A paradox of non-monotonicity in stability of pipes conveying fluid

The paradoxical result of the non-monotonous relationship between the critical speed of the fluid that is conveyed in the elastic pipe, and the mass ratio was reported first some four decades ago. Since then this result was reproduced in numerous books and articles. In this study the paradox is revisited. It appears that it is a numerical artifact; instead of non-monotonicity there are jumps

Hybrid probabilistic and convex modeling of excitation and response of periodic structures

In this paper, a periodic finite-span beam subjected to the stochastic acoustic pressure with bounded parameters is investigated. Uncertainty parameters exist in this acoustic excitation due to the deviation or imperfection. First, a finite-span beams subjected to the random acoustic pressure field are studied, the exact analytic forms of the cross-spectral density of both the transverse displacement and the bending moment responses of the structure are formulated. The combined probabilistic and convex modeling of acoustic excitation appears to be most suitable, since there is an insufficient information available on the acoustic excitation parameters, to justify the totally probabilitic analysis. Specifically, we postulate that the uncertainty parameters in the acoustic loading belong to a bounded, convex set. In the special case when this convex set is an ellipsoid, closed form solutions are obtained for the most and least favorable mean square responses of both the transverse displacement and bending moment of the structure. Several finite-span beams are exemplified to gain insight into proposal methodology

Who needs refined structural theories?

This paper discusses the question posed in the title and available options for the structural analysis of metallic and composite structures concerning the choice of 1D, 2D, and 3D theories. The focus is on the proper modeling of various types of mechanical behaviors and the associated solution’s efficiency. The necessity and convenience of developing higher‐order structural theories are discussed as compared to 3D models. Multiple problems are considered, including linear and nonlinear analyses and static and dynamic settings. Some possible guidelines on the proper selection of a model are outlined, and quantitative estimations on the accuracy are provided. It is demonstrated that the possibility of incorporating higher‐order effects in 1D and 2D models continues to remain attractive in many structural engineering problems to alleviate the computational burdens of 3D analyses

Further Insights Into the Timoshenko–Ehrenfest Beam Theory

In this paper, the theory of a Timoshenko–Ehrenfest beam is revisited and given a new perspective with particular emphasis on the relative significances of the parameters underlying the theory. The investigation is intended to broaden the scope and applicability of the theory. It has been shown that the two parameters that characterize the Timoshenko–Ehrenfest beam theory, namely the rotary inertia and the shear deformation, can be related, and hence, they can be combined into one parameter when predicting the beam’s free vibration behavior. It is explained why the effect of the shear deformation on the free vibration behavior of a Timoshenko–Ehrenfest beam for any boundary condition will be always more pronounced than that of the rotary inertia. The range of applicability of the Timoshenko–Ehrenfest beam theory for realistic problems is demonstrated by a set of new curves, which provide considerable insights into the theory

Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data

Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimisation problems from disparate domains. Many such problems arising from the commercial world are permeated by data uncertainty. Existing CP approaches that accommodate uncertainty are less suited to uncertainty arising due to incomplete and erroneous data, because they do not build reliable models and solutions guaranteed to address the user's genuine problem as she perceives it. Other fields such as reliable computation offer combinations of models and associated methods to handle these types of uncertain data, but lack an expressive framework characterising the resolution methodology independently of the model. We present a unifying framework that extends the CP formalism in both model and solutions, to tackle ill-defined combinatorial problems with incomplete or erroneous data. The certainty closure framework brings together modelling and solving methodologies from different fields into the CP paradigm to provide reliable and efficient approches for uncertain constraint problems. We demonstrate the applicability of the framework on a case study in network diagnosis. We define resolution forms that give generic templates, and their associated operational semantics, to derive practical solution methods for reliable solutions.Comment: Revised versio

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Life Cycle Management of Infrastructures

By definition, life cycle management (LCM) is a framework “of concepts, techniques, and procedures to address environmental, economic, technological, and social aspects of products and organizations in order to achieve continuous ‘sustainable’ improvement from a life cycle perspective” (Hunkeler et al.\ua02001). Thus, LCM theoretically integrates all sustainability dimensions, and strives to provide a holistic perspective. It also assists in the efficient and effective use of constrained natural and financial resources to reduce negative impacts on society (Sonnemann and Leeuw\ua02006; Adibi et al.\ua02015). The LCM of infrastructures is the adaptation of product life cycle management (PLM) as techniques to the design, construction, and management of infrastructures. Infrastructure life cycle management requires accurate and extensive information that might be generated through different kinds of intelligent and connected information workflows, such as building information modeling (BIM)