Altair Case Studies Innovative Design Optimization for Efficient Automotive Powertrains: A Renault Case Study

	Innovative Design Optimization for Efficient Automotive Powertrains: A Renault Case Study Altair

Innovative Design Optimization for Efficient Automotive Powertrains: A Renault Case Study

Altair

Technology Category	Analytics & Modeling - Digital Twin / Simulation Sensors - Temperature Sensors
Applicable Industries	Automotive Life Sciences
Applicable Functions	Procurement Product Research & Development
Use Cases	Time Sensitive Networking Virtual Reality
Challenge	Renault, one of Europe's largest automotive manufacturers, was faced with the challenge of developing a new, efficient, and lightweight vehicle powertrain. The company aimed to further decrease the weight and increase the performance of existing and in-development engines by redesigning key components to use a minimum amount of material. The challenge was compounded by the need to meet tighter regulations across Europe, US, and Asia, and a shift in consumer demand for more fuel-efficient vehicles. The company also wanted to use design optimization techniques from the start of the development process, rather than as a tactical tool to combat weight problems in the detailed design phase. However, the complexity and time-consuming nature of creating powertrain models posed a potential barrier to this approach. Read More
About Customer	Renault is one of Europe’s largest automotive manufacturers, producing a wide range of passenger cars and commercial vehicles from its facilities located around the world. The company has been pushing the development of high performance, fuel-efficient engines for many years. In 2009, Renault figured among Europe's three best-performing car manufacturers for average CO2 emissions. The company is now aiming to move to the top of the order by further decreasing the weight and increasing the performance of its engines. Renault is investing heavily in the development of lighter vehicles and more economical engines that can go further on less fuel, in response to tighter regulations and a shift in consumer demand. Read More
Solution	Renault collaborated with Altair, a provider of simulation solutions, to develop the required optimization design methods and processes for use at component and sub-system levels. Altair supplied Renault with extensive support for the design optimization technology within its HyperWorks simulation suite and provided engineering expertise through its product development division, Altair ProductDesign. The collaboration involved conducting a simulation 'Grand Challenge' to demonstrate the potential impact of optimization technology on the performance of the powertrains. Altair used new design methods and processes to rapidly develop an innovative solution to the defined engineering challenge. The solution involved using Altair’s SimLab finite element modeling environment to automate the build of complex structures based on the concept CAD models, and OptiStruct to perform topology optimization on the external rib network for the global powertrain assembly. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Digital Twin / Simulation

Sensors - Temperature Sensors

Applicable Industries

Automotive

Life Sciences

Applicable Functions

Procurement

Product Research & Development

Use Cases

Time Sensitive Networking

Virtual Reality

Challenge

Renault, one of Europe's largest automotive manufacturers, was faced with the challenge of developing a new, efficient, and lightweight vehicle powertrain. The company aimed to further decrease the weight and increase the performance of existing and in-development engines by redesigning key components to use a minimum amount of material. The challenge was compounded by the need to meet tighter regulations across Europe, US, and Asia, and a shift in consumer demand for more fuel-efficient vehicles. The company also wanted to use design optimization techniques from the start of the development process, rather than as a tactical tool to combat weight problems in the detailed design phase. However, the complexity and time-consuming nature of creating powertrain models posed a potential barrier to this approach.

About Customer

Renault is one of Europe’s largest automotive manufacturers, producing a wide range of passenger cars and commercial vehicles from its facilities located around the world. The company has been pushing the development of high performance, fuel-efficient engines for many years. In 2009, Renault figured among Europe's three best-performing car manufacturers for average CO2 emissions. The company is now aiming to move to the top of the order by further decreasing the weight and increasing the performance of its engines. Renault is investing heavily in the development of lighter vehicles and more economical engines that can go further on less fuel, in response to tighter regulations and a shift in consumer demand.

Solution

Renault collaborated with Altair, a provider of simulation solutions, to develop the required optimization design methods and processes for use at component and sub-system levels. Altair supplied Renault with extensive support for the design optimization technology within its HyperWorks simulation suite and provided engineering expertise through its product development division, Altair ProductDesign. The collaboration involved conducting a simulation 'Grand Challenge' to demonstrate the potential impact of optimization technology on the performance of the powertrains. Altair used new design methods and processes to rapidly develop an innovative solution to the defined engineering challenge. The solution involved using Altair’s SimLab finite element modeling environment to automate the build of complex structures based on the concept CAD models, and OptiStruct to perform topology optimization on the external rib network for the global powertrain assembly.

Impact #1	The use of optimization technology early in the development process proved to be extremely effective for Renault. It allowed the powertrain team to rapidly experiment with hundreds of design variables that an engineer would not typically consider, while still allowing the team to use their judgment in choosing the best solution. The process freed the team to concentrate on added value tasks rather than time-consuming modeling activities. The final design of the new powertrain not only achieved an 8% reduction in weight but also improved in-house NVH and fatigue cumulated performance indicators by 30%, making the performance of the new Renault engine the best in its class. The grand challenge activity demonstrated that using optimization technology early in the development process can help to reduce the development time by shortening costly design loops and minimizing the effort required for product validation.

Impact #1

The use of optimization technology early in the development process proved to be extremely effective for Renault. It allowed the powertrain team to rapidly experiment with hundreds of design variables that an engineer would not typically consider, while still allowing the team to use their judgment in choosing the best solution. The process freed the team to concentrate on added value tasks rather than time-consuming modeling activities. The final design of the new powertrain not only achieved an 8% reduction in weight but also improved in-house NVH and fatigue cumulated performance indicators by 30%, making the performance of the new Renault engine the best in its class. The grand challenge activity demonstrated that using optimization technology early in the development process can help to reduce the development time by shortening costly design loops and minimizing the effort required for product validation.

Benefit #1	Weight of the new powertrain was reduced by 8%
Benefit #2	NVH (Noise, Vibration, and Harshness) & fatigue improved by 30%
Benefit #3	Modeling efficiency improved by 60%

Benefit #1

Weight of the new powertrain was reduced by 8%

Benefit #2

NVH (Noise, Vibration, and Harshness) & fatigue improved by 30%

Benefit #3

Modeling efficiency improved by 60%

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Overview

Innovative Design Optimization for Efficient Automotive Powertrains: A Renault Case Study

Operational Impact

Quantitative Benefit