Public display of deposited theses

DOCTORAL DEGREE IN AEROSPACE SCIENCE AND TECHNOLOGY

• GASPARINO FERREIRA DA SILVA, LUCAS: High-performance low-dissipation algorithms for simulation of turbulent compressible flows
  
  Author: GASPARINO FERREIRA DA SILVA, LUCAS
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN AEROSPACE SCIENCE AND TECHNOLOGY
  Department: Department of Physics (FIS)
  Mode: Normal
  Deposit date: 03/04/2024
  Deposit END date: 16/04/2024
  Thesis director: LEHMKUHL BARBA, ORIOL | MIRA MARTÍNEZ, DANIEL
  Committee:
       PRESIDENT: RUBIO CALZADO, GONZALO
       SECRETARI: JOFRE CRUANYES, LLUÍS
       VOCAL: COLOMBO, ALESSANDRO
  Thesis abstract: Motivated by recent advances in computational technology aiming at exascale capabilities, which implies a need for applicationscapable of taking advantage of these new supercomputing architectures, this work will present two algorithms aimed at implementing an efficient and low-dissipation algorithm focused on LESand DNS of turbulent compressible flows.The basis for the algorithms is the Continuous Galerkin method applied to elements whose nodes and quadrature points areformed from the Gauss-Lobatto-Legendre roots, resulting in a SpectralElements Method. Throughout this work, it will be evidenced that this formulation leads to highly efficient kernels for discretizingthe convective and diffusive terms of the compressible Navier-Stokes equations, with the added benefit that the order of the scheme is coupled with the order of the shape functionpolynomials employed by the elements themselves, significantly simplifying the process of increasing the order of the scheme.To achieve a stable Total Variational Diminishing algorithm, the \acrshort{sem} scheme will be paired with an EntropyViscosity-based stabilization model and a suitable splitting of the nonlinear convective terms will be employed to eliminate aliasing issues that occur in the \acrshort{sem} formulation.This spatial discretization is then coupled with both an explicit and a semi-implicit scheme to account for the temporal nature ofthe flow equations. The explicit version of the algorithm is expected to be simple and efficient per time step, but due to its \acrshort{cfl} condition limitation, the semi-implicit version is alsoproposed to allow for better overall performance incases where the time-step becomes overly limited, such as in wall-bounded flows.Considering the focus on producing a \acrshort{cfd} application towards the exascale future, an important aspect of this work isthat both algorithms are proposed with a full \acrshort{gpu}implementation in mind: the use of accelerators is expected to be a key aspect of future supercomputing architectures, and thus itis important to design these algorithms with this in mind.Examples detailing the performance of both algorithms will be presented both in the case of a single device and when distributedarchitectures using multiple devices are employed.
  
• RADHAKRISHNAN, SARATH: NON-EQUILIBRIUM WALL MODELING IN LES OF HIGH-SPEED TRANSITIONAL FLOWS
  
  Author: RADHAKRISHNAN, SARATH
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN AEROSPACE SCIENCE AND TECHNOLOGY
  Department: Department of Physics (FIS)
  Mode: Normal
  Deposit date: 05/04/2024
  Deposit END date: 18/04/2024
  Thesis director: LEHMKUHL BARBA, ORIOL | MIRA MARTÍNEZ, DANIEL
  Committee:
       PRESIDENT: VINUESA MOLTIVA, RICARDO
       SECRETARI: RODRIGUEZ PEREZ, IVETTE MARIA
       VOCAL: ALCÁNTARA ÁVILA, FRANCISCO
  Thesis abstract: Wall-modeled large eddy simulation (WMLES) is a practical tool to perform the wall-bounded large eddy simulation (LES) with less computational cost by avoiding explicit resolution of the region near the wall. However, its use is limited in flows that have high non-equilibrium effects, like separation and/or transition. In this work, three wall modeling strategies are presented, two of them based on high-fidelity data. First, a technique is presented to improve the robustness of the state-of-the-art algebraic wall shear stress model. Second, an equilibrium-data-driven wall shear stress model is developed using the LES of the channel data. The key purpose of this is to estabilish the methodology of model development using high-fidelity data. The model is built using a machine learning technique that uses gradient-boosted regression trees (GBRT). The objective of the model is to learn the boundary layer of a turbulent channel flow so that it can be used in significantly different flows where the equilibrium assumptions are valid. The importance of selecting the appropriate data for training and the importance of choosing the input of the model are described. The model is validated a priori and a posteriori. A posteriori tests are conducted by implementing the model in a multiphysics solver and using it in the turbulent channel flow and in the flow over a wall-mounted hump. The performance of the model is compared with an algebraic wall shear stress model to understand the strengths and shortcomings of the data-based models and further improve the same. In the next step, the model is upgraded to a non-equilibrium wall model by using non-equilibrium data for the training. The high-fidelity data chosen for training include the Direct Numerical Simulation (DNS) of a double diffuser that has strong non-equilibrium flow regions and LES of a channel flow. The ultimate purpose of this model is to distinguish between equilibrium and non-equilibrium regions and to provide the appropriate wall shear stress. The ML system used for this study is also GBRT. The model is tested a priori and a posteriori. A posteriori tests are conducted on the diffuser, channel flows, flow over the hump, and junction flows. These tests showed that the model is very effective for corner flows and flows that involve relaminarization, while it performs rather less effectively in recirculation regions.
  

DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY

• BENINCA, LETIANE: Multi-objective optimization for social multifamily housing: Minimizing heating and cooling demand
  
  Author: BENINCA, LETIANE
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY
  Department: Department of Architectural Technology (TA)
  Mode: Change of supervisor
  Deposit date: 11/04/2024
  Deposit END date: 24/04/2024
  Thesis director: CRESPO SÁNCHEZ, EVA | PASSUELLO, ANA CAROLINA
  Committee:
       PRESIDENT: DE MOURA FERREIRA DANILEVICZ, ANGELA
       SECRETARI: CÓSTOLA, DANIEL
       VOCAL: KAMPOUROPOULOS, KONSTANTINOS
  Thesis abstract: The field of architecture and engineering is currently experiencing significant changes due to advances in technology and the growing role of Artificial Intelligence. This shift is largely driven by the growing urgency of promoting more efficient buildings, especially considering its substantial impact on global greenhouse gas emissions and energy usage. Consequently, it is becoming important to focus on practical design choices and utilize effective strategies to enhance energy efficiency and overall building performance. This thesis presents a comprehensive approach to optimize the shape, solar orientation, and envelope configuration of social residential buildings in a humid subtropical climate (Koppen classification: Cfa) in the southern region of Brazil. The main objective is to simultaneously minimize both heating and cooling demands, and present optimal performance design and parameter ranges to improve efficiency energy in multifamily buildings. To achieve this, the study utilizes multi-objective optimization techniques with the support of a non-dominated sorting genetic algorithm (NSGA-II). The simulations are conducted using the EnergyPlus while the optimization process is implemented through Python programming. This extensive computational effort involves a total of 480,000 simulations. The results of the optimization process demonstrate that by carefully selecting the optimal solar orientation, significant reductions in energy demand can be achieved. For instance, optimizing the solar orientation alone can lead to energy demand reductions of up to 5% for linear buildings and 11% for H buildings, when linked to the surroundings. Furthermore, when the envelope is properly addressed the energy demand between shapes achieves almost the same value. Moreover, the optimization of the building envelope configuration further enhances energy efficiency, resulting in remarkable reductions in total energy demand. In particular, linear buildings can achieve up to 60% reduction in energy demand, while H buildings reach up to 63% reduction. These findings highlight the potential benefits of considering solar orientation, surrounding shadows, and envelope design simultaneously during the early design stages of a project. The proposed three-phase optimization framework evaluates different parameter alternatives and presents a pratical guidelines to make informed decisions about the most energy-efficient configurations.
  

DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING

• SAYOLS BAIXERAS, NARCÍS: Cognitive Robot Control Strategies for Complex Surgical Environments
  
  Author: SAYOLS BAIXERAS, NARCÍS
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING
  Department: Department of Automatic Control (ESAII)
  Mode: Normal
  Deposit date: 11/04/2024
  Deposit END date: 24/04/2024
  Thesis director: CASALS GELPI, ALICIA | HERNANSANZ PRATS, ALBERTO
  Committee:
       PRESIDENT: DALL\'ALBA, DIEGO
       SECRETARI: FRIGOLA BOURLON, MANEL
       VOCAL: AVILÉS RIVERO, ANGÉLICA
  Thesis abstract: This thesis aims to contribute to the development of robotics autonomy in complex tasks based on the cognitive control paradigm. Cognition is a multidisciplinary approach aimed to provide robotic systems with intelligent and autonomous behaviour that should learn and reason about how to respond in front of complex tasks and environments.Cognition involves aspects as perception, awareness, interpretation of human actions, learning, planning, anticipating and dynamic response to changes in the working conditions and in the interaction with humans. Autonomy is intended to partially substitute and/or complement the human faculties at the level of perception, analysis and execution. Increasing the level of autonomy of robots allows focusing the humans cognitive load on high level decisions and actions, in aspects where the human factor is essential: contextualisation of information, specific expertise, medical knowledge and complex decision-making among others. Furthermore, robots improve the properties of humans in certain aspects such as precision, repeatability, absence of fatigue or response efficiency in terms of time and accuracy.This thesis addresses different key aspects of robotic autonomy: perception, planning and dynamic execution of actions and, finally, the control structures required for efficient control and their integration in robotic systems.This thesis combines a global theoretical approach supported by practical applications based on the field of robot-assisted minimally invasive surgery. This field has been chosen for two main reasons: the social impact involved in the improvement of surgery and, secondly, because this field of application is highly demanding from both, human and robotic perspective.The experimental phases have focused on various surgical robotic. First, a teleoperated platform with a single robot has been used aimed at minimally invasive fetal surgery in which a cognitive system offers a certain level of autonomy to generate trajectories in collision-free spaces, increasing patient safety and decreasing the cognitive load of surgeons in navigation and interaction tasks within the intra-uterine region. Second, a multi-robot architecture to execute auxiliary actions in a human-robot cooperative system: the main surgeon performs the surgical actions while the auxiliary robots perform, autonomously, auxiliary surgical tasks. With this configuration the experimentation focuses on minimally invasive radical prostatectomy surgery.Thus, the thesis addresses the perception of the anatomical environment, considering the limitations of data acquisition in terms of quality and quantity, as well as the absence of anatomical markers. The next topic that the thesis addresses is the dynamic planning of actions. Different application paradigms have been studied, such as direct human-robot interaction using haptic guidance, movement planning in pseudo-structured environments and, active planning and control in dynamic environments. These proposed environments respond to different surgical scenarios within minimally invasive techniques.Finally, cognitive control applied to robotic platforms is addressed. The followed approach is based on the multi-level decomposition of complex tasks (e.g. surgical procedure) defining all potential states and transitions. This decomposition translates into the use of deterministic and robust control structures that restrict falling into uncontrollable or unexpected situations that put at risk, in the application case, the patient, the surgeons or the auxiliary personnel.Control structures also consider human-robot interaction, robots coordination and cooperation, interaction with the work environment and restrictions imposed by surgery and patient safety.The integration of all these modules: perception, planning and cognitive control, demonstrates the advances achieved in cognitive robotics and their applicability towards a more autonomous robotic surgery.
  

