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HEEDS Siemens

Streamlining the Design Exploration Process With Siemens HEEDS

Featured Aerospace Case Study: Optimization of Aircraft Bleed-Air System Design

Here at Siemens HEEDS, our Singapore team aims to Illustrate how a recognizable industry-leading company applied this technology to solve a real problem as shown in a step-by-step process.

Now that you’ve seen the enabling technologies, let’s take a closer, step-by-step look at how modern design exploration is streamlining the virtual product development process for one of our customers. To do that, we’ll use a real-world example from Airbus. They set out to improve the performance of the bleed-air system on one of their aircraft. The system takes in both hot air from the compressor and cold bleed air from the engine fan and mixes that air before delivering it to the cabin air-conditioning and wing ice protection systems.

A challenging and realistic Aerospace design problem

The intake air had an initial temperature variation of nearly 200 degrees K.  The challenge was to find a static air mixer design that could reduce that variation to under 20 degrees Kelvin by the time the air reached the temperature sensor while maintaining a pressure drop less than 5 kiloPascals.

At this stage, we can now carefully consider multiple design concepts. With Siemens HEEDS software, we are solving the problem as it appears rather than changing the problem to fit the software. Instead of focusing in on just one specific design, engineers identified a number of possible design concepts and agreed to look closer at two of these using design exploration techniques.

The first was a helical mixer design with three basic design parameters. The second was a concentric mixing blade concept fully described by 11 variables governing the number, configuration, and shape of blades.
For today, I’ll focus on how they explored the second, more comprehensive concept.

With Siemens HEEDS Software, a broad range of design variation that can be considered via this approach. This helps to illustrate the importance of this design space breadth for driving product innovation.
For this problem, the process automation step was straightforward.  They needed to connect a parametric CAD geometry model, with 11 design variables, to a 3D CFD model with automatic meshing and flow solution.

They ran an initial CAD robustness study shown here to ensure that they could effectively capture a broad range of design families.

Siemens HEEDS is able to visually delivers design insight and is always searching both locally and globally across the design space to identify better designs.
For this project, HEEDS was able to find an optimized design meeting strict targets in under 1 week

The Design Exploration Process

So, let’s run through the required steps for design exploration:

First, you have to be able to build a complete suite of virtual prototype variants. This process needs to be robust and simple to make design changes…remember, we’re going to run tens or hundreds of design variants automatically.

Second, you need to quickly and cost-effectively test these variants with multi-physics simulation. What’s critical to understand in this step is that there is no need to oversimplify geometry and that you can include the necessary physics to predict real-world performance, while still ensuring quick throughput of these virtual tests by relying on parallelization and high-performance computing.

Next, you need to efficiently and automatically search the design space for better families of designs. This process needs to be seamless and properly manage the resulting data … remember, tens or hundreds of design variants might be searched.

Here you see that feasible designs appear as blue dots in our plot while infeasible designs that violate one or more constraints appear as red dots. Of course, once you have explored the design space, you need to have the ability to quickly assess the best families of designs to gain the necessary engineering insight for making design decisions.
Here we can easily look at the results of temperature uniformity versus pressure drop and pick the design characteristics that we like best.

So that’s the process: Build, Test, Explore and Assess.
What’s different about this process is the fact that it is streamlined and completely automated. This enables searching the design space for better families of designs first, before making any design decisions. So rather than needlessly spending time assessing the quality of an inferior baseline design and restricting your vision to only designs around this baseline, with this process, you can cast a much wider net and search for better families of designs throughout the design space. This is what will give you the insight and design direction needed to be innovative.

For this example, after evaluating 280 design variants, HEEDS identified a design that reduced temperature variations by over 90%, while still maintaining the pressure drop constraint.

It’s a straightforward concept, but using a modern process makes all the difference. In fact, in this case, Airbus initially attempted to explore the simpler helical design by stringing together various tools in their toolbox (preprocessor, solver, post-processor, optimization, etc.) to look for design improvements around a baseline design. After 6 months they got a result…but it did not provide the temperature uniformity desired, and the process was deemed too long, too cumbersome, and too fragile to use in practice; so it was shelved.

By using a streamlined process that is centered on design exploration, Airbus was able to overcome the roadblocks they faced … and moreover, because the process facilitates exploring designs throughout the design space, they were able to achieve their goals and do it in only 10 days start-to-finish.

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Additional background information on the Airbus case:

1. Design variables: blade number, inner blade angle, outer blade angle, blade curvature.
2. Image shown in the assess step: Left image – shows temperature variation at temperature sensor location + pressure drop behind the mixer. The right image is an example of a trade-off plot – for example, size represents outer blade angle, color represents inner blade angle and we are plotting pressure drop vs temperature variation to look for families of designs.