REORDENAMIENTO VIAL E IMPACTO AMBIENTAL DEL TRANSPORTE EN LA CIUDAD DE HEREDIA

Este artículo aborda la problemática del reordenamiento vial en la ciudad de Heredia, Costa Rica, se parte de que la temática debe ser analizada dentro del contexto de la Gran Área Metropolitana. Se toman en cuenta aspectos como el volumen de tráfico en las calles y avenidas de la ciudad, las principales rutas de autobuses, las paradas y terminales, la ubicación de parqueos públicos y privados y la modalidad municipal de parqueos con boleta. Ante la imposibilidad de hacer ensanches en la ciudad, los pocos proyectos viales como la radial a Heredia, la construcción de un puente elevado en la Valencia y la utilización de la vía férrea con un tren moderno que una las cuatro ciudades más importantes del país, son las pocas alternativas que son posibles de realizar a corto y mediano plazo. Ante esa realidad es necesario optimizar las vías de la ciudad, mediante un reordenamiento vial. Se estima que de los problemas de tráfico que tienen las ciudades un 50% obedece a la falta de medidas de reordenamiento.   La utilización de indicadores de contaminación del aire y de contamina­ción sónica se usan como parámetros, como elementos que contribuyan al reordenamiento vial y no como un fin en sí mismo. ABSTRACT: This article analyzed the vial planning of Heredia city, Costa Rica and its environmental impact. This city is located in the main metropolitan región of Costa Rica and it is considered in this study such part of this important urban área. The study analyzed several factors such as; traffic volume of the main streets and avenues of the city, location of the public and private parking lot, and the use of the "boleta" (permission) from the Municipality. There are many factor has been considered in order to improved conditions of the street and avenues such as: to built a bright over the Valencia and the possibility to use the old train route with modern machine. There are not many option the short and medium time to overcome the traffic problem in Heredia city. For which reason, it is necessary to improved the street and avenues conditions by vial replanning due this is the cause of the 50% vial problems. In this study, the air and sonic pollution indicators are using as parameters and as element that support the making decision about vial planning.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.

Search for heavy neutrinos in $D_s$ decays with the CMS detector at LHC

This thesis presents a search for a sterile right-handed neutrino $N$ produced in $D_s$ meson decays, using proton-proton collisions collected by the CMS experiment at the LHC. The data set used for the analysis, the B-Parking data set, corresponds to an integrated luminosity of $41.7\,\textrm{fb}^{-1}$ and was collected during the 2018 data-taking period. The analysis is targeting the $D_s^+\rightarrow N(\rightarrow\mu^{\pm}\pi^{\mp})\mu^{+}$ decays, where the final state muons can have the same electric charge allowing for a lepton flavor violating decay. To separate signal from background, a cut-based analysis is optimized using requirements on the sterile neutrino vertex displacement, muon and pion impact parameter, and impact parameter significance. The expected limit on the active-sterile neutrino mixing matrix parameter $|V_{\mu}|^2$ is extracted by performing a fit of the $\mu\pi$ invariant mass spectrum for two sterile neutrino mass hypotheses, 1.0 and 1.5 GeV. The analysis is currently blinded, following the internal CMS review process. The expected limit ranges between approximately $10^{-4}$ for a 1.0 GeV neutrino to $7\times10^{-5}$ for a 1.5 GeV neutrino. This is competitive with the best existing results from collider experiments over the same mass range.

Light coloured and draining wearing courses for low-impact urban pavements

Urban health and well-being are becoming current issues of modern cities due to local climate change and environmental noise. The Urban Heat Island and the Urban Noise Island have a direct impact on the economic, social, and environmental aspects of urban life, negatively affecting the well-being of worldwide citizens. The present research is focused on the study of innovative materials employed in the production of wearing course mixtures aiming to mitigate these phenomena. In particular, a synthetic transparent binder substituting bitumen and recycled aggregates produced from construction and demolition waste. Four mixtures were analysed. Among them, Mix 1 and Mix 2 are conventional wearing courses. The first is exclusively made of natural aggregates, while the second is constituted of 45 % of recycled aggregates (RA). Mix 3 and Mix 4 are draining wearing courses and, in this case, Mix 4 was produced by using 55 % of RA. Laboratory tests were required to fully characterize all the produced samples, allowing a proper comparison of results. Overall, all the mixtures studied provide prominent results suggesting potential applications of these innovative wearing courses in cycle lanes, historical centres, plazas, and parking lots. Among the conventional mixtures, Mix 2 is the most likely to assure the best performance in terms of road safety, efficiency, and durability while as far as the draining mixtures are concerned, Mix 4 is preferable due to its high content of recycled aggregates.

Numerical modeling on the short-term response of fiber-reinforced concrete round panels

Fiber-reinforced concrete is a composite material consisting of discrete, discontinuous, and uniformly distributed fibers in plain concrete primarily used to enhance the tensile properties of the concrete. FRC performance depends upon the fiber, interface, and matrix properties. The use of fiber-reinforced concrete has been increasing substantially in the past few years in different fields of the construction industry such as ground-level application in sidewalks and building floors, tunnel lining, aircraft parking, runways, slope stabilization, etc. Many experiments have been performed to observe the short-term and long-term mechanical behavior of fiber-reinforced concrete in the last decade and numerous numerical models have been formulated to accurately capture the response of fiber-reinforced concrete. The main purpose of this dissertation is to numerically calibrate the short-term response of the concrete and fiber parameters in mesoscale for the three-point bending test and cube compression test in the MARS framework which is based on the lattice discrete particle model (LDPM) and later validate the same parameters for the round panels. LDPM is the most validated theory in mesoscale theories for concrete. Different seeds representing the different orientations of concrete and fiber particles are simulated to produce the mean numerical response. The result of numerical simulation shows that the lattice discrete particle model for fiber-reinforced concrete can capture results of experimental tests on the behavior of fiber-reinforced concrete to a great extent.