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Öğe An experimental investigation of the effect of thermophysical properties on time lag and decrement factor for building elements(Gazi University, 2020-06-01) Oktay, Hasan; Yumrutaş, Recep; Argunhan, ZekiThe 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.Öğe Prediction of the thermal conductivity of lightweight building materials utilizing backpropagation neural network method(Fırat Üniversitesi, 2015) Oktay, Hasan; Polat, Süleyman; Fidan, ŞehmusThe growing concern about energy consumption of heating and cooling of buildings has led to a demand for improved thermal performances of building materials. In this study, an experimental investigation is performed to predict the thermal insulation properties of wall and roof structures of which the mechanical properties are known, by using backpropagation artificial neural network (ANNs) method. The produced samples are cement based and have relatively high insulation properties for energy efficient buildings. In this regard, 102 new samples and their compositions are produced and their mechanical and thermal properties are tested in accordance with ASTM and EN standards. Then, comparisons have been made between the determined thermal conductivity of the newly produced structures, which are obtained from experimental method and ANN method that uses mechanical properties as input parameters. From the test results, since the percentage errors in the thermal conductivity values between experimental data and neural network prediction vary from - 1.09% to 6.4%, It can be concluded that the prediction of the artificial neural network has proceed in the correct manner.Öğe Experimental and articial neural network based studies on thermal conductivity of lightweight building materials(European Journal of Technique (EJT), 2017-04-01) Oktay, Hasan; Fidan, Şehmus; Sevim, Davut; Polat, SüleymanThe growing concern about energy consumption of heating and cooling of buildings has led to a demand for improved thermal performances of building materials. In this study, an experimental investigation is performed to predict the thermal insulation properties of wall structures of which the mechanical properties are known; by using Levenberg-Marquardt training algorithm based artificial neural network (ANNs) method for energy efficient buildings. The produced samples are cement based and have relatively high insulation properties for energy efficient buildings. In this regard, 102 new concrete samples and their compositions are produced and their mechanical and thermal properties are tested in accordance with ASTM and EN standards. Then, comparisons have been made between the experimental results and the ANN predicted results. It can be concluded that thermal performance of lightweight materials could be predicted with high accuracy using artificial neural network approach.Öğe Effects of fuels produced from fish and cooking oils on performance and emissions of a diesel engine(Elsevier, 2014-07-15) Oktay, Hasan; Yumrutaş, Recep; Behçet, RasimIn this study, two fuels called as FOME (Fish Oil Methyl Ester) and COME (Cooking Oil Methyl Ester) were produced from waste fish and cooking oils using the transesterification method. Commercial D2 (Diesel fuel) and two fuel samples obtained by blending the FOME and COME with the D2 with a ratio of 25% on volume basis were used as fuels in a Diesel test engine. An experimental study was performed for investigating the performance and exhaust emissions of the Diesel engine using the fuels. According to the test results, it was observed that the fish oil based fuel indicated better performance and exhaust emission parameters than those of cooking oil. Results clearly showed that the engine power and torque values were lower than those of the Diesel fuel with values of 3.05% and 1.25% for FB25, and 4.07% and 2.2% for CB25, respectively. Also, brake specific fuel consumption for the produced fuels increased up to 5.69% compared to Diesel fuel. However, HC and CO emission reductions compared to the Diesel fuel were found to be around 16.24% and 19.81%, respectively. But, the amount of increase in NOx emissions for the same biodiesel fuels reached up to 17.2%.Öğe Investigation of the thermal and acoustic performance of perlite- based building materials(European Journal of Technique (EJT), 2016-12) Oktay, Hasan; Argunhan, Zeki; Doğmuş, RecepPerlite is a siliceous volcanic glass that provides heat and sound insulation, whose volume can expand substantially under the effect of heat. Perlite ore is one of the most important mineral resources for Turkey where holds a large portion reserves in the world. Evaluation of perlite in building industry, which has advantages in terms of heat and sound insulation, will make an important contribution to the national economy. In this context, experimental investigations are performed for obtaining new concrete types with relatively high strength, low density and good thermal and acoustic properties for energy efficient buildings. For this purpose, 