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Hierarchical model effectiveness factor

Effectiveness factor approaches Macrohomogeneousmodel of ionomer-bound CL Structural (percolation) model of ionomer-bound CL Structural model coupled with water balance in pores Thin-film morphology of ionomer in CL Hierarchical Model, coupling of meso-and macroscale... [Pg.164]

As for the first assumption, the electrolyte phase must be treated as a mixed phase. It consists of a thin-film structure of ionomer at the surface of Pt/C agglomerates and of water in ionomer-free intra-agglomerate pores. The proton density is highest at the ionomer film (pH 1 or smaller), and it is much smaller in water-filled pores (pH > 3). However, the proton density distribution is not incorporated in the statistical utilization Tstat, but in an agglomerate effectiveness factor, defined in the section Hierarchical Model of CCL Operation. ... [Pg.174]

In the section Hierarchical Model of CCL Operation, a hierarchical modeling framework will be presented it couples multiple effects of proton and oxygen transport at the mesoscopic scale of agglomerates and at the macroscopic scale of the layer. In that general case, the CL effectiveness factor can be defined by... [Pg.175]

In the hierarchical model, macroscale transport processes will be coupled to transport and reaction at the mesoscale, as illustrated in Figure 4.1 an explicit treatment of agglomerate effects is needed to properly assess the effectiveness factor of Pt utilization, which transpires as the key parameter in the structural optimization of CCLs. [Pg.273]

Sadeghi, E., Putz, A., and Eikerling, M. 2013b. Hierarchical model of reaction rate distribuiton and effectiveness factors in catalyst layers of polymer electrolyte fuel cells. [Pg.501]

This paper suggests an evaluation of an airport security system based on a hierarchical model of fuzzy reasoning. This approach results from the fact that the tested system is heavily influenced by the human factor and other elements, which are not subject to unequivocal and precise description. It is impossible to detect the functional relationships between the various factors which influence the effectiveness of the security system and an evaluation of the security level. In such cases it is required to use expert opinions. As we have to deal with expert opinions, it is a known fact that very often they are formulated in a descriptive and an imprecise way. We must therefore view the decision making problem in context of uncertainty related to decision making (Dubois Prade 1992). All this locates the decision making problem in an area described by e.g. the theory of fuzzy sets or rough sets (Greco et al. 2001). [Pg.799]

Again, p — 1/3 gives a fair fit to Fig. 11.17. The mass-metallicity relation is associated with the decrease in gas fraction concomitant with the increasing mass, and the G-dwarf problem is neatly solved models of hierarchical structure formation make use of this and several other factors leading to rather similar effects to those of inflow discussed in Chapter 8 (Nagashima Okamoto 2006). [Pg.369]

ABSTRACT By analyzing the hierarchy of safety factors in purification plant of natural gas, they are divided into personnel, equipment, environment and management. After the study of safety index, evaluation methods and fuzzy arithmetic method for each hierarchy, its fuzzy evaluation flow is given. During the evaluation, the weight of the factors and each hierarchy is decided by analytical hierarchical process, and the operational criterion adapts maximum membership degree. And this model is exemplified in purification plant of natural gas. The results show that the second fuzzy evaluation is effective to assess purification plant of natural gas. [Pg.327]

Based on the risk factors identified in Section 2, as shown in Figure 1, a model with hierarchical structure is developed. Following a review of Wang et al. (1995) for effective information processing of a safety expression, linguistic terms need to be in the range of four to seven. As a result, within this paper five linguistic terms are defined to evaluate the likelihood and consequence of each identified risk factor, as shown in Table 2. [Pg.594]

Generally, the use of BBNs to model the impact of MOFs aims to explicitly model their multilevel and hierarchical influences on the HEPs, as discussed in Li et al. (2012), Cai et al. (2013) and Martins Maturana (2013). Many of the influencing factors typically considered by HRA methods can be thought of having direct influence on the HEP, e.g. the quality of the human machine interface and the time available for the personnel to carry out their tasks. In contrast, many of those referred to as MOFs have indirect effects for example the management s commitment to safety influences the quality of personnel training, which then directly influences the HEP. The BBN ability to represent multi-level relations helps to model these types of hierarchies. Figure 1 shows an example of the hierarchical influences. [Pg.1075]

Statistical treatment of the results involved the calculation of the mean and of the standard deviation of the mean as well as a two-factor hierarchical analysis of variance. The mathematical model was Y(ij) = m -H treatmentj -l- e- for experiment 1 and = m + treatmentj + periodj + square, + pig(square)]j[ + e j j for experiment 2. The analysis of variance was followed by a Duncan multiple range test when a significant i effect without) effect was observed (Snedecor and Cochran, 1989). [Pg.413]


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See also in sourсe #XX -- [ Pg.288 , Pg.289 , Pg.290 ]




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