ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

In this paper, the widely known path integral method, derived from the application of the Chapman–Kolmogorov equation, is described in details and discussed with reference to the main results available in literature in several decades of contributions. The most simple application of the method is related to the solution of Fokker–Planck type equations. In this paper, the solution in the presence of normal, α-stable, and Poissonian white noises is first discussed. Then, application to barrier problems, such as first passage problems and vibroimpact problems is described. Further, the extension of the path integral method to problems involving multi-degrees-of-freedom systems is analyzed. Lastly, an alternative approach to the path integration method, that is the Wiener Path integration (WPI), also based on the Chapman–Komogorov equation, is discussed. The main advantages and the drawbacks in using these two methods are deeply analyzed and the main results available in literature are highlighted. This article is available in the ASME Digital Collection at .

The second-order saddlepoint approximation (SOSPA) has been used for component reliability analysis for higher accuracy than the traditional second-order reliability method (SORM). This work extends the second-order saddlepoint approximation (SPA) to system reliability analysis. The joint distribution of all the component responses is approximated by a multivariate normal distribution. To maintain high accuracy of the approximation, the proposed method employs the second-order SPA to accurately generate the marginal distributions of the component responses; to simplify computations and achieve high efficiency, the proposed method estimates the covariance matrix of the multivariate normal distribution with the first-order approximation to the component responses. Examples demonstrate the high effectiveness of the second-order SPA method for system reliability analysis. This article is available in the ASME Digital Collection at .

In pressurized water nuclear reactors, the seismic performance of fuel assemblies is governed by their spacer grids (SGs) which may experience impacts with neighboring fuel assembly SGs or with the core barrel, depending on the intensity of the seismic event. Nonlinear dynamic analysis aiming at computing the maximum permanent deformation in a statistic framework is computationally demanding due to the different possible core configurations and the dimension of the dataset of seismic excitations. Hence, surrogate models trained by the physics-based dynamic model are proposed to analyze different scenarios, i.e., explore the space of potential core configurations and seismic excitations. Starting from ground motion records corresponding to six levels of seismic hazard, the dynamic excitation at the elevation of the reactor pressure vessel is obtained via transfer functions. Correlation between different seismic intensity measures and the maximum permanent deformation is evaluated. The performance of two well-established surrogate models, namely, artificial neural networks (ANN) and Gaussian process (GP) for regression problems is analyzed and discussed. Bayesian techniques are adopted to enhance the robustness of the trained surrogate models by training sets of neural networks and estimating the hyper-parameter of the GP. This article is available in the ASME Digital Collection at .

In recent decades, many accidents involving pressure vessels and piping components (PVP) have occurred in North America. Several studies have been conducted to understand their causes and find suitable solutions to reduce their frequency. Most of the researches have focused on the technical causes of these accidents and the subsequent implementation of safety management strategies. These researches highlight that the main technical causes are leak and rupture. From this standpoint, it is important to deepen the study of these causes to better understand the risk of accident in PVP applications. In Alberta alone, the Alberta Energy Regulator (AER) showed that more than 15 root causes initiated leak and rupture failures in PVP since 1990. This paper presents an analysis of the frequency of accidents, their severity, their causes, and the risk associated in the Alberta petrochemical industry from 2008 to 2017. This study proposes an exponential decay function to estimate the frequency of accidents involving PVP and identifies the most important causes based on a severity analysis. The results based on the frequency model show that there is a good agreement between the predicted and observed accidents frequency from 2008 to 2017. The severity analysis results shows that the main factors contributing to accidents are corrosion, construction deficiency, and overpressure. Finally, the proposed model of frequency and severity of observed and predicted PVP failures, is a useful tool for risk assessment and prevention program implementation. This article is available in the ASME Digital Collection at .

Design and testing of machine guards are provided by international standards in which the inadequacy/suitability of the tested materials for machine guards is obtained by the perforation/nonperforation of the guard in an experimental test at the maximum foreseeable speed of a standardized projectile. Uncertainties and limitations of this standardized test are known by researchers, but a better testing procedure is not already agreed on the standardization level. In this paper, finite element analysis of the impact of three different projectiles of a given mass on polycarbonate guards is presented and compared with experimental tests made using a standardized gas cannon. The good correlation found in terms of ballistic limit, energy absorbed, and residual velocity is presented trying to open the discussion to a “probability of perforation.” Moreover, a statistical analysis of experimental results, based on a nonlinear regression model, is briefly introduced. The penetration behavior is described by the well-known Recht and Ipson equation, and an evaluation of the statistical dispersion of the ballistic limit for each type of projectile is presented through the calculation of confidence intervals. This article is available in the ASME Digital Collection at .

