Powered by OpenAIRE graph
Found an issue? Give us feedback

CENAERO

CENTRE DE RECHERCHE EN AERONAUTIQUE ASBL - CENAERO
Country: Belgium
44 Projects, page 1 of 9
  • Funder: European Commission Project Code: 604999
    more_vert
  • Funder: European Commission Project Code: 314744
    more_vert
  • Funder: European Commission Project Code: 314180
    more_vert
  • Funder: European Commission Project Code: 211861
    more_vert
  • Funder: European Commission Project Code: 101138080
    Overall Budget: 3,436,750 EURFunder Contribution: 3,436,750 EUR

    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 workflows. 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, and AI/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.

    more_vert
  • chevron_left
  • 1
  • 2
  • 3
  • 4
  • 5
  • chevron_right

Do the share buttons not appear? Please make sure, any blocking addon is disabled, and then reload the page.

Content report
No reports available
Funder report
No option selected
arrow_drop_down

Do you wish to download a CSV file? Note that this process may take a while.

There was an error in csv downloading. Please try again later.