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- ItemA Parametric Model for Local Air Exchange Rate of Naturally Ventilated Barns(Basel : MDPI AG, 2021) Doumbia, E. Moustapha; Janke, David; Yi, Qianying; Prinz, Alexander; Amon, Thomas; Kriegel, Martin; Hempel, SabrinaWith an increasing number of naturally ventilated dairy barns (NVDBs), the emission of ammonia and greenhouse gases into the surrounding environment is expected to increase as well. It is very challenging to accurately determine the amount of gases released from a NVDB on-farm. Moreover, control options for the micro-climate to increase animal welfare are limited in an NVDB at present. Both issues are due to the complexity of the NVDB micro-environment, which is subject to temporal (such as wind direction and temperature) and spatial (such as openings and animals acting as airflow obstacles) fluctuations. The air exchange rate (AER) is one of the most valuable evaluation entities, since it is directly related to the gas emission rate and animal welfare. In this context, our study determined the general and local AERs of NVDBs of different shapes under diverse airflow conditions. Previous works identified main influencing parameters for the general AER and mathematically linked them together to predict the AER of the barn as a whole. The present research study is a continuation and extension of previous studies about the determination of AER. It provides new insights into the influence of convection flow regimes. In addition, it goes further in precision by determining the local AERs, depending on the position of the considered volume inside the barn. After running several computational fluid dynamics (CFD) simulations, we used the statistical tool of general linear modeling in order to identify quantitative relationships between the AER and the following five influencing parameters, the length/width ratio of the barn, the side opening configuration, the airflow temperature, magnitude and incoming direction. The work succeeded in taking the temperature into account as a further influencing parameter in the model and, thus, for the first time, in analysing the effect of the different types of flow convection in this context. The resulting equations predict the barn AER with an R2 equals 0.98 and the local AER with a mean R2 equals around 0.87. The results go a step further in the precise determination of the AER of NVDB and, therefore, are of fundamental importance for a better and deeper understanding of the interaction between the driving forces of AER in NVDB.
- ItemParticulate Matter Dispersion Modeling in Agricultural Applications: Investigation of a Transient Open Source Solver(Basel : MDPI, 2021) Janke, David; Swaminathan, Senthilathiban; Hempel, Sabrina; Kasper, Robert; Amon, ThomasAgriculture is a major emitter of particulate matter (PM), which causes health problems and can act as a carrier of the pathogen material that spreads diseases. The aim of this study was to investigate an open-source solver that simulates the transport and dispersion of PM for typical agricultural applications. We investigated a coupled Eulerian–Lagrangian solver within the open source software package OpenFOAM. The continuous phase was solved using transient large eddy simulations, where four different subgrid-scale turbulence models and an inflow turbulence generator were tested. The discrete phase was simulated using two different Lagrangian solvers. For the validation case of a turbulent flow of a street canyon, the flowfield could be recaptured very well, with errors of around 5% for the non-equilibrium turbulence models (WALE and dynamicKeq) in the main regions. The inflow turbulence generator could create a stable and accurate boundary layer for the mean vertical velocity and vertical profile of the turbulent Reynolds stresses R11. The validation of the Lagrangian solver showed mixed results, with partly good agreements (simulation results within the measurement uncertainty), and partly high deviations of up to 80% for the concentration of particles. The higher deviations were attributed to an insufficient turbulence regime of the used validation case, which was an experimental chamber. For the simulation case of PM dispersion from manure application on a field, the solver could capture the influence of features such as size and density on the dispersion. The investigated solver is especially useful for further investigations into time-dependent processes in the near-source area of PM sources.
