Short Course Offerings
Computational Fluid-Structure Interaction
Yuri Bazilevs, UC San Diego
Kenji Takizawa, Waseda University
Tayfun Tezduyar, Rice University
In this two-day course, the lectures will focus on the fundamental concepts and advanced topics in computational fluid–structure interaction (FSI). The fundamental concepts will include the stabilized formulations, ALE method and ALE-VMS technique, space–time (ST) method and ST-VMS technique, mesh update methods for flows with moving interfaces, iterative solution techniques and parallel computing concepts, and isogeometric analysis. The advanced topics are the ST computational FSI techniques, ALE-VMS computational FSI techniques, and FSI coupling techniques. The topics to be covered include the core technologies and the special techniques targeting specific classes of problems.
*Note: This is a TWO-DAY Short Course that will take place on Saturday, June 3rd and Sunday, June 4th. For more information about this short course, visit the course website http://www.tafsm.org/SanDiegoFSI2017/.
Resilience-Based design of structures and infrastructures during emergencies
Gian Paolo Cimellaro, Politecnico di Torino
Steve Mahin, University of California, Berkeley
Current work in the field of resilience is the product of theoretical and practical constructs that have seen refining and reshaping of the disaster paradigm over the past three decades. This has led to multiple definitions of Resilience and to the need for new terminology and/or metrics that necessitate to be harmonized. For this reason, in the course various definitions of resilience are presented for different spatial and temporal scales.
The course will define resilience and describe the state of art about resilience. It will provide performance metrics to evaluate resilience at the building level as well as the regional level and it will outline gaps which need to be addressed to evaluate resilience while comparing different approaches to evaluate resilience. The course will introduce the concepts of Resilience-Based Design (RBD) as an extension of Performance –Based design. A state of art of different methodologies to evaluate resilience will be provided clarifying the differences among Resilience, Risk, Vulnerability and Sustainability. The course will focus also on the different types of uncertainties which will appear in the Resilience evaluation. An entire module is dedicated to the analytical and to the experimental recovery functions. Finally starting from the definition of Resilience provided by MCEER an extension of the methodology is provided introducing the seven dimensions of Community Resilience: Population and demographics, Environmental/Ecosystem, Organized Governmental Services, Physical infrastructures, Lifestyle and Community Competence, Economic Development, Socio-Cultural Capital. For each dimension, components and subcomponents are defined and the related indices are provided. More emphasis is provided for the physical infrastructure dimension in this Course. Several examples of applications are provided for the transportation, hydraulic, gas and electric network and economic community. Finally, one day will focus on the different methodologies to improve disaster preparedness and the engineering mitigation strategies, such as base isolation and visco-elastic dampers. The last day of the course will focus on the description of the different computer platforms available in the market to evaluate Community Resilience.
The main objective of the course is to familiarize Engineers with the various innovative systems that have demonstrated considerable potential through analytical studies, experimental testing and actual structural implementation in reducing the losses and improving the restoration process of buildings, infrastructures and communities in general. The discussion will focus on innovative technologies such as structural health monitoring and smart phones, but also more traditional techniques such as passive energy dissipation systems and base isolation systems.
Bayesian Model Updating and Uncertainty Quantification: Theory, Computational Tools, and Applications
Babak Moaveni, Tufts University
Costas Papadimitriou, University of Thessaly, Greece
In simulations of complex physical systems, uncertainties arise from imperfections in the mathematical models introduced to represent the systems and their interactions with the environment. Such uncertainties lead to significant uncertainties in the predictions using simulations. Since such predictions form the basis for making decisions, the knowledge of these uncertainties is very important. The course will present the Bayesian model updating framework, the associated computational tools, and selected applications, along with the main challenges for quantifying and propagating uncertainties in complex structural dynamic simulations.
The short course consists of the following five lectures (subject to change):
Lecture 1. Bayesian uncertainty quantification and propagation in structural dynamics simulations
1.1 Bayesian model parameter estimation / model updating
1.2 Bayesian model class selection
1.3 Updating robust predictions and robust reliability
1.4 Structural health monitoring using Bayesian model selection and updating
Lecture 2. Bayesian computational tools
2.1 Asymptotic approximations
2.2 Sampling techniques
Lecture 3. High performance computing for Bayesian UQ of complex models
3.1 Component mode synthesis
3.2 Surrogate techniques (kriging, polynomial chaos)
3.3 Parallel computing
3.4 Demonstration on high fidelity linear/nonlinear bridge models
Lecture 4. Optimal experimental design
4.1 Expected information gain
4.2 Optimal sensor placement
4.3 Optimal excitation characteristics
Lecture 5. Case studies