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====== UNH Boundary Layer Workshop, 2015 ======

** High Reynolds Number Boundary Layer Turbulence: ** \\
** Advancing Wall-Turbulence Model Development and Implementation ** \\
** University of New Hampshire, November 19-20, 2015 **

^ [[unh2015:program|scientific program]] ^ [[unh2015:participant|participants]] ^ [[unh2015:sponsors|sponsors]] ^ [[unh2015:travel|travel and lodging]] ^ [[unh2015:iam|about IAM]] ^

Over the past decade there has been increasing activity relating to the construction and implementation of more rigorously founded and thus higher fidelity models of wall turbulence.  Toward furthering these exciting new developments, in 2013 the Integrated Applied Mathematics Program at the University of New Hampshire, in concert with the NSF CBET/Fluid Dynamics Program, hosted a workshop entitled: //High Reynolds Number Boundary Layer Turbulence: Integrating Descriptions of Statistical Structure, Scaling and Dynamical Evolution.//  This workshop has helped to cultivate a community of applied mathematicians, computational scientists, and experimentalists who are working on a variety of inter-related modeling approaches.

Since then, however, it has become apparent that further advances can be accelerated by fostering among the members of this emerging community, a deeper understanding of the practical details of the various models under development.  This is the aim of the //UNH/IAM Workshop on Advancing Wall-Turbulence Model Development and Implementation//.  This workshop will be held on November 19-20, 2015 at the UNH Durham campus, i.e., just prior to the APS-DFD meeting November 22-24 in nearby Boston.

Even more so than previously, the aim is an authentic workshop, from which the participants develop a practically useful understanding of the relevant modeling and analysis methods.  As such it will only have four sessions (two per day), each of which will focus on one of four primary modeling themes,

  * Exact Coherent Structures (John Gibson, UNH) 
  * Data Informed Sparse Representations (Maziar Hemati, Univ. Minnesota)
  * Quasi-linearized Models (Brad Marston, Brown)
  * Asymptotically Reduced PDE Models (Keith Julien, UC Boulder)
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