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Hesam Salehipour
Hesam Salehipour
Principal Research Scientist, Autodesk Research
Dirección de correo verificada de autodesk.com - Página principal
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Diapycnal diffusivity, turbulent Prandtl number and mixing efficiency in Boussinesq stratified turbulence
H Salehipour, WR Peltier
Journal of Fluid Mechanics 775, 464-500, 2015
1342015
Efficiency of turbulent mixing in the abyssal ocean circulation
A Mashayek, H Salehipour, D Bouffard, CP Caulfield, R Ferrari, ...
Geophysical Research Letters 44 (12), 6296-6306, 2017
1142017
Turbulent diapycnal mixing in stratified shear flows: the influence of Prandtl number on mixing efficiency and transition at high Reynolds number
H Salehipour, WR Peltier, A Mashayek
Journal of Fluid Mechanics 773, 178-223, 2015
992015
Turbulent mixing due to the Holmboe wave instability at high Reynolds number
H Salehipour, CP Caulfield, WR Peltier
Journal of Fluid Mechanics 803, 591-621, 2016
802016
A new characterization of the turbulent diapycnal diffusivities of mass and momentum in the ocean
H Salehipour, WR Peltier, CB Whalen, JA MacKinnon
Geophysical Research Letters 43 (7), 3370-3379, 2016
652016
Deep learning of mixing by two ‘atoms’ of stratified turbulence
H Salehipour, WR Peltier
Journal of Fluid Mechanics 861, R4, 2019
572019
Robust identification of dynamically distinct regions in stratified turbulence
GD Portwood, SM de Bruyn Kops, JR Taylor, H Salehipour, CP Caulfield
Journal of fluid mechanics 807, R2, 2016
562016
Self-organized criticality of turbulence in strongly stratified mixing layers
H Salehipour, WR Peltier, CP Caulfield
Journal of Fluid Mechanics 856, 228-256, 2018
472018
A higher order discontinuous Galerkin, global shallow water model: Global ocean tides and aquaplanet benchmarks
H Salehipour, GR Stuhne, WR Peltier
Ocean Modelling 69, 93-107, 2013
212013
A deep learning approach to extract internal tides scattered by geostrophic turbulence
H Wang, N Grisouard, H Salehipour, A Nuz, M Poon, AL Ponte
Geophysical Research Letters 49 (11), e2022GL099400, 2022
192022
A coupled kinematics–energetics model for predicting energy efficient flapping flight
H Salehipour, DJ Willis
Journal of theoretical biology 318, 173-196, 2013
172013
XLB: A differentiable massively parallel lattice Boltzmann library in Python
M Ataei, H Salehipour
Computer Physics Communications 300, 109187, 2024
82024
A multi-fidelity framework for designing compliant flapping wings
DJ Willis, H Salehipour
5th ECCOMAS Conference, Lisbon, Portugal, June, 14-17, 2010
62010
A coupled kinematics and energetics model for flapping flight
H Salehipour, D Willis
48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and …, 2010
32010
Optimized GPU Implementation of Grid Refinement in Lattice Boltzmann Method
AH Mahmoud, H Salehipour, M Meneghin
22024
Reduced-order modeling of unsteady fluid flow using neural network ensembles
R Halder, M Ataei, H Salehipour, K Fidkowski, K Maki
arXiv preprint arXiv:2402.05372, 2024
22024
Preliminary design of three-dimensional flapping wings from a wake-only energetics model
D Willis, H Salehipour
AIAA Atmospheric Flight Mechanics Conference, 7630, 2010
22010
A Wake-Only Energetics Model for Preliminary Design of Biologically Inspired Micro Air Vehicles
H Salehipour, D Willis
AIAA Atmospheric Flight Mechanics Conference, 8232, 2010
12010
A Novel Energetics Model for Examining Flapping Flight in Nature and Engineering
H Salehipour, DJ Willis
at CFD, 2010
12010
A fast, low fidelity computational model for analyzing flapping flight energetics in nature and engineering
H Salehipour
University of Massachusetts Lowell, 2010
12010
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Artículos 1–20