Research output per year
Research output per year
Personal profile
Qualifications
BSc, MSc, PhD
Biography
Saleh Rezaeiravesh is a Lecturer in Engineering Simulation and Data Science within the Department of Mechanical and Aerospace Engineering (MAE). He earned his PhD in Scientific Computing (Numerical Analysis) from Uppsala University, Sweden, in 2018 and then joined the FLOW Centre at KTH Royal Institute of Technology, Sweden. His research interests include CFD, scale-resolving simulation of (wall-bounded) turbulent flows, development and application of uncertainty quantification (UQ) and data-driven techniques such as Bayesian optimization and multifidelity models to fluid mechanics problems.
Teaching
MECH20042&20542: Numerical Methods & Computing (Level 2)
AERO40041: Data-driven Modelling & Simulation (Level 4)
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Doctor of Philosophy, University of Uppsala
Award Date: 23 Nov 2018
Keywords
- Uncertainty Quantification
- Data-driven Methods
- Turbulent Flows
- Computational Fluid Dynamics (CFD)
- Time Series
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Collaborations and top research areas from the last five years
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An uncertainty-quantification framework for assessing accuracy, sensitivity, and robustness in computational fluid dynamics
Rezaeiravesh, S., Vinuesa, R. & Schlatter, P., 30 Apr 2022, In: Journal of Computational Science. 62, 101688.Research output: Contribution to journal › Article › peer-review
Open Access -
Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems
Morita, Y., Rezaeiravesh, S., Tabatabaei, N., Vinuesa, R., Fukagata, K. & Schlatter, P., 15 Jan 2022, In: Journal of Computational Physics. 449, 110788.Research output: Contribution to journal › Article › peer-review
Open Access -
A library for wall-modelled large-eddy simulation based on OpenFOAM technology
Mukha, T., Rezaeiravesh, S. & Liefvendahl, M., Jun 2019, In: Computer Physics Communications. 239, p. 204-224 21 p.Research output: Contribution to journal › Article › peer-review
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UQit: A Python package for uncertainty quantification (UQ) in computational fluid dynamics (CFD)
Rezaeiravesh, S., Vinuesa, R. & Schlatter, P., 20 Apr 2021, In: Journal of Open Source Software.Research output: Contribution to journal › Article › peer-review
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On numerical uncertainties in scale-resolving simulations of canonical wall turbulence
Rezaeiravesh, S., Vinuesa, R. & Schlatter, P., 15 Sept 2021, In: Computers and Fluids. 227, 105024.Research output: Contribution to journal › Article › peer-review
Open Access