This paper investigates thermal walls behavior as elements of buildings and low-consumption energy residences. The problem is formulate based on the transient and unidimensional model, and numerically solved using the finite volumes method to discretize space and the implicit formulation to discretize time. Numerical tests were realized to optimize the mesh. Three different materials, heat transfer coefficient and color incorporation were tested in each wall layer A and B. The influence of these variations in the internal wall was calculated for each case. It was found that the best material for A and B walls are plaster mortar and common brick, respectively. This is a result of the inverse correlation between thermal resistance and the heat transfer rate. For a lower temperature in the external and internal walls, the thickness ratio for the external wall was  < 0.5 and for the inner wall was  > 0.5. The best value of absorptivity was 0.156 and the external heat transfer coefficient was variated to increase heat change. With all these parameters analyzed, it was possible to decrease significantly the heat transfer to the inner space and enhance the thermal comfort.

No abstract

This paper presents a numerical investigation of the aerodynamic flow through airfoils with and without flaps for low Reynolds numbers. A two-dimensional, permanent, viscous model is adopted in the problem. The mass conservation and Navier-Stokes equations are discretized using two numerical methods, the panels method and the finite volume method, using CFD (Computational Fluid Dynamics) XFLR5® and ANSYS Fluent™ softwares. Initially, the work aims to compare the results obtained in the numerical simulation for different mesh refining with the analytical model available in the literature. Afterwards, it was verified how the pressure fields, velocity, current lines, drag and carry coefficients for symmetrical and asymmetrical airfoils work. Then, which airfoil is the most aerodynamically efficient, and finally, which numerical method is more feasible for two-dimensional and incompressible aerodynamic simulations.

Ocean’s surface currents, although not easily measured by ship’s sensors, affect the movements of any vessel at sea. Especially when dealing with Turret-Moored FPSOs, due to its weathervaning property, there is an intrinsic relationship between the Platform’s motion and environmental conditions. In this sense, we propose using Machine Learning regression algorithms to estimate the surface currents that affect a Turret-Moored FPSO based on data commonly measured on board. These data are expressed by wind speed and direction (measured via anemometer and anemoscope); Platform’s heading (obtained by GPS or Magnetic/Gyro compass); and FPSO’s oscillating motion that is given by the standard deviation of pitch, roll, heave and yaw (measured by MRU sensors and considered as proxy variables of first-order waves’ forces). The prior dataset was composed by local environmental conditions at a specific offshore Basin (in Brazil), observed over ten years at a 3-hour time stamp. This corresponds to approximately 30,000 conditions, each used as input into numerical simulations for a partially loaded FPSO (length between perpendiculars: 257 m, beam: 52 m, draught: 15.6 m). Simulation results provide the Platform’s motion time-series used to generate the final dataset previously mentioned. After dividing this final dataset for train/validation/test into 70/20/10 proportion, a K-means algorithm was fitted to the training data, which grouped it into 3 clusters in which, for each one, 2 ‘specialized’ MultiLayer Perceptron (MLP) Neural Network (one for current’s velocity and another for direction) were implemented for. The mean measured value of current’s velocity of the test dataset is 0.33 m/s, whereas the mean prediction with the method proposed is 0.32 m/s. In current’s direction, the mean measured value is 220°, while the mean prediction is 225°.