Modeling deep-bed grain drying using Comsol Multiphysics

CFD simulations were carried out to predict the convective heat and mass transfer coefficients in the rice bed, and correlations were developed for the convective heat and mass transfer coefficients as a function of drying air flow rate. The developed correlations were used to extend the coupled CFD and diffusion model developed by ElGamal et al. (2013) for thinlayer rice drying to volumetric heat and mass transfer in a deep-bed of rice. All mathematical models were solved using the Comsol Multiphysics® simulation program v4.3 (Comsol Inc, Palo Alto), which uses the finite element method to solve the model equations. The model was used to predict the air temperature, as well as the grain moisture content and temperature at different locations of the dryer during the drying process. The theoretical predictions of moisture and temperature profiles inside a deep-bed of rice were verified by experimental data from literature

Multi-scale model for heat and mass transfer during rice drying

Grain drying is a simultaneous heat and moisture transfer problem. The modeling of such a problem is of significance in understanding and controlling the drying process. The main goal of this study was to predict the heat and mass transfer processes during deep-bed rice drying. To achieve this, first, CFD simulations were carried out to analyze the external flow and temperature fields at steady-state for a control volume of a stationary rice bed. The model was used to predict the convective heat and mass transfer coefficients in the rice bed, and correlations were developed for the convective heat and mass transfer coefficients as a function of drying air flow rate. Then, the coupled CFD and diffusion model developed by ElGamal, Ronsse, Radwan & Pieters (2013) to investigate the heat and mass transfer for thin-layer drying of rice was extended to volumetric heat and mass transfer in a deep-bed of rice using the predicted heat and mass transfer coefficients. All models were solved numerically using the finite element method. The model was used to predict the air temperature, as well as the grain moisture content and temperature at different locations of the dryer during the drying process. The theoretical predictions of moisture and temperature profiles inside a deep-bed of rice were verified by experimental data from literature. The average mean relative deviation values for the prediction of grain moisture content varied between 1.00 to 3.13%

Sunspot Time Series Forecasting using Deep Learning

In order to forecast solar cycle 25, sunspot numbers(SSN) from 1700 ∼ 2018 was used as a time series to predict the next eleven years. deep long short-term memory(LSTM) was exploited to do the forecast, first the dataset was split into training set(80%) and (20%) for the test set, the achieved accuracy led us to forecast the next eleven years. The result shows that the cycle will be from 2019 ∼ 2029 with peak at 2024

In-Field Solar Panel Assessment and Fault Diagnosis

Photovoltaic energy is a green energy that suit from small houses to high-power stations spanning large areas. In such large areas, monitoring individual panels can be a tedious task, especially if it was required to identify operational faults of these panels. Photovoltaic 4.0 technology depend on collecting data from each station and feeding them to a central processing system that can analyze operation data and hopefully locate when a fault happens. In such method, it is crucial to be accurate as much as possible and for measuring device to be accurate as well to have a clear judgement. In this work, we build an analysis module at the center of a photovoltaic 4.0 station implemented in the American University in Cairo. The model is comprehensive in nature and is capable of modelling from individual cell level to the whole panel level as well as dealing with measurement issues to have a good judgement at the end. The used model is based on single-diode model of a solar panel and is capable of modelling solar panels in different environmental conditions and is validated against datasheet and actual measurement. Source code for the analysis module and the dataset are provided. It was shown that Laudani’s method of parameter extraction is more successful compared to Stonelli’s method and translating circuit parameters at different environmental conditions proved to be successful and matching to datasheets. Besides, it provided sufficient predictions without need to an actual weather station. The proposed analysis module provided insights about dusty conditions and irregularities that may exist in solar panel characterizer

Between collective victimization and victimhood: the experience of the Ethiopian refugees (Anuak and Highlanders) in Nakivale settlement. Uganda

Master's thesis in Migration and intercultural relationsThis thesis examines collective and competitive victimhood among Ethiopian refugees in Nakivale settlement, Uganda and how these experiences of victimhood are influenced by the humanitarian setting of the camp. The study is based on fieldwork conducted by the researcher during the time she spent as an intern in the settlement from September until November 2019. The study adopted a qualitative approach with in-depth interviews, focus group discussion and personal observation as methods for data collection. The findings have revealed how collective victimization of the Ethiopian Anuak which have been historically subjected to by the Ethiopian government and the Ethiopian Highlanders is negatively influencing and shaping the way they perceive the Ethiopian Highlander refugees in the same settlement. The findings have also shown how the competitive victimhood has developed mainly among the Anuak refugees in a way that shows them as the ultimate sufferers and the ideal "victims" which enhance their exclusive victim consciousness. Through claiming victimhood, Anuak refugees have made their voices more heard and their suffering more legitimate compared to the other refugees including the Highlander. In this sense, being a "victim" goes beyond the normalized image that has been set by the humanitarian discourse which represents refugees as apolitical, passive, dependent but rather reveals their consciousness about their past and the power they have to bring this suffering to the surface

Calibrated 3D Computational Modeling of Soil-Structure Systems and Liquefaction Scenarios

Three-dimensional (3D) computational simulation is increasingly allowing for insights into the mechanics of seismic soil-structure system response. Calibration is being facilitated by field, full-scale, and centrifuge model laboratory data. Computational algorithms and scenario-specific graphical user-interfaces are gradually permitting the routine adoption of such geometrically realistic simulation environments. This paper presents an overview of salient recent 3D soil-foundation-structure earthquake response simulations. Developments related to graphical user-interfaces (OpenSeesPL, http://cyclic.ucsd.edu/openseespl) are summarized, demonstrating the current and evolving capabilities towards performance-based earthquake engineering (PBEE). From an OpenSeesPL-generated lateral push-over analysis of a large pile-group, it is shown that corner piles may shoulder a significantly higher level of load (axial, shear, and bending). Evolution of large tensile forces in these piles may warrant careful consideration. Modeling of liquefaction response mechanisms are also discussed, highlighting the role of cyclic mobility and influence of permeability in dictating the level of associated ground shear deformations, and related countermeasure performance