scholarly journals Sensitivity Analysis and Stochastic Modeling of the Effective Characteristics for the Reinforced Elastomers

10.5772/9743 ◽  
2010 ◽  
Author(s):  
Marcin Kaminski ◽  
Bernd Lauke
Keyword(s):  
Sensitivity Analysis ◽  
Stochastic Modeling ◽  
Effective Characteristics

Probabilistic Analysis of the Combined Slamming and Wave-Induced Responses

1985 ◽  
Vol 29 (03) ◽  
pp. 170-188
Author(s):  
G. Ferro ◽  
A. E. Mansour
Keyword(s):  
Sensitivity Analysis ◽  
Stochastic Modeling ◽  
Structural Design ◽  
Arrival Time ◽  
Extreme Value ◽  
Induced Responses ◽  
Combined Load ◽  
Random Intensity ◽  
Important Input ◽  
Wave Induced

The success of implementing reliability analysis in structural design depends to a large extent on the ability to combine the loads acting on the structure, and on extrapolating their magnitudes to obtain the extreme value of the total combined load. In this paper, a new theory is proposed to combine the slamming and wave-induced responses of a ship moving in irregular seas. The slamming and wave-induced responses are both considered as stochastic processes, and the properties of the combined response are determined on that basis. The slamming loads alone are considered as a train of impulses of random intensity and random arrival time as has been shown by Mansour and Lozow [1],3 but the dependence between the intensity and arrival time is considered in the stochastic modeling. The extreme value of the combined response is then investigated for use in design applications. An example of application to a cargo ship is given and a sensitivity analysis is conducted to determine how sensitive the results are to some of the important input parameters.

scholarly journals Assessment of the Supply Chain under Uncertainty: The Case of Lithium

Minerals ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 604
Author(s):  
Daniel Calisaya-Azpilcueta ◽  
Sebastián Herrera-Leon ◽  
Freddy A. Lucay ◽  
Luis A. Cisternas
Keyword(s):  
Sensitivity Analysis ◽  
Supply Chain ◽  
Lithium Ion Batteries ◽  
Stochastic Modeling ◽  
Global Sensitivity Analysis ◽  
Lithium Ion ◽  
Lithium Carbonate ◽  
Lithium Hydroxide ◽  
Deterministic Models ◽  
Global Sensitivity

Modeling the global markets is complicated due to the existence of uncertainty in the information available. In addition, the lithium supply chain presents a complex network due to interconnections that it presents and the interdependencies among its elements. This complex supply chain has one large market, electric vehicles (EVs). EV production is increasing the global demand for lithium; in terms of the lithium supply chain, an EV requires lithium-ion batteries, and lithium-ion batteries require lithium carbonate and lithium hydroxide. Realistically, the mass balance in the global lithium supply chain involves more elements and more markets, and together with the assortment of databases in the literature, make the modeling through deterministic models difficult. Modeling the global supply chain under uncertainty could facilitate an assessment of the lithium supply chain between production and demand, and therefore could help to determine the distribution of materials for identifying the variables with the highest importance in an undersupply scenario. In the literature, deterministic models are commonly used to model the lithium supply chain but do not simultaneously consider the variation of data among databases for the lithium supply chain. This study performs stochastic modeling of the lithium supply chain by combining a material flow analysis with an uncertainty analysis and global sensitivity analysis. The combination of these methods evaluates an undersupply scenario. The stochastic model simulations allow a comparison between the known demand and the supply calculated under uncertainty, in order to identify the most important variables affecting lithium distribution. The dynamic simulations show that the most probable scenario is one where supply does not cover the increasing demand, and the stochastic modeling classifies the variables by their importance and sensibility. In conclusion, the most important variables in a scenario of EV undersupply are the lithium hydroxide produced from lithium carbonate, the lithium hydroxide produced from solid rock, and the production of traditional batteries. The global sensitivity analysis indicates that the critical variables which affect the uncertainty in EV production change with time.

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