The short courses being offered at USNC/TAM 2018 will take place on Tuesday morning, June 5, from 8:00 am – 12:00 pm.
SC001-Modeling Mass Transport in PEM Fuel Cells
Instructor: Ezequiel Médici (Michigan Technological University)
Electrochemical devices such as fuel cells, batteries, and electrodes are generally made by stacking porous layers. Each layer has unique porous properties, such as porosity, tortuosity, permeability, electrical conductivity, and thermal conductivity, among others. These properties are specified depending on the particular transport process intended to be controlled on a given layer, such as heat transport, mass transport, phase change, proton transport, etc. However, all these transport processes are coupled and act over the whole stack. Modeling these coupled transport processes on porous materials where the localized properties (pore level) change stochastically is highly challenging. Many computational models such as continuum models, pore network models, and the lattice Boltzmann model have been developed and implemented in the past with different degrees of success. This short course will provide an overview of the fundamental aspects of the most common transport mechanisms in electrochemical devices, including capillary-driven flows in porous structures. Fundamental concepts such as capillarity, hydrophobicity, phase change (diffusive versus kinetic models), effective permeability, effective thermal conductivity, and effective diffusivity at the microscale will be discussed. This short course will also provide attendees with the background needed to implement those transport processes using a pore network model.
SC002-Mechanics of Defects in 2D Materials Description
Instructor: Harley Johnson (University of Illinois at Urbana-Champaign)
In the past 10-15 years, graphene has become one of the most important and exciting material systems for mechanicians interested in nanoelectronics, energy conversion and storage, composite materials, and other applications. New and exotic 2D materials have emerged more recently, including h-BN and transition metal dichalcogenides. Compared with conventional 3D bulk materials, 2D materials have unique properties that require a fresh mechanics perspective. This short course will cover the mechanics of defects in 2D materials, including the following topics: crystallographic structure of in-plane defects including dislocations and grain-boundaries; dislocation motion in 2D materials; interfacial misfit dislocations; defects in layered 2D materials including van der Waals dislocations and Moiré patterns; effects of defects on mechanical, electrical, and optical properties of 2D materials; atomistic and continuum computational methods for studying defects in 2D materials.
SC003-Peridynamics Theory of Solid Mechanics: Modeling, Computation, and Applications
Instructors: , John Foster (University of Texas at Austin), David Littlewood (Sandia National Laboratory), Pablo Seleson (Oak Ridge National Laboratory)
Peridynamics is a nonlocal reformulation of classical continuum mechanics, based on integral equations, suitable for material failure and damage simulation. In contrast to classical constitutive relations, peridynamic models do not require spatial differentiability assumptions of displacement fields, leading to a natural representation of material discontinuities such as cracks. Furthermore, peridynamic models possess length scales, making them suitable for multiscale modeling. This short course will review the peridynamics theory, and it will discuss various applications as well as related modeling and computational aspects.
SC004-The Reciprocal Theorem in Fluid Dynamics and Transport Phenomena
Instructors: Hassan Masoud (Michigan Technological University), Bhargav Raliabandi (Princeton University)
The main objective of the proposed short course is to introduce graduate students and young researchers to the concept and application of the Reciprocal Theorem. This theorem provides a framework for calculating various integral quantities in continuum mechanics, electricity, magnetism, and optics. The approach is closely related to the Green’s second identity, which is almost certainly familiar to all attendees from introductory courses on partial differential equations. The lecture begins by outlining a brief history of the Reciprocal Theorem followed by a discussion of various ways in which the Theorem gives insights into fluid dynamics problems, as well as problems in elasto-hydrodynamics and heat/mass transfer. In particular, it will be shown that the Reciprocal Theorem provides a means to: (i) derive integral equation representations for the velocity distribution; (ii) learn about various symmetries of tensorial properties of a flow; (iii) calculate integrated properties associated with a flow, such as forces, torques, net pressure drops, heat fluxes, etc. Despite its importance and practicality, the topic of this short course is rarely covered in conventional graduate courses in theoretical and applied mechanics.
SC005 – Cancelled
SC006-Stochastic Mechanics of Materials and Structures
Instructor: Martin Ostoja-Starzewski (University of Illinois at Urbana-Champaign)
This course presents an array of methods to study mechanics of spatially random material microstructures involving several scales. The course begins with lectures on random geometry and stochastic processes and fields, including spatial point processes, mathematical morphology, geodesics, ergodicity, and entropy. Subsequent topics include: periodic versus disordered truss- and beam-type lattices, and a construction of corresponding classical and non-classical (Cosserat, non-local, strain-gradient, chiral …) continua; introduction to statistical continuum theories, including thermomechanics of random media; scaling to Representative Volume Element (RVE) in conductivity, linear or finite (thermo)elasticity, elasto-plasticity, permeability, and coupled field phenomena; methods for problems below the RVE (i.e., those lacking the separation of scales) via micromechanically-based stochastic finite elements; tensor random fields, effects of microscale material randomness on waves and wavefronts (e.g. shocks) in linear and non-linear elastic/dissipative media; introduction to stochastic damage mechanics; elements of fractional calculus and fractals.
SC007-Mechanistic Data-driven Multiscale Analysis and Applications
Instructors: Wing Kam Liu, (Northwestern University, Director of Global Center on Advanced Material Systems and Simulation; and President of International Association for Computational Mechanics)
Miguel Bessa (Delft University of Technology)
An open frontier in mechanical science is the efficient and accurate description of heterogeneous material behavior that strongly depends on complex microstructure. This short course introduces the latest efforts to expand this frontier: a data-driven computational framework for discovery and design of new materials and structures. The framework includes a new reduced order model called Self-consistent Clustering Analysis (SCA) allowing for concurrent homogenization of different material systems. SCA was developed to avoid the limitations of phenomenological models by directly generating material laws on-the-fly, using an efficient two-stage solution to compute microscale material response from a statistically Representative Volume Element (RVE). The first stage, known as the offline or training stage, uses data science theories such as k-means clustering and self-organizing maps to “compress” the RVE. The second, online stage solves the Lippmann-Schwinger equation to predict the response of each compressed RVE (CRVE) to an applied load using any constitutive relationship desired within the CRVE, thus effectively predicting the material law produced by the overall response of the CRVE. The CRVE may then be considered a material point in the larger concurrent simulation and/or used to generate large amounts of data necessary for the data-driven framework. In summary, this short course will detail the multiple steps involved in data-driven Computational Mechanics, including Design of Experiments, computational analysis from SCA, Machine Learning and Genetic Optimization. A schedule of the short course may be found here.