DOCTORAL DEGREE IN CHEMICAL PROCESS ENGINEERING

• AGUILAR MORENO, MIGUEL: Liquid-Liquid membrane contactors for sustainable ammonia recovery and valorization: experimental insights, novel approaches and applications
  
  Author: AGUILAR MORENO, MIGUEL
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CHEMICAL PROCESS ENGINEERING
  Department: Department of Chemical Engineering (EQ)
  Mode: Normal
  Deposit date: 16/04/2024
  Deposit END date: 29/04/2024
  Thesis director: CORTINA PALLAS, JOSE LUIS | VALDERRAMA ANGEL, CESAR ALBERTO
  Committee:
       PRESIDENT: DOSTA PARRAS, JOAN
       SECRETARI: GIBERT AGULLO, ORIOL
       VOCAL: REZAKAZEMI, MASHALLAH
  Thesis abstract: This comprehensive research represents a significant stride in the exploration of innovative strategies aimed at enhancing ammonia recovery within diverse wastewater streams. The study is structured into distinct phases, each addressing crucial aspects of the ammonia recovery process. In the initial phase, the research focuses on augmenting membrane contactor performance, employing coagulation-flocculation (C/F) and aeration as preliminary treatments. The outcomes of this phase demonstrate substantial increases in both the mass transfer coefficient and overall efficiency ofammonia recovery, particularly notable when treating the real sidestream centrate. A pivotal finding underscores the efficacy of dosing aluminum sulphate (Al2(SO4)3) at 30 mg Al+/L in the C/F process, yielding remarkable efficiencies in the removal of chemical oxygen demand (COD), turbidity, and total suspended solids (TSS). Into the second phase, the study delves into the sustainable application of liquid-liquid membrane contactors (LLMC) for ammonia recovery. An array of experimental conditions is meticulously explored, with the results illuminating the considerable impact of replacing the acid washing liquid between steps on the overall performance of the LLMC. Additionally, the study highlights the nuanced relationship between the initial ammonia concentration and the subsequent recovery, providing valuable insights. This phase effectively showcases the potential versatility and efficiency of LLMCs in the valorization of ammonia within wastewater streams. The third and final phase introduces a novel asymmetric hollow fiber liquid-liquid membrane contactor (HF-LLMC) with distinctive selectivity for ammonia over water. The investigation entails a comprehensive examination of various operational parameters, including feed and acid flow rates, mass transfer coefficients, and acid consumption. Notably, the results affirm the high selectivity of the HF-LLMC for ammonia, coupled with minimal water transfer. This establishes the HF-LLMC as a promising technology for the recovery and concentration of ammonium in diluted urban and industrial streams. The amalgamation of these findings, approached with a global perspective, significantly contributes not only to the advancement of sustainable nutrient recovery technologies but also underscores their pragmatic feasibility for implementation within the frameworks of the circular economy and efficient resource management.
  
• MESA GÓMEZ, ADRIANA MARÍA: Analysis and modelling of natech accidents originated by strong winds
  
