Identification of thermal parameters of a building envelope based on the cooling process of a building object

2019 ◽  
Vol 43 (6) ◽  
pp. 503-527 ◽  
Author(s):  
Iwona Pokorska-Silva ◽  
Artur Nowoświat ◽  
Lidia Fedorowicz

Thermal properties of building envelopes are often described using thermal conductivity or thermal resistance. And the opposite task involves the identification of thermal parameters of building envelopes based on the measurements of their cooling process. In this article, the authors proposed a method of identifying thermal parameters of a building envelope based on cooling measurements, using a multiple regression model for this purpose. To satisfy the research objectives, two basic experiments were carried out. The first experiment was performed in laboratory conditions. The research model was a cube of the dimensions of 1.1 m × 1.1 m × 1.1 m. The second experiment was carried out in semi-real conditions, and the used model was a small house of the dimensions of 6.0 m × 4.15 m × 5.2 m. The measurement results were also used to calibrate numerical models made in the ESP-r program. The research studies have demonstrated that the model can be used to identify thermal parameters of a building envelope. Based on the measurements and simulations, the cooling equations of the object were determined and the 95% confidence interval for the heat retention index was estimated. On that basis, using the multiple regression model, such parameters of the model as density, specific heat, and thermal conductivity were estimated. It turned out that using the Gauss–Newton approximation, we obtained the correlation of the measurement results and the analytical model with the correlation coefficient of 0.9971 (for the laboratory scale). And the multiple regression improved not only the correlation between the measurement and the analytical model, but it also allowed to obtain “almost identical” results. Similarly, promising results were obtained for the semi-real scale.

Paradigm ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 181-193
Author(s):  
Nitya Garg

Banking sector is the backbone of any economy, so it is necessary to focus on its performance which is largely affected by its non-performing assets (NPAs). In the year 2018–2019, NPA of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. The purpose of this study is to identify the determinants based on analysis from previous literatures, and majorly macroeconomic and bank specific factors which are affecting NPAs using the relative weight analysis and to frame a model to predict future NPAs using multiple regression model using SPSS. The study also attempts to focus on actions and remedies that banks should make to control future NPAs. Findings of the study will act as a scaffolding for financial analysts and policymakers to prevent the conversion of its performing assets into NPAs and also help in proper management of banks and also in the recovery of economy.


2018 ◽  
Vol 65 (3) ◽  
pp. 115-121
Author(s):  
Zorana Lanc ◽  
Milan Zeljković ◽  
Aleksandar Živković ◽  
Branko Štrbac ◽  
Miodrag Hadžistević

Abstract This paper presents the experimental determination of the dependence of emissivity of brass on surface roughness and temperature. The investigation was conducted using the infrared thermographic technique on brass alloy C27200 workpieces with different degrees of surface roughness, during the continuous cooling process. The results obtained showed that the emissivity of the chosen brass alloy increases with greater surface roughness and decreases during the cooling process, its value ranging from 0.07 to 0.19. It was concluded that surface roughness has a greater influence on the increase of the emissivity at higher temperatures, which can be seen in the three-dimensional infrared images. Multiple regression analysis confirmed a strong correlation between the examined parameters and the emissivity, and an original multiple regression model was determined.


2020 ◽  
Vol 12 (07) ◽  
pp. 527-544
Author(s):  
Assoué Kouakou Sylvestre Kouadio ◽  
Ouedraogo Moussa ◽  
Ismaïla Ouattara ◽  
Issiaka Savane

2014 ◽  
Vol 644-650 ◽  
pp. 5319-5324
Author(s):  
Tian Jiu Leng

In this paper, the relevant factors of PM2.5 and the degree of correlation between them were analyzed.The multiple regression model was established using stepwise regression analysis method and the temporal spatial evolution of PM2.5 was obtained by setting the initial and boundary conditions.


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