An algorithm to predict the transient moisture distribution for wall condensation under a steady flow field

doi:10.1016/j.buildenv.2013.04.028

Building and Environment

Volume 67, September 2013, Pages 56-68

https://doi.org/10.1016/j.buildenv.2013.04.028 Get rights and content

Highlights

•
An analytical model is proposed to quantify the indoor transient humidity distribution.
•
The indoor moisture distribution with wall condensation is easily obtained.
•
The proposed model is validated with experiment and CFD methods.
•
The model can be applied in ventilation design and control of local environment.

Abstract

The prediction and control of the indoor moisture distribution, especially for wall condensation, is very important for a healthy and energy-efficient environment. However, existing methods cannot accurately predict the transient indoor moisture distribution for wall condensation. In this study, the prediction of indoor transient moisture distribution is facilitated by using transient accessibility indices, which characterize the transport of the passive contaminants to describe boundary effects on the transient moisture distribution. An algebraic expression is obtained for the indoor moisture distribution, and a computational model is presented for the wall condensation problem. The model is validated by experiments and computational fluid dynamics (CFD) simulations, and the model is implemented for a wall condensation case study. The new model can easily predict the indoor moisture distribution under different boundary conditions and for a range of condensation time on the wall surfaces. This study provides an effective means of indoor moisture prediction and control, which may become an important tool for designing a non-uniform indoor environment.

Introduction

The humidity is an important parameter in an indoor environment both in terms of human comfort [1], [2], [3] and the indoor air quality (IAQ) [4], [5]. High humidity may lead to the spread of germs, allergic reactions and asthma: the literature shows that smoking, furry and feathered pets and dampness are the three major factors that cause asthma in children [6]. Low humidity may result in static electricity and cause discomfort to and even inflame the skin, eyes and respiratory system [7]. Therefore building design standards contain humidity requirements [8]. Indoor humidity is even more important in buildings with special moisture control requirements, such as natatoriums, operating rooms in hospitals, valuable storerooms in museums and libraries etc. [9], [10], [11]. Suitable indoor air humidity is also quite important in greenhouses and animal rooms for plant and animal survival [12], [13].

Condensation on the surfaces of the walls, the roof and the ground is also a serious problem. Surface condensation is caused by a larger air humidity ratio near the boundary relative to the saturated humidity ratio corresponding to the temperature of the boundary and may result in corrosion and mildew of the material, IAQ degradation and other damage [14], [15]. In recent years, condensation from air-conditioning systems and other devices has become prevalent in indoor environments, especially with the emergence of new air conditioning systems such as cold air distribution systems or radiant cooling systems, which cause condensation on the exposed cold surface of indoor terminals [16]. Condensation usually occurs at local wall areas in most rooms, e.g., a thermal bridge or a roof above vapor source [17]. There are mainly two ways of preventing condensation in ventilated rooms, which are improving thermal isolation of envelope and controlling indoor air humidity. Normally, indoor humidity control is the most effective way, which can save initial costs and is easy to implement [18]. Indoor humidity prediction is critical for controlling the indoor humidity in a built environment. The conventional method for predicting indoor humidity is based on a lumped parameter theory, in which the indoor air is assumed to be well-mixed. However, the environment is usually non-uniform in practical applications and the lumped parameter method cannot be used to predict the indoor humidity because the assumption that the air is well-mixed is not satisfied.

The two main prediction methods for the indoor moisture distribution in non-uniform indoor environments are experimental measurements and numerical simulations. Experimental measurements are simple to perform and reliable and have been used by researchers to determine the moisture distribution under different boundary conditions [19]. However, the experimental method has disadvantages such as long required measurement periods, significant financial investment and limited data production. Numerical simulations have been used to analyze the indoor moisture distribution and can produce more data [20], [21]. However, numerical methods cannot determine the contribution of each boundary to the indoor humidity distribution, necessitating a case-by-case analysis when a significant amount of computation is needed to obtain an optimal design or a suitable control strategy.