  Author: MESA GÓMEZ, ADRIANA MARÍA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CHEMICAL PROCESS ENGINEERING
  Department: Department of Chemical Engineering (EQ)
  Mode: Change of supervisor
  Deposit date: 11/04/2024
  Deposit END date: 24/04/2024
  Thesis director: CASAL FABREGA, JOAQUIM | MUÑOZ GIRALDO, FELIPE | SANCHEZ SILVA, EDGAR MAURICIO
  Committee:
       PRESIDENT: PLANAS CUCHI, EULALIA
       SECRETARI: PALACIOS ROSAS, ADRIANA
       VOCAL: DEMICHELA, MICAELA
  Thesis abstract: In recent decades, there has been an increase in the frequency of natural events, coinciding with the simultaneous development of industrial activities in many countries. Consequently, the frequency of Natech accidents, which are technological disasters triggered by natural hazards, has also risen. This trend has spurred researchers to explore new risk analysis methods to prevent and mitigate potential damage to populations, the environment, and industrial facilities. There is a growing awareness in the literature about the impact of natural events, particularly when they occur concurrently, cascade, or accumulate over time.This thesis proposes a research initiative to conduct a risk assessment that includes the Natech risk associated with strong winds. The primary objective is to develop a methodology for analyzing Natech risk in storage units in coastal zones that are particularly vulnerable to extreme weather events.Firstly, the thesis introduces the integration of natural events, specifically strong winds, into a quantitative Natech risk analysis methodology. This integration represents a significant advancement in assessing the potential impacts of technological accidents triggered by natural events. By incorporating strong winds as a hazard, the methodology offers a more comprehensive approach to evaluating the vulnerability of industrial facilities, especially storage tanks, to natural-technological events. This integration enables stakeholders to better understand and quantify the risks posed by Natech events involving strong winds, facilitating the implementation of targeted mitigation measures and enhancing preparedness. Ultimately, it contributes to improving the resilience of industrial facilities and surrounding communities to the risks posed by natural events.Secondly, the thesis describes the development of two models for environmental and socioeconomic risk assessment, respectively. These models provide a comprehensive framework for evaluating the potential environmental and socioeconomic impacts of Natech events, thereby enhancing the understanding of the overall risk landscape. By incorporating previously overlooked vulnerable elements, such as cultural heritage sites, sensitive environmental areas, water catchment sites, and so on, the models offer a more holistic perspective on Natech risks, ensuring that mitigation strategies can protect not only human safety and infrastructure, but also socioeconomic and environmental assets.Thirdly, the thesis outlines the development of a computational tool designed to facilitate the implementation of these models. This tool streamlines the risk assessment process, enabling stakeholders to analyze and manage Natech risks efficiently.Overall, the generation of these models and the accompanying computational tool represents a significant advancement in Natech risk management. By integrating environmental and socioeconomic considerations into the risk assessment process, these models provide a more robust foundation for decision-making and emergency preparedness, ultimately contributing to the resilience of communities and ecosystems in the face of Natech events. Finally, the methodology is applied in a case study to verify its applicability
  

DOCTORAL DEGREE IN CIVIL ENGINEERING

• GÓMEZ DUEÑAS, SANTIAGO: Unraveling Hydrological Dynamics: Climate and Human Implications in the Magdalena River Streamflow and its Interaction with Ciénaga Grande de Santa Marta Wetland
  
  Author: GÓMEZ DUEÑAS, SANTIAGO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CIVIL ENGINEERING
  Department: (DECA)
  Mode: Normal
  Deposit date: 16/04/2024
  Deposit END date: 29/04/2024
  Thesis director: BATEMAN PINZON, ALLEN
  Committee:
       PRESIDENT: SOLÉ, AURELIA
       SECRETARI: DE MEDINA IGLESIAS, VICENTE CÉSAR
       VOCAL: LA ROCCA, MICHELE
  Thesis abstract: This study offers a comprehensive understanding of the hydrological dynamics of the Magdalena River (MR) basin, located in Colombia. Multiple elements that affect streamflow were analyzed, such as climate-forcing drivers and human-induced ones, to understand the complex interactions that shape the region's hydrology.Firstly, the influence of many factors on the flow of water downstream of the Magdalena River was studied. The research identified El Niño southern oscillation (ENSO) episodes as crucial climate-forcing drivers and human-induced modifications such as reservoir evaporation. The complex nature of streamflow changes over time was highlighted by showing the variations in the average, volume, and maximum streamflow, as well as oscillations in evaporation and minimum streamflow, especially during positive ENSO episodes. These findings offer important insights into the changing hydrological regime of the MR basin, emphasizing the complex combination of elements that influence its flow patterns throughout time.Moreover, the study explored the hydrological connection between the MR and the Ciénaga Grande de Santa Marta (CGSM) wetland, revealing the interdependence of these two ecosystems. For this, the study explores the vulnerability of downstream habitats, especially wetlands, to changes in streamflow inputs by taking a broad approach that views the entire wetland as a unified unit. It identified crucial threshold ranges where the inflow from the Magdalena River to the CGSM becomes uncertain. This analysis highlights the urgent need to understand the interactions between water flow and wetland ecosystems and their significant impact. Furthermore, the present research utilizes Long Short-Term Memory (LSTM) neural network models to predict streamflow changes at the Calamar gauging station. The goal was to improve the precision of streamflow predictions by combining data from several gauging stations and reservoir evaporation records. Finally, this study can help enhance the comprehension of the intricate hydrological processes in the MR basin, revealing the interconnected effects of climate fluctuations, human actions, and ecosystem dynamics. In this context, this research sets the foundation for creating well-informed water resource management strategies in Colombia that protect wetland ecosystems' ecological health and adaptability in the face of ongoing environmental changes.
  
