1.5k citations · 2.3k across the 33 of their papers we have counts for
34 papers
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…
Spinning-enabled Wireless Amphibious Origami Millirobot
Qiji Ze, Shuai Wu, Jize Dai +5
Wireless millimeter-scale origami robots that can locomote in narrow spaces and morph their shapes have recently been explored with great potential for biomedical applications. Exi…
Simultaneous Identification and Denoising of Dynamical Systems
Jeffrey M. Hokanson, Gianluca Iaccarino, Alireza Doostan
In recent years there has been a push to discover the governing equations dynamical systems directly from measurements of the state, often motivated by systems that are too complex…
An integrated heterogeneous computing framework for ensemble simulations of laser-induced ignition
Kazuki Maeda, Thiago Teixeira, Jonathan M. Wang +6
An integrated computational framework is introduced to study complex engineering systems through physics-based ensemble simulations on heterogeneous supercomputers. The framework i…
Task-parallel in-situ temporal compression of large-scale computational fluid dynamics data
Heather Pacella, Alec Dunton, Alireza Doostan +1
Present day computational fluid dynamics simulations generate extremely large amounts of data, sometimes on the order of TB/s. Often, a significant fraction of this data is discard…
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.…
Pass-efficient methods for compression of high-dimensional turbulent flow data
Alec M. Dunton, Lluís Jofre, Gianluca Iaccarino +1
The future of high-performance computing, specifically on future Exascale computers, will presumably see memory capacity and bandwidth fail to keep pace with data generated, for in…
Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence
Hillary R. Fairbanks, Lluis Jofre, Gianluca Geraci +2
Efficiently performing predictive studies of irradiated particle-laden turbulent flows has the potential of providing significant contributions towards better understanding and opt…
Immersed-finite-element method for deformable particle suspensions in viscous and viscoelastic media
Amir Saadat, Chris J. Guido, Gianluca Iaccarino +1
Deformable elastic bodies in viscous and viscoelastic media constitute a large portion of synthetic and biological complex fluids. We present a parallelized 3D-simulation methodolo…
Cutting the Double Loop: Theory and Algorithms for Reliability-Based Design Optimization with Statistical Uncertainty
Zachary del Rosario, Richard W. Fenrich, Gianluca Iaccarino
Statistical uncertainties complicate engineering design -- confounding regulated design approaches, and degrading the performance of reliability efforts. The simplest means to tack…
Quadrature Strategies for Constructing Polynomial Approximations
Pranay Seshadri, Gianluca Iaccarino, Tiziano Ghisu
Finding suitable points for multivariate polynomial interpolation and approximation is a challenging task. Yet, despite this challenge, there has been tremendous research dedicated…
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…
Multi-fidelity uncertainty quantification of irradiated particle-laden turbulence
Lluis Jofre, Gianluca Geraci, Hillary Fairbanks +2
The study of complex systems is often based on computationally intensive, high-fidelity, simulations. To build confidence in the prediction accuracy of such simulations, the impact…
Lurking Variable Detection via Dimensional Analysis
Zachary del Rosario, Minyong Lee, Gianluca Iaccarino
Lurking variables represent hidden information, and preclude a full understanding of phenomena of interest. Detection is usually based on serendipity -- visual detection of unexpla…
Data-driven dimensional analysis: algorithms for unique and relevant dimensionless groups
Paul G. Constantine, Zachary del Rosario, Gianluca Iaccarino
Classical dimensional analysis has two limitations: (i) the computed dimensionless groups are not unique, and (ii) the analysis does not measure relative importance of the dimensio…
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…
A Low-rank Control Variate for Multilevel Monte Carlo Simulation of High-dimensional Uncertain Systems
Hillary Fairbanks, Alireza Doostan, Christian Ketelsen +1
Multilevel Monte Carlo (MLMC) is a recently proposed variation of Monte Carlo (MC) simulation that achieves variance reduction by simulating the governing equations on a series of…
Many physical laws are ridge functions
Paul G. Constantine, Zachary del Rosario, Gianluca Iaccarino
A ridge function is a function of several variables that is constant along certain directions in its domain. Using classical dimensional analysis, we show that many physical laws a…
Density-Matching for Turbomachinery Optimization Under Uncertainty
Pranay Seshadri, Geoffrey Parks, Shahrokh Shahpar
A monotonic, non-kernel density variant of the density-matching technique for optimization under uncertainty is developed. The approach is suited for turbomachinery problems which,…