Dynamic and reliability analysis of stochastic structure system using probabilistic finite element method

On the formulation of a finite element method for the general pipe-in-pipe structure system: Impact buckling analysis

Clinical databases require accurate entity resolution (ER). One approach is to use algorithms that assign questionable cases to manual review. Few studies have compared the performance of common algorithms for such a task. Furthermore, previous work has been limited by a lack of objective methods for setting algorithm parameters. We compared the performance of common ER algorithms: using algorithmic optimization, rather than manual parameter tuning, and on two-threshold classification (match/manual review/non-match) as well as single-threshold (match/non-match). We manually reviewed 20,000 randomly selected, potential duplicate record-pairs to identify matches (10,000 training set, 10,000 test set). We evaluated the probabilistic expectation maximization, simple deterministic and fuzzy inference engine (FIE) algorithms. We used particle swarm to optimize algorithm parameters for a single and for two thresholds. We ran 10 iterations of optimization using the training set and report averaged performance against the test set. The overall estimated duplicate rate was 6%. FIE and simple deterministic algorithms allowed a lower manual review set compared to the probabilistic method (FIE 1.9%, simple deterministic 2.5%, probabilistic 3.6%; p<0.001). For a single threshold, the simple deterministic algorithm performed better than the probabilistic method (positive predictive value 0.956 vs 0.887, sensitivity 0.985 vs 0.887, p<0.001). ER with FIE classifies 98.1% of record-pairs correctly (1/10,000 error rate), assigning the remainder to manual review. Optimized deterministic algorithms outperform the probabilistic method. There is a strong case for considering optimized deterministic methods for ER.

This paper presents a Generalized Complementary Intersection Method (GCIM) that can predict system reliability for series, parallel, and mixed systems. The GCIM is an extension of the original study, referred to as the Complementary Intersection Method (CIM). The CIM was developed to assess system reliability for series systems. The contribution of this paper is to generalize the original CIM so that it can be used for system reliability analysis regardless of system structures (series, parallel, and mixed system). First, we derive a closed-form system reliability formula for a parallel system through its transformation into a series system using De Morgan’s law. Second, a unified system reliability analysis framework is proposed for mixed systems by defining a new System Structure matrix (SS-matrix) and employing the Binary Decision Diagram (BDD) technique. The SS-matrix is used to present any system structure in a comprehensive matrix form. Then the BDD technique together with the SS-matrix automates the process to identify system’s mutually exclusive path sets, of which each path set is a series system. As a result, system reliability with any system structure can be decomposed into the probabilities of the mutually exclusive path sets. Five engineering examples are used to demonstrate that the proposed GCIM can assess system reliability regardless of the system structures.

Photovoltaic systems are expected to play a key role in the planning and operation of future distribution systems due to the benefits associated with their use. Unfortunately, a great problem is involved in photovoltaic power utilization, i.e., the unpredictability of the solar source. Thus, many forecasting methods have been developed in order to provide tools with adequate consistency, quality, and value. The methods can provide either deterministic or probabilistic forecasts; the latter seem to be the most appropriate for taking into account the unavoidable uncertainties of the solar source. In this paper, a new probabilistic method based on a competitive ensemble of different base predictors is proposed for the short-term forecasting of photovoltaic power. Three probabilistic methods were selected and trained as base predictors in order to obtain an ensemble of the predictive distribution with optimal characteristics of sharpness and reliability. Numerical applications based on actual data were performed to test the effectiveness of the proposed method with respect to single predictors and to a benchmark method.

In recent years, there often exist multiple priors from experienced experts or historical experiments with the rapid development of system structure in engineering fields. Bayesian Melding Method is commonly used for integrating multiple priors, which is based on the deterministic system structure. However, if the system model cannot be described by an explicit expression, the traditional Bayesian Melding Method is not feasible for system reliability analysis anymore. In order to describe the structure relationship clearly, Bayesian Network is applied in this paper to construct the complex system structure model and the system reliability is calculated by node probability tables rather than explicit expressions. Combining the advantages of the Bayesian Melding Method and Bayesian Network, a multi-prior integration and updating algorithm is developed for the system reliability analysis of complex system structures. Finally, a satellite attitude control system is used to demonstrate the proposed method. The system is established by the Bayesian Network and the comparison between natural prior and updated prior is discussed at length.

Shape-instability life scatter prediction of 40Cr steel: Damage-coupled crystal plastic probabilistic finite element method

Study on Design Method of Series Traction Rod of Heavy Machinery

The central difference is a popular algorithm used to integrate the equations of motion, yet suffers from two drawbacks: (1) it is only conditionally stable and requires a small-time step to maintain numerical stability; (2) it is non-dissipative, and high-frequency spurious oscillations may appear and compromise the accuracy of the solution. These drawbacks are detrimental to applying the algorithm to the real-time hybrid simulation of large, complex nonlinear structural systems. In this paper, the conventional central difference algorithm is modified to overcome these drawbacks, and the modified algorithm is applied to the real-time hybrid simulation of complex structural systems.

