Structural reliability methodology — application at the system level

We theoretically study the dynamic and static effects of mechanical stress on a straintronics device that includes a piezoelectric film combined with a magnetic tunneling junction. The inverse magnetostriction effect is studied in detail by realizing the varying magnetic susceptibility of the nanomagnet under stress. A dynamic model is developed based on the Landau-Lifshitz-Gilbert (LLG) equation, which provides a platform to simulate the magnetization vector's behavior, critical flipping voltage, and delay properties. Furthermore, by converting the LLG equation into a 2nd order damping differential equation, we develop a proximate approach. This approach predicts the dynamic behavior of the magnetization vector and its dependency on material properties and applied voltage across the device without using sophisticated numerical calculations of the LLG model. Different dynamic and static material properties are observed by simulating five common magnetostrictive materials, including a newly discovered alloy, Galfenol.

The impact of spheres and bodies with spherical surfaces is frequently occurring in engineering applications. Only little research on repeated impacts of spheres is available and the variation of the COR (Coefficient of Restitution) due to repeated impacts is not fully understood yet. Further, the variation of the COR for impact repetition of visco-plastic materials, such as steel, has not been investigated in full detail yet. Therefore, the aim of this study is to investigate the behavior of steel spheres during repeated impact in detail in both, experiments and numerical simulations. In the experiments, two steel spheres are suspended like pendula, and the two spheres collide at the same position with the same initial velocity for every repeated impact. The COR is obtained from the velocity change of the spheres which is measured by LDVs (Laser Doppler Vibrometers) set at both sides of the spheres. The static and dynamic material properties are obtained from material tests and are incorporated into an FEM (Finite Element Method) analysis. The experimental results and the FEM results agree fairly well. It is observed that the COR increases toward to 1 by the repetition of impacts, indicating decreasing amount of plastic deformation in the successive impacts.

Recent progress in shape memory alloy reinforced composites: A review of the properties, production, mechanisms involved and functional applications

Influence of printing direction on the dynamic response of additively-manufactured polymeric materials and lattices

Maintenance, rehabilitation and replacement of existing bridges by using cost effective management are increasingly important topics in efforts to deal with aging and deteriorating infrastructure. Recently, engineers have shifted the emphasis from design and construction of new bridges to maintenance and rehabilitation of existing bridges. This paper uses the structural reliability method with a predefined performance function to assess the component performance of the main load carrying members of an inner span in a multi-span bridge. The performance of the components is a function of various random variables with certain probabilistic distributions. The analysis is extended using the same performance function to evaluate the reliability of the bay as subsystem and bridge as a system. System reliability in this case is defined as the probability that a system (bridge) performs its intended function under operating conditions for a specified period of time using second order reliability method (SORM) and Monte Carlo simulations. In addition, deterioration in the main carrying elements (components) due to environmental impacts is introduced and the sensitivity parameters for certain random variables are re-evaluated to determine the inspection and maintenance period of the bridge. The deterioration is imposed as a percentage reduction in certain random variables. The paper involves use of multi reliability analysis at component, sub-system and system levels. Results show that probability of failure (Pf) increases with decrease in distance of the bridge from source of deterioration. Reliability index (β) decreased with increase in the age of the bridge because of corrosion of reinforcing steel. Results also show that the rate of probability of failure for bridges exposed to deteriorating effects increased dramatically after 45-50 years.

A general methodology for motion error and motion reliability analysis of planar parallel manipulators (PPMs) with random and interval variables is presented. The inherent uncertainties of the manipulator, including tolerances in manufactures, errors in inputs as well as joint clearances are taken into account. The error model of a 3-RRR parallel manipulator is built and the global sensitivity coefficients of motion errors to variations are defined and obtained. The joint clearances are treated as interval variables while the others are treated as random variables. As a result, the motion error of the manipulator could turn out to be the mixture of a random variable and an interval variable. A new motion reliability analysis method based on the First Order Second Moment (FOSM) method and the Monte Carlo simulation (MCS) method is developed for the manipulator with random and interval variables. This paper provides a new idea to better understand the motion reliability affected by the inherent uncertainties of PPMs.

Thermal-pulse method is a powerful tool for measuring space charge distributions in polymer films. The data analysis for thermal-pulse method involves the Fredholm integral equation of the first kind, which requires an appropriate numerical procedure to obtain a solution. Various numerical techniques, including scale transformation and regulation method, are proposed. Of those numerical methods, the scale transformation (ST) is the simplest and the most widely used method. However, it presents a high spatial resolution only near the sample surface. Monte Carlo (MC) method is one of the recently proposed ways to solve the equation numerically and has been successfully applied to the analysis of laser intensity modulation method data, which also involves the Fredholm integral equation of the first kind. In this paper we attempt to analyze thermal-pulse data in frequency domain with the MC method and discuss its effectiveness based on some numerical simulations. The simulation results indicate that the electric field profiles can be effectively extracted by the MC method. The computed profiles by the MC method consist well with the supposed distributions in the entire thickness of the sample, while the profiles reconstructed by the ST method fit very well to the supposed one at the vicinity of the target surface and distort sharply along the direction of the thermal pulse propagation in the sample bulk. On the other hand, the oscillations in the computed results by the MC method could deteriorate its accuracy in this study. The influence of noise level on the analysis based on the MC method is also tested by the use of the simulated data. The results show that the computed profiles would become more fluctuant as the noise level increases. This problem can be solved by selecting a larger value of tolerance during the singular value decomposition procedure. Thus, the value of tolerance is considered to be one of the key parameters in this algorithm, which is actually hard to determine. Additionally, the experimental data obtained from a polypropylene film under applied electric field are analyzed to illustrate the feasibility of MC method to be applied to the thermal-pulse experimental data. The results also show that the spatial accuracy by the MC method in the entire sample thickness is higher than by the ST method, which verifies that the MC method is more suitable for detecting the electric field distribution in the deep bulk of the sample. Owing to noise and error, the accuracy of MC calculation depends on the chosen tolerance value, which is now considered to be an obstacle in applying this method to the practical thermal-pulse measurement.

