# On Finding the Right Sampling Line Height through a Parametric Study of Gas Dispersion in a NVB
## 1 Introduction  
Gas emissions from natural ventilated barns (NVBs) are often estimated using the tracer gas method, which relies on the assumption that the tracer and target gas are well mixed at the location of measurement (e.g., for CO\({}_{2}\) balancing where CO\({}_{2}\) is a naturally produced tracer) [1]. Measurement uncertainty is high since gas mixing is not perfect [2]. Line measurements with sample points every 10 m are typically used to reduce the uncertainty, but there is no general recommendation on the measurement points' height [3]. The VERA test protocol [4], which is a guideline on the measurement strategy for naturally ventilated buildings, recommends a height of at least three meters for the sampling lines. These guidelines were formulated based on expert rating of common flow conditions; however, environmental conditions are not constant in and around real NVBs, and different farms have specific building characteristics. This situation leads to the fact that, under on-farm conditions, systematic investigation of the impact of changing boundary conditions on indoor climate and emissions is rather difficult. In this context, parametric studies with validated computational fluid dynamics (CFD) models can help to fill this gap and gain better understanding of the general gas emission pattern. [PERSON] et al. [5] found that theflow convection type (natural, mixed, forced) affects the gas concentrations and temperature distribution. Further investigations are needed to deepen the understanding of those kinds of impacts. With Reynolds-averaged Navier-Stokes (RANS) simulations, reasonable computing times can be achieved, but the right choice of the turbulent Schmidt number is essential to derive a meaningful gas emission pattern. The turbulent Schmidt number (\(\mathrm{Sc_{t}}\)-number), which is the ratio of momentum diffusivity to mass diffusivity, plays an essential role in the numerical RANS modeling of gas dispersion in CFD. As was reminded in [PERSON] et al. [6] and confirmed in the present work, the influence of the turbulent Schmidt number on gas dispersion simulations is huge. The main reason for this is that, as will be detailed in Section 2.2.4, in the RANS equations, the coefficient influencing the gas dispersion is not interpreted by the solver, but is given by the user. Thus, an appropriate selection of the \(\mathrm{Sc_{t}}\)-number is crucial to derive meaningful recommendations for practical measurements from the CFD model. Research has been done in order to find the ways and methods for a systematic determination of the turbulent Schmidt number. The results of the studies showed that many factors influence the turbulent Schmidt number such as the flow medium (for example, air or water) and the flow conditions (for example, close or open space). The review paper of [PERSON] [7] summarized the recent research done on the topic of environmental flows using the CFD method. The review pictured a range of \(\mathrm{Sc_{t}}\)-number values for dispersion phenomena used in both air and water systems. He also concluded the impossibility of identifying a universal \(\mathrm{Sc_{t}}\) acceptable for all the diverse flow cases. [PERSON] et al. [5] did the CFD model validation with \(\mathrm{Sc_{t}}=0.6\) based on experimental methane and ammonia emissions from one cow. However, the work focused on the effects of buoyancy flow in a closed space, and the effect of the turbulent Schmidt number on the gas dispersion has not been explored. [PERSON] et al. [8] studied the gas dispersion in a street canyon with trees by means of a wind tunnel and numerical investigations. He varied the value of the \(\mathrm{Sc_{t}}\)-number from 0.2 to one and found that \(\mathrm{Sc_{t}}=0.3\) best matched the experiments data. [PERSON] et al. [9], in order to predict the dispersion in the wake of a rectangular building, found and applied a \(\mathrm{Sc_{t}}\)-number depending on the RANS turbulence model k-epsilon constants, \(C_{o}\) and \(C_{\mu}\).