Understanding trend and its variability of rainfall and temperature over Patna (Bihar)

Gupta, Nitesh; Mahato, Pradeep K.; Patel, Jitendra; Omar, Padam Jee; Tripathi, Ravi Prakash

doi:10.1016/b978-0-323-91910-4.00030-3

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…Patna also has unique geographical and climatic features. 56 Table 1 displays all of the location and weather-related characteristics. The HAP software’s weather properties tab is where these settings are taken as input.…”

Section: Simulation Model For Cooling Load Calculationmentioning

confidence: 99%

Optimization of air-conditioning load due to different thermal parameters for a residential building using different techniques

Arif,

Mondal,

Mandal

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Better insulation or material with low thermal conductivity is a crucial factor in new construction and retrofitting existing structures where energy efficiency is the objective. Effective energy savings need the careful selection of construction materials to accomplish this. For this purpose, in this research work, a typical residential flat is taken into account. Using the hourly analysis program (HAP 4.90) software, the cooling and heating load is determined. Eight influencing factors, including building orientation (A), overall heat transfer coefficient of wall (B), roof insulation material (C), roof insulation thickness (D), heat transfer coefficient of glass (E), window to wall ratio (F), air infiltration flow rate (G), and bypass factor of air-conditioner (H) at three levels, are chosen to optimize the cooling and heating load. An orthogonal L27 layout is considered for the optimization, and the cooling and heating load has been determined for each experimental attempt. At first using Taguchi method, ANOVA analysis is done separately for each response variable and later using GRA and TOPSIS based Taguchi analysis, multi-objective optimization has been done, and the results are compared with each other. By determining each factor’s percentage contribution to the cooling and heating load, the ANOVA approach is also used to identify the primary components that have a major impact on the cooling and heating load. The outcome demonstrates that A3B2C2D3E1F1G1H1 is the best possible combination of all eight variables, and overall heat transfer coefficient of wall is the most influencing parameter for the residential buildings.

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Section: Simulation Model For Cooling Load Calculationmentioning

confidence: 99%