abdolazim ghanghermeh; Gholamreza Roshan; smaeil shahkooeei
Abstract
Extended Abstract
Introduction
One of the practical indices in determining required energy for providing climatic comfort is the degree day index. The total mean deviation of daily temperature of human comfort temperature (threshold temperature) is called degree day temperature that provides many ...
Read More
Extended Abstract
Introduction
One of the practical indices in determining required energy for providing climatic comfort is the degree day index. The total mean deviation of daily temperature of human comfort temperature (threshold temperature) is called degree day temperature that provides many applications in estimating required energy in cooling and heating section. It is notable that various studies around the world have used different temperatures to calculate HDD and CDD considering their climatic and geographical location. In Iran, 18 degrees centigrade is used for HDD and 24 degrees centigrade for CDD calculation, while climatic and geographical diversity of Iran causes new base temperatures to be recommended for HDD and CDD calculations. The present study plans to present a proper base temperature for calculating HDD and CDD with regard to specific characteristics of each city's climate.
Materials and Method
In the present study to determine the new threshold temperatures in order to provide the energy required for climatic comfort conditions, Olgyay diagram is used. Therefore, the average daily temperature and relative humidity data have been used to draw bioclimatic conditions. Since Iran has different climatic diversity, 10 stations that represent different climatic conditions of Iran were selected and analyzed (Figure 1). It should be mentioned that the duration of time series used includes the statistical period of 1950 to 2010 and these data was collected from Iran`s Meteorological Organization. Since hand drawing of each of the events on Olgyay diagram is cumbersome and time consuming considering the wide range of studied data, therefore, Olgyay diagram was digitalized to receive the output for each station quickly and easily. It is also noteworthy that in this study, Olgyay diagram is divided into 12 bioclimatic classes and the frequency of occurrence of each of the bioclimatic classes for each station in Table (1) has been reported. However, the most important section of this study is related to the determination of new base temperatures for calculating HDD and CDD indices of observational stations. Therefore, based on the days in the comfort zone, three regions in the form of percentile thresholds of 40 to 60 were selected as the representative of the central 20 percent of the data, percentile threshold of 25 to 75 percent as the representative of the dominant central 50% of the data, and finally percentile threshold of 10 to 90 as the central 80 % of the data were selected, and these domains were introduced as new thermal comfort for determining the base temperatures for HDD and CDD calculation (equation 1):
Equation 1:
In equation 1, LP is an equivalent for the threshold rank of the percentiles 10, 25, 40, 60, 75 and 90 percent, n is an equivalent for the number of samples and s is an equivalent for percentiles.
In the final step, after determining the base temperature, required cooling day-degree values (Equation 2) and heating (Equation 3) are calculated as follows:
Equation 2:
Equation 3:
In formula (2) and (3), cooling requirement is calculated by CDD and heating requirement is calculated by HDD for a given period of N days. In these formulae, T is the average daily temperature and è is the base temperature that with regard to the threshold of different percentiles, different numbers are proposed for each station.
Findings
Findings of this section showed that Shiraz and Esfahan have experienced the most ideal conditions of comfort with 35.22 and 33.22 percent of frequency of days in the comfort zone respectively and Babolsar with 83.2 percent of frequency has had the lowest percentage of days with thermal comfort. Among the observational stations, the most frequent occurrence experience of frost and freezing belongs to Sanandaj, and for the stations in Makoo, Shiraz, Tehran and Tabas, the most important preventive factor for the occurrence of comfort conditions is frost and freezing. But, Jask and Bushehr have had the most experience of the days with heat stroke risks and this factor is the most important preventive factor for comfort in these two stations. Although extreme dryness is the most important preventive factor for comfort in Ahvaz, but in Rasht and Babolsar, excess moisture is the most important factor of the lack of comfort. The results indicated that Olgyay diagram has perfectly shown the climatic and bioclimatic differences of various regions. For example, for the coastal cities of the Persian Gulf and Oman Sea, the type of data distribution on the diagram showed that climatic and bioclimatic characteristics of the two cities of Bushehr and Jask differ from Ahwaz, so that the dominant climatic regime of Bushehr and Jask due to the high humidity experience, are affected by the water zone of the Persian Gulf and Oman Sea, but Ahwaz is affected both by the water body of the Persian Gulf and hot and dry systems that pass directly through the Saudi Arabia.
Conclusion
Based on the main objective of this research, new thermal comfort thresholds for all study stations were proposed and the results showed that according to various percentiles, minimum base temperature for calculating HDD belonged to Babolsar station and maximum base temperature for calculating CDD belonged to Shiraz station. It is also worth noting that the sensitivity of the proposed method is such, that minimum differences in the domain and base temperature of thermal comfort are visible even for the stations located in a nearly similar geographical area, and this could indicate the validity of the proposed method. Finally, monthly and annual long-term average of HDD and CDD indices were calculated for the studied cities using proposed thresholds and base temperatures. The results of this section showed that in most observational stations, the months of January, December and February have had the maximum HDD requirements and the maximum CDD requirement was calculated for the months of July and August. The research findings reveal that maximum average annual HDD and CDD requirements belong to Makoo and Jask respectively. The results of this study point to the fact that the need for heating energy has been higher than the need for cooling energy for most of the studied cities. Therefore, the findings show that, based on the proposed method, which is derived from the climatic characteristics and experimental data of each station, a more logical thermal comfort thresholds for the studied stations are presented.
Abdolazim Ghangormeh; Javad Malek
Abstract
The Caspian Sea like all other seas andgreat lakes is constantly turbulent under the influence of various hydrodynamic phenomena such as storm tide, storm surges caused bythe wave and wind, rise of wave in the coastal zone, and the phenomenon of resonance.But the reason for all of these phenomena is ...
Read More
The Caspian Sea like all other seas andgreat lakes is constantly turbulent under the influence of various hydrodynamic phenomena such as storm tide, storm surges caused bythe wave and wind, rise of wave in the coastal zone, and the phenomenon of resonance.But the reason for all of these phenomena is the forces caused by the air pressure and wind fields on the sea.In this research, the effects of air and wind pressure data (components) on observational levels in the level measuring stations of Bandar Anzali, Noshahr, Bandar Sadra (Neka) and Ashuradeh were investigated.For this purpose, the hourly data of the water level of the aforementioned stations during a four-year period (2000-2003) and also the data of air and wind pressure on the Caspian Sea were used with temporal-spatial resolutions of 6 hours and 0.25 degrees (25 km) during a 4 year period onwards.To study the range of the effects of these factors (air andwind pressure) on the data of the level measuring stations, the 50x50 to 250x250 km networks (with 50km sequence) were extended towards the main body of the sea. A linear multivariate regression model was used to determine the coefficients and the amount of the effect of wind and air pressure components, and the results were obtained monthly for each station. The results show that, by modeling a wider field of air and wind pressure effectiveness on the water zone overlooking each station, the calculated and observed results of the water level data are more correlated, so that the maximum correlation coefficient for the 250x250 km field for the Anzali stationsis 0.864 in November, for Noshahris 0.909 in November, for Sadra (Neka) is 0.893 in March and for Ashoradeis 0.873in April.
