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Sci-Fi-Turbo aims to revolutionise the aero engine design process by advancing and integrating high-order scale-resolving simulations (SRS) and optimization methodologies into standard industrial workflow.

SRS are a key enabler for developing ultra-efficient propulsion systems that drastically reduce GHG emissions by 2035 and achieve the EU's target to be climate-neutral by 2050. The advancements will boost design process capabilities and reduce product development cycles. Future engine concepts require opening up the design space and solving complex design problems out of reach for today's standard industrial design processes within the required timeframe.

To achieve the necessary step change in engine design, a similar step change is needed for the design approach. Sci-Fi-Turbo fills this urgent need by exploiting opportunities in three foundation technologies:

  • High-performance computing

  • High-order numerical methods

  • Artificial Intelligence (AI) / Machine Learning (ML)

 

The combination is used to implement and demonstrate two key advancements.

First, a highly integrated high-order SRS design process is established for modern CPU/GPU hardware, meeting robustness, accuracy, and turnaround time requirements. It will provide increased functionality and effectivity at an industrial level and pave the way for the uptake of SRS-based design by the industry. The high accuracy of the methodology will also reduce the need for low-TRL testing and enable new concepts and extended operating conditions.

Second, an SRS-assisted multi-fidelity, data-driven optimisation framework is developed, which embeds and exploits the advantages of highly accurate high-order SRS while leveraging AI/ML methods to increase the predictive capability of lower-fidelity simulations and maximize overall process accuracy and speed. Dedicated experiments support the technology advancement and will enable the design of net-zero-emission engines in due time and contribute to the digital transformation of the aviation industry.

 

 
      KEYWORDS

     #ScaleResolvingSimulations, #MachineLearning, #DataDrivenTurbulenceModelling,
     #MultiFidelityOptimization, #TurbomachineryDesign, #ClimateNeutralAviation

  

 

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Funded by the European Union under grant number 101138080. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.