Growing concerns on energy consumption of buildings by heating and cooling applications have led to a demand for improved insulating performances of building materials. The establishment of thermal property for a building structure is the key performance indicator for energy efficiency, whereas high accuracy and precision tests are required for its determination which increases time and experimental costs. The main scope of this study is to develop a model based on artificial neural network (ANN) in order to predict the thermal properties of concrete through its mechanical characteristics. Initially, different concrete samples were prepared, and their both mechanical and thermal properties were tested in accordance with ASTM and EN standards. Then, the Levenberg–Marquardt algorithm was used for training the neural network in the single hidden layer using 5, 10, 15, 20, and 25 neurons, respectively. For each thermal property, various activation functions such as tangent sigmoid functions and triangular basis functions were used to examine the best solution performance. Moreover, a cross-validation technique was used to ensure good generalization and to avoid overtraining. ANN results showed that the best overall R² performances for the prediction of thermal conductivity, specific heat, and thermal diffusivity were obtained as 0.996, 0.983, and 0.995 for tansig activation functions with 25, 25, and 20 neurons, respectively. The performance results showed that there was a great consistency between the predicted and tested results, demonstrating the feasibility and practicability of the proposed ANN models for predicting the thermal property of a concrete.

The time lag (TL) and decrement factor (DF) are essential for the heat storage capabilities of building elements, which strictly depend on the thermophysical properties of the elements. Many investigations are presented in literature arguing to find the influence of each thermophysical property on TL and DF by keeping the other properties constant. This study aims to investigate the effect of each property on TL and DF, utilizing relationships between the measurement values of the thermophysical properties of wall materials. Therefore, first, 132 new concrete wall samples were produced, and their thermophysical properties were tested. Secondly, TL and DF values for each building element are computed from the solution of the problem by Complex Finite Fourier Transform (CFFT) technique. Finally, a multivariate regression analysis has been performed, and the variations of each thermophysical property versus TL and DF are presented, and also the findings are compared with literature. The results show that each property alone (keeping the other properties constant) is not adequate to identify the thermal inertia and thermal performance of a wall element. Besides, 87.3 % decrease in thermal diffusivity corresponds to 6.03 h increase in the value of TL and 88.8 % decrease in value of DF; respectively, for W1 wall assembly.

Bu çalışmada, hava giriş konumu ve hızının, hesaplamalı akışkanlar dinamiği (HAD) simülasyonları kullanılarak elektrikli araçlarda (EA'lar) nikel-metal hidrür (Ni-MH) batarya paketinin soğutma performansı üzerindeki etkisi incelenmiştir. EA'lara olan talebin artması, araçların güvenliğini sağlamak için gelişmiş batarya soğutma performansına ihtiyaç duyulmasına neden olmuştur. Ni-MH batarya takımının soğutma performansı, batarya takımının 0 ila 60 mm arasında değişen giriş konumları ve sabit çıkış konumu ile hem U hem de Z kanalı geometrilerinde analiz edilmiştir. Hava giriş hızları 2 ila 6 m/s arasında değişmektedir. Sonuçlar, hava giriş konumu arttıkça sıcaklık değerlerinin düştüğünü, ancak 30 mm'lik giriş konumundan sonra düşüşün önemsiz hale geldiğini göstermektedir. Bu, hava akışını batarya takımını tamamen dolaştırmaya ve ısı transfer hızını artırmaya yönlendirmenin, hızı artırmaktan daha etkili olduğunu göstermektedir. En iyi soğutma performansının sırasıyla 316,86-327,75 K ve 316,27-317,46 K sıcaklık değerleri ile hem U hem de Z tipi kanallar için, 30 ve 60 mm giriş konumunda ve 6 m/s hızında olduğu bulunmuştur. Ek olarak, Z-tipi kanalın U-tipi kanaldan yaklaşık %6 daha fazla ısıyı dağıttığı görülmüştür. Çalışmanın sonuçları, EA’larda batarya ısıl yönetim sistemlerinin (BIYS) enerji verimliliğini artırmak için kullanılabilir. Havayı daha düşük hızlarda yönlendirerek, soğutma sisteminin enerji tüketimi azaltılabilir ve gerekli soğutma performansı korunabilir. Bu, sonuçta EA'ların menzilinin artmasına ve performansının gelişmesine yol açacaktır. Ek olarak, çalışma aynı zamanda EA’larda soğutma performansını optimize etmek ve araçların genel enerji verimliliğini artırmak için kullanılabilecek batarya paketi düzeninin tasarımı hakkında da fikir vermektedir.

The estimation of the cooling load through the building envelope is an essential task in the selection of proper HVAC system components that influences the building’s performance. For this task, ASHRAE has presented several methods to calculate the building cooling load due to heat gain, such as the total equivalent temperature difference method (TETD), the cooling load temperature difference method (CLTD), and the radiant time series method (RTS). The present study aims to explore the accuracies of those calculation methods in terms of energy efficiency. In this regard, an analytical solution method utilizing Complex Finite Fourier Transform Technique (CFFT) was developed for the calculation of cooling load due to heat gain to compare the temperature differences obtained from the TETD and CLTD methods. Then, a computer program was prepared in MATLAB to perform the calculations based on an analytical methodology. Besides, the estimated CLTD and TETD values by the CFFT were compared with those values presented in the Handbook of the ASHRAE. The calculation results revealed there is a good agreement between the analytical and presented results in the ASHRAE Manual for the selected building envelopes. However, several differences were found between the estimated TETD and CLTD cooling load values and those presented in the Handbook of ASHRAE.