https://hdl.handle.net/20.500.12008/38539 2026-09-17T19:13:32Z https://hdl.handle.net/20.500.12008/56673 Título: Desempeño energético de la lana ovina aplicada como aislante térmico en programa de vivienda rural para la zona cálida de Uruguay Autor: Gonzalvez, Cecilia M.; Hongn, Marcos; Vitale, Gerardo Resumen: Por su conjunto de características y propiedades físicas, la lana ovina es un material con óptimas condiciones para ser utilizado como aislante térmico, es un excelente aislante acústico, tiene un bajo índice de toxicidad y es un material ignífugo. El objetivo de esta investigación es obtener un material apto para ser utilizado en obras edilicias, conocer sus propiedades térmicas y evaluar su desempeño. Para ello, se describen los resultados del proceso de acondicionamiento del material que permiten viabilizar su puesta en obra; se estudia su conductividad térmica a través de ensayos de muestras de distintas densidades; y finalmente se evalúa el desempeño energético de una vivienda aislada con lana ovina a través de la simulación de un modelo numérico. El material analizado arroja resultados que aseguran un comportamiento térmico adecuado de la lana en su función aislante. La masa térmica aislada es ampliamente beneficiosa como estrategia de diseño pasivo para los requerimientos del período frío, donde las horas de disconfort pueden ser reducidas entre un 30 y 55% respecto al caso base. Sin embargo, puede suponer un efecto de sobrecalentamiento de los locales durante el período caluroso, esto implica que es necesario optimizar el diseño edilicio para mejorar los requerimientos de confort en los espacios habitables.; Due to its set of characteristics and physical properties, sheep wool has optimal conditions for use as thermal insulation, it’s an excellent acoustic insulator, has a low toxicity index, and is fire-resistant. The aim of this research is to develop a material suitable for use in building construction, to understand its thermal properties, and to evaluate its performance. To this end, the results of the material conditioning process are described, which enable its practical application; its thermal conductivity is studied through tests on samples of varying densities; and finally, the energy performance of a dwelling insulated with sheep wool is studied through the simulation of a numerical model. The tests carried out confirm the adequate thermal behavior of wool as an insulation material. It can be concluded that insulated thermal mass is highly beneficial as a passive design strategy for cold-season requirements, with reduction in the percentage of discomfort hours between 30% and 55% compared to the base case. However, it may lead to overheating of indoor spaces during the warm season, implying the need to optimize architectural design to improve comfort requirements in living spaces. 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12008/56672 Título: Extrapolación espacial de un modelo de aprendizaje automático basado en imágenes satelitales para la estimación de la irradiancia global horizontal Autor: Iturbide, Paula; Orsi, Ximena; Stern, Valeria; Ronchetti, Franco; Alonso-Suárez, Rodrigo Resumen: La disponibilidad de estimaciones precisas de irradiancia solar en zonas sin mediciones directas resulta decisiva para el diseño y planificación de sistemas de energía solar. En este trabajo se analiza la capacidad de extrapolación espacial de un modelo empírico basado en redes neuronales artificiales (RNA) e imágenes satelitales, entrenado exclusivamente con datos de la estación de Luján, Argentina. Se evalúa su desempeño al aplicarlo en las estaciones de Paraná, Concepción del Uruguay y General Villegas, comparando las estimaciones con mediciones en tierra y con modelos físicos de referencia (CAMS y NREL). Los resultados muestran que el modelo mantiene un buen desempeño incluso en estaciones alejadas, con una ligera disminución en la precisión que se mantiene dentro de parámetros aceptables. En todos los casos, el modelo RNA supera en hasta un 16 % en términos de error cuadrático medio porcentual (RMSEn) a las estimaciones de CAMS y NREL. Al analizar los errores por condición de cielo, el modelo RNA obtiene los mejores resultados en cielo nublado y parcialmente nublado. En días despejados presenta un rendimiento inferior al de los modelos físicos, lo que es esperable porque no incorpora información de aerosoles o vapor de agua, ni en forma directa como entrada ni a través de un modelo de cielo claro. Estos hallazgos permiten tener mayor confianza en un enfoque basado únicamente en imágenes satelitales y su potencial para ser aplicado en regiones con escasa infraestructura de medición.; The availability of accurate solar irradiance estimates in areas without ground measurements is crucial for the design and