Summary
The flow dynamics within turbomachinery flows are complex and still not fully understood. Such flows are rich in turbulent phenomena driven by high levels of unsteadiness. Despite much progress we are still unable to reliably predict many of these phenomena (e.g. wake interactions, transition, separations) using standard modelling techniques which hinders further aero-engine efficiency improvements.
To investigate these phenomena, researchers are employing high-fidelity techniques both in experiments and in computations, e.g. direct numerical simulations. These techniques give unprecedented resolution and a wealth of data but extracting knowledge and distilling it into reduced-order models applicable to a wide range of flows is challenging, thus limiting their impact.
The objective of this project is to address this limitation by adapting a theoretical framework of data-driven flow decomposition to turbomachinery flows. This methodology will be used to study inter-scale energy transfers between the different flow dynamics within the multi-stage compressor. The project aims to discern between scales contributing towards the turbulence production, dissipation and those regulating the flow of energy down the turbulence cascade. The analysis will be facilitated by combining multiple high-fidelity datasets from computations at engine representative conditions. The primary goal of the project is to establish a new paradigm for industrial flow modelling that utilises all the information embedded within the 3D unsteady flowfield. Consequently, a new set of low-order models will be derived, each catering to a different range of scales present in the compressor flowfield.
The project will leverage the expertise of three world-leading groups in high-performance computing data decomposition and turbomachinery modelling. Results of this project are expected to provide much needed insight into multi-scale interactions in complex industrial flows such as those of aero-engines.
To investigate these phenomena, researchers are employing high-fidelity techniques both in experiments and in computations, e.g. direct numerical simulations. These techniques give unprecedented resolution and a wealth of data but extracting knowledge and distilling it into reduced-order models applicable to a wide range of flows is challenging, thus limiting their impact.
The objective of this project is to address this limitation by adapting a theoretical framework of data-driven flow decomposition to turbomachinery flows. This methodology will be used to study inter-scale energy transfers between the different flow dynamics within the multi-stage compressor. The project aims to discern between scales contributing towards the turbulence production, dissipation and those regulating the flow of energy down the turbulence cascade. The analysis will be facilitated by combining multiple high-fidelity datasets from computations at engine representative conditions. The primary goal of the project is to establish a new paradigm for industrial flow modelling that utilises all the information embedded within the 3D unsteady flowfield. Consequently, a new set of low-order models will be derived, each catering to a different range of scales present in the compressor flowfield.
The project will leverage the expertise of three world-leading groups in high-performance computing data decomposition and turbomachinery modelling. Results of this project are expected to provide much needed insight into multi-scale interactions in complex industrial flows such as those of aero-engines.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101026928 |
| Start date: | 01-04-2022 |
| End date: | 31-03-2025 |
| Total budget - Public funding: | 257 209,92 Euro - 257 209,00 Euro |
Cordis data
Original description
The flow dynamics within turbomachinery flows are complex and still not fully understood. Such flows are rich in turbulent phenomena driven by high levels of unsteadiness. Despite much progress we are still unable to reliably predict many of these phenomena (e.g. wake interactions, transition, separations) using standard modelling techniques which hinders further aero-engine efficiency improvements.To investigate these phenomena, researchers are employing high-fidelity techniques both in experiments and in computations, e.g. direct numerical simulations. These techniques give unprecedented resolution and a wealth of data but extracting knowledge and distilling it into reduced-order models applicable to a wide range of flows is challenging, thus limiting their impact.
The objective of this project is to address this limitation by adapting a theoretical framework of data-driven flow decomposition to turbomachinery flows. This methodology will be used to study inter-scale energy transfers between the different flow dynamics within the multi-stage compressor. The project aims to discern between scales contributing towards the turbulence production, dissipation and those regulating the flow of energy down the turbulence cascade. The analysis will be facilitated by combining multiple high-fidelity datasets from computations at engine representative conditions. The primary goal of the project is to establish a new paradigm for industrial flow modelling that utilises all the information embedded within the 3D unsteady flowfield. Consequently, a new set of low-order models will be derived, each catering to a different range of scales present in the compressor flowfield.
The project will leverage the expertise of three world-leading groups in high-performance computing data decomposition and turbomachinery modelling. Results of this project are expected to provide much needed insight into multi-scale interactions in complex industrial flows such as those of aero-engines.
Status
SIGNEDCall topic
MSCA-IF-2020Update Date
28-04-2024
Images
No images available.
Geographical location(s)
Structured mapping
Search
