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基于键合图方法的中国太阳能温室微环境预测模型。

A microenvironment prediction model for Chinese solar greenhouses based on the bond graph approach.

机构信息

College of Information and Electrical Engineering, Shenyang Agricultural University, Shenhe District, Shenyang, China.

National & Local Joint Engineering Research Center of Northern Horticultural Facilities Design & Application Technology (Liaoning), Shenhe District, Shenyang, China.

出版信息

PLoS One. 2022 May 3;17(5):e0267481. doi: 10.1371/journal.pone.0267481. eCollection 2022.

DOI:10.1371/journal.pone.0267481
PMID:35503764
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9064107/
Abstract

To improve the prediction accuracy of temperature and humidity in typical Chinese solar greenhouses, this paper proposed a new longwave/shortwave radiation modeling method using bond graph. This model takes into account sun position, useful incoming solar radiation model, sky longwave radiation model, inside longwave, and shortwave radiation model. The approach solves the problems caused by underestimating the effects of longwave radiation on night temperature and relative humidity. The study found that after a period of t = 7.5 h, with the increase of sun altitude angle, the internal temperature was significantly affected by the temperature rise of outside environment on sunny day. The sun altitude angle gradually falls over a period of t = 12.5 h (beginning at 12.30 p.m.). The decline in night temperature steadily slowed after a period of t = 20.5 h. On the other hand, the temperature variation has a multi-peak distribution and the warming rate of the CSG slows down on cloudy days. Furthermore, a good agreement between the experimental and simulation data were obtained, with a maximum temperature deviation of 2°C and maximum humidity deviation of 5%. The developed model is a universal and valuable approach that can be used for greenhouse climate simulation. Furthermore, it can be used as a support system during decision-making processes to help manage Chinese solar greenhouses more efficiently, which provides several control perspectives on the low-energy greenhouse in the future. This work has also provided several control perspectives on the low energy greenhouse in the future.

摘要

为了提高中国典型太阳能温室温度和湿度的预测精度,本文提出了一种使用键合图的新的长波/短波辐射建模方法。该模型考虑了太阳位置、有用入射太阳辐射模型、天空长波辐射模型、内部长波和短波辐射模型。该方法解决了低估长波辐射对夜间温度和相对湿度影响的问题。研究发现,在 t = 7.5 h 后,随着太阳高度角的增加,内部温度在晴天受外部环境温度升高的影响显著。在 t = 12.5 h(从下午 12 点 30 分开始)期间,太阳高度角逐渐下降。在 t = 20.5 h 后,夜间温度的下降速度逐渐放缓。另一方面,温度变化具有多峰分布,在阴天 CSG 的升温速度减缓。此外,实验和模拟数据之间具有良好的一致性,最大温度偏差为 2°C,最大湿度偏差为 5%。开发的模型是一种通用且有价值的方法,可用于温室气候模拟。此外,它还可以作为决策过程中的支持系统,帮助更有效地管理中国太阳能温室,为未来的低能耗温室提供了几种控制角度。这项工作还为未来的低能耗温室提供了几种控制角度。

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本文引用的文献

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Effect of internal surface structure of the north wall on Chinese solar greenhouse thermal microclimate based on computational fluid dynamics.基于计算流体动力学的北墙内表面结构对中国太阳能温室热微气候的影响。
PLoS One. 2020 Apr 15;15(4):e0231316. doi: 10.1371/journal.pone.0231316. eCollection 2020.
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Implementation of Virtual Sensors for Monitoring Temperature in Greenhouses Using CFD and Control.使用 CFD 和控制技术实现温室温度虚拟传感器的监测
Sensors (Basel). 2018 Dec 24;19(1):60. doi: 10.3390/s19010060.