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The following study investigates the effectiveness of a deep learning-based method for predicting the flow field and flow-driven rotation of a vertical-axis hydrokinetic turbine operating in previously unseen free-stream velocities. A Convolutional Neural Network (CNN) is trained and tested using the solutions of five two-dimensional (2-D), foil-resolved Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations, with free-stream velocities of 1.0, 1.5, 2.0, 2.5, and 3.0 m/s. Based on the boundary conditions of free-stream velocity and rotor position, the flow fields of x-velocity, y-velocity, pressure, and turbulent viscosity are inferred, in addition to the angular velocity of the rotor. Three trained CNN models are developed to evaluate the effects of (1) the dimensions of the training data, and (2) the number of simulations used as training cases. Reducing data dimensions was found to diminish mean relative error in predictions of velocity and turbulent viscosity, while increasing it in predictions of pressure and angular velocity. Increasing the number of training cases from two to three was found to reduce relative error for all predicted unknowns. With the best achieved CNN model, the variables of x-velocity, y-velocity, pressure, turbulent viscosity, and angular velocity were inferred with mean relative errors of 6.93%, 9.82%, 10.7%, 7.48%, and 0.817%, respectively.

Parabolic solar trough systems have seen limited deployments in cold northern climates as they are more suitable for electricity production in southern latitudes. A numerical dynamic model is developed to simulate troughs installed in cold climates and validated using a parabolic solar trough facility in Winnipeg. The model is developed in Simulink and will be utilized to simulate a trigeneration system for heating, cooling and electricity generation in remote northern communities. The main objective of this simulation is to obtain operational data of solar troughs in cold climates and use the model to determine ways to improve the economics and address cold weather issues. In this paper the validated Simulink model is applied to simulate a solar assisted absorption cooling system along with electricity generation using Organic Rankine Cycle (ORC) and thermal storage. A control strategy is employed to distribute the heated oil from solar collectors among the above three systems considering the temperature requirements. This modelling provides dynamic performance results using measured meteorological data recorded every minute at the solar facility location. The purpose of this modeling approach is to accurately predict system performance at each time step considering the solar radiation fluctuations due to passing clouds. Optimization of the controller in cold temperatures is another goal of the simulation to for example minimize heat losses in winter when energy demand is high and solar resources are low. The solar absorption cooling is modeled to use the generated heat from the solar trough system and provide cooling in summer for a greenhouse which is located next to the solar field. The results of the simulation are presented for a summer day in Winnipeg which includes comparison of performance parameters of the absorption cooling and ORC systems at different heat transfer fluid (HTF) temperatures., {"references":["H. M. Henning, \"Solar assisted air conditioning of buildings – an\noverview,\" Applied Thermal Engineering, vol. 27, 2007, pp. 1734–1749.","K. F. Fong, T. T. Chow, C. K. Lee, Z. Lin, L. S. Chan, \"Comparative\nstudy of different solar cooling systems for buildings in subtropical\ncity,\" Solar Energy, vol. 84, 2010, pp. 227–244.","V. Boopathi Raja, V. Shanmugam, \"A review and new approach to\nminimize the cost of solar assisted absorption cooling system,\"\nRenewable and Sustainable Energy Reviews, vol. 16, 2012, pp. 6725-\n6731.","M. Citterio, G. Corallo, G. Guj, A. Vangelista, B. Di Pietra,\"Solar air\nconditioning with high temperature solar collectors and water ammonia\nabsorption heat pump,\" 61st ATI National Congress, International\nSession \"Solar Heating and Cooling\".","N. Hartmann, C. Glueck, F.P. Schmidt, \"Solar cooling for small office\nbuildings: Comparison of solar thermal and photovoltaic options for two\ndifferent European climates,\" Renewable Energy, vol. 36, 2011, pp.\n1329-1338.","J. P. Praene, O. Marc, F. Lucas, F. Miranville, \"Simulation and\nexperimental investigation of solar absorption cooling system in\nReunion Island,\" Applied Energy, vol. 88, 2011, pp. 831–839.","H, Price, V. Hassani, \"Modular Trough Power Plant Cycle and Systems\nAnalysis,\" Technical report, National Renewable Energy Laboratory,\nJanuary 2002.","A. C. McMahan, \"Design & Optimization of Organic Rankine Cycle\nSolar-Thermal Power plants,\" University of Wisconsin-Madison, 2006.","R. Forristall, \"Heat Transfer Analysis and Modeling of a Parabolic\nTrough Solar Receiver Implemented in Engineering Equation Solver,\"\nTechnical report, National Renewable Energy Laboratory, October\n2003."]}

A two-phase experimental loop was designed and built to simulate the operating conditions of the SLOWPOKE reactor. Void growth was measured for both upflow and downflow for velocities between 0.07 to 0.46 m/s and at a pressure of 155 kPa. The buoyancy effect causes the Onset of Significant Void, OSV, to occur at higher subcooling for downflow than for upflow. This effect is maximum close to the bubble rise velocity (0.23 m/s) and decreases at higher velocities. The results indicate that bubble detachment is not the only critical parameter affecting OSV. The OSV correlations from the literature did not predict the experimental results well. Investigation of the heat transfer mechanisms indicates that fully developed sub cooled boiling occurs prior to OSV.