LS-DYNA & JSTAMP Forum 2018

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Welcome to the LS-DYNA & JSTAMP Forum 2018

JSOL Corporation holds an annual LS-DYNA & JSTAMP Forum to provide our users a wide range of information including the latest simulation technologies and case studies and also to offer the opportunity for information exchange among our users.

This year the venue of the LS-DYNA & JSTAMP Forum 2018 moves from Tokyo to Nagoya. It will be held at NAGOYA TOKYU HOTEL, on Wednesday 31 October 2018. Our engineers will showcase the latest simulation technologies and poster sessions will be held. We welcome any inquiry, consultation and discussion about your day-to-day work.

We encourage our users to take advantage of this once a year opportunity. We look forward to your attendance in the event.

JSOL Corporation
Engineering Technology Division

General Session

Overview

Title:
LS-DYNA & JSTAMP Forum 2018
Dates:
Wednesday, October 31, 2018
Organizer:
JSOL Corporation
Venue:
NAGOYA TOKYU HOTEL
4-6-8 Sakae Naka-ku Nagoya Aichi 460-0008 Japan  map
Contents:
Conference / Partner exhibition / JSOL poster sessions
Expected number of participants:
400
Registration Fee:
Users : Free
Non-subscribed users : 50,000.-JPY (exc.tax)
* Please contact our sales staff or the event office for detailed information.
Register:
Pre-registration required.

Registration has now closed

For details about the LS-DYNA & JSTAMP Forum, please contact here.

Contact

Users Meeting Secretariat
JSOL Corporation, Engineering Technology Division

E-mail : event@sci.jsol.co.jp

Application of Reduced Model for Simulations of Occupant Protection and Crashworthiness at Toyota

Mr. Yasuo Yamamae
Group Manager
Safety CAE Technology Development Dept. Advanced CAE Div.
TOYOTA MOTOR CORPORATION

Reduced model for predicting injury values of Hybrid III dummy in sled of frontal collision was established by machine learning from FE simulation results. Prediction error of injury values was within approximately 10% of FE simulation results. Reduced model for predicting displacements of front side rail in full car of frontal collision simulation was also established by machine learning. FE results were clustered by deformation modes of front side rail and reduced models were established in each mode. Accuracy of reconstructed displacements of reduced models was better than without clustering.

タイトル

Livermore Software Technology Corporation

概要

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