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From Conventional Simulation to Predictive Engineering Analysis

September 25, 2017

Summer has been the perfect time to enjoy all kinds of outdoor activities such as cycling.
If you enjoy this sport, you probably know that it is important to adopt an efficient and ergonomic position before going out. Several factors can optimize your performance: seat height, seat and shim position, etc. It is usually a question of trial and error to find the optimal position and this task can become long and laborious. You can also go to a professional who will adjust these details for you. However, there is now a new cutting-edge technology that allows you to scan your body and identify a range of potentially optimal positions for you. Following the calculations, you will then only have to try the proposed solutions. This method is called predictive analysis and it is used in several areas, including engineering.

What is predictive engineering analysis?

Predictive analysis is a method used especially in the development of products to design complex systems often including intelligent systems, mechatronics and control. It is used to predict the behavior of a system even before that system is subjected to the forces and movements of physical tests.

More specifically, predictive engineering analysis makes it possible to design a digital clone of the complete product that reflects reality and the environment in which it will evolve.

Manufacturers in the automotive, aerospace, marine or other industries must reinvent the way they design to meet customer expectations in terms of costs and deadlines. The popularity of online purchases and shorter delivery times, for example, require shorter production times and lower costs while delivering high-quality products.

Traditionally, manufacturers design and optimize their products before their market entry by using various methods such as:

  • computer aided design (CAD)
  • finite element method (FEM)
  • computational fluid dynamics (CFD)
  • dynamic behavior studies, control loops, part validations
  • physical testing

These traditional simulations, both for simple parts and for complex systems, are carried out sequentially and require a lot of time and money, thus limiting the number of prototypes that can be tested.

This is why companies must find new solutions to achieve better results. One of these solutions consists of a predictive analysis.

Take the example of an active vehicle suspension which allows the suspension to be controlled according to the dynamic behavior of the vehicle, but also according to the defects of the road ahead. Predictive analysis is using the results of the 1D elements simulations (hydraulic or pneumatic, electrical and mechatronic systems) as well as data from the sensors on the suspension test bench and vehicle prototypes. Added to that, it is also using the history of vehicles already in use through the Internet of the things as input data to the 3D model (FEM). This analysis makes it possible to know the forces and the constraints, but also the dynamic and vibratory behaviors of the suspension using a co-simulation software. The output data of this model is transmitted to the other elements of the analysis by the co-simulation software and continuously recalculated until an optimized suspension is obtained.

Implementation of a predictive analysis strategy

Implementing a predictive analysis strategy has several benefits. Predictive engineering analysis, in part because of the industry 4.0 and connected products, allows product optimization beyond development and marketing by providing information to both the user and the manufacturer.
Moreover, this method makes it possible to respond more quickly and easily to the client’s expectations and to offer them a high quality product. Predictive analysis is a good way to reduce product development costs and production times. To generate a predictive analysis with value-added, Merkur’s mechanical, mechatronic and electrical engineers use a combination of expertise and tools in systems engineering such as numerical simulations based on objectives and costs.

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