthaworn, K., Kruenate, J., Dechaumphai, P. 2012. Computational fluid dynamics modeling to improve natural flow rate and sweet pepper productivity in greenhouse. Adv. Mech. Eng. 4:158563.
Madona, E., Yulastri, Nasution, A., Prayogi. 2022. Implementation of Lora for controlling and monitoring broiler cage temperature. J. Phys. Conf. Ser. 2406:012009.
Mondaca, M.R., Choi, C.Y., Cook, N.B. 2019.Understanding microenvironments within tunnel-ventilated dairy cow freestall facilities: examination using computational fluid dynamics and experimental validation. Biosyst. Eng. 183:70-84.
National standard of the People’s Republic of China: AQSIQ & SAC, 2008. Environmental parameters and environmental management of large-scale pig farms. GB/T 17824.1-2008.
Standards Press of China, Beijing, China. Nurmalisa, M., Tokairin, T., Kumazaki, T., Takayama, K., Inoue, T. 2022. CO2 distribution under CO2 enrichment using computational fluid dynamics considering photosynthesis in a tomato greenhouse. Appl. Sci-Basel. 12:7756.
Pakari, A., Ghani, S. 2021.Comparison of different mechanical ventilation systems for dairy cow barns: CFD simulations and field measurements. Comput. Electron. Agr. 186:106207.
Rong, L., Bjerg, B., Zhang, G. 2015. Assessment of modeling slatted floor as porous medium for prediction of ammonia emissions- Scaled pig barns. Comput. Electron. Agr. 117:234-44.
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-Basel. 10:6054.
Sousa, V., Sabino, L.A., Moura, D.J., Nunhez, J.R., Sonoda, L.T., Oliveira, A., Prada, R.J., Volpin, D. 2018. Application of computational fluid dynamics on a study in swine facilities with mechanical ventilation system. Sci. Agr. 75:173-83.
Tabase, R.K., Bagci, O., De Paepe, M., Aarnink A.J.A., Demeyer, P. 2020. CFD simulation of airflows and ammonia emissions in a pig compartment with underfloor air distribution system: model validation at different ventilation rates. Comput. Electron. Agr. 171:105297.
Tomasello, N., Valenti, F., Cascone, G., Porto, S.M.C.. 2019. Development of a CFD model to simulate natural ventilation in a semi-open free-stall barn for dairy cows. Buildings-Basel. 9:183.
Wang, X., Zhang, G., Choi, C.Y. 2018. Effect of airflow speed and direction on convective heat transfer of standing and reclining cows. Biosyst. Eng. 167:87-98.
Wang, X., Cao, M., Hu, F., Yi, Q., Amon, T., Janke, D., Xie, T., Zhang, G., Wang, K. 2022. Effect of fans’ placement on the indoor thermal environment of typical tunnel-ventilated multifloor pig buildings using numerical simulation. Agriculture-Basel. 12:891.
Xin, Y. 2021. Research on the distribution law of ammonia inside and outside the building pig house based on CFD simulation. Degree diss., Zhejiang University, Zhejiang, China.
Xu, F., Lu, H., Chen, Z., Guan, Z.C., Chen, Y.W., Shen, G.W., Jiang, Z. 2021. Selection of a computational fluid dynamics (CFD) model and its application to greenhouse pad-fan cooling (PFC) systems. J. Cl. Ean. Prod. 302:127013.
Yeo, U.H., Decano-Valentin, C., Ha, T., Lee, I.B., Kim, R.W., Lee, S.Y., Kim, J.G. 2020. Impact analysis of environmental conditions on odour dispersion emitted from pig house with complex terrain using CFD. Agronomy-Basel. 10:1828.
Zeng, Z., Zeng, F., Han, X., Elkhouchlaa, H., Yu, Q., Lü, E. 2021. Real-time monitoring of environmental parameters in a commercial gestating sow house using a ZigBee-based wireless sensor network. Appl. Sci-Basel. 11:972.
Zeng, Z., Wei, X., Lü, E., Liu, Y., Yi, Z., Guo J. 2020. Numerical simulation and experimental verification of temperature and humidity in centralized ventilated delivery pigsty. Trans. Chin. Soc Agric. Eng. 36:210-7.
Zhang, G., Fu, Z., Yang, M., Liu, X., Dong, Y., Li, X. 2019. Nonlinear simulation for coupling modeling of air humidity and vent opening in Chinese solar greenhouse based on CFD. Comput. Electron. Agr. 162:337-47.
Zhao, W., Choi, C.Y., Du, X., Guan, H., Li, H., Shi, Z. 2023. Effects of ventilation fans and type of partitions on the airflow speeds of animal occupied zone and physiological parameters of dairy pre-weaned calves housed individually in a barn. Agriculture-Basel. 13:1002.
Zou, Z., Zhou, M., Zhao, Z., Wen, B. 2017. Design of ZigBeebased Environmental Parameter Monitoring System for Henhouse. Proceedings of the 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing. pp. 1879-94.

How to Cite

Yang, H., Li, Y., Fu, C., Zhang, R., Li, H., Feng, Y., Zhang, Y., Cong, H. and Nie, F. (2024) “Research on inspection route of hanging environmental robot based on computational fluid dynamics”, Journal of Agricultural Engineering, 55(2). doi: 10.4081/jae.2024.1565.