• NÚÑEZ CORBACHO, MARC: Aerodynamic shape optimization under uncertainties using embedded methods and adjoint techniques
  
  Author: NÚÑEZ CORBACHO, MARC
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CIVIL ENGINEERING
  Department: Barcelona School of Civil Engineering (ETSECCPB)
  Mode: Normal
  Deposit date: 09/04/2024
  Deposit END date: 22/04/2024
  Thesis director: ROSSI BERNECOLI, RICCARDO | BAIGES AZNAR, JOAN
  Committee:
       PRESIDENT: LEHMKUHL BARBA, ORIOL
       SECRETARI: MARTINEZ FRUTOS, JESUS
       VOCAL: RICCHIUTO, MARIO
  Thesis abstract: This thesis develops a framework to perform shape optimization under uncertainties for a body under the action of aerodynamic forces. The solution of the flow is performed with finite elements using the full potential equation with an embedded approach, where the object of study is defined implicitly with a level set function. The optimization problem is solved by combining different software packages to perform the solution of the flow, advance in the optimization loop and perform uncertainty quantification. The first contribution of the thesis is the development of a full embedded approach for the solution of the full potential equation. Due to the inviscid hypothesis of potential solvers, these require the definition of a gap in the computational mesh in order to generate lift, known as the wake. Based on previous works where the wake is defined implicitly with an embedded approach, this work also considers the geometry as an embedded body. Mesh refinement and numerical terms are employed to improve the definition of the geometry in the mesh and ensure the definition of the Kutta condition. The solver is validated for two and three dimensions for subsonic and transonic flows with different reference data. Another contribution of the thesis is the development of the adjoint analysis for the subsonic full potential equation with embedded geometries in two dimensions. Each coordinate of the object of study is considered a design parameter in the adjoint analysis, where the effect of the level set function is considered. The sensitivities of the objective function with respect to the design parameters are validated by comparing them to the sensitivities obtained by using a finite differences approach. A shape optimization problem where the lift coefficient is maximized with geometrical constraints is solved as an example of application of the adjoint sensitivities. The embedded shape optimization problem is extended to consider uncertainties in the inlet condition. The optimization problem is reformulated by choosing a risk measure, the Conditional Value-at-risk, which is minimized. The adjoint sensitivities are adapted for the stochastic case, considering the selected risk measure. The estimation of the risk measure is performed thanks to an external uncertainty quantification library, by applying a novel approach which uses Monte Carlo methods to estimate the Conditional Value-at-risk. The stochastic case is solved in a distributed environment, where each optimization step deploys a Monte Carlo hierarchy to estimate the objective function and its gradients.
  

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

• FERRIOL GALMÉS, MIQUEL: Network modeling using graph neural networks
  
  Author: FERRIOL GALMÉS, MIQUEL
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN COMPUTER ARCHITECTURE
  Department: (DAC)
  Mode: Normal
  Deposit date: 10/04/2024
  Deposit END date: 23/04/2024
  Thesis director: CABELLOS APARICIO, ALBERTO | BARLET ROS, PERE
  Committee:
       PRESIDENT: PESCAPÈ, ANTONIO
       SECRETARI: ARIAS VICENTE, MARTA
       VOCAL: RÉTVÁRI, GÁBOR
  Thesis abstract: Network modeling is central to the field of computer networks. Models are useful in researching new protocols and mechanisms, allowing administrators to estimate their performance before their actual deployment in production networks. Network models also help to find optimal network configurations, without the need to test them in production networks. Arguably, the most prevalent way to build these network models is through the use of discrete event simulation (DES) methodologies which provide excellent accuracy. State-of-the-art network simulators include a wide range of network, transport, and routing protocols, and are able to simulate realistic scenarios. However, this comes at a very high computational cost that depends linearly on the number of packets being simulated. As a result, they are impractical in scenarios with realistic traffic volumes or large topologies. In addition, and because they are computationally expensive, they do not work well in real-time scenarios.Another network modeling alternative is Queuing Theory (QT) where networks are represented as inter-connected queues that are evaluated analytically. While QT solves the main limitation of DES, it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks.In this context, Machine Learning (ML) has recently emerged as a practical solution to achieve data-driven models that can learn complex traffic models while being extremely accurate and fast. More specifically, Graph Neural Networks (GNNs) have emerged as an excellent tool for modeling graph-structured data showing outstanding accuracy when applied to computer networks. However, some challenges still persist:1. Queues and Scheduling Policies: Modeling queues, scheduling policies, and Quality-of-Service (QoS) mappings within GNN architectures poses another challenge, as these elements are fundamental to network behavior.2. Traffic Models: Accurately modeling realistic traffic patterns, which exhibit strong autocorrelation and heavy tails, remains a challenge for GNN-based solutions.3. Training and Generalization: ML models, including GNNs, require representative training data that covers diverse network operational scenarios. Creating such datasets from real production networks is unfeasible, necessitating controlled testbeds. The challenge lies in designing GNNs capable of accurate estimation in unseen networks, encompassing different topologies, traffic, and configurations.4. Generalization to Larger Networks: Real-world networks are often significantly larger than testbeds. Scaling GNNs to handle networks with hundreds or thousands of nodes is a pressing challenge, one that requires leveraging domain-specific network knowledge and novel architectural approaches.This dissertation represents a step forward in harnessing Graph Neural Networks (GNN models) for network modeling, by proposing a new GNN-based architecture with a focus on addressing these critical challenges while being fast and accurate.
  
