Experimental and simulation study on temperature rise prediction of spiral bevel gears based on similarity theory

Abstract

The temperature rise of the tooth surface in spiral bevel gears plays a crucial role in lubrication performance and surface failure. However, existing studies primarily investigate scuffing by analyzing tooth surface temperature through experiments and simulations, without using similarity theory to examine comparable systems. By leveraging similarity theory, researchers efficiently translate insights from controlled experiments to real-world applications, fostering innovation while conserving resources. Similarity theory is used in the study to analyze the temperature rise of the tooth surface, and it is possible to determine that gears in different systems may exhibit analogous thermal behavior under specific scaling conditions. A thermal fluid–structure coupled model is employed to conduct a precise analysis of the original system for thermal assessment and experimental validation. Similarity theory effectively predicts tooth surface temperature rise and optimizes lubrication strategies. Notably, the temperature rise is more pronounced near the tooth crest. The maximum temperature in the similarity model reaches 140.59 °C, while that in the original model is 134.5 °C. The deviation between simulation and experimental results for the original model is 6.43%, and the discrepancy between the original and similarity models remains within 4.53%. This similarity-based modeling approach accurately captures the thermal behavior of analogous systems, significantly reducing the cost of manufacturing test gears and the workload associated with tooth surface temperature experiments and simulations.