1.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of Colorado BoulderUS112 papers
- Colorado School of MinesUS59 papers
- King's College LondonGB30 papers
- Lawrence Berkeley National LaboratoryUS28 papers
- Fudan UniversityCN17 papers
- Radboud University NijmegenNL15 papers
- Stanford UniversityUS15 papers
- University of MinnesotaUS15 papers
- University of California, BerkeleyUS14 papers
- Colorado State UniversityUS13 papers
- North Carolina State UniversityUS12 papers
- Princeton UniversityUS12 papers
12 papers · 1 filter
Improved pressure-gradient sensor for the prediction of separation onset in RANS models
Kevin Patrick Griffin, Ganesh Vijayakumar, Ashesh Sharma +1
We improve upon two key aspects of the Menter shear stress transport (SST) turbulence model: (1) We propose a more robust adverse pressure gradient sensor based on the strength of…
A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
Graham Pash, Malik Hassanaly, Shashank Yellapantula
While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical s…
Direct numerical simulation of a high-pressure hydrogen micromix combustor: flame structure and stabilisation mechanism
Thomas L. Howarth, Mark A. Picciani, Edward S. Richardson +2
A high-pressure hydrogen micromix combustor has been investigated using direct numerical simulation with detailed chemistry to examine the flame structure and stabilisation mechani…
Near-wall model for compressible turbulent boundary layers based on an inverse velocity transformation
Kevin Patrick Griffin, Lin Fu, Parviz Moin
In this work, a near-wall model, which couples the inverse of a recently developed compressible velocity transformation [Griffin, Fu, & Moin, PNAS, 118:34, 2021] and an algebraic t…
A Moving Embedded Boundary Approach For The Compressible Navier-Stokes Equations In A Block-Structured Adaptive Refinement Framework
Mahesh Natarajan, Ray Grout, Weiqun Zhang +1
A computational technique has been developed to perform compressible flow simulations involving moving boundaries using an embedded boundary approach within the block-structured ad…
Analytical solution for the cumulative wake of wind turbines in wind farms
Majid Bastankhah, Bridget L. Welch, Luis A. Martínez-Tossas +2
This paper solves an approximate form of conservation of mass and momentum for a turbine in a wind farm array. The solution is a fairly simple explicit relationship that predicts t…
The Area Localized Coupled Model for Analytical Mean Flow Prediction in Arbitrary Wind Farm Geometries
Genevieve M. Starke, Charles Meneveau, Jennifer R. King +1
This work introduces the Area Localized Coupled (ALC) model, which extends earlier approaches to coupling classical wake superposition and atmospheric boundary layer models in orde…
Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine
Aliza Abraham, Luis A. Martinez-Tossas, Jiarong Hong
The current study uses large eddy simulations to investigate the transient response of a utility-scale wind turbine wake to dynamic changes in atmospheric and operational condition…
Wake steering of multirotor wind turbines
Gustav A. Speakman, Mahdi Abkar, Luis A. Martínez-Tossas +1
In this paper, wake steering is applied to multirotor turbines to determine whether it has the potential to reduce wind plant wake losses. Through application of rotor yaw to multi…
Assessment of low-altitude atmospheric turbulence models for aircraft aeroelasticity
Georgios Deskos, Alfonso del Carre, Rafael Palacios
We investigate the dynamic response of flexible aircraft in low-altitude atmospheric turbulence. To this end, three turbulence models of increasing fidelity, namely, the one-dimens…
Data recovery in computational fluid dynamics through deep image priors
Marc T. Henry de Frahan, Ray W. Grout
One of the challenges encountered by computational simulations at exascale is the reliability of simulations in the face of hardware and software faults. These faults, expected to…
From Deep to Physics-Informed Learning of Turbulence: Diagnostics
Ryan King, Oliver Hennigh, Arvind Mohan +1
We describe tests validating progress made toward acceleration and automation of hydrodynamic codes in the regime of developed turbulence by three Deep Learning (DL) Neural Network…