- ItemComparison of Methane Emission Patterns from Dairy Housings with Solid and Slatted Floors at Two Locations(Basel : MDPI, 2022) Hempel, Sabrina; Janke, David; Losand, Bernd; Zeyer, Kerstin; Zähner, Michael; Mohn, Joachim; Amon, Thomas; Schrade, SabineMethane (CH4) emissions from dairy husbandry are a hot topic in the context of active climate protection, where housing systems with slatted floors and slurry storage inside are in general expected to emit more than systems with solid floors. There are multiple factors, including climate conditions, that modulate the emission pattern. In this study, we investigated interrelations between CH4 emission patterns and climate conditions as well as differences between farm locations versus floor effects. We considered three data sets with 265, 264 and 275 hourly emission values from two housing systems (one slatted, one solid floor) in Switzerland and one system with solid floors in Germany. Each data set incorporated measurements in summer, winter and a transition season. The average CH4 emission was highest for the slatted floor system. For the solid floor systems, CH4 emissions at the Swiss location were around 30% higher compared to the German location. The shape of the distributions for the two solid floor systems was rather similar but very different from the distribution for the slatted floor system, which showed higher prevalence for extreme emissions. Rank correlations, which measure the degree of similarity between two rankings in terms of linear relation, were not able to detect dependencies at the selected significance level. In contrast, mutual information, which measures more general statistical dependencies in terms of shared information, revealed highly significant dependencies for almost all variable pairs. The weakest statistical relation was found between winds speed and CH4 emission, but the convection regime was found to play a key role. Clustering was consistent among the three data sets with five typical clusters related to high/low temperature and wind speed, respectively, as well as in some cases to morning and evening hours. Our analysis showed that despite the disparate and often insignificant correlation between environmental variables and CH4 emission, there is a strong relation between both, which shapes the emission pattern in many aspects much more in addition to differences in the floor type. Although a clear distinction of high and low emission condition clusters based on the selected environmental variables was not possible, trends were clearly visible. Further research with larger data sets is advisable to verify the detected trends and enable prognoses for husbandry systems under different climate conditions.
- ItemAirflow Characteristics Downwind a Naturally Ventilated Pig Building with a Roofed Outdoor Exercise Yard and Implications on Pollutant Distribution(Basel : MDPI AG, 2020) Yi, Qianying; Janke, David; Thormann, Lars; Zhang, Guoqiang; Amon, Barbara; Hempel, Sabrina; Nosek, Štěpán; Hartung, Eberhard; Amon, ThomasThe application of naturally ventilated pig buildings (NVPBs) with outdoor exercise yards is on the rise mainly due to animal welfare considerations, while the issue of emissions from the buildings to the surrounding environment is important. Since air pollutants are mainly transported by airflow, the knowledge on the airflow characteristics downwind the building is required. The objective of this research was to investigate airflow properties downwind of a NVPB with a roofed outdoor exercise yard for roof slopes of 5°, 15°, and 25°. Air velocities downwind a 1:50 scaled NVPB model were measured using a Laser Doppler Anemometer in a large boundary layer wind tunnel. A region with reduced mean air velocities was found along the downwind side of the building with a distance up to 0.5 m (i.e., 3.8 times building height), in which the emission concentration might be high. Additional air pollutant treatment technologies applied in this region might contribute to emission mitigation effectively. Furthermore, a wake zone with air recirculation was observed in this area. A smaller roof slope (i.e., 5° slope) resulted in a higher and shorter wake zone and thus a shorter air pollutant dispersion distance.
- ItemSupervised Machine Learning to Assess Methane Emissions of a Dairy Building with Natural Ventilation(Basel : MDPI, 2020) Hempel, Sabrina; Adolphs, Julian; Landwehr, Niels; Willink, Dilya; Janke, David; Amon, ThomasA reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach.
- ItemOpening Size E ects on Airflow Pattern and Airflow Rate of a Naturally Ventilated Dairy Building : A CFD Study(Basel : MDPI, 2020) Saha, Chayan Kumer; Yi, Qianying; Janke, David; Hempel, Sabrina; Amon, Barbara; Amon, ThomasAirflow inside naturally ventilated dairy (NVD) buildings is highly variable and difficult to understand due to the lack of precious measuring techniques with the existing methods. Computational fluid dynamics (CFD) was applied to investigate the effect of different seasonal opening combinations of an NVD building on airflow patterns and airflow rate inside the NVD building as an alternative to full scale and scale model experiments. ANSYS 2019R2 was used for creating model geometry, meshing, and simulation. Eight ventilation opening combinations and 10 different reference air velocities were used for the series of simulation. The data measured in a large boundary layer wind tunnel using a 1:100 scale model of the NVD building was used for CFD model validation. The results show that CFD using standard k-ε turbulence model was capable of simulating airflow in and outside of the NVD building. Airflow patterns were different for different opening scenarios at the same external wind speed, which may affect cow comfort and gaseous emissions. Guiding inlet air by controlling openings may ensure animal comfort and minimize emissions. Non-isothermal and transient simulations of NVD buildings should be carried out for better understanding of airflow patterns.
