Thermal mass, including the building envelope, the interior partition, the furnishing, or even the air inside building, is defined as the mass that can store thermal energy (heat or cooling energy). For storing heat in buildings, there are two important thermal properties of the materials that need to be considered, i.e. the heat capacity by volume and the heat-absorption rate. The first property determines the ability of the element to store thermal energy, and the second property determines the ability of the element to conduct the thermal energy.

The well-known case of a simple naturally ventilated building with two openings, uniform internal temperature and opposing wind and buoyancy forces is re-visited. In particular, it is shown that the effect of wind turbulence can play a deciding role on whether or not multiple solutions occur. It is also argued that in practice the number of possible solutions is three rather than two.

This paper presents results of CFD simulations of flow around and through a cubic building with symmetric openings on two opposite sides. The case where the incoming wind is perpendicular to the front face of the building, and so parallel to the openings, is considered. An unsteady method, Detached Eddy Simulation, is used to capture the unsteady cross-ventilation.

In this paper experimental results are used to verify and validate a computational fluid dynamics (CFD) model based on a commercial package. The validation is used to ensure that the CFD model is able to simulate stratum ventilation. The low speed air jet is the basis for a newly proposed ventilation mode whereby jets are placed strategically around a room at breathing height level to create a layer or stratum of relatively fresher air.

Growing concern about negative effects on the environment and increasing energy prices stress theimportance of energy efficiency. Support processes such as heating, ventilation and air-conditioning(HVAC) use large amounts of energy in the dairy industry. In this paper the energy aspects of the support processes at two large dairies, built at three different points in time, are analyzed and compared with energy use throughout the rest of the company. Significant differences in the use of energy and the resulting indoor climate were found.

Macroscopic methods of building ventilation analysis developed in the past fifty years have proven to be accurate and thus useful for purposes of single- and multi-zone building infiltration, air quality, smoke spread, thermal comfort, and integrated HVAC/building ventilation system analysis. These methods fail, however, to provide the same level of accuracy when applied to the analysis of wind-driven airflow through porous buildings.

The amount of outdoor air ventilation in buildings is one of the most important determinants of indoor air quality, but many critical questions and misunderstandings exist. First, given the importance of ventilation, how well do we know how much outdoor air is even needed in buildings? While research has been done on ventilation and odour perception and on ventilation and symptom prevalence, is it adequate to support the ventilation requirements in our standards and regulations?

The design protocol for wind-driven cross ventilation in buildings should include two processes, namely the determination of required ventilation rate to reduce the room temperature and the prediction of the ventilation rate resulting from the arrangement of openings and wind pressure on those. Computer fluid dynamic techniques (CFD) have the potential to be a useful tool in such calculations but another more practical way is the airflow network model combined with the wind pressure coefficient (Cp) values for buildings with different shapes and surrounding conditions.

Passive cooling by cross-ventilation has been considered to be a key technology for reducing cooling requirements. While various applications have been introduced in both domestic and non-domestic buildings, the strategy of operating windows and air-conditioners to utilize cooling potential has not been well investigated. This paper illustrates how occupants behaviour related to window operation affects the reduction of cooling requirements in domestic buildings.

Human thermal comfort is influenced by psychological as well as physiological factors. Several comfort indices, such as PMV, PPD, TSENS, ET*, DISC, and SET* (see nomenclature) have been developed. These indices attempt to correlate human thermal comfort with environmental conditions. This paper describes the use of a learning algorithm "support vector machine (SVM) learning" for prediction of the thermal comfort indices. The SVM is an artificial intelligent approach that can capture the input/output mapping from the given data.