This study was carried out in order to determine the indoor air quality of the classrooms existing in university (Turkey). Relative humidity, temperature, carbon dioxide, radon, and particulate matters (PM_0.5, PM_1.0, PM_2.5, PM_5.0, and PM₁₀) were taken into account as the parameters of indoor air quality measurements. The results obtained from the present work were interpreted by comparing them with the standards of different countries. The relations between all parameters were statistically examined by means of correlation and regression analysis in SPSS 17 statistical program. As a result, it was observed that indoor temperature was lower than the standards, yet carbon dioxide and PM values were higher than the upper limit, but relative humidity level was within comfort conditions. The average indoor radon concentrations were found to be below the recommended reference levels for International Commission on Radiological Protection (ICRP), yet it was seen that the results were relatively higher in comparison with the worldwide values. In addition, it was determined that there was a meaningful relation between outdoor relative humidity, indoor relative humidity, and particulate matters in different diameters. Some solutions were suggested for the treatment of the indoor air quality for each parameter.

Insulating is the most effective method that is used to save energy in buildings. Samples from cities from different climatic zones from TS 825 (Turkey) first. Optimum insulation thickness ( $x_{opt}$) analysis is based on two types of insulating and four different fuels (natural gas, coal, fuel oil and electric) of related cities. Cost accounts, payback period and CO₂-SO₂ emission calculations were performed based on these analyses. Second of all, the relationship between a number of degree day (NDD) and optimum insulation thickness ( $x_{opt}$) was developed by linear, quadratic and cubic correlations. Thirty different mathematical correlations based on different fuel types by using XPS and EPS insulating materials. Twenty-four of these models that were developed were generated peculiar to the fuel type; six of them were generated based on average insulation thickness. R² values of related correlations are between 0.9989 most and 0.9952 at least as well as it is pretty close to (R ≤ 1) one value. The model among these models is the $x_{opt}=a+b\left(NDD\right)+c{\left(NDD\right)}^2+d{\left(NDD\right)}^3$ cubic mathematical model that gives the best average value. a = 0.0036, b = 5E-05, c = – 7E-09 and d = 6E-13 are the values for XPS material. Following values are for EPS material; a = 0.0028, b = 5E-13, c = – 7E-09 and d = 4E-05. R² determination coefficient of both two equations is pretty close to 0.9989 and 1; the models obtained are less-than-stellar. Optimum insulation thickness of the area can be known based on the type of material via the number of degree day without the need for long analyses. According to the R² values, the use of all models is recommended for academic and industrial users.

A prototype of a solar chimney power plant was performed by modeling in this study. The performed prototype was experimentally confirmed. Temperature, velocity, and radiation values were measured to actualize the confirmation. Experimental data that were obtained to determine the performance of solar chimney whose prototype was actualized by the help of measured values were computationally analyzed. The geometry of a solar chimney in the analysis was bidimensionally (2D) drawn on an axis of symmetry. The numerical simulation was analyzed with computational fluid dynamics (CFD) method. Since analysis results show that there is turbulent flow in system (RNG), k-ɛ turbulence model was used. Continuity, momentum, and energy equations were applied to the solar chimney system via the finite volume method. Moreover, DO (discrete ordinates) model was inserted in analysis to evaluate the radiation effect in the collector area. In addition to all these, correlation results between SPSS 17 statistics program and data obtained were evaluated. Finally, with reference to the comparison between numerical and experimental results, data obtained and numerical data are close to each other; the prototype is applicable to the real systems.

In the pursuit of sustainable and clean energy, biofuels and nanofuels are increasingly investigated as practical solutions to improve diesel engine efficiency and emission characteristics. This study evaluates the effects of Nickel (III) oxide nanoparticles added to biodiesel (B20) and biodiesel–n-butanol (B20But10) blends on combustion, engine performance, emissions, and optimization using a single-cylinder, four-stroke, water-cooled, direct injection diesel engine. Experiments were conducted at multiple engine loads and nanoparticle concentrations ranging from 0 to 100 ppm. At full load and 100 ppm Ni₂O₃, peak in-cylinder pressure increased to 55.86 bar for B20 and 55.45 bar for B20But10, while maximum heat release rates reached 29.45 and 30.02 J/°CA, indicating enhanced premixed combustion behavior. Brake thermal efficiency increased to 24.89% for B20 and 24.94% for B20But10, accompanied by reductions in brake specific fuel consumption to 0.309 and 0.333 kg/kWh, respectively. Emission results showed reductions of 13–28% in HC, 8–43% in smoke opacity, and 12–21% in NO _x . Response surface methodology was employed to develop both performance- and emission-oriented predictive models, yielding high reliability (R ² = 90.9–99.9%) and identifying optimal nanoparticle levels between 50 and 75 ppm. Overall, Ni₂O₃-enhanced blends provide measurable performance and emission benefits without requiring engine modifications.