Doctorado en Ciencias de la Ingeniería (DOCI)

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Browsing Doctorado en Ciencias de la Ingeniería (DOCI) by Subject "Ciencias del Medio Ambiente"

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    A novel fluid dynamic study of the gas-liquid flows in biotrickling filters through CFD simulations and digital imaging techniques
    (Universidad de los Andes, 2022) Carreño López, Felipe Antonio; Moreno Casas, Felipe; Vergara Fernández, Alberto
    Daily, tons of volatile organic compounds (VOCs) which negatively affect the environment and human health are emitted into the atmosphere from anthropogenic and natural sources. Biotrickling filtration (BTF) is becoming one of the most promising treatment technologies for odor control. Within the last decades, the treatment of pollutants have been studied, and diverse numerical models for predicting the mass transfer have been intensively developed. However, the current state of the art is mainly based on using the two-film, penetration, and surface renewal theories which do not account for local variations of the fluid velocities, physical properties, or flow regimes. To account for variations on the local physical processes, a detail description of porous media, the multiphase fluid dynamics, and the biomass film is required. This work investigates and extends a three-dimensional computational fluid dynamic (CFD) model coupled with computerized tomography (CT) with the novel incorporation of a contrast agent as a first attempt to assess the local biofilm formation inside a realistic porous structure used in biotrickling filtration of VOCs. The validation of these models was accomplished in terms of the gas and liquid phase residence time distribution (RTD), and the volumetric mass transfer coefficient. The gas phase RTD was obtained using a novel methodology based low cost MOx sensor; the liquid phase RTD was obtained from a methylene blue pulse method, while the mass transfer characterization was carried out by using the sulphite method. Finally, the column was operated for the treatment of toluene vapours and a contrast agent was added after reaching the steady state in order to obtain a 3D description of the local biofilm formation. These results were used to validate the CFD-CT models. The mean RTD and the normalized variance estimated in the simulation were 43.709 s and 0.326, respectively. Compared with the experimental results, a relative difference of 4.167% for the mean RTD and 32.515% for the normalized variance were found. The computed surface area was available for biodegradation was 0.366 m2. This work results in a validated gas RTD model, whereas for the liquid RTD and mass transfer coefficient the proposed approaches seem promising but requires additional computational resources to assess the steady state behavior. This methodology demonstrated the feasibility to obtain the local biofilm formation but additional imaging procedures are required to reconstruct the closed manifold geometry to use this image as a computational mesh.
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    Efficient uncertainty quantification and propagation in performance-based earthquake engineering
    (Universidad de los Andes, 2025-04) Birrell Arangua, Matías; Astroza Eulufí, Rodrigo
    In recent decades, the constant deterioration of existing infrastructure and the increasing exposure to natural hazards driven by geological processes and changing climate conditions have motivated the development of a new philosophical approach to structural engineering, known as performance-based engineering. Its goal is to provide a rigorous, science-based framework through a comprehensive assessment of structural risk, ultimately delivering a decision variable that is useful for practical decision-making. To this end, performance-based engineering establishes a probabilistic framework that aims to address uncertainty regarding (i) the hazards to which the structure is exposed, (ii) the actual behavior of the structure versus that predicted by the engineering model, and (iii) the damage caused when certain intensity levels are exceeded. At each of these stages, properly quantifying uncertainty and subsequently propagating it through the following stages is critical for a successful risk assessment. In this context, methodological progress has been gradual, supported by technological advances that have enabled the implementation of probabilistic methods. However, the cost of adopting a probabilistic framework has been high, especially due to the need for largescale simulation of finite element models, which requires significant computational and time investment. For this reason, developing methods that enable efficient uncertainty quantification and propagation in performance-based engineering remains an open challenge and a key area of current research. This thesis presents two approaches aimed at providing efficient methods for uncertainty quantification and propagation by supporting structural simulations with machine learning surrogate models using Gaussian processes. The first approach focuses on quantifying and decomposing parameter-induced uncertainty in structural responses under specific hazard scenarios. Its goal is to support probabilistic sampling-based analyses, including model calibration and updating, iterative performancebased design, and sensitivity analysis. The second approach focuses on the quantification, propagation, and decomposition of uncertainty in structural vulnerability assessment under a broad range of seismic events. This approach implements and discusses the performance-based engineering framework from a philosophical standpoint, although applied to a real-world case study. Available definitions of damage states in bridge components and the relationships between these and their consequences are discussed. Both approaches are developed in a fully probabilistic setting, including probabilistic seismic hazard analysis, probabilistic structural modeling, and uncertainty decomposition.