Scale-resolving Simulations ​for Innovations in Turbomachinery Design

Climate, Energy & MobilityHORIZON-RIAID: 101138080
EC Contribution
€34,368
Consortium Size
11 orgs
Start Year
2024
Summary

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.

Consortium (11)

Project Results (14)

Source: CORDIS, the EU research results database.

Publications (12)
A Comparative Study of Varying Incidence Angle Effects on a Low-Reynolds-Number Compressor Cascade Based on Experiments and Low-Fidelity and High-Fidelity Numerical Simulations
International Journal of Turbomachinery, Propulsion and Power· 2025DOI
Michael Bergmann, Christian Morsbach, Felix M. Möller, Björn F. Klose, Alexander Hergt, Georgios Goinis
Comparison of multi-fidelity surrogate models for multi-objective aerodynamic optimization in turbomachinery under extreme cost imbalance
Advanced Modeling and Simulation in Engineering Sciences· 2025DOI
Marc Schouler, Anca Belme, Paola Cinnella
Examining the Potential of High-Order Scale-Resolving Simulation to Support RANS-Based Compressor Airfoil Optimization
Journal of Turbomachinery· 2025DOI
Georgios Goinis, Sutharsan Satcunanathan, Michael Bergmann
Examining the Potential of High-Order SRS to Support RANS-Based Compressor Airfoil Optimization
Volume 11: Turbomachinery — Deposition, Erosion, Fouling, and Icing; Design Methods & CFD Modeling for Turbomachinery; Ducts, Noise & Component Interactions· 2025DOI
Georgios Goinis, Sutharsan Satcunanathan, Michael Bergmann
PyFR v2.0.3: Towards industrial adoption of scale-resolving simulations
Computer Physics Communications· 2025DOI
Freddie D. Witherden, Peter E. Vincent, Will Trojak, Yoshiaki Abe, Amir Akbarzadeh, Semih Akkurt, Mohammad Alhawwary, Lidia Caros, Tarik Dzanic, Giorgio Giangaspero, Arvind S. Iyer, Antony Jameson, Marius Koch, Niki Loppi, Sambit Mishra, Rishit Modi, Gonzalo Sáez-Mischlich, Jin Seok Park, Brian C. Vermeire, Lai Wang
Status of LES of Wall-Bounded Flows in Industrial CFD
· 2025DOI
Adaptive-Mesh Optimizations for Scale-Resolving Simulations using Dynamic Closures
· 2024DOI
Krzysztof Fidkowski
Cache blocking for flux reconstruction: Extension to Navier-Stokes equations and anti-aliasing
Computer Physics Communications· 2024DOI
Semih Akkurt, Freddie Witherden, Peter Vincent
Learning PDEs
· 2024DOI
Optimizing Chaos: Aerodynamic Design using High-Fidelity Scale Resolving Simulations
· 2024DOI
Brian Vermeire
Turbulence Modeling supported by Statistical Machine Learning Models
· 2024DOI
Public Website and Presence in Social Networks
Other Results (1)
Periodic Reporting for period 1 - Sci-Fi-Turbo (Scale-resolving Simulations ​for Innovations in Turbomachinery Design)
Deliverables (1)