Design Optimization

Design Optimization aims at reducing the time required to engineer a new product, when successively passing through research, virtual prototyping and production. At every stage, specific design optimization aspects help you gain deeper insight into the model behavior. A good statistical understanding makes it a lot easier to improve the performance of your product. Noesis Solutions can be your partner in design optimization.

Design Optimization

Design exploration

Thanks to parametric modeling, design exploration techniques follow a systematic, mathematical approach to acquire model behavior to the maximum extent. Design exploration is a powerful asset to the design optimization process because it can be started at the earliest stages of development, requiring minimum computational effort.

  • Gain a deep understanding of design statistics
  • Identify variation sources through intelligent sampling
  • Obsolete trial-and-error in generating variant designs
  • Eliminate costly or useless design variants instantly
  • Identify the most important influencing factors
  • Identify critical points for creating an accurate mathematical model
  • Provide a set of interesting starting points for design optimization
  • Check the robustness of a solution

Design optimization

By minimizing, maximizing or zero-mizing the design manually or automatically, numerical optimization techniques help you improve the performance of the original configuration. With the recent quest for a sustainable future, manufacturers everywhere seek to reduce the ecologic footprint of their products without compromising performance characteristics. Finding the right balance between environmental, social and economic considerations translates into a truly, multi-disciplinary optimization process.  Noesis Solutions software leverages numerical optimization techniques to help you strike this balance.

The ability to create and manufacture products that are appealing and sustainable at the same time, requires numerical optimization involving multiple disciplines. During the design phase, each discipline is minimized, maximized or zero-mized sequentially, to learn how the design can be improved. The information picked up supports smart design optimization decisions early on in the process.

  • Build a better-performing design faster and more affordably
  • Reach a design’s ultimate performance
  • Meet the optimum and fit the constraints
  • Balance conflicting objectives
  • Obtain a trade-off between multiple simulation disciplines
  • Troubleshoot an attribute-driven discipline
  • Use simulation models to correlate measurements
  • Calibrate simulation models to maximize virtual simulation accuracy