In this work, a physics-based first-order reliability method (FORM) algorithm is proposed for the flutter reliability analysis of an aircraft wing in the frequency domain. The limit state function, which is an implicit function of random variables, is defined in terms of the damping ratio of the aeroelastic system in a conditional sense on flow velocity. Two aeroelastic cases, namely, an airfoil section model and a cantilever wing model, are considered for carrying out the studies. These aeroelastic models have well separated mean bending and mean torsional modal frequencies. The geometric, structural, and aerodynamic parameters of airfoil and wing systems are modeled as independent Gaussian random variables. The effects of these on the statistics of frequency and damping ratio, and the cumulative distribution functions (CDFs) of flutter velocity are studied. In the case of the wing, the effects of modeling stiffness parameters as Gaussian random fields on the CDFs of flutter velocity are also studied. Here, spectral stochastic finite element method (SFEM) based on Karhunen–Loeve (K–L) expansion is used to discretize the random fields into random variables. From the study of an airfoil system, it is observed that parameters like torsional stiffness, elastic axis location, free stream density, and mass moment of inertia are more sensitive as compared with other parameters. However, in the case of the wing parameters such as torsional stiffness, free stream density, mass moment of inertia, and mass are observed to be more sensitive. The CDFs of flutter velocity obtained using the proposed algorithm are compared with Monte Carlo simulations (MCS) and found to be accurate. A comparative study of aeroelastic reliability for the wing is also carried out by treating stiffness parameters as random variables and random fields. It is observed that the CDFs of flutter velocity in the tail region are conservative when stiffness parameters are treated as random variables.

In this study we present the design, analysis, and experimental validation of a tunable follicle-sinus complex (FSC) unit for whisker-like sensors. The tunable FSC unit displays variable viscoelasticity enabled by a dielectric elastomer (DE) mechanism. Our previous studies on whisker-like sensors for underwater applications have shown the dependence between both static and dynamic sensor performance and FSC material properties. Variations in FSC modulus and viscosity affect its effective stiffness and damping and in turn the sensitivity and range of the sensors. A new FSC design is presented that uses a DE mechanism to tune the effective stiffness and damping of the follicle region. A model to better predict the resulting sensitivity and measurement range is described. The model accounts for the viscoelastic of the FSC components, pre-loading of the FSK volume, and stiffness ratios between the sensor whisker-like shaft and the FSC region. The changes in both the stiffness and damping of the whisker-like sensors are characterized experimentally using loading and impulse tests and compared with model estimates.

A material (polymer + glass fibers) is characterized by its inhomogeneity and anisotropy. This material is subjected an injection molding simulation at first (generally unnewton type of fluid). Then the material is cooled and common structural analysis (static, dynamic and thermal) is performed. The cooled material has dissimilar mechanical properties for each of discrete element. Thus the mechanical properties (after simulation of load) will completely have different values when influence of injection molding is included.

Fast integration algorithms for time-dependent structural reliability analysis considering correlated random variables

Robust mechanism synthesis with random and interval variables

Monte Carlo and deterministic calculation of the mutual information of the fission source in k-eigenvalue calculations

This paper presents a sensitivity and reliability analysis of a self-anchored suspension bridge by applying a new hybrid method proposed by the authors based on integration of the Latin hypercube sampling technique (LHS), artificial neural network (ANN), first-order reliability method (FORM), Pearson's linear correlation coefficient (PLCC), and Monte Carlo simulation with important sampling technique (MCS-IS). The framework consists of three stages of analysis: (1) selection of training, validation, and test datasets for establishing an ANN model by the LHS technique; (2) formulation of a performance function from the well-trained ANN model; and (3) sensitivity analysis using PLCC, identification of the most probabilistic failure point based on FORM, and estimation of the failure probability using the MCS-IS technique. Upon demonstration of its efficiency through analysis of a 12-story frame structure, the method is applied to sensitivity and reliability analysis of the Jiangxinzhou Bridge, a five-span self-anchored suspension bridge, in which both structural parameters and external loads are considered as random variables. The analysis identified a number of structural parameters, as well as external loads, that have a significant influence on structural serviceability and safety.

In the past, residual factor R in strain-softening soil slopes has been included, either directly or indirectly, as a deterministic variable in both deterministic and probabilistic studies. This paper discusses the uncertainties associated with R and outlines a systematic approach for the reliability analysis of a natural slope in which shear strength parameters and pore pressure ratio are random variables, each assumed with a lognormal probability distribution. For the residual factor R, seven probability distribution options under the generalized beta-distribution system are considered. Slope reliability is computed based on the first order reliability method (FORM) and validated against Monte-Carlo simulation (MCS). Results obtained from two illustrative examples indicate that the probability of failure, with R as one of six random variables, can be orders of magnitude higher than that based on five random variables with R considered as a deterministic parameter. The magnitude of influence of R as a random variable is, however, highly dependent on its probability distribution, the left-skewed triangular distribution having the most significant influence in both the examples. Results of sensitivity analyses reveal that, for almost all of its assumed probability distributions, R is the most dominant among the six random variables. Effects of variation of some of the statistical and correlation properties of the other random variables, viz. the shear strength parameters and the pore pressure ratio, on the results of reliability analyses are also studied.