Data Driven CAE

Solution Description

Auto OEMs have been using Computer Aided Engineering (CAE) for vehicle development and virtual validation for many years. The current changes in trend and market competition in developing electrical, autonomous and eco-friendly cars are forcing automakers to explore optimized methodologies that reduce time & cost in product development. While it is clear that CAE helps produce better quality vehicles with greater reliability and durability, there are a number of challenges in data management. These include:

Complex and heterogeneous data manipulated through distributed workflows
Compatibility issues, software dependency to access output and different file format across various CAE software
Demand for more customized scripting for process automation that makes the process expensive
Enormous manual intervention required for interpretation of results.

EinNel’s Data-driven CAE platform

EinNel data-driven CAE solutions elevate auto industries from Simulation-Driven Engineering to Objective-Driven Engineering by utilizing the power of Big Data, Artificial Intelligence (AI) and GPU computing.

We provide Big Data solution to store, manage and to perform scalable query-driven processing of scientific data. EinNel’s CAE data lake ingests both structured and unstructured numerical simulation data from automotive Crash, Durability, NVH, Aerodynamics, and UHTM.

EinNel AI based Data driven CAE platform gives flexibility in carrying out DoE explorations, sensitivity analysis, MOO, MDO and reliability studies more efficiently and economically without any constraints to identify the optimal robust model.

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Big Data solutions

Sift through large volumes of data and utilize analytics & insights. Extract from output files across different CAE software for centralized cloud storage.

CAE Data Warehouse

Data Segregation for post processing for various analyses. Data filtration based on specific developmental requirements.

Machine Learning for CAE

Stochastic approach for Design Space exploration. Make use of EinNel’s AI platform to predict performance parameters with high accuracy using less computing and processing time.

Design Sensitivity

Identification of critical components/parameters related to each response value, constraint and objective with the help of ML models.

Design Optimization

Make use of EinNel’s AI platform to Identify optimal robust models with all the critical design parameters in consideration.

Design Data Visualization

ML models visualized for stress, strain, displacement etc. Analyze all the available design parameters in customized and intuitive visualizations.

Case Study