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Engineering, United Kingdom

Thomas M.E.,University of LeedsLeeds | Neuberg J.W.,University of LeedsLeeds
Geochemistry, Geophysics, Geosystems | Year: 2014

The estimation of the magma ascent rate is key to predicting volcanic activity and relies on the understanding of how strongly the ascent rate is controlled by different magmatic parameters. Linking potential changes of such parameters to monitoring data is an essential step to be able to use these data as a predictive tool. We present the results of a suite of conduit flow models Soufrière that assess the influence of individual model parameters such as the magmatic water content, temperature or bulk magma composition on the magma flow in the conduit during an extrusive dome eruption. By systematically varying these parameters we assess their relative importance to changes in ascent rate. We show that variability in the rate of low frequency seismicity, assumed to correlate directly with the rate of magma movement, can be used as an indicator for changes in ascent rate and, therefore, eruptive activity. The results indicate that conduit diameter and excess pressure in the magma chamber are amongst the dominant controlling variables, but the single most important parameter is the volatile content (assumed as only water). Modeling this parameter in the range of reported values causes changes in the calculated ascent velocities of up to 800%. © 2014. American Geophysical Union. All Rights Reserved. Source


Ramirez J.A.,University of LeedsLeeds | Baird A.J.,University of LeedsLeeds | Coulthard T.J.,Environment and Earth SciencesUniversity of HullHull | Waddington J.M.,McMaster University
Water Resources Research | Year: 2015

Bubble dynamics in porous media are of great importance in industrial and natural systems. Of particular significance is the impact that bubble-related emissions (ebullition) of greenhouse gases from porous media could have on global climate (e.g., wetland methane emissions). Thus, predictions of future changes in bubble storage, movement, and ebullition from porous media are needed. Methods exist to predict ebullition using numerical models, but all existing models are limited in scale (spatial and temporal) by high computational demands or represent porous media simplistically. A suitable model is needed to simulate ebullition at scales beyond individual pores or relatively small collections (<10-4 m3) of connected pores. Here we present a cellular automaton model of bubbles in porous media that addresses this need. The model is computationally efficient, and could be applied over large spatial and temporal extent without sacrificing fine-scale detail. We test this cellular automaton model against a physical model and find a good correspondence in bubble storage, bubble size, and ebullition between both models. It was found that porous media heterogeneity alone can have a strong effect on ebullition. Furthermore, results from both models suggest that the frequency distributions of number of ebullition events per time and the magnitude of bubble loss are strongly right skewed, which partly explains the difficulty in interpreting ebullition events from natural systems. © 2015 American Geophysical Union. Source


Paton J.,University of LeedsLeeds | Khatir Z.,University of LeedsLeeds | Thompson H.,University of LeedsLeeds | Kapur N.,University of LeedsLeeds | Toropov V.,University of LeedsLeeds
Applied Thermal Engineering | Year: 2013

Energy usage in bread ovens is analysed using a generic methodology applicable to all types of mass-production tunnel ovens. The presented methodology quantifies the energy required to bake the dough, and to conduct a detailed analysis of the breakdown of losses from the oven. In addition, a computational fluid dynamics (CFD) optimisation study is undertaken, resulting in improved operating conditions for bread baking with reduced energy usage and baking time. Overall, by combining the two approaches, the analyses suggest that bake time can be reduced by up to 10% and the specific energy required to bake each loaf by approximately 2%. For UK industry, these savings equate to more than £0.5 million cost and carbon reduction of more than 5000 tonnes CO2 per year. © 2012 Elsevier Ltd. Source

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