Laetitia Adelard

A detailed weather data generator for building simulations

Thermal buildings simulation softwares need meteorological files in thermal comfort, energetic evaluation studies. Few tools can make significant meteorological data available such as generated typical year, representative days, or artificial meteorological database. This paper deals about the presentation of a new software, RUNEOLE, used to provide weather data in buildings applications with a method adapted to all kind of climates. RUNEOLE associates three modules of description, modelling and generation of weather data. The statistical description of an existing meteorological database makes typical representative days available and leads to the creation of model libraries. The generation module allows the generation of non-existing sequences. This software tends to be usable for the searchers and designers, by means of interactivity, facilitated use and easy communication. The conceptual basis of this tool will be exposed and we will propose two examples of applications in building physics for tropical humid climates.

Building ventilation: A pressure airflow model computer generation and elements of validation

The calculation of airflows is of great importance for detailed building thermal simulation computer codes, these airflows most frequently constituting an important thermal coupling between the building and the outside on one hand, and the different thermal zones on the other. The driving effects of air movement, which are the wind and the thermal buoyancy, are briefly outlined and we look closely at their coupling in the case of buildings, by exploring the difficulties associated with large openings. Some numerical problems tied to the resolving of the non-linear system established are also covered. Part of a detailled simulation software (CODYRUN), the numerical implementation of this airflow model is explained, insisting on data organization and processing allowing the calculation of the airflows. Comparisons are then made between the model results and in one hand analytical expressions and in another and experimental measurements in case of a collective dwelling.

Study of moisture in buildings for hot humid climates

Humidity in buildings generates many disorders or disadvantages. A dry-bulb temperature of the air relatively low, strong moisture and wall surface temperatures very low characterize the interior conditions of the highland dwellings in Reunion Island, during the southern winter. This causes many disorders related to phenomena of condensation on walls: deterioration of the envelope, odor of mould. It is thus, significant to precisely know the evolution of the moisture in a building to avoid any disorder on the frame. In this study we will expose a series of experiments carried out on real residences in order to highlight main parameters of the problem. On the basis of these results, numerical simulations were used to extrapolate the behavior of this building on unusual climatic sequences, holding account various improvements of its constitution. A curative study and a preventive study were carried out on two different types of residences. The aim is to propose solutions to prevent deteriorations of the coatings due to the surface condensation.

Sky temperature modelisation and applications in building simulation

The sky temperature is an important parameter for simulation codes in building studies. A preliminary campaign of validation of a simulation software CODYRUN has demonstrated the misinterpretation of the radiative exchanges of long waves between the building and its environment. A bibliographical research has then led to the use models using dry air temperature to estimate sky temperature. However, these models has not been completely satisfactory as far as night clear sky are concerned. In this case, sky temperature remains overestimated. A research of a non linear model has been undertaken, leading to the use of neural networks with satisfactory results. Sky temperature is then calculated and reinjected into the simulation code. Comparison between simulated temperature and measures has turned to be acceptable.

A validation methodology aid for improving a thermal building model : case of diffuse radiation accounting in a tropical climate

As part of our efforts to complete the software CODYRUN validation, we chose as test building a block of flats constructed in Reunion Island, which has a humid tropical climate. The sensitivity analysis allowed us to study the effects of both diffuse and direct solar radiation on our model of this building. With regard to the choice and location of sensors, this stage of the study also led us to measure the solar radiation falling on the windows. The comparison of measured and predicted radiation clearly showed that our predictions over-estimated the incoming solar radiation, and we were able to trace the problem to the algorithm which calculates diffuse solar radiation. By calculating view factors between the windows and the associated shading devices, changes to the original program allowed us to improve the predictions, and so this article shows the importance of sensitivity analysis in this area of research.

Bayesian Parameter Estimation of Convective Heat Transfer Coefficients of a Roof-Mounted Radiant Barrier System

This paper deals with the application of Bayesian methods to the estimation of two convective heat-transfer coefficients of a roof-mounted radiant barrier system. As part of an empirical validation of the thermal model of the roofing complex, a parametric sensitivity analysis highlighted the importance of convective coefficients in the thermal behavior of a roofing complex. A parameter estimation method is then used in order to find the values of the coefficients that lead to an improvement of the thermal model. However, instead of using a classical parameter estimation method, we used a Bayesian inference approach to parameter estimation. The aim of the paper is to introduce the basic concepts of this powerful method in this simple two-parameter case. We show that Bayesian methods introduce an explicit treatment of uncertainty in modeling and a corresponding measure of reliability for the conclusions reached. DOI: 10.1115/1.2188957