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The main objective of this study is to investigate and model the viscosity of\nmulticomponent natural silicate melts and constrain the compositional effects which affect\nsuch a parameter. The results of this study, relevant to all petrological and volcanological\nprocesses which involve some transport mechanism, will be applied to volcanic setting.\nAn extensive experimental study was performed, which constituted the basis for the\ngeneral modelling of Newtonian viscosity in terms of composition and temperature.\nComposition, viscosity and density of selected samples were investigated at different water\ncontents. The experimental method involved measuring the viscosity of dry and hydrated\nmelts under superliquidus and supercooled conditions. In the high temperature range (1050 \u2013\n1600 \xb0C) viscosities from 10-0.5 to 105 Pa\xb7s were obtained using a concentric cylinder\napparatus. Measurements of both dry and hydrated samples in the low temperature (616-860\n\xb0C) - high viscosity (108.5 \u2013 1012 Pa\xb7s) interval, from glassy samples quenched after high\ntemperature viscometry, were performed using the dilatometric method of micropenetration.\nHydrated samples measured in the supercooled state were synthesized, using a piston cylinder\napparatus, between 1100\xb0 and 1600\xb0 C at 10 kbar. Water contents were measured using the\nKarl Fischer Titration (KFT) method. Fourier-Transform Infrared (FTIR) spectroscopy was\nused before and after the experiments in order to check that the water content was\nhomogeneously distributed in the samples and that water had not been lost. Major element\ncompositions of the dry remelted samples were determined using an electron microprobe.\nNewtonian viscosities of silicate liquids were investigated in a range between 10-1 to\n1011.6 Pa s and parameterised using the non-linear 3 parameter (ATVF, BTVF and T0) TVF\nequation. The data provided in this work are combined also with previous data from\nWhittington et al. (2000, 2001); Dingwell et al. (1996); Neuville et al. (1993).\nThere are strong numerical correlations between parameters (ATVF, BTVF and T0) that\nmask the effect of composition. Wide ranges of ATVF, BTVF and T0 values can be used to\ndescribe individual datasets. This is true even when the data are numerous, well-measured and\nspan a wide range of experimental conditions. In particular, \u201cstrong\u201d liquids (liquids that are\nArrhenian or slightly deviate from Arrhenian behaviour) place only minor restrictions on the\nabsolute ranges of ATVF, BTVF and T0. Therefore, strategies for modelling the effects on\ncompositions should be built around high-quality datasets collected on non-Arrhenian liquids.\nx\nThe relationships between important quantities such as the fragility F, characterizing the\ndeviation from Arrhenian rheological behaviour, are quantified in terms of the chemical,\nstructure-related parameter NBO/T. Initial addition of network modifying elements to a fully\npolymerised liquid (i.e. NBO/T=0) results in a rapid increase in F. However, at NBO/T values\nabove 0.4-0.5 further addition of a network modifier has little effect on fragility. This\nparameterisation indicates that this sharp change in the variation of fragility with NBO/T is\ndue to a sudden change in the configurational properties and rheological regimes, owing to the\naddition of network modifying elements.\nThe resulting TVF parameterisation has been also used to build up a predictive model\nfor Arrhenian to non-Arrhenian melt viscosity. The model accommodates the effect of\ncomposition via an empirical parameter called here the \u201cstructure modifier\u201d (SM). SM is the\nsummation of molar oxides of Ca, Mg, Mn, half of the total iron Fetot, Na and K. This\napproach is validated by the highly predictive capability of the viscosity model. The model\nreproduces all the original data set with about 10%, of the measured values of log\u03b7 over the\nentire range of composition in the temperature interval 700-1600 \xb0C.\nThe combination of calorimetric and viscosimetric data has enabled a simple expression\nto be used to predict shear viscosity at the glass transition, that is the temperature which\ndefines the transition from a liquid-like to a solid-like rheological behaviour. The basis for\nthis stems from the equivalence of the relaxation times for both enthalpy and shear stress\nrelaxation in a wide range of silicate melt compositions (Gottsmann et al., 2002). A shift\nfactor that relates cooling rate data with viscosity at the glass transition appears to be slightly\ndependent on the melt composition.\nFinally, the effect of water content on decreasing the viscosity of silicate melts has also\nbeen parameterised using a modified TVF expression (Giordano et al., 2000). This leads to an\nimprovement in our knowledge of the non-Arrhenian behaviour of silicate melts over a wide\ncompositional range from basaltic to rhyolitic and from trachytic to peralkaline phonolite\ncompositions in the temperature interval pertaining to volcanic and subvolcanic processes.\nThe viscosities of natural hydrous basaltic liquids are shown to be lower than those of\nhydrous phonolites, whereas thachytes show viscosity that are higher than those of phonolites\nand lower that those of rhyolites. This is consistent with the style of eruption associated with\nthese compositions, with trachytes generating eruptions that are dominantly explosive (e.g.\nxi\nPhlegrean Fields volcano), compared to the highly explosive style of rhyolitic volcanoes, the\nmixed explosive-effusive style of phonolitic volcanoes (e.g. Vesuvius) and the dominantly\neffusive style of basalts. Variations in composition between the trachytes translate into\ndifferences in liquid viscosity of nearly two orders of magnitude in dry conditions, and less\nthan one order of magnitude in hydrous conditions. These differences increase significantly\nwhen the estimated eruptive temperatures of different eruptions at Phlegrean Fields are taken\ninto account.\nAt temperatures close to those of natural magmas and in the case of low viscosity\nhydrous liquids the uncertainty of the calculations is large, although it cannot be quantified,\ndue to a lack of measurements under these conditions.