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[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.This paper describes the study of cooling performance of cold storage in a pharmaceutical industry. It was intended to investigate the temperature distribution inside the storage that is an important performance factor in pharmaceutical industry cold storage. Cold storage that used is a ceiling type with the liquid bottle loading. Temperature distribution and the storage cooling performance were studied using experimental measurement and numerical simulation. Some variation of bottle arrangement and rack arrangement have been observed to show the impact of distribution temperature and cooling performance of cold storage. Surface temperatures of the bottles were measured with different bottles and rack arrangement. The temperature of cold storage was set to 5°C. In numerical simulation, a transient three dimensional Computational Fluid Dynamic (CFD) model was developed to investigate the cooling performance and temperature distribution inside bottle. At this stage, the results showed that rack arrangement that parallel with the cold room fan and V shape bottle layout has given a good cooling performance (it takes 1480 minutes to reach a stable temperature at the setpoint) and an optimum temperature distribution (with temperature difference of 0.58 °C). For the measurement of the distribution of temperature in the bottle, the mean deviation value between the simulation and the experiment on the measurement of 2 coordinate points (X = 0,1 m, Y = 0,3 m, Z = 0 m and X = -0,1 m, Y = 0,3 m, Z = 0 m) were 5,5 % and 7,6 %.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Distribution of temperature,Distribution temperature,Numerical investigations,Performance factors,Pharmaceutical industry,Surface temperatures,Temperature differences,Three dimensional computational fluid dynamics[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1742-6596/1090/1/012012[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]