Engine bay optimization improves fuel efficiency

Designing cars that satisfy consumer expectations is hard enough as it is. Car buyers want it all—safety, speed, power, beauty, fuel efficiency—without sacrificing any of the amenities. To add to the challenge, the White House has mandated that new cars average 35.5 miles per gallon (mpg) by 2016 and 54.5 mpg by 2025.

This may seem a daunting demand, but Ford is no stranger to such challenges. A longtime symbol of American ingenuity, the company is focusing its creative energy on reaching these milestones.

Visualization of a vehicle cooling airflow which has been optimized using advanced Design of Experiment-based CAE processes.

Visualization of a vehicle cooling airflow which has been optimized using advanced Design of Experiment-based CAE processes.

“The continuous improvement in fuel economy of our vehicles is one of the key goals of the Ford product development team,” said Burkhard Hupertz, the thermal and aerosystems computer-aided engineering (CAE) supervisor at Ford of Europe. “Delivering a more energy-efficient vehicle while maintaining design leadership and high-package efficiency, and fulfilling future safety standards, is a very complex task requiring a new engineering approach.”

A vehicle’s engine bay is one important piece of the fuel efficiency puzzle. Air flowing in through the front grill plays a critical role in cooling the engine, but the process creates aerodynamic drag that increases the engine’s workload.

Better understanding of the engine bay airflow and the key parameters influencing the cooling airflow is key to developing underhood and cooling packages that maximize the cooling and minimize the drag. It is a problem well suited to supercomputer simulation. Hupertz and his Ford colleagues wanted to optimize the engine bay airflow while considering a significant number of design parameters, a job that required supercomputing resources on a completely new scale. To do so, they enlisted the help of Oak Ridge National Laboratory (ORNL) and its Jaguar supercomputer, which has since been upgraded to Titan, the most powerful computer in the U.S.

Henry’s history

Ford’s first successful design, the Model T, looks like it started with the idea that it’s easier to make a box than a ball. Combine one box for the cab with another for the engine and two sets of tires, and the “Tin Lizzie” rolled off the assembly line as the world’s first widely affordable automobile.

Of course, the process wasn’t quite so easy. Designers relied heavily on trial and error, manufacturing prototype after prototype until they found the right fit. This method of physical testing would be the standard for decades to come.

The company and its vehicles have come a long way since Henry Ford’s first hand-cranked Model T. CAE with high-performance computers (HPC) started to take hold in the mid-1980s, dramatically changing the way the auto industry designed cars. Around this time Ford began using HPC in its design process with a Cray X-MP and created its first computational fluid dynamics (CFD) code, Underhood 3D (UH3D).

CFD refers to the use of computers to simulate the flow of liquids and gases. UH3D was developed specifically to simulate the flow of air through the engine compartment to determine the most effective engine bay and cooling package design.

“CAE with high-performance computing is unquestionably an integral part of today’s design process in all development phases,” said Hupertz.

Get the best out of airflow

Senior HPC technical specialist and lead investigator Alex Akkerman is working with Hupertz from Detroit. They and a team of Ford specialists have focused on a large-scale engine bay optimization project that could potentially be the cornerstone for the designs of many future Ford vehicles.

“Our goal here,” said Akkerman, “is to deliver new vehicles which meet or exceed our customers’ expectations.”

The airflow in and around an engine bay has a significant impact on the vehicle’s fuel consumption and overall performance. Each of the many components within the compartment alters the airflow and complicates the task of understanding it.

Design parameters with a strong influence on the cooling airflow are geometric parameters such as the size and location of the front-end openings, the selection and position of heat exchangers, active flow-control features like active grill shutters and speed flaps, and nongeometric parameters such as fan power.

The necessity to deliver good engine cooling performance at a wide range of operating conditions also complicates the problem. These include varying external temperatures, going from idle to maximum speed, driving on flat terrain at more than 60 mph, driving caravan style, driving uphill, driving in sand, and towing heavy loads.

The simulations to examine these design parameters and operating conditions reveal how the cooling airflow efficiency can be maximized and the resulting cooling drag minimized.

“Any change in the size and position of just one component can have a significant impact on the computational model as a whole,” Hupertz said. “Making one more efficient could result in the loss of cooling or increased drag for another.”

With 20 years of HPC experience in automotive CAE, Hupertz knew that the need for greater fuel efficiency demanded a 3-dimensional CFD optimization of the complete underhood package, something that Ford had never attempted. This would push the research and development (R&D) further than ever before. Instead of testing individual parameters and operating conditions individually, Ford researchers would simultaneously test multiple parameters with multiple operating conditions. But therein resided the roadblock. The project would require thousands of simulations, but number crunching on this scale would require an extraordinarily powerful HPC system.

The power of Jaguar

Ford’s research needs led Akkerman and Hupertz to ORNL. Through the lab’s HPC Industrial Partnerships Program, the team was awarded time on ORNL’s Jaguar supercomputer, at the time the Department of Energy’s most powerful HPC system and one of the most powerful in the world.

After scaling the UH3D code to run on Jaguar, the Ford team used approximately 1 million processor hours to test 11 geometric and nongeometric parameters against four different operating conditions. In total they ran 1,600 simulation cases. It was the first time Ford designers were able to collect this much data in such a short amount of time.

“Access to Jaguar enabled us to develop a new methodology that allowed Ford, for the first time, to conduct engine bay analysis with the required number of design variables and operating conditions for a true design optimization,” said Akkerman.

In addition to being a high-powered workhorse, Jaguar enabled the Ford engineers to run the large number of CFD analyses without compromising on the simulation accuracy. Using CFD models of more than 50 million computational cells enabled an accurate, microscopic view of problematic areas.

“Before we got access to Jaguar we were unable to reach this level of analysis complexity,” noted Akkerman.

The Ford team also demonstrated it could run these complex simulations fast enough to enable their use within the time constraints of a vehicle development project. The new methodology was ready to go.

R&D drives bottom-line results

Thanks to Jaguar, Akkerman and Hupertz were able to deliver an R&D breakthrough with bottom line benefits. Results will be evident in a new vehicle platform Ford already has in development.

“Being able to understand the interactions between the performance of the cooling system and the large number of design parameters in such great detail has provided us with a level of insight we have not been able to gain before,” said Hupertz.

The team also expects that this project will lead to more robust cooling system designs in the early vehicle development phases—thereby cutting the number of physical prototypes and physical tests required and the costs associated with those. “In particular,” said Hupertz, “we should be able to significantly reduce the unexpected and unplanned retesting, which are the most expensive.

“The ability to assess the system performance for several different vehicle operating conditions added a significant benefit to our business. We are convinced that the developed cooling system optimization methodology gives us a competitive advantage with respect to our analytical cooling system development capabilities.”

Not only did Jaguar validate the methodology of design while adding a competitive advantage in the market; it also gave Ford management a look at how the company can benefit from upgrading its own HPC resources.

“Access to Jaguar provided Ford the ability to evaluate the impact of using a large-scale system in these important engine bay package optimization studies,” said Akkerman. “The results provided important return-on-investment justification for a significant upgrade to Ford’s in-house computing resources for similar and other automotive CAE projects.”

The team’s work on Jaguar will help Ford maximize the effectiveness and fuel efficiency of engine bay designs throughout the company. According to Hupertz, Ford will standardize the computational underhood package design process and deploy it to all new vehicles.

Thanks to these efforts, not only will the country benefit from the increased fuel efficiency, but Ford customers will be driving around with a little more cash in their pockets.—by Jeremy Rumsey