Yokohama develops simulation technology for multi-objective design
3 Dec 2015
Tokyo – Yokohama Rubber Co. Ltd has developed a simulation technology for multi-objective design exploration of rubber materials, the company announced on 2 Dec.
The new technology was developed to create tires that meet standards in areas that normally are at odds with each other, such as low fuel consumption and safety or ultralight weight and high rigidity.
The new simulation technology, said Yokohama, creates rubber material models based on virtual morphologies, enabling simulations of various morphologies.
The new method differs from the previous simulation technique that assumed the use of actual rubber materials.
According to Yokohama, the new technology can allow the changing of morphological parameters (variables), enabling users to “create huge-scale simulation models consisting of about one billion elements with various morphologies”.
A performance evaluation run on the Tsubame2.5 supercomputer in the Tokyo Institute of Technology, confirmed that the new simulation technology can complete huge-scale computations consisting of one billion elements in 75 minutes.
YRC went on to explain that the challenges to establishing the simulation technology were the modelling technology that enables complete control of the morphology and the large-scale viscoelastic simulation technology needed to calculate the mechanical properties of rubber material.
To address the problem, Professor Dominique Jeulin of MINES ParisTech and Centre de Morphologie Mathematique (CMM) in France developed a modelling technology, in a joint research, which offers a random morphological model and a new computational scheme for large-scale viscoelastic simulations.
The establishment and combination of these two technologies, said YRC, provided the finishing touches to Yokohama Rubber’s simulation technology for multi-objective design exploration of rubber materials.
Multi-objective design exploration is a technique for deducing knowledge useful to the design process.
The technique focuses on organisms’ evolutionary processes and uses a multi-objective genetic algorithm to search for more optimal solutions.