Redesigned Optimus 2020.1 streamlines Process Integration and Design Optimization process

Leuven (Belgium), April 8 2020 – Noesis Solutions announces the availability of Optimus 2020.1, featuring a user-centered redesign of the entire working environment. Intelligent context-dependent toolbars ensure that relevant functionality is always close at hand whenever users need it. In addition to this game-changing UI redesign and new or enhanced interfaces for JMAG Designer, Motor-CAD, Cradle scSTREAM & scFLOW, and Solidworks, Optimus 2020.1 introduces powerful Multi-fidelity Kriging modeling and Multi-thread model building.

A new standard in usability

Optimus’ re-engineered user interface goes far beyond a new set of clean, modern icons. Above all, it streamlines how users interact with Optimus’ powerful Process Integration and Design Optimization (PIDO) capabilities. Intelligent context-dependent toolbars are at the core of this redesign as they organize Optimus functionality according to the Automate – Discover – Postprocess steps that build a PIDO process. Whether users are looking for functionality to create or edit an engineering simulation workflow, create an optimization method, or post-process results, the new Optimus 2020.1 release lets them achieve this incredibly fast.

New interface for optimizing electric machine design

In addition to enhanced interfaces for JMAG Designer, Cradle scSTREAM & scFLOW, and Solidworks, Optimus 2020.1 brings a new interface for Motor-CAD. Motor-CAD is the world-leading dedicated electric motor design software package for the multiphysics simulation of electrical machines across the full torque-speed operating range. With this new interface, engineers interact with Motor-CAD design parameters and analysis results directly from Optimus. This tight integration empowers them to get from concept design to the final optimized electric machine design in less time than ever before.

Introducing Multi-fidelity Kriging modeling and Multi-thread model building

Optimus 2020.1 Multi-fidelity Kriging modeling enables users to fuse data from different sources into a single model. Data fusion combines experiments belonging to different data sets or methods while accounting for a fidelity score assigned by the user. Multi-thread model building uses modern CPUs’ multithreading capability to boost the creation of (Multi-fidelity) Kriging, Radial Basis Function (RBF), Deep Neural Network (DNN) and Relevance Vector regression (RVR) models. Model-thread model building performance is subject to the hardware that is used.