### ### ME-CSE PhD Thesis Defense Announcement

Tuesday, December 6, 2016 | 3:00 PM | 66-110

### ME-CSE PhD Thesis Defense Announcement

Tuesday, December 6, 2016 | 3:00 PM | 66-110

**Experimental study and modeling analysis of ion transport membranes for methane partial oxidation and oxyfuel combustion **

*Georgios Dimitrakopoulos *

The atmospheric concentration of CO_2 has recently exceeded 400 ppm (up from 285 ppm in 1850), largely because of the burning of fossil fuels. Despite the growth of alternatives, these fuels will continue to play a major role in the energy sector for many decades. In accordance with international agreements, action to curtail CO_2 emissions is necessary, including carbon capture, reuse and storage. For this purpose, some of the leading technologies are oxy-combustion for power generation and partial oxidation for syngas production. Both require significant quantities of oxygen, whose production can impose considerable energy and economic penalties. Alternative technologies, such as intermediate-temperature ceramic membranes, operating under reactive conditions, promise to ameliorate both. Challenges include the long term stability of the material, reactor design and integration into the overall system.

The goal of this thesis is to develop a framework for the thermochemical and electrochemical modeling of oxygen-conducting membranes that can be used in reactor design, based on experimental measurements and detailed surface exchange kinetics and charged species transport. 〖La〗_0.9 〖Ca〗_0.1 FeO_(3-δ) (LCF) perovskite membranes have been used because of their long term stability in a reducing environment. Using experimental measurements, we examine the impact of hydrogen, carbon monoxide and methane on oxygen permeation and defect chemistry. While LCF exhibits low flux under non-reactive conditions, in the presence of fuel oxygen permeation increases by more than one order of magnitude. Our experiments confirm that hydrogen surface oxidation is faster compared to carbon monoxide. With methane, syngas production is slow and oxygen permeation is limited by surface exchange on the permeate side. Adding CO_2 to the fuel stream doubles the oxygen flux and increases syngas production by an order of magnitude. Application of nickel catalyst on the permeate surface reduces the surface kinetics limitations.

Our modeling analysis shows that different oxidation states of Fe participate in the electron transfer process. To account for this dependency, oxygen transport is modeled using a multi-step (fuel dependent) surface reaction mechanism that preserve thermodynamic consistency and conserve site balance and electroneutrality. Charged species diffusion is modeled using the dilute-limit Poisson-Nernst-Planck formulation that accounts for transport due to concentration gradient as well as electromigration. We use the experimental data to extract kinetic parameters of the model. We couple the aforementioned model with CFD of the gas-phase transport and thermochemistry in an effort to design membrane reactors that exhibit high oxygen permeation and fuel conversion.

Thesis Committee

Ahmed F. Ghoniem

Ronald C. Crane (1972) Professor of Mechanical Engineering at MIT

Committee Chair and Thesis Supervisor

Martin Z. Bazant

E. G. Roos (1944) Professor Chair of Chemical Engineering at MIT

Committee Member

Nicolas Hadjiconstantinou

Professor of Mechanical Engineering at MIT

Committee Member

Youssef M. Marzouk

Class of 1942 Associate Professor of Aeronautics and Astronautics at MIT

Committee Member

Yuriy Roman

Associate Professor of Chemical Engineering at MIT

Committee Member

### ### Thursday, December 8th, 2016 | 12:00 PM* | 37-212

### Thursday, December 8th, 2016 | 12:00 PM* | 37-212

**Explicit parametric solutions: the “extra-mile” for Model Order Reduction **

*Antonio Huerta *

Computational Mechanics techniques are well integrated in today’s applied sciences and engineering practice. However, their cost (man-hours for pre- and post-processing, as well as CPU time) is unaffordable for a large number of applications in a daily industrial/engineering production environment as well as for many scientific simulations. This is substantiated when the simulation requires a large number of possible scenarios, corresponding to different instances of a parametric family of problems (for instance in the context of support for decision-making, optimization and uncertainty quantification).

A standard strategy to reduce costs and increase the practical interest of these computational techniques is using pre-computed solutions or the more formal framework of reduced order models. This requires pre-computing offline some representative samples of the parametric family of solutions (viz. snapshots for Reduced Basis methods, principal components for POD, …). Then, any other instance is computed online with a small computational overhead. In the case of the Proper Generalized Decomposition, the offline phase provides an explicit description of the parametric solution, i.e. an explicit parametric solution. Thus, the offline phase is more involved but the online phase is a simple functional evaluation with a negligible computational overhead.

This approach allows to easily adapt parameterized solutions in Graphical User Interfaces (GUIs) to readily visualize solutions in fast decision-making processes (supported by human-computer interaction).

But more important, these explicit parametric solutions can be further employed as an ingredient in more sophisticated computational strategies.

* Lunch provided at 11:45

### ### Thursday, March 23rd 2017 | 12:00 PM* | 37-212

### Thursday, March 23rd 2017 | 12:00 PM* | 37-212

*Serkan Gugercin *

* Lunch available at 11:45

### ### Thursday, April 27th 2017 | 12:00 PM* | 37-212

### Thursday, April 27th 2017 | 12:00 PM* | 37-212

*Misha Kilmer *

* Lunch provided at 11:45

### ### To Be Rescheduled - New Date TBA

### To Be Rescheduled - New Date TBA

**A NONPARAMETRIC PROBABILISTIC APPROACH FOR QUANTIFYING UNCERTAINTIES IN LOW- AND HIGH-DIMENSIONAL NONLINEAR MODELS **

*Charbel Farhat *

A. Bos^1 and C. Farhat^{1,2,3}

^1 Department of Aeronautics and Astronautics

^2 Department of Mechanical Engineering

^3 Institute for Computational and Mathematical Engineering

Stanford University, Stanford, CA 94305, USA

C. Soize

Laboratoire Mod\'elisation et Simulation Multi Echelle

MSME UMR 8208 CNRS, Universit\'e Paris-Est, 5 bd Descartes

77454 Marne-la-Vallee, France

A nonparametric probabilistic approach for modeling uncertainties in projection-based, nonlinear, reduced-order models is presented. When experimental data is available, this approach can also quantify uncertainties in the associated high-dimensional models.

The main underlying idea is two-fold. First, to substitute the deterministic Reduced-Order Basis (ROB) with a stochastic counterpart. Second, to construct the probability measure of the Stochastic Reduced-Order Basis (SROB) on a subset of a compact Stiefel manifold in order to preserve some important properties of a ROB. The stochastic modeling is performed so that the probability distribution of the constructed SROB depends on a small number of hyperparameters. These are determined by solving a reduced-order statistical inverse problem.

The proposed nonparametric probabilistic approach for taking into account model form uncertainties can be interpreted as a stochastic-based method for extracting fundamental information or knowledge from test or High-Dimensional Model (HDM) data that is not captured by a deterministic HDM or ROM, respectively. In this approach, one essentially parameterizes the approximation basis in order to capture the variabilities instead of parameterizing the governing equations. Its mathematical properties are analyzed through theoretical developments and numerical simulations. Its potential is demonstrated through several example problems from nonlinear computational structural dynamics.

* Lunch available at 11:45