Probabilistic fatigue analysis of all-ceramic crowns based on the finite element method

The recent tailing dam failure in Brazil has again emphasized the need of performing robust stability analysis prior to construction. This paper demonstrates the probabilistic dynamic stability analysis of tailing dams considering an existing rock-fill tailing dam in India. The stability analysis was performed using 2D finite element-based package RS2. In the probabilistic analysis, the strength parameters such as cohesion (c) and the friction angle (φ) were considered as random variables. In total, 3000 numbers of samples were generated assuming a normal distribution. Monte Carlo simulation was used to evaluate the probability of failure (PoF) and reliability index. Strength reduction method was used for the finite element analyses. A pseudo-static seismic loading was incorporated in the strength reduction analysis to check the seismic stability of the dam. A factor of safety (FoS) of 1.15 was observed from the deterministic analysis for downstream slope. For the same case, the probabilistic analysis provided a mean FoS of 1.19 with 5.46% probability of failure. The FoS values and the locations of the critical failure surface obtained by the limit equilibrium method and finite element method were compared. The observed FoS values were found to be higher than the values specified in the IS 7894-1975 (reaffirmed in 1997) and ANCOLD (1999). In the case of pseudo-static approach, the maximum displacement of 0.53 m was observed in the slope. Furthermore, nonlinear dynamic stability analysis was performed to simulate a true earthquake event. The permanent deformation of the slope after the earthquake was found to be 0.40 m. The zone of failure observed in both pseudo-static and nonlinear dynamic stability analyses was found to be the same. Overall, the results revealed that the spatial variability of the soil significantly influences the FoS values.

ABSTRACTIn this work a probabilistic structural analysis of a container terminal wharf is presented. Through the application of the Monte Carlo Simulations in a finite element structural model, the statistics of the axial forces at the pile heads were evaluated. Considering only ship berthing conditions, but varying equipment type, number and positions, ten external load combinations were analyzed and compared. Both finite element model and probabilistic analysis were performed in ANSYS software, v.16, on its APDL Mechanical and Probabilistic Design System packages respectively. The random input parameters considered were the concrete structure, pavement and rear landfill self-weight, live loads and ship sizes, for which statistical parameters were attributed based on bibliography or data collection. PIANC’s berthing energy method for fender systems design was applied for the evaluation of ship berthing forces acting on the structure. Considering the axial forces as the main parameters in the design of partially embedded piles, the random output parameters obtained were the minimum and maximum axial forces on each simulation. That approach, applied due to the possibility of ANSYS to be programmed through its Parametric Design Language (APDL), proved to be a powerful tool for probabilistic analysis of complex structural systems. The results present a comparison between the statistical parameters and probability density curves for the ten cases analyzed, by which the worst combinations from a probabilistic point of view could be defined. Keywords: Container terminal berth; Probabilistic Analisys; Monte Carlo Method; Finite Element Method; ANSYS.

Finite element methods have been applied to evaluate in vivo joint behavior, new devices, and surgical techniques but have typically been applied to a small or single subject cohort. Anatomic variability necessitates the use of many subject-specific models or probabilistic methods in order to adequately evaluate a device or procedure for a population. However, a fully deformable finite element model can be computationally expensive, prohibiting large multisubject or probabilistic analyses. The aim of this study was to develop a group of subject-specific models of the patellofemoral joint and evaluate trade-offs in analysis time and accuracy with fully deformable and rigid body articular cartilage representations. Finite element models of eight subjects were used to tune a pressure-overclosure relationship during a simulated deep flexion cycle. Patellofemoral kinematics and contact mechanics were evaluated and compared between a fully deformable and a rigid body analysis. Additional eight subjects were used to determine the validity of the rigid body pressure-overclosure relationship as a subject-independent parameter. There was good agreement in predicted kinematics and contact mechanics between deformable and rigid analyses for both the tuned and test groups. Root mean square differences in kinematics were less than 0.5 deg and 0.2 mm for both groups throughout flexion. Differences in contact area and peak and average contact pressures averaged 5.4%, 9.6%, and 3.8%, respectively, for the tuned group and 6.9%, 13.1%, and 6.4%, respectively, for the test group, with no significant differences between the two groups. There was a 95% reduction in computational time with the rigid body analysis as compared with the deformable analysis. The tuned pressure-overclosure relationship derived from the patellofemoral analysis was also applied to tibiofemoral (TF) articular cartilage in a group of eight subjects. Differences in contact area and peak and average contact pressures averaged 8.3%, 11.2%, and 5.7% between rigid and deformable analyses in the tibiofemoral joint. As statistical, probabilistic, and optimization techniques can require hundreds to thousands of analyses, a viable platform is crucial to component evaluation or clinical applications. The computationally efficient rigid body platform described in this study may be integrated with statistical and probabilistic methods and has potential clinical application in understanding in vivo joint mechanics on a subject-specific or population basis.

