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Accepted Manuscripts

BASIC VIEW  |  EXPANDED VIEW
research-article  
Kenji Iino, Ritsuo Yoshioka, Masao Fuchigami and Masayuki Nakao
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4040570
The Great East Japan Earthquake on March 11, 2011 triggered huge tsunami waves that attacked Fukushima Daiichi Nuclear Power Plant (Fukushima-1). Units 1, 3, and 4 had hydrogen explosions. Units 1, 2, and 3 had core meltdowns and released a large amount of radioactive material. Published investigation reports did not explain how the severity of the accident could have been prevented. We formed a study group to find what preparations at Fukushima-1 could have avoided the severity of the accident. We concluded that the severity could have been avoided if the plant had prepared a set of equipment, and had exercised actions to take against such tsunami. Necessary preparation included (1) A number of DC batteries, (2) Portable underwater pumps, (3) Portable AC generators with sufficient gasoline supply, (4) High voltage AC power trucks, and (5) Drills against extended loss of all electric power and seawater pumps. The most important preparation was item (5), i.e., to study plans and carry out exercises against huge tsunami. That alone would have identified all other necessary preparations.
TOPICS: Accidents, Risk, Fukushima nuclear disaster, Japan, 2011, Tsunamis, Pumps, Earthquakes, Generators, Hydrogen, Nuclear power stations, Seawater, Trucks, Gasoline, Electricity (Physics), Explosions, Alternating current (Electricity), Drills (Tools), Radioactive substances, Waves
research-article  
Stephen Wu, Panagiotis Angelikopoulos, James L. Beck and Petros Koumoutsakos
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4040571
Hierarchical Bayesian models have been increasingly used for various engineering applications. We classify two types of Hierarchical Bayesian Model found in the literature as Hierarchical Prior Model (HPM) and Hierarchical Stochastic Model (HSM). Then, we focus on studying the theoretical implications of the HSM. Using examples of polynomial functions, we show that the HSM is capable of separating different types of uncertainties in a system and quantifying uncertainty of reduced order models under the Bayesian model class selection framework. To tackle the huge computational cost for analyzing HSM, we propose an efficient approximation scheme based on Importance Sampling and Empirical Interpolation Method. We illustrate our method using two engineering examples --- a Molecular Dynamics simulation for Krypton and a pharmacokinetic/pharmacodynamic model for cancer drug.
TOPICS: Engineering systems and industry applications, Approximation, Risk, Uncertainty, Pharmacokinetics, Electromagnetic weapons, Anticancer drugs, Interpolation, Polynomials, Molecular dynamics simulation
Review Article  
Yali Ren, Ning Wang, Jinwei Jiang, Junxiao Zhu, Gangbing Song and Xuemin Chen
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4040407
In the challenging downhole environment, drilling tools are normally subject to high temperature, severe vibration and other harsh operation conditions. The drilling activities generate massive field data, namely Field Reliability Big Data (FRBD), which includes downhole operation, environment, failure, degradation and dynamic data. FRBD has large volume, high variety and extreme complexity. FRBD presents abundant opportunities and great challenges for drilling tool reliability analytics. Consequently, as one of the key factors to affect drilling tool reliability, downhole vibration factor plays a critical role in the reliability analytics based on FRBD. This paper reviews the important parameters of downhole drilling operations, examines the mode, physical and reliability impact of downhole vibration, and presents the features of reliability big data analytics. Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring, and maintenance planning and optimization. Moreover, the authors highlight the future researches about how to better utilize the downhole vibration in reliability big data analytics to further improve tool reliability and optimize maintenance planning.
TOPICS: Drilling, Reliability, Vibration, Risk, Maintenance, Failure, High temperature, Condition monitoring, Optimization
research-article  
David Eager and Hasti Hayati
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039999
More than four decades have passed since the introduction of safety standards for impact attenuation surfaces (IAS) used in playgrounds. Falls in children’s playground are a major source of injuries and IAS is one of the best method of preventing severe head injuries. However, the ability of IAS in prevention of other types of injuries such as upper limb fractures is unclear. Accordingly, in this paper 10 synthetic playground surfaces were tested to examine their performance beyond the collected Head Injury Criterion (HIC) and maximum G-force (Gmax) outputs recommended by ASTM F1292. The aim of this work was to investigate any limitations with current safety criteria and proposing additional criteria to filter hazardous IAS that technically comply with the current 1000 HIC and 200 Gmax thresholds. The proposed new criterion is called the impulse force criterion (If). If combines two important injury predictor characteristics, namely: HIC duration that is time duration of the most severe impact; and the change in momentum that addresses the IAS properties associated with bounce. Additionally, the maximum jerk (Jmax), the bounce and the IAS absorbed work are presented. HIC, Gmax, If and Jmax followed similar trends regarding material thickness and drop height. Moreover, the bounce and work done by the IAS on the falling missile at increasing drop heights was similar for all surfaces apart from one viscoelastic foam sample. The results presented in this paper demonstrate the limitations of current safety criteria and should therefore assist future research to reduce long-bone injuries in playgrounds.
TOPICS: Wounds, Risk, Safety, Fracture (Materials), Impulse (Physics), Bone, Fracture (Process), Filters, Missiles, Momentum, ASTM International
research-article  
Zhangli Hu and Xiaoping Du
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4040000
In traditional reliability problems, the distribution of a basic random variable is usually unimodal; in other words, the probability density of the basic random variable has only one peak. In real applications, some basic random variables may follow bimodal distributions with two peaks in their probability density. When binomial variables are involved, traditional reliability methods, such as the First Order Second Moment (FOSM) method and the First Order Reliability Method (FORM), will not be accurate. This study investigates the accuracy of using the saddlepoint approximation for bimodal variables and then employs saddlepoint approximation based reliability methods with first order approximation to predict the reliability. A limit-state function is at first approximated with the first-order Taylor expansion so that it becomes a linear combination of the basic random variables, some of which are bimodally distributed. The saddlepoint approximation is then applied to estimate the reliability. Examples show that the saddlepoint approximation based reliability methods are more accurate than FOSM and FORM.
TOPICS: Reliability, Approximation, Risk, Density, Probability
research-article  
Mohammad Al Sharman, Mohammad Al Jarrah and Mamoun Abdel-Hafez
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039943
The high estimated position error (EPE) in current commercial-off-the-shelf (GPS/INS) impedes achieving precise autonomous takeoff and landing flight operations. To overcome this problem, in this paper we propose an integrated GPS/INS/Optical Flow (OF) solution in which the OF provides an accurate augmentation to the GPS/INS. To ensure accurate and robust OF augmentation, we have used a robust modeling method to estimate OF based on a set of real-time experiments conducted under various simulated helicopter-landing scenarios. Knowing that the accuracy of the OF measurements are dependent on the accuracy of the height measurements; we have developed a real time testing environment to model and validate the obtained dynamic OF model at various heights. The performance of the obtained OF model matches the real OF sensor with 87.70 % fitting accuracy. An accuracy of 0.006 m/s mean error between the real OF sensor velocity and the velocity of the OF model is also achieved. The velocity measurements of the obtained OF model and the position of the GPS/INS are used in performing a dynamic model-based sensor fusion algorithm. In the proposed solution, the OF sensor is engaged when the vehicle approaches a landing spot that is equipped with a predefined landing pattern. The proposed solution has succeeded in performing a helicopter auto takeoff and landing with a maximum position error of 27 cm.
TOPICS: Flow (Dynamics), Risk, Sensors, Errors, Fittings, Velocity measurement, Flight, Algorithms, Modeling, Testing, Vehicles

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