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Fatigue Crack Growth In Linear Elastic Bodies (Cont’d)
Published in Vladimir V. Bolotin, Mechanics of Fatigue, 2020
Random factors influencing fatigue crack growth can be classified in several ways. In particular, one could distinguish randomness of material properties inherent to a material’s structure, random variability of properties within a sample of specimens or structural components, and random variability of properties of commercial materials for which suppliers are responsible. In the paper by Bolotin [26] names are suggested for these kinds of variability as within-specimen, specimen-to-specimen, and batch-to-batch scatters, respectively. Some aspects of this classification have been already discussed in Chapter 2 in the context of fatigue crack nucleation and early growth. Hereafter we turn to the crack propagation in the presence of various random factors. It is not easy to draw a border between some groups of random factors. In particular, instabilities of manufacturing enter, perhaps, all three groups. Nevertheless, from the viewpoint of analytical modeling, the difference is rather distinctive. To describe randomness on the within-specimen scale, random fields must be considered. The fields are continuous for amorphous polymers, and piece-continuous for multi-phase materials or materials with multiple microcracking. The batch-to-batch and specimen-to-specimen scatters are described by random variables. Some of these variables are parameters characterizing random fields; they enter probability distributions and spectral densities of local material properties.
Random Vibration: Probabilistic Forces
Published in Haym Benaroya, Mark Nagurka, Seon Han, Mechanical Vibration, 2017
Haym Benaroya, Mark Nagurka, Seon Han
We now introduce some uncertainty into the experimentally determined parameters of the deterministic inverse vibration problem of Section 6.12. Quantities involved in the design and analysis of engineering systems generally exhibit some degree of randomness. This can be attributed to several sources. One source is the uncertainty involved in measurements. A measurement may be made, in some cases, as accurately as is needed for a particular application. In other situations, a measurement can be made only as accurately as the measurement system will allow. In either case, there is some uncertainty in the resulting values.
Concept of Randomness
Published in Franklin R. Nash, Reliability Assessments, 2017
Random numbers can be generated, for example, by placing 10 balls, numbered 0 to 9, in a bowl and drawing one ball at a time, replacing the ball after each drawing. The use of random numbers in computers, however, has required their generation in a deterministic manner. The numbers so generated are termed pseudorandom. The quality of their randomness is determined by statistical tests. The digits of pi (π) calculated from an infinite series have been shown to be as random as possible, that is, they are random according to statistical tests even though the digits are derived from a simple formula [61]. There is no way to distinguish with any degree of confidence a truly random sequence from one that is pseudorandom [3].
Probability analysis of train-bridge coupled system considering track irregularities and parameter uncertainty
Published in Mechanics Based Design of Structures and Machines, 2023
Xiang Liu, Lizhong Jiang, Ping Xiang, Zhipeng Lai, Lili Liu, Shanshan Cao, Wen Zhou
In engineering structures, randomness is inevitable and must be considered in the design process to ensure the safety and reliability of dynamic systems (G. Chen, Meng, et al. 2018; G. Chen and Yang 2019; X. Li et al. 2021). Similarly, characteristics of HSR bridges will have certain amount of variability due to uncertain material characteristics, construction variables, environmental factors, and structural properties of the bridge itself (Wang, Zhou, et al. 2019). Railway tracks are not completely smooth (Feng et al. 2019; Lai, Jiang, Liu, Zhang, et al. 2020; Lai et al. 2020). The entire TBCS is a complex system with randomness, and as the speed of the train increases, the stability and safety requirements become more important and randomness of the TBCS must be scientifically and reasonably assessed.
Optimization of maintenance tasks of spatially distributed assets with non-preemptive overtime
Published in International Journal of Management Science and Engineering Management, 2022
Moreover, the model is deterministic and does not incorporate randomness in variables, this means that all parameters such as distances, durations, routes, costs are constant and do not change during the planning horizon. To address randomness in some parameters, stochastic programming or simulation studies can be helpful. In addition, the number of tasks in preventive maintenance is normally constant and predictable; however, owed to the interference of corrective maintenance, the number of tasks may vary depending on the state of the asset and the average number of breakdowns during the asset’s lifetime. This highlights the model’s suitability for deterministic scenarios. While all costs and durations are considered to be predictable and probably well-defined, these costs and durations may change over time. Stochastic models must be examined in the situation of varying durations.
Drug recall management and channel coordination under stochastic product defect severity: a game-Theoretic analytical study
Published in International Journal of Production Research, 2021
Seyyed-Mahdi Hosseini-Motlagh, Mohammadreza Nematollahi, Nazanin Nami
In practice, manufacturing systems may face unavoidable short-term disruptions (e.g. labour strike, machine failure, etc.), which might lead to producing defective items and therefore having substantial negative impacts on the manufacturing performance and the whole supply chain (Namdar et al. 2018). In the pharmaceutical sector, there are restrictive obligations regarding defective medications, while in other industries, such obligations are scarce. This is because defective pharmaceuticals may significantly threaten public health and therefore they should be immediately recalled thanks to the health legislations and laws (FDA). A drug recall is the act of collecting distributed pharmaceutical products that cannot meet basic standards set by the Food and Drug Administration (FDA). There are various factors causing the drug recall, including but not limited to incorrect labelling, error in product formulation, and adverse side effects or drug reactions not included in the package insert (WHO). Hence, production disruption can be considered as one of the major factors causing defective or hazardous pharmaceuticals that did not follow manufacturing guidelines (Nagaich and Sadhna 2015). Due to the randomness in disruptions, the occurrence of disruption within manufacturing processes may cause different product defects with random severities, which determine the potential hazard level of the produced items. In practice, FDA’s regulations characterise the recall’s category and strategy based on the products’ potential hazard (Nagaich and Sadhna 2015).