Strength Evaluation and Fatigue Prediction of a Pressurized Thick-Walled Cylinder

By design, a large caliber gun barrel routinely operates closer to its fatigue envelope than virtually any other device. The lifetime of a gun barrel is limited by bore damage and by fatigue crack growth, which depends crucially upon near-bore thermal damage arising from initial firing, the thermomechanical basis for early cracking and subsequent loss of liner material. The proper understanding and prediction of the strength and fatigue failure of a pressurized thick cylinder is an important prerequisite for any reliable application. In this paper, an analysis of two-dimensional stresses in a thick walled pressurized cylinder using an analytical method followed by fatigue calculation was performed. The effects of wall thickness and internal firing pressure, considering the material properties, on the stress distribution were investigated. This analytical method of stress analysis can be used as a valuable tool for evaluating strength and predicting failure phenomena of a large caliber gun barrel.

Blade Material Fatigue Assessment Using Elman Neural Networks

Material degradation evaluation and life prediction of major components such as blades, rotors, valves of steam turbines not only guarantees reliable, efficient and continuous operation of electric plants, but also offers the promise of substantially reducing the cost of repair and replacement of defective parts, and may even result in saving lives. In this paper, a recurrent neural network based strategy was developed for material degradation assessment and fatigue damage propagation prediction. Two Elman Neural Networks were developed for fatigue severity assessment and trend prediction correspondingly. The performance of the proposed prognostic methodology was evaluated by using blade material fatigue data collected from a material testing system. The prognostic method is found to be a reliable and robust material fatigue predictor.

A Simplified Hydroplaning Simulation for a Straight-Grooved Tire by Using FDM, FEM and an Asymptotic Method

Much research has been conducted to simulate the hydroplaning phenomenon of tires using commercial explicit FEM (finite element method) codes such as MSC.Dytran and LS-DYNA. However, it takes a long time to finish such a simulation because its model has a great number of Lagrangian and Eulerian elements and a contact should be defined between the two different types of elements, and the simulation results of the lift force and the contact force are oscillatory. Thus, in this study a new methodology was proposed for the hydroplaning simulation using two separate mathematical models; an FDM (finite difference method) code was developed to solve Navier-Stokes and continuity equations and consequently to obtain the pressure distribution around a tire with the inertia and the viscous effects of water taken into account, and an FE tire model was used to obtain the deformed shape of the tire due to the vertical load and the pressure distribution. The two models were iteratively used until a converged pressure distribution was obtained. Since the converged pressure distribution could not be obtained near or at the contact zone due to very shallow water, an asymptotic method was also proposed to estimate the pressure distribution. This new simulation methodology was applied to a straight-grooved tire, and its hydroplaning speed was determined for the water depth of 5 mm, 10 mm, 15 mm and 20 mm. In addition, a simplified simulation method was proposed instead of the fully iterative method. Only one iteration was conducted at each speed to reduce the total number of iterations, still resulting in a similar hydroplaning speed. Moreover, a new simulation methodology of using LS-DYNA was proposed, and its results were compared with those from the iterative method in terms of accuracy and efficiency.

Prospective System Assessments Used to Enhance Patient Safety: Case Studies From a Collaboration of Engineers and Hospitals in Southwest Washington State

It is no secret that healthcare, in general, has become an increasingly complicated mixture of technical systems, complex processes and intricate skilled human interactions. Patient care processes have followed this same trend. The healthcare industry, itself, has acknowledged that it is fraught with high-risk and error prone processes and cite medication management systems, invasive procedures and diagnostic methods. Complexity represents opportunity for unanticipated events, process failures and undesirable outcomes. Traditionally when a patient care process fails, accountability was focused on the individual clinician error. However, increasing, healthcare is following the lead of other high-risk industries (e.g. chemical, aerospace, nuclear, etc.) that give attention to the characteristics the overall system that contribute to the failure. The focus has shifted to identification of systemic weaknesses and vulnerabilities. Increasing the healthcare industry is using prospective system assessment methods to evaluate the high-risk systems and processes. This paper describes results of collaboration between engineers and community hospitals in Southwest Washington State between 2002 and 2007 in applying prospective system assessment methods to a range of the high-risk healthcare systems and processes. The methods used are Failure Mode Effects and Criticality Analysis and Probabilistic Risk Assessment. The two case studies presented are: 1) an interhospital FMEA on patient transfer and 2) a risk assessment of mental health patients who present themselves in a hospital Emergency Department.

Optimization Based Approach to Automate Fatigue Durability Test Load Development

Loads for structural fatigue durability tests are often developed by considering only the load data and ignoring the structure and its failure modes. Typically, in these procedures, the load data is directly used with a fatigue damage calculation method to compute the most damaging loading direction and magnitude. These processes ignore the underlying structure and hence may not arrive at loading modes that are sensitive to the failure modes of the structure. The structure, with its failure modes, wield considerable influence on the test load selection and ought to be considered in the test development. FEA tools can be employed for this purpose. However, due to the iterative nature of the test development process and the repeated FEA analysis it entails, the development task can become tedious. Here, an optimization based approach to automate the test load development process is proposed. This methodology leverages optimization algorithms to arrive at the test load cycles with proper load phasing even when a large number of load channels are involved. This method permits linear or nonlinear FEA procedures with component or system level test setups. This method also allows for maximizing the fatigue damage at the primary ‘key life’ failure location. A range of loading constraints — from constraints based on durability loading histories to constraints due to testing rig limitations — may be applied. In this discussion, a unique approach to setup lab test development problems that are conducive to optimization algorithms is delineated. As a part of this process, a novel approach to set loading constraints by utilizing multidimensional scatter plots of the existing loading histories will also be shown. The effectiveness of well known optimization methods in searching and arriving at the test load cycles will be also highlighted.

