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Warehouse commodity classification from fundamental principles. Part II: Flame heights and flame spread
In warehouse storage applications, it is important to classify the burning behavior of commodities and rank them according to their material flammability for early fire detection and suppression operations. In this study, a preliminary approach towards commodity classification is presented that models the early stage of large-scale warehouse fires by decoupling the problem into separate processes of heat and mass transfer. Two existing nondimensional parameters are used to represent the physical phenomena at the large-scale: a mass transfer number that directly incorporates the material properties of a fuel, and the soot yield of the fuel that controls the radiation observed in the large-scale. To facilitate modeling, a mass transfer number (or B-number) was experimentally obtained using mass-loss (burning rate) measurements from bench-scale tests, following from a procedure that was developed in Part I of this paper. Two fuels are considered: corrugated cardboard and polystyrene. Corrugated cardboard provides a source of flaming combustion in a warehouse and is usually the first item to ignite and sustain flame spread. Polystyrene is typically used as the most hazardous product in large-scale fire testing. The nondimensional mass transfer number was then used to model in-rack flame heights on 6.19.1 m (2030 ft) stacks of 'C' flute corrugated cardboard boxes on rack-storage during the initial period of flame spread (involving flame spread over the corrugated cardboard face only). Good agreement was observed between the model and large-scale experiments during the initial stages of fire growth, and a comparison to previous correlations for in-rack flame heights is included. © 2011 Elsevier Ltd. All rights reserved
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Warehouse commodity classification from fundamental principles. Part I: Commodity & burning rates
An experimental study was conducted to investigate the burning behavior of an individual Group A plastic commodity over time. The objective of the study was to evaluate the use of a nondimensional parameter to describe the time-varying burning rate of a fuel in complex geometries. The nondimensional approach chosen to characterize burning behavior over time involved comparison of chemical energy released during the combustion process with the energy required to vaporize the fuel, measured by a B-number. The mixed nature of the commodity and its package, involving polystyrene and corrugated cardboard, produced three distinct stages of combustion that were qualitatively repeatable. The results of four tests provided flame heights, mass-loss rates and heat fluxes that were used to develop a phenomenological description of the burning behavior of a plastic commodity. Three distinct stages of combustion were identified. Time-dependent and time-averaged B-numbers were evaluated from mass-loss rate data using assumptions including a correlation for turbulent convective heat transfer. The resultant modified B-numbers extracted from test data incorporated the burning behavior of constituent materials, and a variation in behavior was observed as materials participating in the combustion process varied. Variations between the four tests make quantitative values for each stage of burning useful only for comparison, as errors were high. Methods to extract the B-number with a higher degree of accuracy and future use of the results to improve commodity classification for better assessment of fire danger are discussed. © 2011 Elsevier Ltd. All rights reserved
Vertical wind profile characterization and identification of patterns based on a shape clustering algorithm
Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. The present paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on z-normalization, shape-based distance metric solution and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using a commercialWindcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.The authors are grateful for the financial support from the Spanish Ministry of the Economy and Competitiveness and the European Union —ENE2016-78214-C2-2-R—and the Spanish Education, Culture and Sport Ministry —FPU16/042
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