2 resultados para VLE data sets

em Greenwich Academic Literature Archive - UK


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The SB distributional model of Johnson's 1949 paper was introduced by a transformation to normality, that is, z ~ N(0, 1), consisting of a linear scaling to the range (0, 1), a logit transformation, and an affine transformation, z = γ + δu. The model, in its original parameterization, has often been used in forest diameter distribution modelling. In this paper, we define the SB distribution in terms of the inverse transformation from normality, including an initial linear scaling transformation, u = γ′ + δ′z (δ′ = 1/δ and γ′ = �γ/δ). The SB model in terms of the new parameterization is derived, and maximum likelihood estimation schema are presented for both model parameterizations. The statistical properties of the two alternative parameterizations are compared empirically on 20 data sets of diameter distributions of Changbai larch (Larix olgensis Henry). The new parameterization is shown to be statistically better than Johnson's original parameterization for the data sets considered here.

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In this paper, the buildingEXODUS evacuation model is described and discussed and attempts at qualitative and quantitative model validation are presented. The data sets used for validation are the Stapelfeldt and Milburn House evacuation data. As part of the validation exercise, the sensitivity of the building-EXODUS predictions to a range of variables is examined, including occupant drive, occupant location, exit flow capacity, exit size, occupant response times and geometry definition. An important consideration that has been highlighted by this work is that any validation exercise must be scrutinised to identify both the results generated and the considerations and assumptions on which they are based. During the course of the validation exercise, both data sets were found to be less than ideal for the purpose of validating complex evacuation. However, the buildingEXODUS evacuation model was found to be able to produce reasonable qualitative and quantitative agreement with the experimental data.