TLG Aerospace Case Study

TLG Aerospace, based in Seattle, Washington, is an aerospace engineering services company that provides CFD analysis and other services to aerospace customers worldwide. The company specializes in engineering for small and large aircraft. It focuses on vehicle analysis and design, including static and dynamic loads, flutter, stability and control, aerodynamics, and airframe stress analysis and design.

Like many aerospace engineering firms, TLG employs STAR-CCM+, a leading industry application, to perform CFD simulations. TLG uses the application to conduct aerodynamic simulations on aircraft and predict the pressure and temperature surrounding airframes. However, the company wanted to reduce the costs associated with running simulations. “We were using a cloud provider to host our simulations, but the cost per simulation was high,” says Andrew McComas, engineering manager at TLG Aerospace. “Running a typical simulation was costing us hundreds of dollars per case, and there may be hundreds of cases per project.”

TLG also wanted the ability to scale its high-performance computing (HPC) applications to take on larger simulations. “The trend in our industry is toward doing more complex simulations that require more compute resources,” McComas says. “But with the internal HPC cluster we were using, we were limited as far as the maximum size problem we could run. We were limited to a small number of nodes and couldn’t allocate enough memory to run large-scale problems.”

Because it wanted to reduce costs and gain scalability, TLG decided to search for a new cloud provider.

TLG ultimately decided to use Amazon Web Services (AWS) to run CFD simulations. “I was familiar with AWS, and I thought the technology could meet our specific needs for lower costs and higher scalability,” says McComas.

TLG takes advantage of Amazon EC2 Spot instances, a way to use unused Amazon Elastic Compute Cloud (Amazon EC2) computing capacity at a discounted price. “We were aiming to find dramatic reductions in cost, and EC2 Spot instances did that,” says McComas. “From a business perspective, it’s all about the dollars per case, and this technology helps us save a lot on simulation costs.”

TLG uses Amazon Simple Storage Service (Amazon S3) buckets to store multiple terabytes of simulation data on the cloud. The company also takes advantage of Amazon Elastic Block Store (Amazon EBS), which offers persistent block-level storage volumes that can be used with Amazon EC2 instances. TLG uses Amazon EBS volumes for the HPC cluster head node. In addition, TLG uses the Amazon CloudWatch monitoring service to track the progress of CFD simulations in an online AWS console. “We created a CloudWatch metric that enables us to monitor simulation status directly from AWS,” McComas says. “As a result, we can monitor solutions from any computer and are not required to utilize the CFD code’s proprietary GUI.”

By using Amazon EC2 Spot instances, TLG is significantly reducing its CFD simulation costs. “We saw a 75 percent reduction in the cost per CFD simulation as soon as we started using Amazon EC2 Spot instances,” says McComas. “We are able to pass those savings along to our customers—and be more competitive. The kind of analysis we do is becoming more of a commodity, with more and more companies getting involved in the space. So there’s more competition to provide services at the lowest price. As a result of our simulation cost reduction, we can be more competitive.”

TLG saves additional money by sending simulation residuals to Amazon CloudWatch to view online. “With Amazon CloudWatch, I can be at home or on the road and I can simply log in to the console to see the status of any of our simulations,” says McComas. “We are definitely saving money by actively monitoring jobs to catch problems early and reduce rework. We can also use it to reduce unnecessary cost in larger jobs that may otherwise run longer than required.”

The company is also now able to scale STAR-CCM+ to more than a thousand cores, providing the opportunity to target both small and large CFD projects. The organization runs STAR-CCM+ on AWS using more than 60 EC2 Spot instances per case. “Previously, we were very limited as far as how much we could scale, so we couldn’t support jobs requiring more than 1,000 nodes,” says McComas. “Even though 1,000 nodes were sufficient most of the time, there was still that limitation. Using AWS, we aren’t limited in our ability to scale STAR-CCM+ jobs. We have the opportunity to go much bigger if we need to, and we will be prepared for very large computational fluid dynamics projects now. That’s important for us, because that’s the way the industry is headed.”

With the ability to scale more easily, TLG expects to take on larger projects going forward. “We can now bid on projects that have a greater scope, because we can run larger grids and run them faster,” says McComas. “That shows the advantage of having the scalability and low cost of AWS. We can do much more with our dollar.”