planning of solar energy systems. This study analyzes the spatial extrapolation capability of an empirical model based on machine learning (ANN) and satellite imagery, trained exclusively with data from the Luján station in Argentina. Its performance is evaluated by applying it to the stations of Paraná, Concepción del Uruguay, and General Villegas, comparing the estimates with ground-based measurements and with two physical reference models (CAMS and NREL). The results show that the model maintains very good performance even at distant stations, with only a slight decrease in accuracy that remains within excellent parameters. In all cases, the model outperforms CAMS and NREL estimates by up to 16% in terms of normalized root mean square error (RMSEₙ). When analyzing errors by sky condition, the ANN model performs best under cloudy and partly cloudy conditions. On clear-sky days, it performs worse than physical models, which is expected given that it does not incorporate solar geometry variables or a clear-sky model as inputs. These findings highlight the robustness of the approach based solely on satellite imagery and its potential for application in regions with limited measurement infrastructure 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12008/56667 Título: Site adaptation of satellite-based GHI and DNI using clustered features and combined local-global modeling Autor: Miranda, Diego Rodrigues de; Salazar, Germán; Alonso-Suárez, Rodrigo; Costa, Alexandre; Costa, Renan Soares Siqueira; Ing Ren, Tsang; Vilela, Olga Castro Resumen: Estimating solar irradiance over several years is essential for assessing large-scale photovoltaic and heliothermic projects. The usual way to achieve this is to adapt long-term satellite-based solar irradiance estimates for specific locations using short-term ground measurements. In this context, a novel site adaptation method is proposed for global horizontal irradiance (GHI) and direct normal irradiance (DNI) based on a two-level modelling strategy incorporating both clustered and unclustered features. These lead to global and local models, which are evaluated separately and in combination. Input variables are provided by the Copernicus Atmosphere Monitoring Service Radiation Service (CAMS) and ECMWF ERA5- Land. The method is tested at four locations – El Rosal and Salta (Argentina), Petrolina (Brazil), and Gobabeb (Namibia), addressing sites in the Southern Hemisphere with similar climates, but different latitudes and satellite viewing angles. A new non-supervised sky conditions classification method is proposed for the local models using the clear sky index, a variability index, and Kernel Density Estimation. This classification forms an integral part of the siteadaptation procedure by capturing local cloud-irradiance interactions. For GHI at 15-minute resolution, the method improves accuracy relative to CAMS at locations near the edge of the Meteosat Second Generation field of view satellites, with RMSE reductions of 26.2% (El Rosal) and 4.8% (Salta). For DNI in Petrolina and Gobabeb, the local and combination models outperform CAMS model, achieving RMSE reductions of 10.2% and 5.8%, respectively. Combination models present the more accurate results as they use the global, local, and CAMS outputs as input variables. 2026-01-01T00:00:00Z https://hdl.handle.net/20.500.12008/51835 Título: Numerical simulation of a solar wood dryer Autor: Corzo, Santiago; Villemur, Juan; Pienika, Rodolfo; Galione, Pedro Resumen: This article addresses a comprehensive analysis of the fluid dynamics in an Oxford-type solar kiln for wood drying. The work focused on a solar dryer located in Tacuarembó (Uruguay) and the approach is both experimental and numerical. A detailed analysis of the internal fluid dynamics in this type of kiln is essential to improve drying efficiency and avoid heterogeneous drying, which can lead to defects such as wood warping and reduced mechanical properties. It is well known and widely analyzed in the literature that ensuring homogeneous flow in the wood castle is crucial to avoid these problems. To address these challenges, an experimental analysis was performed to characterize the flow behavior inside the kiln. Additionally, numerical simulations were performed with OpenFOAM, incorporating a fan model based on data provided by the manufacturer. A rigorous mesh convergence study validated the numerical results with experimental measurements. The simulations revealed key flow characteristics, including stagnation zones, recirculation areas, flow diversion around the stack, and flow heterogeneity within the stack. Potential modifications to the furnace design are proposed to improve flow uniformity and minimize air diversion, leading to improved dryer performance. 2025-01-01T00:00:00Z