Dr Omid Bidar
School of Mechanical, Aerospace and Civil Engineering
Research Associate in Computational Modelling of Boiling Heat Transfer
Full contact details
School of Mechanical, Aerospace and Civil Engineering
Sir Frederick Mappin Building
Mappin Street
Sheffield
S1 3JD
- Profile
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I am a research associate in the Thermofluids group. My research focuses on computational modelling of boiling heat transfer in nuclear reactors.
I hold an MEng in Aerospace Engineering from the University of Sheffield, graduating with first-class honours, and also studied at the Aerospace Department at the University of Michigan, USA, as part of a Global Engineering Exchange program. I recently completed my PhD at the University of Sheffield in data-driven turbulence modelling. My PhD research focused on combining Computational Fluid Dynamics (CFD) with machine learning techniques to enhance turbulence models, particularly for improving the prediction accuracy of complex aerodynamic flows.
- Research interests
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- Computational fluid dynamics
- Turbulence Modelling
- Data assimilation
- Machine Learning
- Engineering Design Optimisation
- Modelling of multiphase flows
- Publications
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Journal articles
- Sensor placement for data assimilation of turbulence models using eigenspace perturbations. Physics of Fluids, 36, 015144. View this article in WRRO
Chapters
- A Hybrid RANS-LES Dataset for Data-Driven Turbulent Mean Flow Reconstruction, Proceedings of the Cambridge Unsteady Flow Symposium 2024 (pp. 121-133). Springer Nature Switzerland
Conference proceedings papers
- Aerodynamic shape optimisation using a machine learning-augmented turbulence model. AIAA SCITECH 2024 Forum. Orlando, FL, USA, 8 January 2024 - 8 January 2024. View this article in WRRO
- A priori sensor placement strategy for turbulent mean flow reconstruction using parametric model perturbations. AIAA SCITECH 2024 Forum. Orlando, FL, USA, 8 January 2024 - 8 January 2024. View this article in WRRO
- Turbulent mean flow reconstruction based on sparse multi-sensor data and adjoint-based field inversion. AIAA AVIATION 2022 Forum. Chicago, IL, USA (and online), 27 June 2022 - 27 June 2022. View this article in WRRO
- An open-source adjoint-based field inversion tool for data-driven RANS modelling. AIAA AVIATION 2022 Forum(2022). Chicago, IL, USA (and online), 27 June 2022 - 27 June 2022. View this article in WRRO
Reports
Theses / Dissertations
- Data-driven Augmentation of Turbulence Models for Complex Fluid Flows.
- Towards statistical inference to improve turbulence RANS closures for multi-element aerofoils.
- Sensor placement for data assimilation of turbulence models using eigenspace perturbations. Physics of Fluids, 36, 015144. View this article in WRRO