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Maseeh Mathematics & Statistics Colloquium Series

(In Neuberger Hall 454 unless otherwise noted.)

The upcoming colloquium:

3:15PM Friday, September 30, 2016
Malgorzata Peszynska, Oregon State University
Adsorption: new mathematics and computations for multiple components

Abstract: Adsorption is a well known process during which (adsorbent) gas particles adhere to a surface (of adsorbent). Adsorption is present in many natural systems and is widely used in biotechnology, pharmaceutical and chemical engineering industry. The mathematical models of adsorption relate the amount adsorbed to that present in the fluid which transports the adsorbent, and range from simple nonlinear parametric algebraic relationships called isotherms to the complex statistical mechanics algorithms which take into account the surface energy and bonding energy of the particles. The mathematical and computational treatment of the transport with adsorption is challenging due to its coupled nonlinear hyperbolic system structure.  
In the talk we present our recent results on hybrid models of multicomponent adsorption in which advection is the prevalent transport mechanism. In particular, we discuss the well-posedness of adsorption-desorption hysteresis and computational stability of non-equilibrium models, and the hyperbolicity of a system formulated with hybrid models in which the isotherms are not given explicitly.
 

The 2016-2017 Colloquium Series:

3:15PM Friday, September 30, 2016:

Malgorzata Peszynska, Oregon State University
Adsorption: new mathematics and computations for multiple components
view abstract

3:15PM Friday, October 7, 2016:

Martin Flashman, Humboldt State University
TBA
view abstract

3:15PM Friday, October 14, 2016:

Dacian Daescu, Portland State University
The value of observations in BIG data assimilation: significance, challenges, and research opportunities
view abstract

3:15PM Friday, October 21, 2016:

Jane-Jane Lo, Western Michigan University
TBA
view abstract


 

Abstracts:

September 30, 2016 (return)

Malgorzata Peszynska, Oregon State University
Adsorption: new mathematics and computations for multiple components  
Adsorption is a well known process during which (adsorbent) gas particles adhere to a surface (of adsorbent). Adsorption is present in many natural systems and is widely used in biotechnology, pharmaceutical and chemical engineering industry. The mathematical models of adsorption relate the amount adsorbed to that present in the fluid which transports the adsorbent, and range from simple nonlinear parametric algebraic relationships called isotherms to the complex statistical mechanics algorithms which take into account the surface energy and bonding energy of the particles. The mathematical and computational treatment of the transport with adsorption is challenging due to its coupled nonlinear hyperbolic system structure.  
In the talk we present our recent results on hybrid models of multicomponent adsorption in which advection is the prevalent transport mechanism. In particular, we discuss the well-posedness of adsorption-desorption hysteresis and computational stability of non-equilibrium models, and the hyperbolicity of a system formulated with hybrid models in which the isotherms are not given explicitly.

October 7, 2016 (return)

Martin Flashman, Humboldt State University
TBA  
TBA

October 14, 2016 (return)

Dacian Daescu, Portland State University
The value of observations in BIG data assimilation: significance, challenges, and research opportunities  
Data assimilation systems (DAS) for numerical weather prediction (NWP) combine information from a numerical model, observational data, and error statistics to analyze and predict the state of the atmosphere. Variational methods (3D-Var, 4D-Var) produce an estimate (analysis) to the true state by solving a large scale nonlinear optimization problem. The rapid growth in the data volume provided by satellite-based instruments has prompted research to assess and improve the forecast impact ("value") of high-resolution observations.  
We discuss mathematical and computational aspects of hyperparameter sensitivity and impact estimation in the context of model-constrained optimization. The evaluation of the sensitivity of a forecast error measure ("quantity of interest") to observations and error covariance parameters is considered in a 4D-Var DAS. Special emphasis is given to the analysis of correlated errors in observations assimilated from hyperspectral remote sensing instruments. The practical significance, challenges, and research opportunities are presented together with illustrative numerical results and a summary of the current status of implementation at operational NWP centers.

October 21, 2016 (return)

Jane-Jane Lo, Western Michigan University
TBA  
TBA