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Abstract (English)

In connection with the methods used so far, this contribution describes a new approach for the modelling of odour dispersion. Using the aid of example cases, the methodology of odour prognosis with different approaches is explained, and their advantages and dis- advantages as well as their limitations are discussed. Particular attention is given to close-range dispersion from odour emis- sion sources with low source heights and a complex fluidic environment. Typical examples of such cases are agricultural sources or biological processing plants (composting, sewage treatment plants). The new dispersion model is a further development of the NaSt3D model with two variants of improved dispersion modelling, an advection- diffusion approach (Euler model) and a Lagrange-particle model. This model is able to conduct time-resolved calculations of flows and disper- sion and hence allows the question of concentration fluctuation, which is important for odour phenomena, to be integrated into the model. The parallelizing of the computer code enables terrain- and source configu- rations which have been too complex thus far to be calculated in a fine division of the calculation grid. At present, computer clusters and high- performance computers can be used for this purpose in anticipation of the fast further development of efficient personal computers. The consistently analytical approach avoids empirical model supple- ments with adaptation parameters, such as the otherwise necessary mo- dels of exceeding probability, and can thus be calibrated on a physical basis.

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Agrartechnische Forschung, 6 (2000), 4. ISSN: 0948-7298
Faculty
Faculty of Agricultural Sciences
Institute
Institute of Agricultural Engineering

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English

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Sustainable Development Goals

BibTeX

@article{Boeker2000, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/10433}, author = {Boeker, Peter and Wallenfang, Oliver and Koster, Frank et al.}, title = {The Modelling of Odour Dispersion with Time-Resolved Models}, journal = {Agrartechnische Forschung}, year = {2000}, volume = {6}, number = {4}, }
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