1.5k citations · 2.3k across the 33 of their papers we have counts for
10 papers · 1 filter
Machine Learning to Predict Aerodynamic Stall
Ettore Saetta, Renato Tognaccini, Gianluca Iaccarino
A convolutional autoencoder is trained using a database of airfoil aerodynamic simulations and assessed in terms of overall accuracy and interpretability. The goal is to predict th…
The discrete Green's function paradigm for two-way coupled Euler-Lagrange simulation
J. A. K. Horwitz, G. Iaccarino, J. K. Eaton +1
We outline a methodology for the simulation of particle-laden flows whereby the dispersed and fluid phases are two-way coupled. The drag force which couples fluid and particle mome…
Quantifying structural uncertainty in the BHR model for variable-density flows
Z. Huang, J. Hayes, G. Iaccarino
In this brief, we try to develop a comprehensive framework to identify, quantify, isolate, and reduce the uncertainties in the original BHR model \citep {Besnard1992} for variable-…
Estimated aleatoric uncertainty from initial and inlet conditions for variable density mixing
Jan Felix Heyse, Zhu Huang, Gianluca Iaccarino
Variable density flows occur in a variety of different systems with a wide range of scales, from astrophysics to atmospheric flows to inertial confinement fusion or reacting flows.…
Turbulence Modeling in the Age of Data
Karthik Duraisamy, Gianluca Iaccarino, Heng Xiao
Data from experiments and direct simulations of turbulence have historically been used to calibrate simple engineering models such as those based on the Reynolds-averaged Navier--S…
An uncertainty estimation module for turbulence model predictions in SU2
Aashwin Ananda Mishra, Jayant Mukhopadhaya, Gianluca Iaccarino +1
With the advent of improved computational resources, aerospace design has testing-based process to a simulation-driven procedure, wherein uncertainties in design and operating cond…
A return to eddy viscosity model for epistemic UQ in RANS closures
W. N. Edeling, G. Iaccarino, P. Cinnella
For the purpose of Uncertainty Quantification (UQ) of Reynolds-Averaged Navier-Stokes closures, we introduce a framework in which perturbations in the eigenvalues of the anisotropy…
Assessing turbulence sensitivity using stochastic Monte Carlo analysis
K. Duraisamy, Anand A., G. Iaccarino
Reynolds Averaged Navier Stokes (RANS) models represent the workhorse for studying turbulent flows in industrial applications. Such single-point turbulence models have limitations…
RANS predictions for high-speed flows using enveloping models
A. A. Mishra, G. Iaccarino
In this investigation, we outline an enveloping models methodology for estimating structural uncertainty bounds on RANS closures. This methodology incorporates both eigenvalue and…
Validation of a new k-epsilon model with the pressure diffusion effects in separated flows
Svetlana V. Poroseva, Gianluca Iaccarino
The contribution of the "rapid" part of the pressure diffusion to the turbulent kinetic energy balance is analyzed, and a new model to describe its effect is suggested. A new trans…