Robust Estimation of Reliability in the Presence of Multiple Failure Modes

In structural design, every component or system needs to be tested to ascertain that it satisfies the desired safety levels. Due to the uncertainties associated with the operating conditions, design parameters, and material systems, this task becomes complex and expensive. Typically these uncertainties are defined using random, interval or fuzzy variables, depending on the information available. Analyzing components or systems in the presence of these different forms of uncertainty increases the computational cost considerably due to the iterative nature of these algorithms. Therefore, one of the objectives of this research was to develop methodologies that can efficiently handle multiple forms of uncertainty. Most of the work available in the literature about uncertainty analysis deals with the estimation of the safety of a structural component based on a particular performance criterion. Often an engineering system has multiple failure criteria, all of which are to be taken into consideration for estimating its safety. These failure criteria are often correlated, because they depend on the same uncertain variables and the accuracy of the estimations highly depend on the ability to model the joint failure surface. The evaluation of the failure criteria often requires computationally expensive finite element analysis or computational fluid dynamics simulations. Therefore, this work also focuses on using high fidelity models to efficiently estimate the safety levels based on multiple failure criteria. The use of high fidelity models to represent the limit-state functions (failure criteria) and the joint failure surface facilitates reduction in the computational cost involved, without significant loss of accuracy. The methodologies developed in this work can be used to propagate various types of uncertainties through systems with multiple nonlinear failure modes and can be used to reduce prototype testing during the early design process. In this research, fast Fourier transforms-based reliability estimation technique has been developed to estimate system reliability. The algorithm developed solves the convolution integral in parts over several disjoint regions spanning the entire design space to estimate the system reliability accurately. Moreover, transformation techniques for non-probabilistic variables are introduced and used to efficiently deal with mixed variable problems. The methodologies, developed in this research, to estimate the bounds of reliability are the first of their kind for a system subject to multiple forms of uncertainty

A multi-objective framework for the optimisation of life-cycle costs of wind turbines

Peer reviewedPublisher PD

Affine arithmetic-based methodology for energy hub operation-scheduling in the presence of data uncertainty

In this study, the role of self-validated computing for solving the energy hub-scheduling problem in the presence of multiple and heterogeneous sources of data uncertainties is explored and a new solution paradigm based on affine arithmetic is conceptualised. The benefits deriving from the application of this methodology are analysed in details, and several numerical results are presented and discussed

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

B

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN