Developing Advanced CAE Model for Door Glass Regulator Operating Noise Reduction through TEST-CAE Correlation

In recent decades, Computer-Aided Engineering (CAE) has become increasingly critical in the early stages of vehicle development, particularly for performance improvement and weight optimization. At the core of this advancement lies the accuracy of CAE models, which directly impacts design insights and reliable TEST-CAE correlation. Yet, accurately replicating real-world physical systems in virtual environments remains a significant challenge. This research introduces a structured methodology for improving correlation in door system models. It focuses specifically on reducing glass regulator operating noise, a common design issue that can lead to unwanted sounds and passenger discomfort. Traditional CAE models often fail to predict this problem, exposing the limitations of virtual-only validation. To address this gap, the study proposes a modal correlation-based approach aligned with actual assembly stage conditions. This strategy enables more precise assessment of the glass regulator’s operating behavior, substantially improving the correlation accuracy when compared to the initial model. The results affirm that this enhanced approach offers a reliable means of detecting noise issues early in the product development cycle. The refined model increases predictive accuracy and lays the foundation for improved cost efficiency, weight reduction, and passenger satisfaction. This methodology holds promise for advancing virtual validation practices in the automotive industry, providing engineers with powerful tools to optimize design and ensure higher-quality outcomes from the earliest development phases.