## 1 Introduction
In a study conducted by [PERSON] and [PERSON]. [14], the thermal performance of lithium-ion battery packs in hybrid electric vehicles was optimized using the DOE method to develop an air-cooled strategy. The Optimal Latin Hypercube DOE method was applied to analyze the effects of cooling channel geometry, cooling plates, and fluid guidance on the temperature distribution and pressure drop of the battery pack. By implementing a tapered design at the inlet and outlet of the flow channels, the temperature difference between battery cells was reduced from 10.8 \({}^{\circ}\)C to 3.6 \({}^{\circ}\)C, and the maximum cell temperature was decreased by 6.3 \({}^{\circ}\)C. Additionally, this design reduced the total pressure drop by approximately 24%. [PERSON] and [PERSON]. [15] used a Design of Experiments (DoE)- and computational fluid dynamics (CFD)-based methodology to achieve thermal optimization of next-generation network servers. In the study, the effects of different design parameters on the temperature distribution were analyzed using the partial factorial design method of DoE. In the DoE analysis, the main components affecting the cooling performance such as disc drives, CPU modules, DIMM memory modules, fans, and air diverters are considered. Thanks to the optimum parameters obtained by DoE, the number of physical prototypes was reduced from four to two, the efficiency of the cooling design was increased, and the cost was reduced. This study demonstrates that the use of DoE in combination with CFD-based analyses is an effective method to improve cooling performance in large-scale electronic systems. In the study by [PERSON] et al. [16], experimental and numerical simulation data were combined with the Design of Experiments (DOE) method to optimize the cooling systems of electronic devices. In the study, the main design parameters such as placement of electronic components, duct dimensions, flow rate, and heat input were tested by the partial factorial DOE method. With DOE analysis, the optimum layout configuration of the components is determined to minimize the surface temperature of the system. According to the results, with the optimum configuration, the electronic device surface temperature is reduced by 15% and the fan power requirement is reduced by 20%. The study accelerated the design process and reduced costs by halving the number of prototypes by using experimental and numerical simulation data together. This method demonstrates the effectiveness and cost advantage of DOE in the optimization process of electronic cooling systems. This study investigates how heatsink design parameters affect IGBT surface temperatures during induction hob operation, using FloEFD 2021 for numerical analysis. First, airflow measurements were taken using the Type 1 heatsink to evaluate measurement reliability. Based on these results, a Design of Experiments (DOE) study was conducted in Minitab v22.2 to optimize the measurement method and reduce operational variability. The DOE considered heatsink positions (aligned, angled, and offset from the fan) and probe stability (fixed vs. free). After identifying the optimal method, experiments were repeated for all three heatsink types (Type 1, Type 2, and Type 3). Air velocities were measured at multiple outlet points, with each test repeated three times by two operators. These repetitions supported both uncertainty analysis and measurement system evaluation. Uncertainty was calculated using both Type A (statistical) and Type B (systematic) components based on standard deviation and mean values. CFD simulations were then run for each heatsink to validate airflow behavior. Once validated, thermal loads from electronic components were applied, and IGBT surface temperatures were predicted. By analyzing measurement data and factor effects, the key parameters influencing cooling performance were identified. The combination of statistical analysis, DOE results, and CFD validation offers a complete evaluation of heatsink performance under real conditions.