All engineering design problems can be characterized by the underlying assumptions around which the problem is formulated. The effect of these assumptions - including everything from general assumptions defining an operating environment to detailed assumptions regarding material properties - is variability in system performance, and resulting deviations from expected performance. Assumptions are made to eliminate uncertainties that would prevent the quantification of design performance. Probabilistic methods have been developed in recent decades to convert such deterministic problem formulations into probabilistic formulations to model and assess the effects of these known uncertainties and thus relax restrictive assumptions. Until very recently, however, the computational expense of probabilistic analysis of a single design has made its application impractical for all but very simplistic design problems. Consequently, probabilistic optimization has been considered prohibitively expensive, particularly for complex multidisciplinary systems. Today a number of enabling technologies are available to support probabilistic design analysis and optimization for complex engineering design problems, including: flexible software frameworks that allow integration and automation of a complex multidisciplinary process; probabilistic analysis and optimization tools that allow uncertainty to be modeled and performance variation to be measured and reduced; large scale parallel processing capabilities leading to greatly increased efficiency; and advanced surrogate modeling capabilities to replace complex nonlinear analyses with computationally efficient approximation models. Along with steady increases in computing power, the combination of these enabling technologies can facilitate effective probabilistic analysis and optimization for complex design problems. In this paper we focus primarily on two of these enabling technologies. We present a comprehensive probabilistic design optimization formulation, a six sigma based approach that incorporates variability within all elements of the formulation - input design variable bound formulation, output constraint formulation, and robust objective formulation. We discuss the applicability of kriging, one surrogate modeling approach that supports the approximation of complex nonlinear analyses, for facilitating probabilistic multidisciplinary design optimization. These enabling technologies, as implemented within the commercial software framework provided by iSIGHT, including capability to facilitate parallel processing, are demonstrated for a multidisciplinary conceptual ship design problem.

The development of a three-dimensional inelastic analysis methodology for the Space Shuttle main engine (SSME) structural components is described. The methodology is composed of: (1) composite load spectra, (2) probabilistic structural analysis methods, (3) the probabilistic finite element theory, and (4) probabilistic structural analysis. The progress in the development of generic probabilistic models for various individual loads which consist of a steady state load, a periodic load, a random load, and a spike, is discussed. The capabilities of the Numerical Evaluation of Stochastic Structures Under Stress finite element code designed for probabilistic structural analysis of the SSME are examined. Variation principles for formulation probabilistic finite elements and a structural analysis for evaluating the geometric and material properties tolerances on the structural response of turbopump blades are being designed.

Increases in bone strains above a certain threshold have a positive effect on bone mass, whereas reductions in strain magnitude lead to bone loss and osteopenia; the term 'mechanostat' has been introduced to describe this tissue-level negative feedback mechanism. The mechanobiology of bone and particularly alveolar bone is poorly understood, and whether the mechanostat theory has any relevance to explaining the osseous changes that occur during orthodontic tooth movement remains unclear. To investigate the relationship further, an expansile force of 0.2 N was applied to the maxillary molars of 36, 6-week-old Wistar rats by helical coil springs. The animals were sacrificed at 1, 2, 4, and 8 days and the tissue response analyzed by histological, biochemical, and finite element (FE) methods. Differences between groups were determined by Student's t-test (two-tailed). The appliance produced an increase in the intermolar width averaging 0.5 mm after 8 days. Tetracycline uptake in the control rats suggested a rapid turnover of bone in both the interradicular domain and the bone-periodontal ligament interface. In the experimental group, however, incorporation of tetracycline into the interradicular domain was reduced and conventional histology revealed evidence of bone loss and osteopenia, in both the experimental and a group of sham-treated positive controls (with inactive, annealed springs). Serum alkaline phosphatase declined significantly in both experimental and sham-treated groups over the 8-day time course, indicating decreased bone formation. Serum acid phosphatase also declined, suggesting a concomitant decrease in bone resorption. Three-dimensional FE analysis of the stresses generated in the bone following occlusal (2 N) and orthodontic loading showed that the orthodontic force created a constant loading condition shielding some areas of bone from mechanical stress. Areas of low mechanical stimulation were coincident with sites of bone loss observed histologically, while bone mass was preserved in areas with higher levels of loading. These findings suggest that (1) the osteopenia resulted from stress shielding of the interradicular bone by the appliance, and a consequent reduction in occlusal loading below the critical threshold required for maintaining normal osseous architecture and (2) the mechanostat model can be employed to explain, at least in part, the response of the bone to orthodontic loading.

