Akademik Veri Yönetim Sistemi
MEASUREMENT, cilt.240, 2025 (SCI-Expanded)
From advanced fairing designs and spoilers to novel airflow management systems, the quest for drag reduction (DR) in truck trailers is critical for cleaner and more sustainable transport. In this study, a particle swarm optimization (PSO) integrated machine learning approach is developed to optimize the DR performance of a bare truck trailer (BTT) with and without half airfoils and roof fairings. The study aims to reach the optimal truck trailer design with fewer experiments by searching the entire solution space with machine learning methods thanks to the developed approach. For this purpose, the experimental setup was initially designed and the experimental results were obtained. The experimental inputs are Airfoil thickness, Roof Fairing (RF) positions, and Reynolds number, while the experimental output is the drag coefficient. These inputs and outputs were arranged via data preprocessing and then modeled using machine learning methods. Modeling was performed with Artificial Neural Network (ANN) and multiple regression models. In addition to the ANN model setup with appropriate topology, linear and non-linear multiple regression models were established. Comparisons were performed using statistical metrics and it was determined that ANN was the superior model. Using the ANN's prediction model, the drag coefficients were derived from the whole parameter values of both airfoil thickness and RF positions in the solution space for all Reynolds number and an optimization process was performed by PSO. As a result of the analysis, optimal parameter values were determined as 0.10 and 5.0 mm for the airfoil thickness and RF position. As a result of the experimental validation, it is seen that the BTT_NACA 0010_RF-0.5 model, designed with the optimum parameter values, demonstrated a DR performance of 34.7 %, which is the highest value compared to others. Thus, the heuristic process was experimentally validated and optimized by saving time and cost.