Ceramic matrix composites (CMCs) exhibit process-induced defects such as matrix porosity at multiple length scales that have a considerable influence on their mechanical and failure behavior. This work focuses on the microscale mechanical behavior of single tow CMCs in the presence of microporosities that exist within fiber bundles of the composite. Microporosities in a single tow C/boron nitride (BN)/SiC CMC minicomposite fabricated by chemical vapor infiltration (CVI) have been characterized by X-ray microcomputed tomography. The porosity distribution in the scanned region has been represented by probability distribution functions (PDFs) that serve as an input to numerical homogenization. Effective elastic properties in the presence of matrix micropores have been obtained by a two-step numerical homogenization approach considering the statistical distributions of pore parameters obtained from experimental characterization. A variation of the approach has been utilized to investigate the severity of pores with respect to their location and orientation relative to the fiber reinforcement. This article is available in the ASME Digital Collection at .

Surrogate models are efficient tools which have been successfully applied in structural reliability analysis, as an attempt to keep the computational costs acceptable. Among the surrogate models available in the literature, artificial neural networks (ANNs) have been attracting research interest for many years. However, the ANNs used in structural reliability analysis are usually the shallow ones, based on an architecture consisting of neurons organized in three layers, the so-called input, hidden, and output layers. On the other hand, with the advent of deep learning, ANNs with one input, one output, and several hidden layers, known as deep neural networks, have been increasingly applied in engineering and other areas. Considering that many recent publications have shown advantages of deep over shallow ANNs, the present paper aims at comparing these types of neural networks in the context of structural reliability. By applying shallow and deep ANNs in the solution of four benchmark structural reliability problems from the literature, employing Monte Carlo simulation (MCS) and adaptive experimental designs (EDs), it is shown that, although good results are obtained for both types of ANNs, deep ANNs usually outperform the shallow ones. This article is available in the ASME Digital Collection at .

The study of the flow interaction and the heat transfer between air jets and a surface is of paramount importance in industrial processes that apply air jet impingement. To ensure a good performance of the process, high heat transfer rates and uniformization of the flow over the target plate are required. In this work, a particle image velocimetry (PIV) technique was implemented for the measurement of the flow velocity fields. However, as any real experiment, the values recorded by the PIV method are subjected to several errors that compromise the reliability and accuracy of the measurements. These errors can have different sources, from the installation and alignment to the particles seeding and calibration procedure. To maximize the accuracy of the experimental results, this paper focuses on the identification of measurement errors and uncertainty quantification of an experimental setup purposely built for the analysis of the interaction between air jets and a target surface. This paper presents an analysis of the system, and the source of errors are identified, quantified, and, when possible, corrected. The particle seeding is characterized, and its efficiency for the flow tracking is analyzed. The setup was tested to fully characterize the flow field in terms of mean velocity profile and turbulence intensity over a wide range of Reynolds numbers and temperature. Several velocity fields are then measured until convergence of the flow quantities is reached. The combination of these measurements with high spatial resolution and low measurement errors allows us to obtain accurate and precise measurements. This article is available in the ASME Digital Collection at .

Reliability assessment of linear discretized structures with interval parameters subjected to stationary Gaussian multicorrelated random excitation is addressed. The interval reliability function for the extreme value stress process is evaluated under the Poisson assumption of independent up-crossing of a critical threshold. Within the interval framework, the range of stress-related quantities may be significantly overestimated as a consequence of the so-called dependency phenomenon, which arises due to the inability of the classical interval analysis to treat multiple occurrences of the same interval variables as dependent ones. To limit undesirable conservatism in the context of interval reliability analysis, a novel sensitivity-based procedure relying on a combination of the interval rational series expansion and the improved interval analysis via extra unitary interval is proposed. This procedure allows us to detect suitable combinations of the endpoints of the uncertain parameters which yield accurate estimates of the lower bound and upper bound of the interval reliability function for the extreme value stress process. Furthermore, sensitivity analysis enables to identify the most influential parameters on structural reliability. A numerical application is presented to demonstrate the accuracy and efficiency of the proposed method as well as its usefulness in view of decision-making in engineering practice. This article is available in the ASME Digital Collection at .

The paper discusses applications of the domain-independent method of algebraic inequalities, for reducing uncertainty and risk. Algebraic inequalities have been used for revealing the intrinsic reliability of competing systems and ranking the systems in terms of reliability in the absence of knowledge related to the reliabilities of their components. An algebraic inequality has also been used to establish the principle of the well-ordered parallel-series systems which, in turn, has been applied to maximize the reliability of common parallel-series systems. The paper introduces linking an abstract inequality to a real process by a meaningful interpretation of the variables entering the inequality and its left- and right-hand parts. The meaningful interpretation of a simple algebraic inequality led to a counterintuitive result. If two varieties of items are present in a large batch, the probability of selecting randomly two items of different variety is smaller than the probability of selecting randomly two items of the same variety. This article is available in the ASME Digital Collection at .