96 citations · 262 across the 11 of their papers we have counts for
12 papers
Uncertainty-driven training for three-dimensional calibrated lung nodule classification
Giuseppe Tripodi, Alessandro De Rosis, Saleh Rezaeiravesh
In this work, we present an uncertainty-driven training framework for three-dimensional computed tomography (CT) lung nodule classification, where validation-based uncertainty esti…
Causally coherent structures in turbulent dynamical systems
Daniele Massaro, Saleh Rezaeiravesh, Philipp Schlatter
The extraction of spatio-temporal coherence in high-dimensional, chaotic, non-linear dynamical systems, such as turbulent flows, remains a fundamental challenge in physics, mathema…
Effects of lower floating-point precision on scale-resolving numerical simulations of turbulence
Martin Karp, Ronith Stanly, Timofey Mukha +11
Modern computing clusters offer specialized hardware for reduced-precision arithmetic that can speed up the time to solution significantly. This is possible due to a decrease in da…
Robustness and uncertainty of direct numerical simulation under the influence of rounding and noise
Martin Karp, Niclas Jansson, Saleh Rezaeiravesh +2
Numerical precision in large-scale scientific computations has become an emerging topic due to recent developments in computer hardware. Lower floating point precision offers the p…
In-situ Estimation of Time-averaging Uncertainties in Turbulent Flow Simulations
Saleh Rezaeiravesh, Christian Gscheidle, Adam Peplinski +2
The statistics obtained from turbulent flow simulations are generally uncertain due to finite time averaging. The techniques available in the literature to accurately estimate thes…
Efficient prediction of turbulent flow quantities using a Bayesian hierarchical multifidelity model
Saleh Rezaeiravesh, Timofey Mukha, Philipp Schlatter
High-fidelity scale-resolving simulations of turbulent flows quickly become prohibitively expensive, especially at high Reynolds numbers. As a remedy, we may use multifidelity mode…
Applying Bayesian Optimization with Gaussian Process Regression to Computational Fluid Dynamics Problems
Y. Morita, S. Rezaeiravesh, N. Tabatabaei +3
Bayesian optimization (BO) based on Gaussian process regression (GPR) is applied to different CFD (computational fluid dynamics) problems which can be of practical relevance. The p…
An Uncertainty-Quantification Framework for Assessing Accuracy, Sensitivity, and Robustness in Computational Fluid Dynamics
Saleh Rezaeiravesh, Ricardo Vinuesa, Philipp Schlatter
A framework is developed based on different uncertainty quantification (UQ) techniques in order to assess validation and verification (V&V) metrics in computational physics problem…
Systematic Study of Accuracy of Wall-Modeled Large Eddy Simulation using Uncertainty Quantification Techniques
Saleh Rezaeiravesh, Timofey Mukha, Mattias Liefvendahl
The predictive accuracy of wall-modeled large eddy simulation is studied by systematic simulation campaigns of turbulent channel flow. The effect of wall model, grid resolution and…
A Library for Wall-Modelled Large-Eddy Simulation Based on OpenFOAM Technology
Timofey Mukha, Saleh Rezaeiravesh, Mattias Liefvendahl
This work presents a feature-rich open-source library for wall-modelled large-eddy simulation (WMLES), which is a turbulence modelling approach that reduces the computational cost…
Effect of grid resolution on large eddy simulation of wall-bounded turbulence
Saleh Rezaeiravesh, Mattias Liefvendahl
The effect of grid resolution on large eddy simulation (LES) of wall-bounded turbulent flow is investigated. A channel flow simulation campaign involving systematic variation of th…
Assessment of uncertainties in hot-wire anemometry and oil-film interferometry measurements for wall-bounded turbulent flows
Saleh Rezaeiravesh, Ricardo Vinuesa, Mattias Liefvendahl +1
In this study, the sources of uncertainty of hot-wire anemometry (HWA) and oil-film interferometry (OFI) measurements are assessed. Both statistical and classical methods are used…