A Preliminary Safety Analysis of an Ultracapacitor Power Subsystem for a Prototype Alternative Fuel Vehicle

In today’s rapidly changing world, the need for safe, more fuel-efficient and environmental friendly vehicles is in high demand. Private and public sector proponents of alternative fuel vehicles have joined forces to create a university-based competition, Challenge-X, to safely increase fuel economy as well as reduce emissions. The safe performance of vehicle testing, maintenance and rescue tasks are integral to the competition. At West Virginia University, a mechanical engineering team is developing a vehicle powered by a 1.9-L direct injection turbodiesel engine using bio-diesel fuel. Energy storage in the vehicle is via 750 kJ ultracapacitors which power two 13 kW AC induction wheel hub motors. A system safety analysis performed by a WVU industrial engineering team focuses on the ultracapacitor portion of the system. Designsafe© software is used to systematically identify tasks, hazards, risks, and risk reduction measures. An emergency rescue plan and a procedure for emergency rescue for vehicles with this design is described. The emergency safety plan identifies ‘no cut’ areas or areas of the car that would be very dangerous to cut due to the high voltage. The risk reduction plan includes procedures for performing maintenance on the electrical system, including the ultracapacitor system.

Reliability Analysis of Water Mist Systems

This paper presents some preliminary results from a reliability study of water mist systems conducted at FM Global. The study includes a detailed Failure Modes and Effects Analysis (FMEA) to identify all the major potential failure modes, which include demand, quiescent and operational failures. Various fault trees are thus constructed for the typical water mist system configurations to evaluate the failure probabilities. However, due to the short history of industrial application of water mist systems, no specific reliability data are available. Therefore, in the calculation of system failure probability, the component failure data are obtained from other applications. The failure probabilities and the confidence bounds of the typical water mist systems listed in the Standard 750 of the National Fire Protection Association are compared in the paper. The major failure modes identified through an importance analysis are also presented.

A Novel Bayesian Framework for Uncertainty Management in Physics-Based Reliability Models

The physics-of-failure (POF) modeling approach is a proven and powerful method to predict the reliability of mechanical components and systems. Most of POF models have been originally developed based upon empirical data from a wide range of applications (e.g. fracture mechanics approach to the fatigue life). Available curve fitting methods such as least square for example, calculate the best estimate of parameters by minimizing the distance function. Such point estimate approaches, basically overlook the other possibilities for the parameters and fail to incorporate the real uncertainty of empirical data into the process. The other important issue with traditional methods is when new data points become available. In such conditions, the best estimate methods need to be recalculated using the new and old data sets all together. But the original data sets, used to develop POF models may be no longer available to be combined with new data in a point estimate framework. In this research, for efficient uncertainty management in POF models, a powerful Bayesian framework is proposed. Bayesian approach provides many practical features such as a fair coverage of uncertainty and the updating concept that provide a powerful means for knowledge management, meaning that the Bayesian models allow the available information to be stored in a probability density format over the model parameters. These distributions may be considered as prior to be updated in the light of new data when they become available. At the first part of this article a brief review of classical and probabilistic approach to regression is presented. In this part the accuracy of traditional normal distribution assumption for error is examined and a new flexible likelihood function is proposed. The Bayesian approach to regression and its bonds with classical and probabilistic methods are explained next. In Bayesian section we shall discuss how the likelihood functions introduced in probabilistic approach, can be combined with prior information using the conditional probability concept. In order to highlight the advantages, the Bayesian approach is further clarified with case studies in which the result of calculation is compared with other traditional methods such as least square and maximum likelihood estimation (MLE) method. In this research, the mathematical complexity of Bayesian inference equations was overcome utilizing Markov Chain Monte Carlo simulation technique.

Spectral Analysis of Deteriorated Offshore Structures

In order to present an efficient, practical technique to determine progressive failure mechanism of structures, modelling of member deterioration by using a spring system is outlined. The procedure uses updates of member stiffness and mass matrices as well as the random load vector in incremental forms. In this procedure, the assembly process produces redistributions of the system stiffness and mass matrices, and the load vector. In the calculation of response spectral values, the original forms remain unchanged. Inversion of the stiffness matrix is calculated by using the Neumann expansion solution in which the original stiffness matrix is inverted only once so that a considerable computation time is saved in the whole calculation process. An incremental solution technique is presented for spectral analyses of both static and dynamic sensitive structures. In the case of dynamic analysis, special attention is paid to estimations of modified natural frequencies and mode shapes of deteriorated structures, which may affect response spectral values considerably. The technique, which is presented in the paper, is attractive in practical applications and can be efficiently used in the reliability calculation as well, and also it can be successfully used to determine a progressive failure mechanism of the structure.

Strength Assessment of a Pressurized Thick-Walled Cylinder Using Structural-Thermal Coupled Finite Element Analysis

Upon analysis, thick wall cylinders designed for use in cannon barrel applications experience thermal and mechanical loading very near their fatigue limit. Chief factors in determining the lifetime of a cannon barrel involve internal thermal and mechanical damage caused by projectile firing. The most significant damage experienced in the cannon barrel is surface crack propagation which aids in surface erosion and fatigue failure. Adequate knowledge of these failure phenomena and the ability to predict the lifetime of gun barrels will greatly increase the successful application of their designs. This study will investigate three-dimensional stress of a pressurized thick cylinder using computer simulation based on structural-thermal coupled finite element analysis. The effects of high temperature and high pressure, as well as nonlinear material behavior, on stress-strain distribution during the firing process will be evaluated. This computer-based stress analysis will prove to be a valuable tool for assessing strength and forecasting the lifetime of cannon barrels.