Saleh Rezaeiravesh

The University of Manchester

United Kingdom

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Articles (16)

Collaborators (9)

Daniele Massaro

KTH Royal Institute of Technology

Fermin Mallor

KTH Royal Institute of Technology

Yasser Mahmoudi Larimi

The University of Manchester

UNITED KINGDOM

Imran Afgan

The University of Manchester

UNITED KINGDOM

mohammad jadidi

University of Bristol

UNITED KINGDOM

Narges Tabatabaei

Lulea University of Technology

Adam Peplinski

KTH Royal Institute of Technology

Timofey Mukha

KTH Royal Institute of Technology

Fazle Hussain

Texas Tech University

UNITED STATES

Saleh Rezaeiravesh

Research Interests

Explore related searches

Contact this professor

Articles (16)

Collaborators (9)

Daniele Massaro

Fermin Mallor

Yasser Mahmoudi Larimi

Imran Afgan

mohammad jadidi

Narges Tabatabaei

Adam Peplinski

Timofey Mukha

Fazle Hussain

How do I reach out?

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Saleh Rezaeiravesh

Research Interests

Explore related searches

Contact this professor

Articles (16)

Erratum: “Assessing the influence of design variables on serpentine duct intake performance using surrogate-based sensitivity analysis” [Phys. Fluids 37(6), 066127 (2025)]

In-situ estimation of time-averaging uncertainties in turbulent flow simulations

Physics-Informed Neural Network for Turbulent Flow Reconstruction in Composite Porous-Fluid Systems

Bayesian Optimization of Wall-Normal Blowing and Suction-Based Flow Control of a NACA 4412 Wing Profile

Generating synthetic turbulence with vector autoregression of proper orthogonal decomposition time coefficients

Energy-based characterisation of large-scale coherent structures in turbulent pipe flows

Autoregressive models for quantification of time-averaging uncertainties in turbulent flows

Wall-modeled large eddy simulation of 90° bent pipe flows with/without particles

Efficient prediction of turbulent flow quantities using a Bayesian hierarchical multifidelity model

Direct numerical simulations of turbulent pipe flow up to

On the potential of transfer entropy in turbulent dynamical systems

An uncertainty-quantification framework for assessing accuracy, sensitivity, and robustness in computational fluid dynamics

Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems

Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems

UQit: A Python package for uncertainty quantification (UQ) in computational fluid dynamics (CFD)

On numerical uncertainties in scale-resolving simulations of canonical wall turbulence

Collaborators (9)

Daniele Massaro

Fermin Mallor

Yasser Mahmoudi Larimi

Imran Afgan

mohammad jadidi

Narges Tabatabaei

Adam Peplinski

Timofey Mukha

Fazle Hussain

How do I reach out?

Meet Kite AI