Assessing the Sensitivity and Uncertainty of an NH3 Emission Reduction Calculator for Dairy Cattle Barns by Means of Monte Carlo Analysis Combined with Least Square Linearization

With regard to Natura 2000, the Flemish government (Belgium) established the Programmatic Approach to Nitrogen (PAS: acronym in Flemish), with the aim of reducing environmental overload of nitrogen compounds. This approach will have substantial consequences for livestock farms located next to or within special areas of conservation and will likely result in generic measures to reduce ammonia (NH3) emissions from livestock facilities. An NH3 emission reduction calculator for dairy cattle systems (AEREC-DC) was adapted based on a mechanistic approach. Reduction coefficients estimated with this tool are used to assess the efficiency of “low NH3 emission” techniques which can be implemented in Flanders at a later stage. Field measurements will be made in the future to confirm/correct them. Emission reduction techniques combining processes such as floor scraping, flushing, manure acidification, and different types of floor were modeled. The tool comprises 36 input variables, some of which have values that are based on experimental measurements. Nevertheless, reliable information concerning other relevant variables are scarce in the literature. Hence, model sensitivity analysis is imperative. We hypothesize that the ranking of input variables in terms of their effect on the model outcome will change if different uncertainty ranges are assigned to them. Hence, this study was conducted to combine Monte Carlo Analysis associated with Least Square Linearization in order to perform sensitivity and uncertainty analyses on AEREC-DC. The sensitivity analysis was performed by assigning each input variables’ probability distribution function (PDF) with a relatively narrow variance (1% of mean value). The uncertainty analysis was carried out by gradually increasing the PDF’s variance up to what is considered realistic. The outcomes of this study will help deciding which variables urgently need to be monitored experimentally in order to improve predictions’ accuracy

Dynamic and stochastic routing for multimodal transportation systems

The authors present a case study of a multimodal routing system that takes into account both dynamic and stochastic travel time information. A multimodal network model is presented that makes it possible to model the travel time information of each transportation mode differently. This travel time information can either be static or dynamic, or either deterministic or stochastic. Next, a Dijkstra-based routing algorithm is presented that deals with this variety of travel time information in a uniform way. This research focuses on a practical implementation of the system, which means that a number of assumptions were made, like the modelling of the stochastic distributions, comparing these distributions, and so on. A tradeoff had to be made between the performance of the system and the accuracy of the results. Experiments have shown that the proposed system produces realistic routes in a short amount of time. It is demonstrated that routing dynamically indeed results in a travel time gain in comparison to routing statically. By making use of the additional stochastic travel time information even better (i.e. faster), more reliable routes can be calculated. Moreover, it is shown that routing in the multimodal network may have its advantages over routing in a unimodal network, especially during rush hours

Assessing airflow rates of a naturally ventilated test facility using a fast and simple algorithm supported by local air velocity measurements

The high spatial and temporal variations of airflow patterns in ventilation openings of naturally ventilated animal houses make it difficult to accurately measure the airflow rate. This paper focuses on the development of a fast assessment technique for the airflow rate of a naturally ventilated test facility through the combination of a linear algorithm and local air velocity measurements. This assessment technique was validated against detailed measurement results obtained by the measuring method of Van Overbeke et al. (2015) as a reference. The total air velocity |u-|, the normal |Y-| and tangential velocity component |x-| and the velocity vector u- measured at the meteomast were chosen as input variables for the linear algorithms. The airflow rates were split in a group where only uni-directional flows occurred at vent level (no opposite directions of |Y-| present in the airflow pattern of the opening), and a group where bi-directional flows occurred (the air goes simultaneously in and out of the opening). For airflow rates with uni-directional flows the input variables u- and |Y-| yielded the most accurate results. For this reason, it was suggested to use the |Y-| instead of |u-| in ASHRAE’s formula of Q = E × A × |u-|. For bi-directional flows a multiple linear model was suggested where input variable u- gave the best results to assess the airflow rate

Methodology for airflow rate measurements in a naturally ventilated mock-up animal building with side and ridge vents

Currently there exists no generally accepted reference technique to measure the ventilation rate through naturally ventilated (NV) vents. This has an impact on the reliability of airflow rate control techniques and emission rate measurements in NV animal houses. As an attempt to address this issue a NV test facility was built to develop new airflow rate measurement techniques for both side wall and ridge vents. Three set-ups were used that differed in vent configuration, i.e. one cross ventilated set-up and two ridge ventilated set-ups with different vent sizes. The airflow through the side vents was measured with a technique based on an automatic traverse movement of a 3D ultrasonic anemometer. In the ridge, 7 static 2D ultrasonic anemometers were installed. The methods were validated by applying the air mass conservation principle, i.e. the inflow rates must equal the outflow rates. The calculated in - and outflow rates agreed within (5 ± 8)%, (8 ± 5)% and (−9 ± 7)% for the three different set-ups respectively, over a large range of wind incidence angles. It was found that the side vent configuration was of large importance for the distribution of the airflow rates through the vents. The ridge proved to be a constant outlet, whilst side vents could change from outlet to inlet depending on the wind incidence angle. The range of wind incidence angles in which this transition occurred could be clearly visualised

Fault tolerant network design inspired by Physarum polycephalum

Physarum polycephalum, a true slime mould, is a primitive, unicellular organism that creates networks to transport nutrients while foraging. The design of these natural networks proved to be advanced, e.g. the slime mould was able to find the shortest path through a maze. The underlying principles of this design have been mathematically modelled in literature. As in real life the slime mould can design fault tolerant networks, its principles can be applied to the design of man-made networks. In this paper, an existing model and algorithm are adapted and extended with stimulation and migration mechanisms which encourage formation of alternative paths, optimize edge positioning and allow for automated design. The extended model can then be used to better design fault tolerant networks. The extended algorithm is applied to several national and international network configurations. Results show that the extensions allow the model to capture the fault tolerance requirements more accurately. The resulting extended algorithm overcomes weaknesses in geometric graph design and can be used to design fault tolerant networks such as telecommunication networks with varying fault tolerance requirements