## 1 Introduction
The seven factors were evaluated at three levels and the number of experiments was reduced by using an L18 orthogonal array. The most effective factors in this study were determined as fan air capacity (36.24%) and blade pitch (30.73%). As a result, the highest temperature value on the heatsink was reduced by about 8 \({}^{\circ}\)C and an improvement of 15% was achieved. In engineering and scientific research, optimizing system performance often requires a structured approach to experimentation and data analysis. Design of Experiments (DOE) is a powerful statistical methodology used to systematically investigate the effects of multiple factors on a response variable, reducing the number of trials while maximizing the information gained. By utilizing techniques such as full factorial and fractional factorial designs, DOE enables researchers to identify key variables that influence system performance, leading to efficient decision-making in experimental studies. Analysis of Variance (ANOVA), on the other hand, is a statistical tool employed to evaluate the significance of differences between multiple sets of experimental data. ANOVA determines whether observed variations in a response variable are due to the influence of independent factors or merely the result of random fluctuations. In the study conducted by [PERSON] and others [11], the arrangement of racks, the location of cooling devices (CRAC units), and the spacing between racks in a raised-floor data center were examined. The DOE method was applied to determine the most effective factors, and a full factorial design was chosen, testing a total of 27 different combinations through CFD analyses. The results of these analyses revealed that corridor width had the least effect, while the positioning of the cooling devices had the greatest impact. Based on DOE and CFD results, the optimal combination was achieved by strategically positioning CRAC units and utilizing corridor containment. In the study conducted by [PERSON] and others [12], the cooling performance and temperature distribution uniformity of a battery pack were analyzed using DOE and Response Surface Methodology (RSM). A full factorial design was applied, and the obtained data were analyzed using the RSM method. The cartridge width, spacing between batteries, and the distance of the batteries to the wall were evaluated at three different levels (\(-\)1, 0, and 1). The results of the study indicated that the temperature difference between batteries was reduced by approximately 22%, achieving a more homogeneous temperature distribution. Due to the compact structure and high-performance requirements of electronic devices, effective management of the generated heat is critical for device reliability and lifespan. In the study, the effects of design parameters of the PPF heatsink module on cooling performance were investigated using the Taguchi method and Analysis of Variance (ANOVA). Seven factors were evaluated at three levels, and the number of experiments was reduced using an L18 orthogonal array. The most influential factors identified in the study were fan air capacity (36.24%) and fin spacing (30.73%). As a result, the maximum temperature on the heatsink was reduced by approximately 8 \({}^{\circ}\)C, achieving a 15% improvement in performance. In the study conducted by [PERSON] and [PERSON]. [13], the heat transfer performance of a vertical sinter cooling bed was optimized using the Taguchi method and ANOVA. The study analyzed the effects of structural and operational parameters influencing gas-solid heat transfer in the sinter cooling bed. The investigated parameters included gas flow rate, air inlet temperature, sinter inlet temperature, and the diameter and height of the cooling bed, each evaluated at three levels. The Taguchi method was applied using an L27 orthogonal array, and the most significant factor was determined to be sinter inlet temperature, contributing 61.65% to the overall effect. As a result, exergy recovery increased by 1.29 times, and heat transfer efficiency (gc) improved by 4%.