- ItemOn Finding the Right Sampling Line Height through a Parametric Study of Gas Dispersion in a NVB(Basel : MDPI, 2021) Doumbia, E. Moustapha; Janke, David; Yi, Qianying; Zhang, Guoqiang; Amon, Thomas; Kriegel, Martin; Hempel, SabrinaThe tracer gas method is one of the common ways to evaluate the air exchange rate in a naturally ventilated barn. One crucial condition for the accuracy of the method is that both considered gases (pollutant and tracer) are perfectly mixed at the points where the measurements are done. In the present study, by means of computational fluids dynamics (CFD), the mixing ratio NH3/CO2 is evaluated inside a barn in order to assess under which flow conditions the common height recommendation guidelines for sampling points (sampling line and sampling net) of the tracer gas method are most valuable. Our CFD model considered a barn with a rectangular layout and four animal-occupied zones modeled as a porous medium representing pressure drop and heat entry from lying and standing cows. We studied three inflow angles and six combinations of air inlet wind speed and temperatures gradients covering the three types of convection, i.e., natural, mixed, and forced. Our results showed that few cases corresponded to a nearly perfect gas mixing ratio at the currently common recommendation of at least a 3 m measurement height, while the best height in fact lied between 1.5 m and 2.5 m for most cases.
- ItemCFD modelling of an animal occupied zone using an anisotropic porous medium model with velocity depended resistance parameters(Amsterdam [u.a.] : Elsevier, 2021) Doumbia, E. Moustapha; Janke, David; Yi, Qianying; Amon, Thomas; Kriegel, Martin; Hempel, SabrinaThe airflow in dairy barns is affected by many factors, such as the barn’s geometry, weather conditions, configurations of the openings, cows acting as heat sources, flow obstacles, etc. Computational fluids dynamics (CFD) has the advantages of providing detailed airflow information and allowing fully-controlled boundary conditions, and therefore is widely used in livestock building research. However, due to the limited computing power, numerous animals are difficult to be designed in detail. Consequently, there is the need to develop and use smart numerical models in order to reduce the computing power needed while at the same time keeping a comparable level of accuracy. In this work the porous medium modeling is considered to solve this problem using Ansys Fluent. A comparison between an animal occupied zone (AOZ) filled with randomly arranged 22 simplified cows’ geometry model (CM) and the porous medium model (PMM) of it, was made. Anisotropic behavior of the PMM was implemented in the porous modeling to account for turbulence influences. The velocity at the inlet of the domain has been varied from 0.1 m s−1 to 3 m s−1 and the temperature difference between the animals and the incoming air was set at 20 K. Leading to Richardson numbers Ri corresponding to the three types of heat transfer convection, i.e. natural, mixed and forced convection. It has been found that the difference between two models (the cow geometry model and the PMM) was around 2% for the pressure drop and less than 6% for the convective heat transfer. Further the usefulness of parametrized PMM with a velocity adaptive pressure drop and heat transfer coefficient is shown by velocity field validation of an on-farm measurement.
- ItemCalculation of ventilation rates and ammonia emissions : Comparison of sampling strategies for a naturally ventilated dairy barn(San Diego, Calif. : Academ. Press, 2020) Janke, David; Willink, Dylia; Ammon, Christian; Hempel, Sabrina; Schrade, Sabine; Demeyer, Peter; Hartung, Eberhard; Amon, Barbara; Ogink, Nico; Amon, ThomasEmissions and ventilation rates (VRs) in naturally ventilated dairy barns (NVDBs) are usually measured using indirect methods, where the choice of inside and outside sampling locations (i.e. sampling strategy) is crucial. The goal of this study was to quantify the influence of the sampling strategy on the estimation of emissions and VRs. We equipped a NVDB in northern Germany with an extensive measuring setup capable of measuring emissions under all wind conditions. Ammonia (NH3) and carbon dioxide (CO2) concentrations were measured with two Fourier-transform infrared spectrometers. Hourly values for ventilation rates and emissions for ammonia over a period of nearly a year were derived using the CO2 balance method and five different sampling strategies for the acquisition of indoor and outdoor concentrations were applied. When comparing the strategy estimating the highest emission level to the strategy estimating the lowest, the differences in NH3 emissions in winter, transition, and summer season were +26%, +19% and +11%, respectively. For the ventilation rates, the differences were +80%, +94%, and 63% for the winter, transition and summer season, respectively. By accommodating inside/outside concentration measurements around the entire perimeter of the barn instead of a reduced part of the perimeter (aligned to a presumed main wind direction), the amount of available data substantially increased for around 210% for the same monitoring period.
- ItemHow the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning(Basel : MDPI AG, 2020) Hempel, Sabrina; Adolphs, Julian; Landwehr, Niels; Janke, David; Amon, ThomasEnvironmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days.