کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10145124 | 1646355 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sensitivity analysis of CSGHEAT model for estimation of heating consumption in a Chinese-style solar greenhouse
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
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چکیده انگلیسی
The sensitivity of a heating simulation model (CSGHEAT) was performed for estimation of the time-dependent heating requirement in a Chinese-style solar greenhouse in cold region. Results showed that the constant value of air thermal conductance is the main default parameter of the model that significantly affected the model output. The results also indicated the heating requirement is highly sensitive to the greenhouse design parameters including the thermal properties of cover, thermal blanket, and greenhouse perimeter. The thermal blanket is the most important design parameter for the Chinese-style solar greenhouse, and the heating requirement could be increased between 32 and 41% during the coldest three months (January, February, and December) for changing the thermal conductivity from 0.01 to 0.05â¯W/mâ¯K. Increasing daytime indoor set-point temperature from 19 to 23â¯Â°C would increase the heating demand between 13 and 20%, whereas the heating demand could be increased by 9-18% for increasing the night-time temperature from 16 to 20â¯Â°C. Results also indicate the heating demand could be reduced up to 20% during the coldest period for increasing the indoor relative humidity from 70 to 90%. The results from this study could be useful for understanding the energy saving management of greenhouse operations and for designing the energy-efficient Chinese-style solar greenhouses in cold regions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Electronics in Agriculture - Volume 154, November 2018, Pages 99-111
Journal: Computers and Electronics in Agriculture - Volume 154, November 2018, Pages 99-111
نویسندگان
Md Shamim Ahamed, Huiqing Guo, Karen Tanino,