Fire performance of phase change material enhanced plasterboard

Sustainable construction materials are increasingly being used to reduce the carbon footprint of modern buildings. These materials have the potential to change the fire dynamics of compartments by altering the compartment energy balance however there is little quantitative understanding of how these materials behave in the event of a real fire. The changes in fire dynamics may be due to increased fuel load in a compartment, reduced time to failure or promotion of flame spread. The objective of this research is to quantify how Phase Change Materials (PCMs) perform in realistic fire scenarios. It was found that a plasterboard product containing microencapsulated PCMs will behave similarly to a charring solid and have the potential to contribute significant fuel to a compartment fire but that they maintain integrity for the duration of flaming period. The critical heat flux for this product was determined in the cone calorimeter to be 17.5 ± 2.5 kW m−2, the peak heat release rate and mass loss rate ranged from 60.2 kW m−2 to 107 kW m−2 and 1.88 g s−1 m−2 to 8.47 g s−1 m−2 respectively for exposures between 20 kW m−2 and 70 kW m−2. Sample orientation was found to increase the peak heat release rate by up to 25%, whilst having little to no effect on the mass loss rate. These parameters, in addition to the in-depth temperature evolution and ignition properties, can be used as design criteria for balancing energy savings with quantified fire performance

Algorithms for survival: a comparative perspective on emotions

The nature and neural implementation of emotions is the subject of vigorous debate. Here, we use Bayesian decision theory to address key complexities in this field and conceptualize emotions in terms of their relationship to survival-relevant behavioural choices. Decision theory indicates which behaviours are optimal in a given situation; however, the calculations required are radically intractable. We therefore conjecture that the brain uses a range of pre-programmed algorithms that provide approximate solutions. These solutions seem to produce specific behavioural manifestations of emotions and can also be associated with core affective dimensions. We identify principles according to which these algorithms are implemented in the brain and illustrate our approach by considering decision making in the face of proximal threat