Progress towards accelerating the unified model on hybrid multi-core systems

he cloud microphysics scheme, CASIM, and the radiation scheme, SOCRATES, are two computationally intensive parts within the Met Office’s Unified Model (UM). This study enables CASIM and SOCRATES to use accelerated multi-core systems for optimal com- putational performance of the UM. Using profiling to guide our efforts, we refactored the code for optimal threading and kernel arrangement and implemented OpenACC directives manually or through the CLAW source-to-source translator. Initial porting re- sults achieved 10.02x and 9.25x speedup in CASIM and SOCRATES respectively on 1 GPU compared with 1 CPU core. A granular per- formance analysis of the strategy and bottlenecks are discussed. These improvements will enable UM to run on heterogeneous com- puters and a path forward for further improvements is provided

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°)

Doctor of Philosophy

dissertationTurbulent transport of momentum, scalars, and heavy particles within plant canopies is strongly impacted by the canopy’s effect on the flow field in the canopy sub-layer (CSL). Although considerable research has been conducted on momentum and particle transport in and above dense homogeneous plant canopies, relatively little has been performed in perennial trellised canopies which have repetitive inhomogeneities at the scale of the canopy height. Particle transport in such canopies is of great interest due to the increasing use of training systems of this type by growers and due to the multitude of particle types regularly dispersed in these canopies, e.g., fungal spores and droplets sprayed by growers. The focus of this work is on the transport of momentum and fungal-spore-sized particles in a trellised vineyard canopy. Due to the discrete two-dimensional nature of the vineyard canopy, CSL flow characteristics differ from those seen in homogeneous canopies and change as a function of the above-canopy wind direction. To determine the specifics of how the trellised canopy geometry and local meteorological conditions combine to determine the characteristics of momentum and particle transport under all possible wind directions, multiple field campaigns were conducted in a vineyard in Oregon. During each of these campaigns, extensive meteorological data were collected while particles were released into the canopy and particle concentrations were sampled at downwind locations. The meteorological and plume data showed that the canopy exerted inhomogeneous nonisotropic drag, caused channeling of the flow along the aisles, and led to persistent coherent flow effects. The combination of these effects led to momentum statistics varying with wind direction, particle transport being biased to along the rows, and plume shapes being more complicated than those seen in homogeneous canopies or freestream flows

A Review of Element-Based Galerkin Methods for Numerical Weather Prediction: Finite Elements, Spectral Elements, and Discontinuous Galerkin

Numerical weather prediction (NWP) is in a period of transition. As resolutions increase, global models are moving towards fully nonhydrostatic dynamical cores, with the local and global models using the same governing equations; therefore we have reached a point where it will be necessary to use a single model for both applications. The new dynamical cores at the heart of these unified models are designed to scale efficiently on clusters with hundreds of thousands or even millions of CPU cores and GPUs. Operational and research NWP codes currently use a wide range of numerical methods: finite differences, spectral transform, finite volumes and, increasingly, finite/spectral elements and discontinuous Galerkin, which constitute element-based Galerkin (EBG) methods.Due to their important role in this transition, will EBGs be the dominant power behind NWP in the next 10 years, or will they just be one of many methods to choose from? One decade after the review of numerical methods for atmospheric modeling by Steppeler et al. (Meteorol Atmos Phys 82:287–301, 2003), this review discusses EBG methods as a viable numerical approach for the next-generation NWP models. One well-known weakness of EBG methods is the generation of unphysical oscillations in advection-dominated flows; special attention is hence devoted to dissipation-based stabilization methods. Since EBGs are geometrically flexible and allow both conforming and non-conforming meshes, as well as grid adaptivity, this review is concluded with a short overview of how mesh generation and dynamic mesh refinement are becoming as important for atmospheric modeling as they have been for engineering applications for many years.The authors would like to thank Prof. Eugenio Oñate (U. Politècnica de Catalunya) for his invitation to submit this review article. They are also thankful to Prof. Dale Durran (U. Washington), Dr. Tommaso Benacchio (Met Office), and Dr. Matias Avila (BSC-CNS) for their comments and corrections, as well as insightful discussion with Sam Watson, Consulting Software Engineer (Exa Corp.) Most of the contribution to this article by the first author stems from his Ph.D. thesis carried out at the Barcelona Supercomputing Center (BSCCNS) and Universitat Politècnica de Catalunya, Spain, supported by a BSC-CNS student grant, by Iberdrola Energías Renovables, and by grant N62909-09-1-4083 of the Office of Naval Research Global. At NPS, SM, AM, MK, and FXG were supported by the Office of Naval Research through program element PE-0602435N, the Air Force Office of Scientific Research through the Computational Mathematics program, and the National Science Foundation (Division of Mathematical Sciences) through program element 121670. The scalability studies of the atmospheric model NUMA that are presented in this paper used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. SM, MK, and AM are grateful to the National Research Council of the National Academies.Peer ReviewedPostprint (author's final draft

Applications of artificial neural networks in three agro-environmental systems: microalgae production, nutritional characterization of soils and meteorological variables management

La agricultura es una actividad esencial para los humanos, es altamente dependiente de las condiciones meteorológicas y foco de investigación e innovación con el objetivo de enfrentar diversos desafíos. El cambio climático, calentamiento global y la degradación de los ecosistemas agrícolas son sólo algunos de los problemas que los humanos enfrentamos para continuar con la esencial producción de alimentos. Buscando la innovación en el sector agrícola, se consideraron tres tópicos principales de investigación para esta tesis; la producción de microalgas, el color del suelo y la fertilidad, y la adquisición de datos meteorológicos. Estos temas tienen roles cada vez más importantes en la agricultura, especialmente bajo la incertidumbre del futuro de la producción de alimentos. Las microalgas son una interesante alternativa para la fertilización de cultivos y la sostenibilidad del suelo; mientras que los parámetros de fertilidad del suelo necesitan ser más estudiados para desarrollar métodos de análisis de menor costo y más rápidos para ayudar al manejo. La agricultura, como actividad altamente dependiente del clima, necesita de datos meteorológicos para anticipar eventos, planificar y manejar los cultivos eficientemente. Estos temas se seleccionaron con el propósito de mejorar el estado actual de la técnica, proponer nuevas alternativas basadas, principalmente, en la aplicación de redes neuronales artificiales (ANN) como una manera novedosa de resolver los problemas y generar conocimiento de aplicación directa en sistemas de cultivos. El objetivo principal de esta tesis fue generar modelos de ANNs capaces de abordar problemas relacionados con la agricultura, como una alternativa a los métodos tradicionales y más costosos empleados en el manejo, análisis y adquisición de datos en los sistemas agrarios.Departamento de Ingeniería Agrícola y ForestalDoctorado en Ciencia e Ingeniería Agroalimentaria y de Biosistema

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings