The measurement and prediction of building material emission rates have been the subject of intensive research over the past decade, resulting in the development of advanced sensory and chemical analysis measurement techniques as well as the development of analytical and numerical models. One of the important input parameters for these models is the diffusion coefficient. Several experimental techniques have been applied to estimate the diffusion coefficient. An extensive literature review of the techniques used to measure this coefficient was carried out, for building materials exposed to volatile organic compounds (VOC). This paper reviews these techniques; it also analyses the results and discusses the possible causes of difference in the reported data. It was noted that the discrepancy between the different results was mainly because of the assumptions made in and the techniques used to analyze the data. For a given technique, the results show that there can be a difference of up to 700% in the reported data. Moreover, the paper proposes what is referred to as the mass exchanger method, to calculate diffusion coefficients considering both diffusion and convection. The results obtained by this mass exchanger method were compared with those obtained by the existing method considering only diffusion. It was demonstrated that, for porous materials, the convection resistance could not be ignored when compared with the diffusion resistance.

Building materials can strongly affect indoor air quality. Porous building materials are not only sources of indoor air pollutants such as volatile organic compounds (VOC) but they are also strong sinks of these pollutants. The knowledge of VOC transfer mechanisms in these materials is an important step for controlling the indoor VOC concentration levels, and for determining the optimum ventilation requirements for acceptable IAQ. This study provides a theoretical investigation of primary and secondary VOC source and sink behavior of porous building materials. A new analytical model was developed based on the fundamental theories of mass transfer mechanisms in porous materials. The proposed model considers both primary and secondary source/sink behavior for the first time. The former refers to the transfer of gas-phase and/or physically adsorbed VOC, while the latter refers to the generation or elimination of VOC within the solid because of chemical reactions like oxidation, hydrolysis, chemical adsorption, etc. The proposed model was assessed with experimental data, namely emission tests of carpets and sorption tests of wood chipboard. It was demonstrated that, unlike the existing analytical models, the proposed analytical model could simultaneously account for the effect of air velocity on both VOC source as well as sink behavior. Case studies were then carried out for secondary VOC source behavior. Due to the lack of experimental studies on mechanisms of secondary behavior, hypothetical generation functions were implemented. It was demonstrated that the proposed analytical model is suitable for describing various mechanisms involved in the secondary behavior due to the little limitations imposed on the generation/elimination term. When VOC generation takes place at the material-air interface, the simulation shows that although the primary emission is not affected by air velocity, the secondary emission, however, is clearly affected. This behavior agrees with the available experimental findings on secondary emissions. PRACTICAL IMPLICATION: The analytical model presented in this paper can predict both primary and secondary VOC source (emission) or sink (sorption) behavior of porous building materials. Since the model considers diffusion and adsorption/desorption within the material, and convection over the material surface, the simulation using the model can readily provide the effects of material properties and airflow properties on the primary and/or the secondary behavior, hence, it can provide a better understanding on the mechanisms. This will enable us to keep the indoor VOC concentration within a desirable level.

A fast, flexible, and robust simulation-based optimization scheme using an ANN-surrogate model was developed, implemented, and validated. The optimization method uses Genetic Algorithm (GA), which is coupled with an Artificial Neural Network (ANN) that uses a back propagation algorithm. The developed optimization scheme was successfully applied to single-point aerodynamic optimization of a transonic turbine stator and multi-point optimization of a NACA65 subsonic compressor rotor in two-dimensional flow, both were represented by 2D linear cascades. High fidelity CFD flow simulations, which solve the Reynolds-Averaged Navier-Stokes equations, were used in generating the data base used in building the ANN low fidelity model. The optimization objective is a weighted sum of the performance objectives and is penalized with the constraints; it was constructed so as to achieve a better aerodynamic performance at the design point or over the full operating range by reshaping the blade profile. The latter is represented using NURBS functions, whose coefficients are used as the design variables. Parallelizing the CFD flow simulations reduced the turn-around computation time at close to 100% efficiency. The ANN model was able to approximate the objective function rather accurately and to reduce the optimization computing time by ten folds. The chosen objective function and optimization methodology result in a significant and consistent improvement in blade performance.

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