Browsing by Author "TURHAN, Cihan"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item INVESTIGATION OF THE EFFECTS OF MOOD STATES ON USERS’ THERMAL COMFORT(2022-06-14) ÇETER, Aydın Ege; TURHAN, Cihan; ALKAN, NeşeProviding indoor thermal comfort to the users has a great importance since most of the people spend 90% of their time in interior zones. On the other hand, accurate calculation of the thermal comfort has been a problematic issue and is the first priority in providing thermal comfort. Recent studies in this field have shown that there is a vital difference between the calculated thermal comfort and thermal sensation. Additionally, researchers have investigated the effects of human psychology, which is thought to be one of the important reasons for this difference. However, ongoing studies have only addressed the physical responses of the human body under psychological disturbance. Moreover, the mood state of the people is an important parameter of the human psychology. Therefore, this thesis investigates the effect of mood states on users’ thermal sensation and offers a novel “Mood State Correction Factor (MSCF)” on the existing thermal comfort model. For this reason, a series of experiments were conducted in a study hall of Atılım University between 16th of August, 2021 and 15th of April, 2022. Predicted Mean Vote (PMV), Actual Mean Vote (AMV) and Profile of Mood States (POMS) were used to investigate the effect of mood state on the thermal sensation. The results of the study indicated that the most effective mood state classes on thermal sensation of the users are very pessimistic, very optimistic and pessimistic, moreover, the users feel significantly warmer than the measurement results. Additionally, the MSCFs are calculated as -0.125, -0.114 and -0.075 for very pessimistic, very optimistic and pessimistic mood state classes, respectively.Item SELECTION OF THE BEST HVAC SYSTEM FOR INDUSTRIAL BUILDINGS BY USING MULTI-CRITERIA DECISION-MAKING TOOLS(2022-01-20) ALALOOSI, KHALID; BAÇ, Uğur; TURHAN, CihanHeating Ventilating and Air Conditioning (HVAC) selection is a difficult task, especially when the selection relates to technical, economic, and environmental criteria. HVAC systems are responsible for 50% of total energy consumption in buildings and play a major role in the ability to reduce harmful emissions. The great demand for energy and the upward trend in the use of HVAC systems with the global need to impose measures on environmental hygiene underlines and focuses the importance of choosing the most appropriate HVAC system during the design process. The case study of an industrial building in Ankara has several machines studied. The Design Builder (DB) program was used in the dynamic building energy simulation and knowledge of the total energy consumption of the building. Stepwise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods has been applied for the purpose of decision making due to its ability to accurately weigh criteria and to choose the most appropriate alternative through subjective and objective assessments with great accuracy. WASPAS is characterized by its ability to facilitate the effective data and vague information and to provide a good systematic decision-making analysis. Eleven HVAC systems were examined based on 27 criteria, including technical, environmental, and economic criteria. The results of selecting the best HVAC system using SWARA and WASPAS showed Sensitivity analysis performed according to different lambda values. The (Water-source Heat Pumps) is preferred at the first rank in all cases and it was identified as the best alternative for industrial buildings. It has been determined that the choice of alternative is economically, technically and environmentally most logical.Item THERMAL COMFORT INVESTIGATION AND RETROFITTING STRATEGIES OF AN EDUCATIONAL BUILDING(2022-03-01) Ghazi, Sanarya; TURHAN, CihanIn terms of global sustainable development, buildings are one of the largest energy consumers. Although technology advancements actively assist in constructing environmentally friendly buildings, current structures still consume a large amount of energy. Thus, we shall investigate educational facilities, one of the essential architectural types. It is vital to establish high-quality school structures to give a high-quality education to future generations. While numerous factors influence the building, thermal comfort significantly impacts the pupils. The pleasure a person feels in their thermal environment is thermal comfort. A suitable temperature environment aids physical and mental well-being. This study considers these aspects and attempts to evaluate the possibility of improving thermal comfort in educational buildings by making minor changes to the architecture rather than reconstructing them. At Atilim University in Ankara, Turkey, Design-Builder Software assessed an existing building model. The simulation was then run on the building's adjusted cases, totally seven retrofitting cases. Changing the window and frame types, as well as installing a Trombe wall, are some of the retrofitting options. In addition, the insulation material was replaced with three different materials in each case. A solar collector was added, the set temperature and airtightness were changed, and the light systems were changed to the led type. The Design-Builder ran the model for annual energy usage and recorded the result considering the building's modification. We conducted a comparative examination of the cases. The most compelling case for student thermal comfort was the use of Rockwool insulating material, which reduced student discomfort hours by 17% and was also the most effective for lowering CO2 emissions and energy consumption, none of the instances affected airtightness. Furthermore, using a solar collector was the most expensive choice.Item UTILIZING ARTIFICIAL NEURAL NETWORKS TO PREDICT HUMAN BODY EXERGY CONSUMPTION(2022-03-10) Layth, Yousif; TURHAN, Cihan; LOTFISADIGH, BahramThe American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) defines thermal comfort as "the state of mind that conveys happiness with the thermal environment”. Energy and Matter can scatter as a system and move toward equilibrium with their surrounding environment, and this is referred to as exergy in thermodynamics. Predicted Mean Vote (PMV)/Percentage of Predicted Dissatisfied (PPD) model and adaptive thermal comfort approach are the two most widely used methods for assessing thermal comfort. However, it is also possible to apply the exergy notion to the human body system as an index of thermal comfort. The relationship between a person's exergy balance and their level of thermal comfort is that effectively dissipating heat and water from the body is essential to human well-being. For this reason, the lowest human body exergy consumption rate mostly gives the optimum thermal comfort level. In this thesis, an Artificial Intelligence-based work was conducted in a room of engineering faculty of the Atilim University, Ankara, Turkey, with an occupant being inside the room to obtain the best condition for his exergy and thermal comfort. Human body exergy consumption is extracted via a computer programme and environmental parameters are measured by objective sensors. Then, an Artificial Neural Network (ANN) model is developed in Python environment. A back propagation and sigmoid function is used in the neural network technique. A total of 133 data are included in the ANN model, with 75% (99 datasets) being used for training and the remaining for testing. A Mean Absolute Percentage Error (MAPE) of 1.98 and an accurate prediction rate (R2 ) of 0.91 are found under the provided conditions, indicating a good coordination between the artificial neural network model outputs and the human body exergy data. Simplicity, speed of analysis, and learning from restricted data sets are all features of an ANN model over human body exergy simulation. This thesis presents a novel concept that uses an ANN model to determine how much exergy rate people consume (HBExC). This is because artificial neural networks (ANNs) are the most commonly used artificial intelligence technique in the field of buildings and thermal comfort fields. After all, they can handle nonlinear variables' interactions rapidly and correctly, especially, exergy concept which has complex nonlinear relationships between its variables.