TOPOLOGY OPTIMIZATION UNDER UNCERTAINTY FOR GAS TURBINE INTERNAL FLOWS
Lead Research Organisation:
Imperial College London
Department Name: Aeronautics
Abstract
Aerospace industries rely heavily on computational fluid dynamic simulations before to prototype and manufacture components. Consequently, it is important that these simulations represent with as much fidelity as possible real conditions. New regulations in the aerospace industry on carbon dioxide emissions have also increased the need for more efficient gas turbines. Increased efficiency can be obtained by increasing the turbine entry temperature, well above the melting point of the alloys used in aerospace. This requires more advanced coolant systems able to reduce the metal temperature and improve the components life.
With the introduction of additive manufacturing (AM), it is possible to have any possible complex geometries designed and manufactured, without the constraints brought by traditional manufacturing methods such as milling or drilling. For example, the idea is that in the structural domain, to develop bones similar structure designs would enable the industries to get lighter components. However, no design methods are currently able to exploit the flexibility offered by AM.
The overall goal of my research is to develop codes able to automatically design such geometries. We managed to obtain bio-inspired structures, similar to the body vein system, that can be used to cool down the components of real gas turbines. Moreover, we managed to obtain new valves without moving parts that can be used in different parts of the engine, such as the internal coolant system. Valves without moving parts have for property to facilitate the flow in one direction while impeding it in the reverse direction, without the use of mechanical components.
An important aspect of my research is to also achieve reliable geometries able to cope with manufacturing errors and uncertainties brought by flow conditions or material properties: a variation of 20 degree C of metal temperature can change the life of hot components by 50%. For these reasons, I am also developing an uncertainty quantification framework which will be able to deal with these problems. The algorithms are developed on an in-house solver TOffee.
With the introduction of additive manufacturing (AM), it is possible to have any possible complex geometries designed and manufactured, without the constraints brought by traditional manufacturing methods such as milling or drilling. For example, the idea is that in the structural domain, to develop bones similar structure designs would enable the industries to get lighter components. However, no design methods are currently able to exploit the flexibility offered by AM.
The overall goal of my research is to develop codes able to automatically design such geometries. We managed to obtain bio-inspired structures, similar to the body vein system, that can be used to cool down the components of real gas turbines. Moreover, we managed to obtain new valves without moving parts that can be used in different parts of the engine, such as the internal coolant system. Valves without moving parts have for property to facilitate the flow in one direction while impeding it in the reverse direction, without the use of mechanical components.
An important aspect of my research is to also achieve reliable geometries able to cope with manufacturing errors and uncertainties brought by flow conditions or material properties: a variation of 20 degree C of metal temperature can change the life of hot components by 50%. For these reasons, I am also developing an uncertainty quantification framework which will be able to deal with these problems. The algorithms are developed on an in-house solver TOffee.
Publications
Gaymann A
(2018)
Random Variable Estimation and Model Calibration in the Presence of Epistemic and Aleatory Uncertainties
in SAE International Journal of Materials and Manufacturing
Gaymann A
(2019)
Fluid topology optimization: Bio-inspired valves for aircraft engines
in International Journal of Heat and Fluid Flow
Gaymann A
(2019)
Deep Neural Network and Monte Carlo Tree Search applied to Fluid-Structure Topology Optimization.
in Scientific reports
Gaymann A
(2019)
Deep Neural Network and Monte Carlo Tree Search applied to Fluid-Structure Topology Optimization.
in Scientific reports
Gaymann A
(2017)
Design for additive manufacturing: valves without moving parts
Pietropaoli M
(2018)
Three-dimensional fluid topology optimization for heat transfer
in Structural and Multidisciplinary Optimization
Pietropaoli M
(2018)
Three-dimensional fluid topology optimization for heat transfer
in Structural and Multidisciplinary Optimization
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/N509206/1 | 30/09/2015 | 29/09/2021 | |||
1662246 | Studentship | EP/N509206/1 | 30/09/2015 | 29/09/2019 | Audrey Gaymann |
Description | Currently General Electrics is studying the possiblity of upgrading some of their component with ones built with the help of topoplogy optimization and additive manufacturing in their gas turbines. The high efficiency of the components designed and built would lead to large economies, both as investment and as fuel cost. Additive manufacturing enables the fast production of key engineering components with a high flexibility in terms of manufactured designs. |
First Year Of Impact | 2017 |
Sector | Aerospace, Defence and Marine |
Impact Types | Economic |
Description | Amelia Earhart Fellowship |
Amount | $10,000 (USD) |
Organisation | Zonta International |
Sector | Multiple |
Country | United States |
Start | 08/2017 |
End | 09/2017 |
Description | GE |
Organisation | General Electric |
Department | GE Oil & Gas |
Country | United Kingdom |
Sector | Private |
PI Contribution | Collaboration to develop some innovative geometries for gas turbines that can be manufactured by Additive Manufacturing |
Collaborator Contribution | Supervision, hosting, experimental testing |
Impact | New design methods for additive manufacturing |
Start Year | 2016 |
Title | TOffee: Topology Optimization for fluid engineering |
Description | TOffee is a topology optimization software which tackles optimization for reverse flow and heat exchange under uncertainty. The software is being developped at the moment. |
Type Of Technology | Software |
Year Produced | 2018 |
Impact | A few companies have shown interests for the outputs of TOffee as it uses state of the art optimization in fluid dynamics. This has lead to a few collaborations on product development but which falls under confidentiality. |
Company Name | TOffeeAM |
Description | TOffeeAM develops an additive manufacturing design platform, allowing users from industries including energy and aerospace to design optimised engineering components. |
Year Established | 2019 |
Impact | TOffee won the second price at Programm/able 2018, which includes USD 4000 AWS credits. TOffee was selected to the semi-final Venture Catalyst Challenge. Hello tomorrow invited us twice. RAEng enterprise fellowship recipient 2019. |
Website | http://www.toffeeam.co.uk |
Description | Invited researcher to the University of Tokyo summer camp |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | International postgraduate students from all over the world met during 4 days. Everyone presented and talked about their own research. During 2 full days, the students were divided in groups and worked on different project together. Good opportunity for networking. |
Year(s) Of Engagement Activity | 2016 |
Description | New Scientist Live Festival |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Public/other audiences |
Results and Impact | Invited by The Knowledge Transfer Network (KTN) which was responsible of a stand in the Technology Zone of the Festival. The stand purpose was to show how mathematics can be used for broad applications (physics, fluid dynamics, structures, etc). Many secondary and high school students visited the stand which sparked curiosity and interest for the different applications presented. People from industry were also present and were able to appreciate what was being done in additive manufacturing. |
Year(s) Of Engagement Activity | 2017 |
URL | https://live.newscientist.com/ |
Description | Special Interest Group (SIG) Steering Group for the Uncertainty Quantification and Management (UQ&M) Programme |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | Talks were done on previous workshop and study group organized on Uncertainty Quantification (UQ). Plans were formulated for the future workshop between industry-academia. Discussions were done on how to bring academia and businesses together on UQ subject. |
Year(s) Of Engagement Activity | 2016 |