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End date: 2021 × Start date: 2021 × DDC: 630 × Publisher: Amsterdam [u.a.] : Elsevier ×

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    CFD 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, Sabrina
    The 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.
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    Quantifying sustainable intensification of agriculture: The contribution of metrics and modelling
    (Amsterdam [u.a.] : Elsevier, 2021) Mouratiadou, Ioanna; Latka, Catharina; van der Hilst, Floor; Müller, Christoph; Berges, Regine; Bodirsky, Benjamin Leon; Ewert, Frank; Faye, Babacar; Heckelei, Thomas; Hoffmann, Munir; Lehtonen, Heikki; Lorite, Ignacio Jesus; Nendel, Claas; Palosuo, Taru; Rodríguez, Alfredo; Rötter, Reimund Paul; Ruiz-Ramos, Margarita; Stella, Tommaso; Webber, Heidi; Wicke, Birka
    Sustainable intensification (SI) of agriculture is a promising strategy for boosting the capacity of the agricultural sector to meet the growing demands for food and non-food products and services in a sustainable manner. Assessing and quantifying the options for SI remains a challenge due to its multiple dimensions and potential associated trade-offs. We contribute to overcoming this challenge by proposing an approach for the ex-ante evaluation of SI options and trade-offs to facilitate decision making in relation to SI. This approach is based on the utilization of a newly developed SI metrics framework (SIMeF) combined with agricultural systems modelling. We present SIMeF and its operationalization approach with modelling and evaluate the approach’s feasibility by assessing to what extent the SIMeF metrics can be quantified by representative agricultural systems models. SIMeF is based on the integration of academic and policy indicator frameworks, expert opinions, as well as the Sustainable Development Goals. Structured along seven SI domains and consisting of 37 themes, 142 sub-themes and 1128 metrics, it offers a holistic, generic, and policy-relevant dashboard for selecting the SI metrics to be quantified for the assessment of SI options in diverse contexts. The use of SIMeF with agricultural systems modelling allows the ex-ante assessment of SI options with respect to their productivity, resource use efficiency, environmental sustainability and, to a large extent, economic sustainability. However, we identify limitations to the use of modelling to represent several SI aspects related to social sustainability, certain ecological functions, the multi-functionality of agriculture, the management of losses and waste, and security and resilience. We suggest advancements in agricultural systems models and greater interdisciplinary and transdisciplinary integration to improve the ability to quantify SI metrics and to assess trade-offs across the various dimensions of SI.