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End-to-end durability for Automotive

Simcenter engineers put their end-to-end durability solution to the test. Read a step-by-step account of a test campaign for automotive.

The interior of the Karma Revero electric car is sleek and acoustically optimized.

Adopt a people-centric approach to electric vehicle sound engineering

How does your electric vehicle sound? Discover why Simcenter adopts a people-centric approach to the NVH performance engineering of electric…

Tire modeling made easy with off-the shelf parameter sets

Testing services for tire modeling are relatively cost intensive and require a specific throughput time. By making pre-defined tire model…

Process automation: Streamlining simulation workflows

This process automation article is based on an interview with Mark Farrall, Business Engagement Manager for Simcenter Engineering at Siemens…

Cross-section view of an automatic transmission

Have downsized engines stifled the love of consumers for their cars?

Vehicles with downsized engines seldom meet consumers’ enthusiasm. Understand where downsizing fails and find paths for improved NVH perfomance engineering.

Digital Testing Symposium

Access all presentation material from our August 26, 2020 Event Learn how Testing is Evolving with the Digital Revolution Future…

Power Cycling and Thermal Testing Engineering Services

Power Cycling and Thermal Testing Engineering Services

In close cooperation with Siemens Simcenter, M4 Engineering will start delivering power cycling and thermal testing as a service in September 2020.

Achieving the perfect cook with CFD

Grill Masters – Unveiling the secrets with CFD

A grill that is too hot or too cold will be a thing of the past when you cook with…

Selected application cases, projected on the automotive development cycle

How artificial neural networks aid in mechatronic system development

In this blog post, we discuss how artificial neural networks aid in mechatronic systems development. We will use examples from different phases of the vehicle systems development cycle. We will also explore the applicability of various types of neural networks for a variety of engineering tasks.