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CTR Tea : Friday, October 22, 2021 - 4:15pm : Speaker(s): Dr. Nagi N. Mansour
Abtract:

A new formulation for near-wall filtering the Navier-Stokes equations without commutation errors will be detailed in the presentation. In addition, a new split of the dependent variables into large scales and sub-filter scales is formulated where the filtered large scales are not affected by further filtering (i.e. the filtered “filtered scales” are the “filtered scales”), thus removing the Leonard terms from the filtered equations. Finally, closure models for the near-wall effects on the large scales are proposed.

Bio:
Dr. Nagi N. Mansour earned his Ph.D. in Mechanical Engineering from Stanford University in 1978 where he pioneered one of the earliest Large-Eddy Simulations of a turbulent mixing layer. Dr.... Read More
CTR Tea : Friday, September 24, 2021 - 4:15pm : Speaker(s): Dr. Laurien Vandewalle
Abtract:

Many innovative catalytic technologies have been developed in the past decade as a response to the world’s rapidly growing demand for a more efficient and sustainable exploitation of energy and material resources. An example is oxidative coupling of methane (OCM), which is considered one of the most promising processes to valorize methane directly into ethylene. The performance of a heterogeneous catalytic process is the result of a complex interaction of phenomena at very different time and length scales. Even in the simplest reactor configurations (i.e., a packed bed reactor), conversion... Read More

Bio:
Dr. Laurien Vandewalle is currently a Postdoctoral Fellow in the Mechanical Engineering Department (NanoEnergy Lab) at Stanford University. She obtained her BSc (2012) and MSc (2014) degree in... Read More
CTR Tea : Friday, May 21, 2021 - 4:30pm : Speaker(s): Prof. Joseph Nichols
Abtract:

Spatial amplification owing to modal instability plays a significant role in determining when and how a hypersonic boundary layer transitions from a laminar state to turbulence. Traditional stability analysis methods rely on the strong assumption of a slowly-varying baseflow, which limits their predictive power. Such methods can calculate downstream amplification only relative to upstream points where the flow has already passed through shock waves (such as a vehicle’s bow shock) and that are far away from complex geometry (such as a blunt nose tip). The estimation of perturbation amplitude... Read More

Bio:
Professor Joseph Nichols’ current interests are in the areas of stability and sensitivity analysis of hypersonic flows and the aeroacoustics of high-speed jets. He has performed some of the largest... Read More
CTR Tea : Friday, May 7, 2021 - 4:30pm : Speaker(s): Dr. Daniel Livescu
Abtract:

This talk summarizes part of the work performed during our 3-year Laboratory Directed Research and Development - Directed Research (LDRD-DR) project, titled MachinE Learning for Turbulence (MELT).  Started in October 2018, the project partially covered ~10 staff members, 7 postdocs, several more summer students, and addressed a diverse set of topics related to turbulence and applications in climate and astrophysics.  Today, in order to highlight the exploratory aspect of such projects, I will survey some of our results on a) neural network (NN) models of scalar turbulence, b) learning Lagra... Read More

Bio:
Dr. Daniel Livescu has been a scientist at Los Alamos National Laboratory since he received his Ph.D. in 2001 and, currently, is leading the fluid dynamics team within the CCS Division and is the PI... Read More
CTR Tea : Friday, April 23, 2021 - 4:30pm : Speaker(s): Prof. Marcus Herrmann
Abtract:

While significant progress has been made in the past decade to predict atomization using detailed numerical simulations, these simulations come at significant computational cost since the range of scales that must be resolved typically exceeds those of a single phase turbulent flow significantly. A switch to a Large Eddy Simulation (LES) approach would be desirable, however, the underlying assumption of LES methods that the dynamics of the unresolved sub-filter scale can be inferred from the dynamics of the resolved scales is questionable when atomization occurs. Similar to viscosity in sin... Read More

Bio:
Marcus Herrmann is a Professor in the School for Engineering of Matter, Transport and Energy at Arizona State University. He received his PhD in Mechanical Engineering from the Technical University... Read More
CTR Tea : Friday, April 9, 2021 - 4:30pm : Speaker(s): Prof. Xiang Yang
Abtract:

The ambitious performance goals set by the aerodynamic as well as the turbomachinery industries call for more accurate, scale-resolving simulation tools. A viable path towards industrial-level scale-resolving simulations of flows at high Reynolds numbers is through wall-modeled large-eddy simulation (WMLES). This talk will discuss the best practice in WMLES of problems in which heat transfer plays a role, covering topics including the gird resolution, the LES/wall-model matching location, the low Mach number limit, and the turbulent Prandtl number. Special attention is given to problems at... Read More

Bio:
Dr. Xiang Yang is an Assistant Professor in the Mechanical Engineering Department at the Pennsylvania State University since 2018. He received his Ph.D. in Mechanical Engineering from Johns Hopkins... Read More
CTR Tea : Friday, March 5, 2021 - 4:30pm : Speaker(s): Dr. Ronald Chan
Abtract:

Turbulent breaking waves entrain air cavities that break up and coalesce to form polydisperse clouds of bubbles. We recently provided theoretical and numerical justification that the dominant mechanism for super-Hinze-scale bubble generation is a fragmentation cascade from large to small sizes sustained by turbulent velocity fluctuations. This behavior should be universal across various turbulent bubbly flows because of the size locality inherent in a cascade. Universality simplifies the development of subgrid-scale (SGS) breakup models in two-phase large eddy simulations (LES). We formulat... Read More

CTR Tea : Friday, February 19, 2021 - 4:30pm : Speaker(s): Prof. Sandip Ghosal
Abtract:

When I first arrived at CTR as a postdoctoral fellow in 1992, I could feel deep rumblings beneath my feet. I knew that California was famous for its seismic activity but this was of a different kind. It originated from something called the “Dynamic Model”: the greatest idea in turbulence modeling in a long time that “worked” but did not make sense. I would like to talk
about our efforts to make sense of it all and the intellectual atmosphere at CTR in the 1990’s.

Link to video file:
... Read More

Bio:
Department of Mechanical Engineering and by courtesy Engineering Sciences and Applied Mathematics, Northwestern University
CTR Tea : Friday, February 5, 2021 - 4:30pm : Speaker(s): Prof. Charles Meneveau
Abtract:

At 30 years old, the Germano identity-based dynamic model for turbulence simulations can be said to have become a classic result for the field of turbulence. In this presentation, I will recall how several of its variants (e.g. Lagrangian, scale-dependent, multiplicative) came about and summarize the main ideas underlying these variations on an elegant theme.

Link to video file:
The dynamic model for LES and variations on the theme

Bio:
Louis M. Sardella Professor of Mechanical Engineering, and Associate Director, Institute for Data Intensive Engineering and Science, Johns Hopkins University
CTR Tea : Friday, January 22, 2021 - 4:30pm : Speaker(s): Prof. Parviz Moin
Abtract:

In the immediate aftermath of the 1990 Summer Program, there was a flurry of activities at CTR seeking to exploit the dynamic modeling concept in canonical turbulent flows which heretofore had challenged the universality of turbulence models. I will discuss extensions to compressible flow, scalar transport, reacting flows and other potential formulations
of the dynamic modeling concept that were considered.

Link to video file:
At last, Turbulence closure modeling minus tu... Read More

Bio:
Franklin P. and Caroline M. Johnson Professor in the School of Engineering and Director, Center for Turbulence Research, Stanford University

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