Discrete Element Method Model of Elastic Fiber Uniaxial Compression

A flexible fiber model based on the discrete element method (DEM) is presented and validated for the simulation of uniaxial compression of flexible fibers in a cylindrical container. It is found that the contact force models in the DEM simulations have a significant impact on compressive forces exerted on the fiber bed. Only when the geometry-dependent normal contact force model and the static friction model are employed, the simulation results are in good agreement with experimental results. Systematic simulation studies show that the compressive force initially increases and eventually saturates with an increase in the fiber-fiber friction coefficient, and the fiber-fiber contact forces follow a similar trend. The compressive force and lateral shear-to-normal stress ratio increase linearly with increasing fiber-wall friction coefficient. In uniaxial compression of frictional fibers, more static friction contacts occur than dynamic friction contacts with static friction becoming more predominant as the fiber-fiber friction coefficient increases.Comment: 30 pages, 14 figures, submitted for publicatio

Uniaxial compression of fibre networks – the synergetic effect of adhesion and elastoplasticity on non-reversible deformation

In this paper we study numerically and experimentally non-reversible deformation of anisotropic, semi-flexible fibre networks. We formulate a Discrete Element Model (DEM) with bonded particles to simulate uniaxial compression of such networks and use this model to describe and quantify the effect of elasto-plastic fibre contacts and fibre-fibre adhesion on non-reversible deformation. Our results show that inter-fibre adhesion plays a role for compression in a low solid volume fraction range where adhesive forces can overcome fibre deformation forces and moments. Also, elasto-plastic contacts between fibres become important at higher solid volume fractions when the yield criterion is exceeded. The combined case of fibres having elasto-plastic contacts and adhesion shows a significant synergetic effect leading to a degree of non-reversible deformation of the network far beyond that of networks with only elasto-plastic fibre contacts or inter-fibre adhesion

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Transdisciplinary top-down review of hemp fibre composites: From an advanced product design to crop variety selection

Given the vast amount of available research in the area of natural fibre composites, a significant step forward in the development of next-generation plant fibre-based products would be to devise a framework for rational design. The authors use a top-down approach, starting with an example final product to define the product specifications for high-performance hemp fibre-reinforced composites. Thereafter, all process steps are critically analysed: from textile preform and reinforcement yarn production, to fibre extraction and the agricultural process chain, to the microbiology of field retting, to cultivation and selection of crop variety. The aim of the analysis is to determine how far the current state of knowledge and process technologies are in order to use hemp fibres in high- performance composites. Based on this critical evaluation of the state-of-the-art, it can be stated that hemp will be found in high-performance composites in the short-to-medium term. There is, however, a need for performance optimisation especially through the selection of crop variety, best practices in retting, and effective fibre extraction methods to obtain more consistent fibre qualities suitable for reinforcement spinning and composite preform manufacturing processes

Transdisciplinary top-down review of hemp fibre composites: from an advanced product design to crop variety selection

Given the vast amount of available research in the area of natural fibre composites, a significant step forward in the development of next-generation plant fibre-based products would be to devise a framework for rational design. The authors use a top-down approach, starting with an example final product to define the product specifications for high-performance hemp fibre-reinforced composites. Thereafter, all process steps are critically analysed: from textile preform and reinforcement yarn production, to fibre extraction and the agricultural process chain, to the microbiology of field retting, to cultivation and selection of crop variety. The aim of the analysis is to determine how far the current state of knowledge and process technologies are in order to use hemp fibres in high-performance composites. Based on this critical evaluation of the state-of-the-art, it can be stated that hemp will be found in high-performance composites in the short-to-medium term. There is, however, a need for performance optimisation especially through the selection of crop variety, best practices in retting, and effective fibre extraction methods to obtain more consistent fibre qualities suitable for reinforcement spinning and composite preform manufacturing processes

