In today’s transportation and mobility industry, the market’s demands for increased fuel economy and reduced emissions are making it harder for vehicle engineers to meet performance targets and requirements. As engineers work to meet these demands, the competitive nature of key performance indicators from different engineering silos are becoming more apparent. For example, vehicle design changes to improve aerodynamic performance can often come at the cost of underhood component thermal performance, and vice-versa. Moreover, design changes are frequently proposed as a solution for performance issues studied only at a single operating point, such as peak power, peak torque, or highway cruise. A design change that solves a problem at one operating point may reduce performance or introduce new problems at other operating points.
The ability to understand tradeoffs, the mechanisms driving them, and consider holistic vehicle performance requires early use of multi-disciplinary optimization (MDO) and drive cycle analysis. MDO enables engineers to make optimal design decisions that account for the trade-offs between competing performance objectives of different engineering teams. Drive cycle analysis enables engineers to evaluate vehicle performance through multiple operating conditions that the vehicle will experience on a regular basis in real-world operation.
In practice, there are several roadblocks preventing the use of MDO studies in the early stages of the vehicle development process. First, engineering analysis is typically done in silos and in a sequential process, rather than in parallel collaboration. Additionally, decision making and design validation within these silos is heavily reliant on physical testing. This can lead to engineering decisions made early on that may be optimal for one physical domain, but far from optimal and even detrimental for another. In addition, drive cycle test data is only available after a physical prototype is built, by which point certain parts of the vehicle design have already been frozen. This limits the ability of engineers to make the right decisions at an early stage, based on holistic performance data at multiple operating conditions.
These problems in the vehicle development process can lead to late-stage failures, product release delays, warranty issues, and cost to the OEM’s bottom line. In fact, certain studies have shown that warranty costs for automotive OEMs are typically about 2-3% of total revenue[1].
Dassault Systèmes SIMULIA enables manufacturers to address these challenges. The video below shows how Isight and PowerFLOW can accurately provide and leverage drive cycle test data in an MDO study, all before any physical prototype is built. This was implemented on a customer technology demonstration project. PowerFLOW provided highly-accurate evaluations of the aerodynamic and thermal performance of the vehicle. Isight automated the PowerFLOW simulation process to efficiently evaluate the performance across a range of operating conditions, and build a thermal drive cycle simulation model to simulate vehicle performance over a long-term drive cycle. The model was then integrated into an MDO study considering multiple design changes to the vehicle’s body. It was demonstrated how the vehicle body shape could be optimized while considering both aerodynamic and thermal performance impact. The design changes contributing to the performance trade-offs were easily visualized with the help of the new PowerFLOW DesignGUIDE capability, which provides intuitive and interactive design guidance for multiple performance objectives. The complex physical mechanisms driving the trade-offs are understood by analyzing the PowerFLOW simulation results. The final result was a set of design changes which simultaneously improved aerodynamic and thermal performance.
For more information, please:
- Register for the PowerFLOW 2020 and DesignGUIDE eSeminars
- Visit the Isight and PowerFLOW product pages
- Barkai, J., “Quality Improvement and Warranty Cost Containment: Better Answers are in the Text,” SAE Technical Paper 2004-01-2666, 2004, https://doi.org/10.4271/2004-01-2666.
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Mark Malinovskiy currently resides in southeast Michigan, where he has spent the entirety of his life. He completed his B.Sc. in Aerospace Engineering and Minor in Computer Science from the University of Michigan. Afterwards he joined Exa Corporation as a CFD Application Engineer. He is currently a CFD Application Engineer with the NA Industry Process Consultants Fluids Team based out of the Livonia, MI field office. Most of his professional experience at 3DS has been focused on DoE, and optimization projects using PowerFLOW. In his free time he enjoys ice hockey, piano music, and reading.