Altair Case Studies Accelerating Design Process with Multi-Disciplinary Optimization: A Daimler Case Study

	Accelerating Design Process with Multi-Disciplinary Optimization: A Daimler Case Study Altair

Accelerating Design Process with Multi-Disciplinary Optimization: A Daimler Case Study

Altair

Technology Category	Cybersecurity & Privacy - Intrusion Detection Robots - Autonomous Guided Vehicles (AGV)
Applicable Industries	Automotive Life Sciences
Applicable Functions	Product Research & Development
Use Cases	Traffic Monitoring Vehicle Performance Monitoring
Challenge	Daimler, a leading producer of premium vehicles, has been using design optimization techniques for many years to maximize performance while minimizing material use and mass. However, the traditional processes of optimizing for different disciplines, such as crash and noise, vibration and harshness (NVH), independently can be slow to deliver a design solution that meets varied attribute targets simultaneously. During the development of a new vehicle variant, Daimler wanted to explore the potential of utilizing a multi-disciplinary approach to optimization (MDO), whereby several attribute performance targets are considered in a single optimization study. The focus of this project was a Mercedes-Benz die cast rear cross member that was not yet meeting its crash and NVH targets. The objective was to increase the stiffness of the casting while minimizing its mass. Read More
About Customer	Daimler is one of the world’s leading producers of premium vehicles and the largest producer of commercial vehicles with its Mercedes-Benz brand known across the globe. In 2016 alone, Daimler is estimated to have sold as many as 3 million vehicles with a workforce of over 280,000 people. Design optimization techniques have been utilized at Daimler for many years, helping the organization to maximize performance while minimizing material use and mass; finding the ideal balance between multiple attributes. Daimler is enthusiastic with the MDO approach and its ability to de-risk later design stages, and is now integrating the methodology into further vehicle development projects. Read More
Solution	Altair, a technology company providing solutions in product development, high-performance computing and data intelligence, was brought in to help Daimler with this challenge. Altair's consultants started with a full vehicle explicit crash model which could be used to derive a set of compliance based constraints due to the section force level for each crash loadcase. An optimization model for HyperWorks’ OptiStruct was built including these and a set of NVH loadcases to determine the dynamic stiffness behaviour. The Altair team reduced the model in size to make it more computationally efficient by extracting a submodel of the casting and using superelements to represent the physical behavior of the rest of the structure. Using NVH loads from Daimler and the draw direction of the ribs as a manufacturing constraint, OptiStruct created a representation of an optimal rib layout which would meet the NVH performance targets. The team then conducted a series of optimization studies to refine the design and minimize mass. Read More Log in to view content
Contents

Technology Category

Cybersecurity & Privacy - Intrusion Detection

Robots - Autonomous Guided Vehicles (AGV)

Applicable Industries

Automotive

Life Sciences

Applicable Functions

Product Research & Development

Use Cases

Traffic Monitoring

Vehicle Performance Monitoring

Challenge

Daimler, a leading producer of premium vehicles, has been using design optimization techniques for many years to maximize performance while minimizing material use and mass. However, the traditional processes of optimizing for different disciplines, such as crash and noise, vibration and harshness (NVH), independently can be slow to deliver a design solution that meets varied attribute targets simultaneously. During the development of a new vehicle variant, Daimler wanted to explore the potential of utilizing a multi-disciplinary approach to optimization (MDO), whereby several attribute performance targets are considered in a single optimization study. The focus of this project was a Mercedes-Benz die cast rear cross member that was not yet meeting its crash and NVH targets. The objective was to increase the stiffness of the casting while minimizing its mass.

About Customer

Daimler is one of the world’s leading producers of premium vehicles and the largest producer of commercial vehicles with its Mercedes-Benz brand known across the globe. In 2016 alone, Daimler is estimated to have sold as many as 3 million vehicles with a workforce of over 280,000 people. Design optimization techniques have been utilized at Daimler for many years, helping the organization to maximize performance while minimizing material use and mass; finding the ideal balance between multiple attributes. Daimler is enthusiastic with the MDO approach and its ability to de-risk later design stages, and is now integrating the methodology into further vehicle development projects.

Solution

Altair, a technology company providing solutions in product development, high-performance computing and data intelligence, was brought in to help Daimler with this challenge. Altair's consultants started with a full vehicle explicit crash model which could be used to derive a set of compliance based constraints due to the section force level for each crash loadcase. An optimization model for HyperWorks’ OptiStruct was built including these and a set of NVH loadcases to determine the dynamic stiffness behaviour. The Altair team reduced the model in size to make it more computationally efficient by extracting a submodel of the casting and using superelements to represent the physical behavior of the rest of the structure. Using NVH loads from Daimler and the draw direction of the ribs as a manufacturing constraint, OptiStruct created a representation of an optimal rib layout which would meet the NVH performance targets. The team then conducted a series of optimization studies to refine the design and minimize mass.

Impact #1	The MDO approach proved to be a highly successful approach to solve this design challenge. Including crash objectives in the NVH constraints and the NVH objectives in the crash constraints was a big departure from the more traditional development process where attribute teams operate in relative silos. The NVH performance of the new rear cross member design had been improved significantly. For crash performance the intrusion level and the material/ connection rupture was drastically reduced. By utilizing an MDO approach, the new design solution for the castings successfully increased the crash and NVH performance but at no change to the weight of the initial design of the component which did not meet performance requirements by a significant margin. Daimler is now integrating the methodology into further vehicle development projects.

Impact #1

The MDO approach proved to be a highly successful approach to solve this design challenge. Including crash objectives in the NVH constraints and the NVH objectives in the crash constraints was a big departure from the more traditional development process where attribute teams operate in relative silos. The NVH performance of the new rear cross member design had been improved significantly. For crash performance the intrusion level and the material/ connection rupture was drastically reduced. By utilizing an MDO approach, the new design solution for the castings successfully increased the crash and NVH performance but at no change to the weight of the initial design of the component which did not meet performance requirements by a significant margin. Daimler is now integrating the methodology into further vehicle development projects.

Benefit #1	Crash and NVH targets achieved.
Benefit #2	No change to component weight.
Benefit #3	More than 2400 optimization jobs run during the DOE.

Benefit #1

Crash and NVH targets achieved.

Benefit #2

No change to component weight.

Benefit #3

More than 2400 optimization jobs run during the DOE.

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Overview

Accelerating Design Process with Multi-Disciplinary Optimization: A Daimler Case Study

Operational Impact

Quantitative Benefit