Agricultural Structures and Mechanization

In our globalized world, the need to produce quality and safe food has increased exponentially in recent decades to meet the growing demands of the world population. This expectation is being met by acting at multiple levels, but mainly through the introduction of new technologies in the agricultural and agri-food sectors. In this context, agricultural, livestock, agro-industrial buildings, and agrarian infrastructure are being built on the basis of a sophisticated design that integrates environmental, landscape, and occupational safety, new construction materials, new facilities, and mechanization with state-of-the-art automatic systems, using calculation models and computer programs. It is necessary to promote research and dissemination of results in the field of mechanization and agricultural structures, specifically with regard to farm building and rural landscape, land and water use and environment, power and machinery, information systems and precision farming, processing and post-harvest technology and logistics, energy and non-food production technology, systems engineering and management, and fruit and vegetable cultivation systems. This Special Issue focuses on the role that mechanization and agricultural structures play in the production of high-quality food and continuously over time. For this reason, it publishes highly interdisciplinary quality studies from disparate research fields including agriculture, engineering design, calculation and modeling, landscaping, environmentalism, and even ergonomics and occupational risk prevention

OPTIMIZATION OF THE CLEANING SYSTEM OF GRAPE HARVESTERS USING THE DISCRETE-ELEMENT METHOD

Grape harvesters are mechanized machines designed to remove grapes from vine trees, process them in a cleaning system, and then store them in onboard bins. These bins are later unloaded into a transport wagon and taken to a vinification facility. Cleaning systems can sometimes fail to completely remove the foreign materials (i.e. leaves, petioles, stems, etc.), which may compromise the vinification process. For this reason, the project focused on the cleaning system by minimizing the presence of foreign materials while maintaining an adequate harvesting throughput. The project main objective was to optimize the cleaning system in grape harvesters by using the Discrete-Element Method (DEM). Individual DEM simulations were validated and used to develop a main crop flow simulation for the optimization of the cleaning system. This optimization included reducing the presence of foreign materials (petioles and leaves) while increasing the crop throughput for the specific grape variety of Cabernet Sauvignon. The physical characteristics and properties of the biological materials (grapes, petioles, leaves) were measured during the 2014 grape harvesting season at three different locations (Aigues-Mortes, Saint-Gervais, and Pauillac) in France. Time constraints limited the number of measured properties at the locations. The results from each location were compared using an ANOVA and a Tukey HSD post-hoc test. Given the natural variability of the biological materials, the three populations were found to be significantly different in most cases. The physical characteristics and properties from the Aigues-Mortes and Pauillac locations were used for the validation process. This was done because these locations had the most complete data sets. During the summer of 2015, a second testing phase took place to validate both the DEM leaf deflection and cleaning system models. The additional experiments consisted of testing the leaf samples in controlled deflections and testing the efficiency of the cleaning system. These experiments used Cabernet Sauvignon leaves shipped from the Vineland Research and Innovation Centre (VRIC) in Ontario. The individual simulations included the inclined plane, rebound surface, leaf deflection, and grape trajectory tests on an inclined conveyor. The inclined plane and rebound simulations were adjusted until the results were within 5% of the experimental test results. The leaf deflection simulations used optimized crop material properties until the simulated leaf behavior matched the actual leaf. Some discrepancies in the DEM simulated leaf shape were identified due to the limitations of the particle creation method. The grape trajectory test results coincided with the DEM simulations at greater conveyor speeds. A moderate difference between the simulations and the experimental tests was present at lower conveyor speeds. A possible cause for this difference may have been the effect of gravity and belt friction on the generation and acceleration of the grapes on the conveyor. A main crop flow simulation that included a conveyor and aspirator was developed using the previously validated simulations. Nine conveyor configurations, which included three belt angles from horizontal (10°, 15°, and 20°) and three speeds (350 rev/min (1.4 m/s), 420 rev/min (1.7 m/s), and 500 rev/min (2.0 m/s)), were tested to optimize the cleaning system performance. Based on the DEM simulations, the 420 rev/min-20° configuration was recommended as the optimal crop conveyor setting. This particular configuration minimized product damage and had an increased aspiration success rate of 9.6% compared to the conventional conveyor settings (420 rev/min-15°)