Probabilistic methods can improve the reliability of fatigue damage evaluation in top tensioned (production) risers because they tend to provide less biased estimators on their safety, which can be used for more reliable decision making concerning their design. Such methods consider the collective impact of uncertainties in the riser system, which is not accurately assessed in conventional fatigue analysis. The large factors of safety that are commonly used in deterministic-based fatigue damage assessment tend to assure the high safety of the design, still they are generic factors that do not take advantage of available data for accurate quantification of system safety. This paper presents a probabilistic method toward fatigue reliability and integrity analysis of TTR systems. By using rules of probability, a simplified method is developed to estimate the probability of failure of the TTR system in its lifetime, considering the uncertainties with the Palmgren-Miner rule, the cyclic loads, and the fatigue strength of the components, and other analysis approximations. The method is then used for a comparative assessment on the fatigue reliability of the TTR components and calculating its fatigue Integrity Index. The method is illustrated in a case study and is used to provide recommendations that could possibly improve the TTR fatigue design by reducing its cost, increasing its safety, and maximizing its integrity.

Structural analysis and design optimization have recently been extended to stochastic approach to take various uncertainties into account. However in areas whwew it is not possible to produce accurate statistical information, the probabilistic method is not appropriate for stochastic structural analysis and design optimization, since improper modeling of uncertainty could cause greater degree of statistical uncertainty than those of physical uncertainty. For uncertainty with insufficient information, possibility-based (or fuzzy set) methods have recently been introduced in stochastic structural analysis and design optimization. The main advantage of the fuzzy analysis is that it preserves the intrinsic random nature of physical variables through their membership functions and, when used for evaluation of designs, yields more conservative design than those from the probabilistic methods. There are two computational aspects in the fuzzy analysis compared to the probability analysis. First, the input fuzzy variables can be defined easier than the input random variables when no or very limited statistical data are available. Secondly, extended fuzzy operations are much simpler than those required to use probability, especially when a number of variables are involved. For possibility-based design optimization (PBDO), like for reliability-based design optimization (RBDO), the performance measure approach (PMA) with an inverse fuzzy analysis is more appropriate than other approaches, such as a possibility index approach. This paper proposes a new formulation of PBDO using PMA. It is also found that PBDO is more desirable than RBDO, since PBDO can inherently handle a system level possibility unlike RBDO. For the inverse fuzzy analysis, the maximal possibility search (MPS) method is proposed to improve numerical efficiency and accuracy comparing with the vertex method and the

Chapter 7 Volumetric Estimation

In operational open pit mines, large rock surfaces are exposed daily and hundreds or thousands of new discontinuities are exposed, all of which may results in the formation of unstable key blocks and wedges. It is impractical to attempt to map each discontinuity and carry out a stability analysis by the traditional kinematic analysis and wedge/planar failure analysis for each discontinuity mapped. The approach presented in this paper is to design the bench berm configuration in such a manner that sufficient key blocks and wedges will be analysed by probabilistic methods so that an acceptable level of safety is achieved. Geotechnical engineers therefore require a design tool which will allow them to evaluate the type and frequency of key blocks that may be formed and the effect of decreasing the bench angle or increasing the berm width on the probability of failure of the key blocks. This paper describes a computer program, SBlock in which a probabilistic method is applied to determine the potential key block dimensions, the back break and the berm width for given acceptability criteria. Also, two case histories of operational open pit mines are presented to validate the methodology and calibrate the results. It is concluded that a probabilistic approach is suitable for the evaluation and design of the bench berm configuration in situations where a large numbers of discontinuities are exposed in the operational open pit benches.

Clinicians who use electromyographic (EMG) signals to help determine the presence or absence of abnormality in a muscle often, with varying degrees of success, evaluate sets of motor unit potentials (MUPs) qualitatively and/or quantitatively to characterize the muscle in a clinically meaningful way. The resulting muscle characterization can be improved using automated analysis. As such, the intent of this study was to evaluate the performance of automated, conventional Means/Outlier and Probabilistic methods in converting MUP statistics into a concise, and clinically relevant, muscle characterization. Probabilistic methods combine the set of MUP characterizations, derived using Pattern Discovery (PD), of all MUPs detected from a muscle into a characterization measure that indicates normality or abnormality. Using MUP data from healthy control subjects and patients with known neuropathic disorders, a Probabilistic method that used Bayes' rule to combine MUP characterizations into a Bayesian muscle characterization (BMC) achieved a categorization accuracy of 79.7% compared to 76.4% using the Mean method (P > 0.1) for biceps muscles and 94.6% accuracy for the BMC method compared to 85.8% using the Mean method (P < 0.01) for first dorsal interosseous muscles. The BMC method can facilitate the determination of "possible," "probable," or "definite" levels for a given muscle categorization (e.g., neuropathic) whereas the conventional Means and Outlier methods support only a dichotomous "normal" or "abnormal" decision. This work demonstrates that the BMC method can provide information that may be more useful in supporting clinical decisions than that provided by the conventional Means or Outlier methods.

The conventional reliability indices of a subtransmission system are generally based on probabilistic methods and include basic load point indices and system performance indices. In spite of the fact that these probabilistic indices are well established and their evaluation techniques are well developed, many power utilities are continuing to use deterministic methods to assess the reliabilities of their systems. The reluctance by a power system planner to accept probabilistic methods in their present form dictates a need to create a bridge between the deterministic methods and the existing probabilistic techniques. This can be achieved by using a well-being framework which links the existing deterministic criteria with a probabilistic method. This paper illustrates an extension of the conventional probabilistic approach utilized in the subtransmission system in the form of health and margin indices that provide system planners with measures of system well-being in addition to more conventional risk indices.

A new damage stability framework based upon probabilistic methods is presented. The proposed framework if based on a probabilistic method, similar to IMO Res. A.265 and the new regulations for cargo ships in SOLAS Ch. II-1, Part B-1. New elements introduced are effect of water on deck and cargo shift. The framework also addresses the damage stability modelling procedures, in particular the standards for intermediate stages of flooding and crossflooding.