Various indices have been developed that describe the contribution of different boundary conditions to the indoor humidity to predict the distribution of indoor parameters efficiently. The scale of ventilation efficiency (SVEs) indices [22], [23] have been used to describe the diffusion properties of indoor contaminants and the ventilation efficiency at steady state. The transient accessibility indices and the response coefficient indices have been used to determine the effects of different boundary conditions on the time-averaged concentrations and the transient concentrations of indoor contaminants [24], [25], [26], [27]. The contribution ratio of indoor climate (CRIs) has been used to determine the effect of heat resources under a fixed flow field [28] and the indoor moisture distribution under a fixed flow field [29]. The aforementioned studies have enabled indoor parameter distributions to be calculated simply and efficiently. Indoor vapor is overheated and has a low density; thus, the dispersion of moisture is similar to that of a passive contaminant [29]. Therefore, these indices can be used for moisture problems, whereby the calculation expressions based on the indices can be used to calculate the indoor humidity distribution. However, moisture may condense on a surface. When condensation occurs, the aforementioned expressions that have been developed for passive contaminants cannot be used to obtain the indoor humidity distribution.

In this paper, an algebraic expression for the indoor moisture distribution is developed to quantitatively determine the effects of the boundary conditions on the humidity at an arbitrary location under transient conditions; the expression holds under condensation conditions and is used in an efficient theoretical model for predicting the indoor humidity with wall condensation. The model is validated by experimental and CFD methods. Finally, a practical case study on wall condensation is used to illustrate the characteristics of the model.

Section snippets

Analytical expression for the transient distribution of indoor passive contaminants

The transport equation for indoor passive contaminants is well-known and is given by Eq. (1): $\frac{\partial ρ C (τ)}{\partial τ} + \frac{\partial ρ C (τ) U_{j}}{\partial x_{j}} = \frac{\partial}{\partial x_{j}} (Γ_{C e f f} \frac{\partial C (τ)}{\partial x_{j}}) + S_{C} (τ)$

The transport equation for contaminant dispersion in a steady flow field is linear; thus, the superposition theorem is applicable [25]. Therefore, the total transient concentration at an arbitrary point is equal to the linear sum of the contributions from three components: the contaminant in the supply air, the contaminant source and the initial contaminant

Validation

The factors affecting the moisture distribution are the supply air, the moisture source, the initial distribution, the condensation and so on. Given the difficulty of observing and measuring the condensation phenomenon in experiments, the model is validated in two steps. The theoretical model is validated for conditions of no condensation by comparing the predicted indoor moisture distribution with the experimental measurements; for condensation conditions, the theoretical model is validated by

Application

In this section, an air-conditioned room in which a radiant cooling system has been installed is used as a case study to analyze condensation on a radiant ceiling. Generally, condensation on the walls and the ceiling is prohibited. In this section, the new theoretical model is used to determine whether the selected supply air parameters prevent condensation. The conventional lumped parameter method is also applied to the case study as a comparison.

Conclusion

Predicting moisture distribution, especially for wall condensation, is very important for thermal comfort and energy conservation. However, existing prediction methods cannot be accurately implemented. In this paper, a theoretical model for the indoor moisture distribution is developed using transient accessibility indices and response coefficients; a computational model for the moisture distribution for wall condensation is also presented. The model is validated by experimental and CFD

Acknowledgments

This study was supported by the National Science Foundation for Distinguished Young Scholars of China (Grant No. 51125030).

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An algorithm to predict the transient moisture distribution for wall condensation under a steady flow field

Highlights

Abstract

Introduction

Section snippets

Analytical expression for the transient distribution of indoor passive contaminants

Validation

Application

Conclusion

Acknowledgments

Energy and Buildings

Energy and Buildings

Energy and Buildings

Energy and Buildings

Scientia Horticulturae

Building and Environment

Building and Environment

Energy and Buildings

Building and Environment

Atmospheric Environment

Building and Environment

Building and Environment

Energy and Buildings

Computer Methods in Applied Mechanics and Engineering

The effect of structures on indoor humidity-possibility to improve comfort and perceived air quality

Indoor Air