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Accepted Manuscripts

BASIC VIEW  |  EXPANDED VIEW
research-article  
Nick Kloppenborg, Tara Amenson, Jacob Wernik and John F. Wiechel
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039357
Go-karts are a common amusement park feature enjoyed by people of all ages. While intended for racing, contact between go-karts does occur. To investigate and quantify the accelerations and forces which result from contact, 44 low-speed impacts were conducted between a stationary (target) and a moving (bullet) go-kart. The occupant of the bullet go-kart was one of two human volunteers. The occupant of the target go-kart was a Hybrid III 50th percentile male anthropomorphic test device (ATD). Impact configurations consisted of rear-end impacts, frontal impacts, side impacts, and oblique impacts. Results demonstrated high repeatability for the vehicle performance and occupant response. Go-kart accelerations and speed changes increased with increased impact speed. Impact duration and restitution generally decreased with increased impact speed. All ATD acceleration, force, and moment values increased with increased impact speed. Common injury metrics such as the Head Injury Criterion (HIC), N_ij, and N_km were calculated and were found to be below injury thresholds. Occupant response was also compared to published activities of daily living data.
TOPICS: Vehicles, Wounds, Bullets, Risk
research-article  
Xinchen Zhuang, Tianxiang Yu, Linjie Shen and Bozhi Guo
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039243
As a common type system, the multi-state weighted k-out-of-n system is of great importance in reliability engineering. The components are usually treated as independent with each other. It's usually not that case in real life and the components are dependent. On the other hand, the performance of the components degrades over time, leading to the change of weight of the components at the same time. As a result, the present paper provides a method to evaluate the dynamic reliability of multi-state weighted k-out-of-n: G system with s-dependent components. The degradation of the components follows a Markov process and the components are non-repairable. The copula function is used to model the s-dependence of the components. Combing the LZ-transform for a discrete-states continuous-time Markov process, the explicit expression for the survival function and the mean time to failure of the system is obtained. The determination of the copula function and the parameter are explicated. A small electricity generating system is studied based on our method in the illustration and detailed comparison result is made for dependent case and independent case. Dynamic reliability with varied levels of electricity generation conforming to the actual situation for this generating system is also calculated.
TOPICS: Reliability, Risk, Markov processes, Weight (Mass), Electric power generation, Failure
research-article  
Yan Wang
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039148
Cyber-physical systems (CPS) are the physical systems of which individual components have functional identities in both physical and cyber spaces. Given the vastly diversified CPS components in dynamically evolving networks, designing an open and resilient architecture with flexibility and adaptability thus is important. To enable a resilience engineering approach for systems design, quantitative measures of resilience have been proposed by researchers. Yet, domain dependent system performance metrics are required to quantify resilience. In this paper, generic system performance metrics for CPS are proposed, which are entropy, conditional entropy, and mutual information associated with the probabilities of successful prediction and communication. A new probabilistic design framework for CPS network architecture is also proposed for resilience engineering, where several information fusion rules can be applied for data processing at the nodes. Sensitivities of metrics with respect to the probabilistic measurements are studied. Fine-grained discrete-event simulation models of communication networks are used to demonstrate the applicability of the proposed metrics.
TOPICS: Design, Risk, Resilience, Entropy, Space, Probability, Simulation models
research-article  
Zhen Hu and Sankaran Mahadevan
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039149
Bayesian Networks (BNs) are being studied in recent years for system diagnosis, reliability analysis, and design of complex engineered systems. In several practical applications, BNs need to be learned from available data before being used for design or other purposes. Current BN learning algorithms are mainly developed for networks with only discrete variables. Engineering design problems often consist of both discrete and continuous variables. This paper develops a framework to handle continuous variables in BN learning by integrating learning algorithms of discrete BNs with Gaussian mixture models (GMMs). We first make the topology learning more robust by optimizing the number of Gaussian components in the univariate GMMs currently available in the literature. Based on the BN topology learning, a new Multivariate Gaussian Mixture (MGM) strategy is developed to improve the accuracy of conditional probability learning in the BN. A method is proposed to address this difficulty of MGM modeling with data of mixed discrete and continuous variables by mapping the data for discrete variables into data for a standard normal variable. The proposed framework is capable of learning BNs without discretizing the continuous variables or making assumptions about their CPDs. The applications of the learned BN to uncertainty quantification and model calibration are also investigated. The results of a mathematical example and an engineering application example demonstrate the effectiveness of the proposed framework.
TOPICS: Design, Risk, Algorithms, Topology, Event history analysis, Engineering design, Modeling, Calibration, Probability, Uncertainty quantification
research-article  
Rakibul Islam, Faisal Khan and Ramachandran Venkatesan
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4039016
The early detection of a kick and mitigation with appropriate well control actions can minimize the risk of a blowout. This paper proposes a downhole monitoring system, and presents a dynamic numerical simulation of a compressible two-phase flow to study the kick dynamics at downhole during drilling operation. This approach enables early kick detection and could lead to the development of potential blowout prevention strategies. A pressure cell that mimics a scaled-down version of a downhole is used to study the dynamics of a compressible two-phase flow. The setup is simulated under boundary conditions that resemble realistic scenarios; special attention is given to the transient period after injecting the influx. The main parameters studied include pressure gradient, rising speed of a gas kick, and volumetric behavior of the gas kick with respect to time. Simulation results exhibit a sudden increase of pressure while the kick enters and volumetric expansion of gas as it flows upward. This improved understanding helps to develop effective well control and blowout prevention strategies. This study confirms the feasibility and usability of an intelligent drill pipe as a tool to monitor well conditions and develop blowout risk management strategies.
TOPICS: Dynamics (Mechanics), Risk, Two-phase flow, Pressure, Flow (Dynamics), Computer simulation, Drills (Tools), Drilling, Transients (Dynamics), Pipes, Boundary-value problems, Monitoring systems, Pressure gradient, Risk management, Simulation results
research-article  
Kasmet T. Niyongabo and Scott B. Nokleby
ASME J. Risk Uncertainty Part B   doi: 10.1115/1.4032634
A proof-of-concept detector prototype capable of collecting and storing radiometric data in the Jet Boring System (JBS) during pilot hole drilling at the Cigar Lake uranium mine is presented. Cigar Lake is the world’s second highest known grade uranium mine and is located in northern Saskatchewan, Canada. Variant design is used to design, develop, test and implement the detector’s firmware, software and hardware. The battery powered detector is attached inside a JBS drill rod to collect radiometric data through the drilling cycle. A readout box is used to initiate the detector, recharge the battery and download radiometric data afterthe pilot hole drilling cycle is complete.Functional testing results are presented and comparative test results between the JBS gamma probe and the AlphaNUCLEAR Hi-Flux probe are evaluated. Field data collected from a pilot hole is plotted against the pilot hole’s driving layout and jetting recipe to show the accuracy of the readings collected.

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