The geotechnical properties of municipal solid waste (MSW) in landfills vary considerably depending on the composition, time, and the rate of waste density. This variability in geotechnical properties leads to many uncertainties in the analysis of landfill slope stability. This study, using probabilistic methods investigates the slope stability and the probability of failure of a Kahrizak landfill under the conditions of spatial variability of physical and geotechnical properties of MSW. To achieve this goal, the random field theory has been used with a finite difference numerical method in the framework of the Monte Carlo simulation. MSW’s shear strength parameters such as cohesion and friction angle as well as the unit weight of layers are considered as random variables. An extensive literature review was conducted to address the probable variation of above-mentioned parameters in Kahrizak landfill. The results of several different laboratory researches on MSW samples collected from Kahrizak landfill were also employed in the modelings. Output results are presented in the form of probability distributions of the factor of safety as well as the probability of failure corresponding to these distributions. Moreover, the effect of various parameters such as coefficient of variation and correlation distance of input parameters on these output results has been investigated. The results show that considering the spatial variability in probabilistic methods would help to determine the various mechanisms affecting the performance and the probability of failure of the landfill slope. In addition, the output of such probabilistic analyses can be used as guidance for engineers to design safe and reliable landfill.

An efficient method for probabilistic finite element analysis with application to contact assessment of CANDU fuel channels

The reliability of a simple turbomachinery model was calculated to demonstrate the application of a newly developing system integration tool, Probabilistic Design and Analysis Framework(PRODAF), along with efficient probabilistic methods using a response surface method. The model represents a system consisting of hypothetical turbine components. The parts include a blade, disk, and shaft with an applied angular velocity. All the components were modeled with the properties of the nickel alloy, Inconel 718. A response surface was calculated for the system of components to improve probabilistic computational efficiency. In addition, a fast probability integration method, Advanced First Order Reliability Method (AFORM), was used for the probabilistic analysis in order to provide an efficient analysis as possible. Geometric dimensions, the applied load, and material yield strength were varied for this study. The probability of failure was determined using the maximum first principal stress response and the material yield strength. A simple G function using the difference between strength and loading stress was used to determine failure limits. The probabilistic sensitivity of the failure response relative to the individual variables was determined also with material yield strength having the greatest influence. The model was recreated with every iteration of the probabilistic analysis in order to vary the geometry. As a result, the response surface method has a significant impact on improving computational efficiency and enabling reliability analysis with rapid turnaround.

Reliability analysis of pipe whip impacts

Probabilistic structural analysis methods for space propulsion system components

An investigation of cuspal deformation and delayed recovery after occlusal loading

Fracture load of composite resin and feldspathic all-ceramic CAD/CAM crowns

This study compared the periodontal health and patient’s satisfaction criteria of maxillary and mandibular incisors restored with porcelain-fused-to-metal and all-ceramic crowns. In this study a total of 40 abutment teeth for receiving 20 Porcelain-Fused-To-Metal and 20 all-ceramic crowns were allocated. Teeth to be restored were divided into two main groups, Group A: consisting of 20 units restored with porcelain-fused-to-metal restorations and Group B: consisting of 20 units restored with all ceramic restorations. The abutment teeth were divided into two groups: Group A for Porcelain-fused-to-metal and Group B for All ceramic with 20 teeth each. Margins for both the restorations were kept supragingival so that less chance of gingival inflammation can occur. Patients were more satisfied with all-ceramic than porcelain-fused-to-metal crowns.

Clinically relevant fracture testing of all-ceramic crowns