heat loss

Buildings represent approximately 40% of global energy demand and heat loss induced by uncontrolled air leakage through the building fabric can represent up to one third of the heating load in a building. This leakage of air at ambient pressure levels, is known as air infiltration and can be measured by tracer gas means, however, the method is disruptive and invasive. Air infiltration models are a non-disruptive way to calculate predictive values for air infiltration in buildings.

With radiant heating, it is possible to set room air temperature lower than when heating withair-conditioning because the human body is heated by a radiation. As room air temperature decreases,heat loss from walls and windows decreases, and so does the ventilation load. It is often said that theradiant heating, such as floor heating saves energy. This study calculates heat flow at the windows andthe walls of a living-room using computational fluid dynamics (CFD).

It is often discussed about the possibilities that more efficient windows offer to reduce the energy loads in residential buildings. Often such results can be achieved reducing the thermal transmittance or optimising the solar gains, not so often the influence of the air permeability is taken into account. This issue is, on the contrary, very important in countries, as Italy, where the age of the building stock is accompanied by the installation of very old windows, characterised by high air leakage, which causes strong heat losses and discomfort phenomena for users.

Direct comparison measurements were made between various prime/storm window combinations and a well-weatherstripped, single-hung replacement window with a low-e selective glazing. Measurements were made using an accurate outdoor calorimetric facility with the windows facing north. The double-hung prime window was made intentionally leaky. Nevertheless, heat flows due to air infiltration were found to be small, and performance of the prime/storm combinations was

A heat and mass transfer model of a walking clothed human has been developed in that study. That model predicts the transient thermal responses of the human and clothing giving temperatures, and latent heat losses. A mathematical model was developed for the simulation of the dynamic thermal behavior of clothing and its interaction with the system of human thermoregulation under walking conditions.

The Building Air Tightness is an important parameter on ventilation systems performanceand energy losses.Yet, the total amount of leakage is as important on performances as their effective positionin the room.Some calculations have been run according to prEN 13465 from TC156 WG2 for differentbuildings (single house, dwellings and commercial buildings) varying air tightness, valueand repartition for different ventilation systems (natural, mechanical exhaust, mechanicalexhaust and supply).All these calculations have been compared focusing on ventilation losses during heatingseason in Paris.So

This short paper demonstrates the existence of an error in instantaneous heat loss calculations due to errors inherent in the input data. By implication, these errors will also be present in thermal simulation programs.

The paper describes a method to calculate the heat flow through a multiple layer wall in a natural climate. The thermal properties needed for the calculation are the thermal resistance and the heat capacity of each layer, and they are assumed to be independent of the temperature. The natural climate can be measured temperatures, either surface temperatures or temperatures of the surrounding air. The method is based on well-known equations for calculating the heat flow due to a sinusoidal temperature variation.

A model for the application of probabilistic methods is the estimation of heat loss caused by convection and heat conduction through the material is developed. Temperature difference (delta T) between inside and outside of a building, air change rate (ACH) and coefficient of thermal transmittance (U-value) of the building structure are treated as random variables. The mean value and standard deviation of heat loss are estimated for different parameters of distribution for temperature difference, air change rate and thermal transmittance.