Turbo Engine Preliminary Optimization

Turbo-fans are one type of airbreathing jet engine that is widely used in aircraft propulsion. Despite being more complex, it can obtain higher efficiencies than its brother the turbojet for subsonic flights.

Challenge

Designers of turbofans have to increase the Force that is able to produce together with the Specific Impulse, which is a measure of the efficiency of the engine. The result of this is normally an engineering trade-off as both outputs are independent.

The objective is to study the performance of multiple staged compressors, and to preliminary design the pressure ratio desired for every stage, taking into account the physical uncertainties of flight conditions.

For this problem, we were studying how seven parameters were affecting both the Thrust Force and the Specific Impulse to get insights into how each variable is affecting the solution and how it is possible to optimize both outputs at the same time. It was a complex problem, as the inputs were interconnected, and multiple combinations of them were left for unfeasible options.

Solution

We started the project by coupling the Uptimai tool with the calculation software. In this case, the external software was a Python script, that was reading the inputs given by Uptimai software, computing all the outputs and feeding its result back. With these results, the AI would choose where would be more efficient to locate the following sample adaptively, and automatically repeat the process until the desired accuracy is achieved.

Our Uptimai Sensitivity Analysis showed us which were the most relevant parameters and which ones could be neglected as its effect was almost negligible. For both outputs, we saw that the inputs in which we should focus to improve the design were in the compressor side, in particular the pressure ratio that both the low and high compressors are able to produce. In the case of the Adimensional Force, the bypass ratio was also a very important factor.

To understand how those relevant variables are actually affecting the outputs we used the Uptimai Increment Functions. That allows us for both, for learning about how each variable was affecting each output, but also as a preliminary optimization tool, where we could distinguish in which ranges each variable should be to maximize the output. In the case of the bypass ratio, we found that the best option for improving the Thrust Force would be to have it between 10 and 13.

Thanks to the Uptimai Histograms we could get a statistical view of how the turbofan was behaving, and the uncertainty around it. As can be seen, there are some combinations of inputs that make the engine behave in a non-desirable way (below Fad = 3), but thanks to the influences we could tune the parameters to be always above 4.5.

As with the studies with the meta-model, we saw that it was impossible to get a particular design point that was maximizing both outputs (some variables had conflictive effects), we used our MultiObjective Optimization feature. With the MOO, we were able to get the Pareto Front that was maximizing both outputs, with the selected constraints for the outputs. That allowed to choose effectively the design point for different engine needs.

Benefits

• Definition of the problem. A clear description of relevant variables
• A deeper understanding of the physics behind the problem, and how each variable affects the outputs
• Multiobjective optimization. Having all the possible values that are maximizing at the same time both outputs