The world is moving towards data driven evolution with wide usage tools & techniques like Artificial Intelligence, Machine Learning, Digital Twin, Cloud Computing etc. In automotive sector, the large amount of data being generated through physical and digital test evaluations. Computer-Aided Engineering (CAE) is one of the highest contributors for data generation as physical testing involves high cost due to prototypes & test set-up.
The Automotive Noise, Vibration & Harshness (NVH) field is advancing exponentially due to new stringent regulatory norms & customer preferences towards comfort, where digitally advanced techniques are playing a key role in the revolution of NVH. Data generation through CAE tool is a crucial aspect of Engineer’s daily activities and selecting such appropriate CAE software and solvers is critical, as it influences user interface experience, accuracy, solution time, hardware requirements, variability expertise, Design of Experiments ability, and integration with other environments.
This study is intended to evaluate and compare these key parameters across leading software and solvers within the automotive NVH CAE domain, using a vehicle finite element model. This paper references the development of a comprehensive matrix which assists engineers in making intended decisions while selecting CAE software and solvers tailored to their specific needs. By using this engineers can improve their proficiency in different analysis with optimized solution time. It also help to identify seamless integration with existing system. This ultimately improves the overall efficiency and effectiveness of their CAE processes. Additionally, the matrix aids software vendors in identifying gaps in existing capabilities and aligning their offerings to meet the needs of CAE engineers.