Structural Dynamics: Theory And Computation
The field of uncertainty quantification that includes the characterization and propagation of uncertainties associated with complex systems has received considerable interest in recent years. A major portion of the engineering dynamics/mechanics community has focused, with considerable success, on problems with stochastic media properties, random excitations and uncertain initial/boundary conditions. Nevertheless, the development of novel mathematical tools and of potent signal processing techniques, the ever-increasing available computational capabilities, and advanced experimental setups offer a unique novel tool for addressing complex problems for the first time and even posing new questions. Specifically, researchers and engineers are faced with the challenge of interpreting and translating measured data at multiple scales into pertinent stochastic models. In this regard, there is a need for developing robust multi-scale statistical descriptors and stochastic models capable of capturing complex uncertainty relationships. Further, there is a need for developing analytical/numerical methodologies for solving nonlinear high-dimensional stochastic (partial) differential equations efficiently, and for propagating uncertainty across various scales in the time and space domains. The objective of this MS is to present recent advances and emerging cross-disciplinary approaches in the broad field of stochastic engineering dynamics/mechanics with a focus on uncertainty modeling, and propagation. Further, this MS intends to provide a forum for a fruitful exchange of ideas and interaction among diverse technical and scientific disciplines. Specific contributions related both to fundamental research and to engineering applications of computational stochastic dynamics/mechanics and signal processing methodologies are welcome. A non-exhaustive list includes joint time/space-frequency analysis tools, spectral analysis/estimation subject to highly incomplete/sparse data, efficient high-dimensional functional representation and identification, stochastic/fractional calculus modeling and applications, nonlinear stochastic dynamics, stochastic stability and control theory, multiscale/multi-physics stochastic modeling and analysis, stochastic model/dimension reduction techniques, Monte Carlo simulation methods, and risk/reliability assessment applications.
Structural Dynamics: Theory and Computation
The mini-symposium deals with structural identification methods and applications, as well as structural health monitoring algorithms for damage detection and reliability prognosis. It covers theoretical and computational issues, applications in structural dynamics, earthquake engineering, mechanical and aerospace engineering, as well as other related engineering disciplines. Topics relevant to the session include: theoretical and experimental modal identification, operational modal analysis, linear and nonlinear system identification, statistical system identification methods (maximum-likelihood, Bayesian inference) for parameter and state estimation, model updating/validation and correlation, uncertainty quantification in model selection and parameter estimation, stochastic simulation techniques for state estimation and model class selection, structural health monitoring and fault detection techniques, optimal strategies for experimental design, optimal sensor and actuator location methods, structural prognosis techniques, updating response and reliability predictions using data. Papers dealing with experimental investigation and verification of theories are especially welcomed.
Quantification of model uncertainty in multidisciplinary analyses can be extremely challenging and computationally intensive for coupled, time-dependent, multi-physics, multi-scale models. Not only does each model component have natural variability in model inputs (e.g., material properties and loading), but there is also model-form error associated with each quantity of interest (QoI) in the multidisciplinary system. In addition, model calibration and validation is often impeded due to high experimental costs (e.g., wind tunnel tests or microstructural material characterization), as well as limited data from the inability to concurrently measure all of the multidisciplinary quantities of interest (e.g., fluid-thermal-structural interaction). This mini-symposium is intended to address fundamental research challenges for multidisciplinary analyses associated with quantifying model uncertainty (e.g., Bayesian model calibration), identifying significant uncertainty sources in coupled model outputs (e.g., sensitivity analysis), reducing model uncertainty through data collection (e.g., experimental design), and assessing models for spatial and temporal validation.
Evaluating and enhancing the long-life performance of critical infrastructure systems require a toolset that maps the system level applications to informed decisions regarding the analysis, design, and management of these systems. Uncertainty quantification and predictive modeling are critical components of such a framework. This PMC2016 mini-symposium will provide the opportunity to discuss recent advances in developing uncertainty-aware computational models for reliable and robust analysis, design, and management of infrastructures systems including, but not limited to, transportation systems, building systems, water and energy networks, and various socio-technical systems. The topics of interest include reliability analysis and optimization, life-cycle performance maintenance and management (structural, environmental, and economical), physics-based models for energy simulation, interdependent infrastructure systems, network modeling and analysis, sustainability and resilience to disasters, decision making under uncertainty and Big Data analytics.
Dramatic advances in computational capabilities and three-dimensional characterization facilities have enabled multi-scale mechanics models with unprecedented levels of resolution that represent the underlying microstructure and lower-scale mechanisms in materials under a variety of stress states. However, the process is riddled with uncertainty at all stages, including random measurement errors associated with the characterization techniques, natural variations between different material samples of a finite size, and uncertainty in the model parameters. This symposium will bring together researchers studying the role that these uncertainties associated with random material heterogeneities play in multi-scale model predictions. For example, contributions could include efforts to address uncertainty associated with microstructural data collection, stochastic simulation of polycrystalline and/or multi-phase materials, upscaling of material properties via stochastic homogenization, stochastic inverse analysis to infer microstructural parameters from experimental observations, or surrogate/reduced-order models to represent lower-scale micro- or nano-mechanics. 041b061a72