RESOURCE LIBRARY

Webinar

Accelerate design predictions with modeFRONTIER’s automated AI/ML workflow

Watch and learn how our AI/ML process data approach for engineering design optimization is orchestrated by modeFRONTIER’s workflow.

eBook

The path to faster, smarter automotive design

Accelerate vehicle design processes with ESTECO’s digital engineering solutions for multidisciplinary design optimization, AI data-driven modeling and simulation data management.

White paper

Enriching PLM’s Landscape with Multi-Physics Simulation Processes and Data

The use of simulations across the product lifecycle has been growing over the years. However, simulation artifacts have largely remained siloed in separate systems in design, testing, and validation workflows. Even proper organization of the data is lacking in most organizations.

Filter by type and industry

All resources

Showing 131 - 131 of 131 resources

Success story
Bombardier uses modeFRONTIER to optimize high-speed trains
Bombardier reduces aerodynamic drag by 20% and saves about the 10% of energy consumption with modeFRONTIER “Rail transportation is a concrete eco-friendly solution for sustainable mobility. Therefore, if we decrease the aerodynamic resistance of our trains, we can increase energy efficiency and further reduce CO2 emissions”, says Alexander Orellano, Head of Aerodynamics at Bombardier, world leader company in aerospace and rail transportation. ## Challenge Reducing energy consumption implies optimizing the aerodynamic shape of a vehicle, thus inevitably facing two main opposing factors: the best models for drag do not have a good crosswind stability, and viceversa. In addition, high passenger capacity conflicts with optimal aerodynamic shape and elegancy and functionality not always go hand in hand. These are some of the reasons that made Bombardier choose modeFRONTIER, the multi-objective, multi-disciplinary optimization platform, for their award-winning ZEFIRO 380 train design. ## Solution “The application of the multi-objective optimization method to Bombardier high-speed trains leads to a highly competitive product, entailing both to energy efficiency and cost reduction, due to a lower traction power dimensioning”, declared Mr. Orellano. The goal was to find a Pareto-optimal, or trade-off, design which would simultaneously give low drag and good cross-wind stability characteristics. The solution was obtained using modeFRONTIER, not only to integrate the various CAE tools in use at Bombardier, but also to drive the geometry modification and simulation process providing the necessary graphical tools for the statistical interpretation of results. modeFRONTIER, de-veloped by ESTECO, uses genetic algorithms to determine Pareto optimal solutions, combining 3D models and simulations of aerodynamic drag and crosswind stability. Bombardier experts considered as many as sixty different design parameters in the modeling phase, taking into account the train’s outer shell, cab, crash structure and ergonomic constraints. ## Benefits The company was therefore able to reduce the aerodynamic resistance by 20%, obtaining a decrease of energy consumption of about 10%. By using modeFRONTIER, Bombardier engineers were able to choose from a selection of designs in order to suit particular styling preferences, but secure in the knowledge that each complies with the principles of optimized energy performance and maximum stability and safety.