Beatris Adriana Escobedo-Trujillo

One of the most critical parameters for the capture of solar radiation is the angle of inclination of the surface and its contribution to optimization to maximize the amount of solar irradiation received. Numerous methods have been used worldwide to determine the angle of inclination. Unfortunately, many of these models are for fixed angles, which do not adapt to the sun's position during the year, resulting in the loss of essential amounts of irradiation that could be used. This work presents the methodology for developing a structure for photovoltaic (PV) panels fixed to the floor, which includes variations in the angle of inclination. The goal is to maximize the solar energy gain (energy per square meter) the panel receives over a year by making monthly tilt angle adjustments. The inclination angles are calculated for a structure, which will be placed in Coatzacoalcos, Veracruz, Mexico. The monthly angle of the prototype can have positive and negative values (tropical region); the maximum is 41.19° for December, and the minimum is - 4.95°, corresponding to June. The study can be applied to build a similar structure, with related variations in the angle of inclination, to work in any city, as long as information from the city in question is

In this paper, a polynomial model is developed to predict the pressure drop in oil wells with two-phase flow. The variables used in the model are gas-oil ratio production, oil production, tubing diameter, solution gas-oil ratio, oil formation volume factor, and oil viscosity. A polynomial model is presented to predict the pressure drop with a coefficient of determination of 0.9901. The residual analysis and level surfaces of the pressure drop against the polynomial model's input variables are presented to validate the model. A regression of the experimental and predicted pressure drops values using the polynomial model is presented. This model contributes to a simpler methodology for the calculation of pressure drops and the consequent application in the modeling of production curves in oil wells for NODAL analysis.

We study optimal control problems where the dynamic system evolves according to a linear stochastic differential equation with (a) multiplicative and additive white noise, (b) a mixture of white noise, and (c) white noise and colored noise. The drift rate and the diffusion matrix of the linear dynamic system depend on a continuous-time Markov chain with finite state space that is (I) partially observed and (II) completely observed. Using the results of Wonham filter theory, we reduce the partially observed problems to one with complete observation. We solve the control optimal problems explicitly with the help of dynamic programing technique, and three applications are presented to illustrate our theoretical results.