6 sets and different types of concrete samples were prepared with a constant watercement ratio, and normal aggregates replaced by expanded perlite aggregates at different volume fractions such as 10%, 20%, 30%, 40%, 50% and 60% of the total aggregate volume. Mechanical and thermal tests were all conducted and the hot disk method was used to establish thermal property values of concrete samples. The results of the experimental studies show that the compressive strength and density decreases, while highly increases the heat and sound insulation features with increasing in perlite content. As a result, it was found out that the reductions in thermal conductivity [Wm–1K–1] and ultrasonic pulse velocity [km/s] of the produced samples reached to 75% and 35%, respectively.Öğe An artificial neural network model to predict the thermal properties of concrete using different neurons and activation functions(Hindawi, 2019-04-01) Fidan, Şehmus; Oktay, Hasan; Polat, Süleyman; Öztürk, SarperGrowing 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.Öğe Sample of Batman in determination of urban solid waste management and recycling potential(International Journal of Physical Sciences, 2012-11-16) Adin, Hamit; Oktay, Hasan; Topkaya, Tolga; Işık, Mehmet Zerrakki; Budak Ziyadanoğulları, NeşeThe collection, transport, treatment, and disposal of solid wastes, particularly wastes generated in medium and large urban centres, have become a relatively difficult problem to solve for those responsible for their management. However, recycling-related activities bring waste reduction, prevention of waste of raw materials and less environmental damage as well as providing an economic benefit to the countries. In this paper, a case study of a developing country has been examined dealing with serious pollution problems due to the ineffective management of the large solid waste generated in the city of Batman in Turkey. The aim of this paper is to estimate the quantity of waste produced that requires collection and the different waste constituents, to analyze the current practices of SWM, to propose an environmentally sound and economically feasible integrated management system for dealing with solid waste. Results showed that the average generation rate of MSW was 0.75 kg/capita/day in Batman and also, it has been anticipated that the wastes could be disposed by using modern methods instead of irregular storageÖğe Mechanical and thermophysical properties of lightweight aggregate concretes(Elsevier, 2015-10-15) Oktay, Hasan; Yumrutaş, Recep; Akpolat, AbdullahIn this study, experimental investigation is performed for producing new cement-based with relatively high strength, low density and good thermal properties for energy efficient buildings. Different types of concretes containing silica fume (SF), superplasticizer (SP) and air-entrained admixtures are prepared with a constant water–cement ratio, and normal aggregates replaced by lightweight aggregates (LWAs) including pumice (PA), expanded perlite (EPA) and rubber aggregates (RA) at different volume fractions of 10%, 20%, 30%, 40% and 50%. 102 samples with different materials and compositions are produced, and their characteristics are tested in accordance with ASTM and EN standards. Based on the experimental results, equations are presented to determine the relation between the thermophysical properties of composite samples. The investigation revealed that the addition of PA, EPA and RA reduced the material bulk density and compressive strength, and improved the insulation characteristics of the composite concretes. Furthermore, it was found out that the reductions in thermal conductivity and diffusivity of the produced samples reached to 82% and 74%, respectively.Öğe Comparison of exhaust emissions of biodiesel–diesel fuel blends produced from animal fats(Journals & Books, 2015-06) Aydın, Hüseyin; Behçet, Rasim; Oktay, Hasan; Çakmak, AbdülvahapThe present paper examines two biodiesels named as fish oil methyl ester (FOME) and chicken oil methyl ester (CFME) produced from low-cost waste fish and chicken oils using the transesterification method, and their fuel properties were compared to EN 14214 and ASTM D6751 biodiesel standards. Then, each methyl esters were blended with the commercial diesel fuel (D2) with a ratio of 20% on volume basis, respectively and two fuel samples named as FOB20 (20% Fish Oil Methyl Ester and 80% D2 fuel) and CFB20 (20% chicken oil methyl ester and 80% D2 fuel) were obtained. An experimental study for investigating the effects of the blended fuels on engine performance and its exhaust emissions was performed by using a single cylinder, four stroke, direct injection and air-cooled diesel engine at different speeds under full load. According to the test results, it was observed that the brake power, torque values and the carbon monoxide (CO), unburnt hydrocarbon (UHC) and carbon dioxide (CO2) concentrations of blended fuels decreased while the NOx concentration and brake specific fuel consumption (bsfc) values increased compared to diesel fuel.