• GÓMEZ SÁNCHEZ, GONZALO: Exploring genomic datasets through machine learning methods leveraging high-performance computing
  
  Author: GÓMEZ SÁNCHEZ, GONZALO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN COMPUTER ARCHITECTURE
  Department: (DAC)
  Mode: Normal
  Deposit date: 16/04/2024
  Deposit END date: 29/04/2024
  Thesis director: BERRAL GARCÍA, JOSEP LLUÍS | CARRERA PÉREZ, DAVID
  Committee:
       PRESIDENT: GARCÍA LÓPEZ, PEDRO
       SECRETARI: RUIZ RAMÍREZ, MARC
       VOCAL: CIRILLO, DAVIDE
  Thesis abstract: In recent years, the exponential increase of generated data has raised the need for implementing new methodologies to process the huge datasets being created. High-Performance Computing (HPC) brings together a set of technologies mainly based on parallel computing that help reduce the time expended analyzing these datasets. A research field where these technologies are needed is Computational Genomics. Furthermore, the complexity of the genomic datasets limits the use of basic conventional methods for the discovery of complex significant relations, introducing the need for Machine learning (ML) algorithms and robust statistical methods to better classify these variants. In the first part of the thesis, we aim to identify complex patterns of somatic genomic rearrangements in cancer samples, which are triggered by internal cellular processes and environmental factors. The problem of classification becomes particularly challenging when considering thousands of rearrangements at a time, often composed of multiple DNA breaks, increasing the difficulty in classifying and interpreting them functionally. Here we present a new statistical approach to analyze structural variants (SVs) from 2,392 tumor samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium and identify significant recurrence. The proposed methodology is able not only to identify complex patterns of SVs across different cancer types but also to prove them as not random occurrences, identifying a new class of pattern composed of three SVs that was not previously described. In the second part of the thesis, we approach another challenge of human genetics, which is the study of the relation between single nucleotide variants (SNVs) and complex diseases, such as Type 2 Diabetes, Asthma, or Alzheimer's. The study of these disease-variant associations is usually performed in a single independent manner, disregarding the possible effect derived from the interaction between genomic variants. Here, we have created a containerized framework that uses Multifactor Dimensionality Reduction (MDR) to detect combinations of variants associated with Type 2 Diabetes (T2D), called Variant Interaction Analysis (VIA). This methodology has been tested in the Northwestern University NUgene project cohort using a subset of 1,883,192 variant pairs with some degree of association with T2D and identifying a subset of 104 significant pairs, two exhibiting a potential functional relationship with T2D. The developed algorithm has been released in an open-source repository, including the containerized HPC framework, which can be used to search for significant pairwise interactions in other datasets.In both frameworks developed within the thesis, the use of large-scale supercomputing architectures has been a hard requirement to find relevant clinical indicators. To ensure open and broad access to HPC technologies, governments, and academia are pushing toward the introduction of novel computing architectures in large-scale scientific environments. This is the case of RISC-V, an emerging open standard instruction-set architecture. To evaluate such technologies, in the last two parts of the thesis, we propose the use of our VIA use case as a benchmarking, providing the first genomic application for RISC-V. With this use case, we provide a representative case for heavy ETL (Extract, Transform, Load) data processing. We developed a version of the VIA workload for RISC-V and adapted our implementation in x86-based supercomputers (e.g. Marenostrum IV at the Barcelona Supercomputing Center (BSC)) to make a fair comparison with RISC-V, since some technologies are not available there. With this benchmark, we have been able to indicate the challenges and opportunities for the next RISC-V developments and designs to come, from a first comparison between x86 and RISC-V architectures on genomic workload executions over real hardware implementations.
  

DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING

• JATIVA GUZMAN, ANDRES: APLICABILIDAD DE LA CENIZA VOLCÁNICA DE BAJA ACTIVIDAD COMO NUEVO RECURSO PARA MATERIALES CEMENTICIOS SOSTENIBLES.
  
  Author: JATIVA GUZMAN, ANDRES
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING
  Department: (DECA)
  Mode: Normal
  Deposit date: 03/04/2024
  Deposit END date: 16/04/2024
  Thesis director: ETXEBERRIA LARRAÑAGA, MIREN
  Committee:
       PRESIDENT: MAURY RAMIREZ, ANIBAL CESAR
       SECRETARI: CASANOVA HORMAECHEA, IGNACIO
       VOCAL: GIRÓ PALOMA, JESSICA
  Thesis abstract: Volcanic ash (VA), abundantly available in various regions globally, serves as an effective supplementary cementitious material (SCM) for partially substituting Portland cement (OPC). However, its inherently low reactivity presents a challenge for its broader utilization. This challenge can be overcome by enhancing VA's reactivity via several approaches: 1) employing mechanical and thermal treatments; 2) adding alkali activators; 3) using corrective additives to balance VA's chemical shortcomings; and 4) applying appropriate curing methods to stimulate pozzolanic reactions.According to ASTM C618 standards, VA falls into the Class N category. The study explored various activation strategies, including VA calcination (CVA) at temperatures ranging from 500 to 900 °C, alkali activation using Na2SiO3 (NSi), CaCl2 (CaCl), Na2SO4 (NS), and Na2CO3 (NC) at 1 to 4% dosages (relative to binder weight), and the strategic inclusion of SCMs like lime (L), fly ash (FA), and slag (EC) in amounts of 10, 20, and 30% (relative to VA weight). The curing process's influence was examined under different conditions: moist and heated environments (40 and 70°C for 3 days). The best mortar mixes underwent evaluations for compressive strength at intervals of 7, 28, and 90 days, alongside assessments of physical characteristics (e.g., porosity, water absorption, density) and microstructural properties. The mortars' durability was further gauged through shrinkage and acid resistance tests (against HCl, H2SO4, HNO3).For mortars comprising 35% VA (VA35) and subjected to moist curing, calcining VA at 700 °C coupled with a 20% lime addition resulted in achieving mortars boasting a peak strength of 49 MPa at 28 days, alongside a 9% reduction in water absorption compared to mortars with unmodified VA (VA35). Similarly, employing alkali activators, particularly NSi and CaCl at 1% and 2% dosages respectively, led to mortars demonstrating superior mechanical and physical properties.In mortars with a 50% VA content, the optimal alkali activator dosages were identified as 2% for NSi and 1% for CaCl. The addition of 20% FA and 10% EC emerged as the most effective corrective additives. Thermal curing (70°C for 3 days) significantly boosted early strength gains, curtailed mortar shrinkage, and enhanced resistance to H2SO4, especially notable in mortars prepared with CVA and 1% CaCl. Notwithstanding, at the 90-day mark, moist chamber curing was found to facilitate greater strength increases. A specific mix utilizing mixed activation (1% CaCl with CVA and 10% EC) notably outperformed, achieving 56 MPa, which is a 32% improvement over mortars with untreated VA (VA50). The presence of hydrated phases (C-S-H/C-S-A-H) and minerals such as portlandite, strätlingite, kuzelite, and Friedel's salt attested to the mortars' commendable performance.For mortars containing 75% VA, the best results were achieved with 2% NSi and 1% CaCl as activators, and the addition of 10% FA and 10% EC as correctives additives. Under moist curing, a mixed-activated mortar (1% CaCl-CVA-10%EC) exhibited the highest compressive strength at 90 days, reaching 44 MPa¿a 29% increase over mortars with untreated VA (VA75). Thermal curing expedited early strength development, minimized shrinkage, and bolstered resistance to H2SO4, along with improving porosity and water absorption rates, with the exception of CaCl-containing mortars. Notably, the VA75 mix showed limited portlandite formation and an absence of strätlingite.This investigation confirms the feasibility of achieving satisfactory compressive strengths in mortars with high VA content. Furthermore, by leveraging the studied activation and curing techniques, it's possible to tailor the mortar mix for specific applications, optimizing for properties such as minimal shrinkage, reduced water absorption, enhanced early-age strength, or heightened resistance to particular acid exposures.
  
• LIPA CUSI, LEONEL: Metodología numérica automatizada para la evaluación de la respuesta dinámica de construcciones prehispánicas de piedra de junta seca en el Perú.
  
  Author: LIPA CUSI, LEONEL
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING
  Department: (DECA)
  Mode: Change of supervisor
  Deposit date: 03/04/2024
  Deposit END date: 16/04/2024
  Thesis director: PELA, LUCA | TARQUE, SABINO NICOLA
  Committee:
       PRESIDENT: GOICOLEA RUIGÓMEZ, JOSÉ MARÍA
       SECRETARI: ROCA FABREGAT, PEDRO
       VOCAL NO PRESENCIAL: SALOUSTROS, SAVVAS
       VOCAL NO PRESENCIAL: SANTA CRUZ HIDALGO, SANDRA CECILIA
       VOCAL NO PRESENCIAL: SANDOVAL MANDUJANO, CRISTIAN
  Thesis abstract: The study and the conservation of stone heritage is a global concern, mainly when these constructions are in seismic zones. Due to its great cultural and historical diversity, Peru has many stone constructions in different archaeological sites, covering different construction typologies. Unfortunately, many of these constructions have not yet been structurally evaluated, so their structural behaviour is unknown. In addition, there is no classification of the stone structural typologies (taxonomy), so the different characteristics of existing constructions are unknown. One way to study the non-linear dynamic behaviour of these stone structures is to use a rigorous -but fast- numerical methodology to adequately reproduce the different failure mechanisms based on the dynamics of rigid bodies within the finite element method.Then, this work presents a taxonomic classification of prehispanic stone constructions in Peru, derived from a field study, as the first contribution. Based on this taxonomy, several archaeological sites in Puno and Cusco were classified, and the most common typologies of these regions were identified. The research also proposes novel algorithms developed in Python to obtain the geometric model of dry-joint stone structures using images taken by a camera, a mobile phone, or an existing photograph (including identification of stones and joints, named image segmentation). These routines allow the creation of a 3D model of each block (stone), assembling them, and exporting them to a finite element program for further evaluation.Regarding developing a numerical methodology, the dynamic of rigid bodies within the finite element method is proposed here. Each stone block is considered a rigid body interconnected with other blocks through nonlinear interfaces. This methodology was validated using Abaqus, based on the results of experimental tests developed in this thesis. The experimental campaign was carried out on three walls built with concrete blocks, simulating the geometry of the Inca structures. The walls were built on a tilting table and tested by rotating them out of the plane of the wall. Then, numerical models of the tests were developed by considering each stone as a rigid body and calibrating the contact properties to simulate the experimental behaviour correctly. The numerical results in weight, collapse angle, relative displacements at different points of the structure and collapse mechanisms were very similar to those obtained in the experimental campaign.As a case study, a section of an Inca stone wall from Sacsayhuaman, Cusco, was numerically evaluated using various seismic records. The complete geometric model of the stone wall was automatically obtained using the Python routines. Furthermore, discrete element particles represented the soil behind the wall. The properties of the numerical model were obtained from the experimental campaign, and the predominant frequencies of the structure were obtained using the vibration approach. As a result, the structure can adequately support these seismic records scaled up to a peak acceleration of 0.1 g. However, it suffers significant residual displacements for scaled records greater than 0.2 g.The proposed numerical methodology allows the rigorous evaluation of dry-jointed stone structures, knowing if the structure should be intervened to ensure its functionality. Therefore, it is expected that the results of this research will be used to study other stone constructions, opening possibilities for improving the methodology for different structural configurations.
  

DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING

• BISCARO, CATERINA: 3D FEM meso-level analysis of sulphate attack in concrete: new results and developments using parallel HP computing
  
  Author: BISCARO, CATERINA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING
  Department: (DECA)
  Mode: Change of supervisor
  Deposit date: 16/04/2024
  Deposit END date: 29/04/2024
  Thesis director: CAROL VILARASAU, IGNACIO | XOTTA, GIOVANNA
  Committee:
       PRESIDENT: SALOMONI, VALENTINA
       SECRETARI: PRAT CATALAN, PERE
       VOCAL: LIAUDAT, JOAQUÍN
       VOCAL: MAROTTI DE SCIARRA, FRANCESCO
       VOCAL: CIANCIO, DANIELA
  Thesis abstract: When concrete is subject to an environment characterised by a high humidity index and rich in sulphate ions, a concrete degradation process may be initiated due to External Sulphate Attack (ESA). The sulphate penetrating into the concrete activates a series of chemical reactions that lead to the formation of secondary ettringite, which may cause non-uniform volumetric expansions that may in turn generate cracking and ultimately culminate in the disintegration of the sample. Because the cracking may in turn facilitate sulphate penetration, ESA may be considered a coupled chemical-mechanical problem. In this study, the numerical analysis of ESA is conducted using the Finite Element Method by considering the specimen at the meso-level composed of larger aggregates embedded in a mortar matrix. Standard continuum finite elements are used to discretise the aggregates and the mortar. Zero-thickness interface elements are inserted along all the aggregate-mortar and selected mortar-mortar contacts to represent potential cracks. The diffusion-reaction of sulphate ions (chemical problem) is formulated following Tixier and Mobasher (2003) and Idiart et al. (2011b). Regarding the mechanical problem, the continuum elements are considered linear elastic, while the interface elements behave according to an elasto-plastic law incorporating concepts of fracture mechanics which was initially developed by Carol et al. (1997) and later extended for 3D analysis by Caballero et al. (2006).The first part of this thesis deals with the verification and use of DRAC5, a completely parallelised version of the in-house code developed within the materials mechanics group (MECMAT) of the Universitat Politècnica de Catalunya (UPC) which now incorporates MPI and PETSC libraries as well as HDF5 i/o files. This new version of the code, which is used to solve both the mechanical problem and the chemical problem through a staggered scheme, has allowed the analysis of new and more challenging 3D studies, producing realistic results that reflect the 'onion peeling' cracking pattern, similar to what has been observed in the laboratory and in previously studied 2D cases (Idiart, 2009). The second part of the thesis, deals with the development of new numerical solving techniques applicable to this type of mesh. In particular, a solution technique based on substructuring and the Schur complement is applied to the analysis of specimens comprising elements of the continuum exhibiting linear elastic behaviour and interface elements characterised by non-linear (elasto-plastic) behaviour. This new technique, which reduces substantially the number of degrees of freedom that need to be considered during the iterative process, has been preliminarily implemented in DRAC4, a simpler series version of the code, and is tested showing great advantages in terms of solution time for a range of 2D application examples. The development of a new formulation using rigid-plastic interfaces is also initiated. This formulation uses relative degrees of freedom at each pair of interface nodes, and leads to the resolution of a Linear Complementarity Problem. This development allows a further reduction in the degrees of freedom of the problem by only considering the nodes of the interface elements involved in the fracture process.
  

DOCTORAL DEGREE IN PHOTONICS

• DÍEZ MÉRIDA, JAIME: Probing Magic-Angle Twisted Bilayer Graphene with Gate Defined homo-Junctions
  
  Author: DÍEZ MÉRIDA, JAIME
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 09/04/2024
  Deposit END date: 22/04/2024
  Thesis director: EFETOV, DMITRI | LEWENSTEIN, MACIEJ
  Committee:
       PRESIDENT: WEITZ, THOMAS
       SECRETARI: RUBIO VERDÚ, CARMEN
       VOCAL: RIBEIRO PALAU, REBECA LISSETTE
  Thesis abstract: In 2018, following a theoretical prediction from 2011, it was found that stacking two layers of graphene with a relative twist angle of 1.1° between them leads to multiple new properties. At this so-called magic angle, the electronic band structure of the material reconstructs, creating a narrow flat band at the Fermi level. The formation of a flat band enhances electron-electron interactions, resulting in the emergence of states of matter not present in the original graphene layers, including correlated insulators, superconductivity, ferromagnetism and non-trivial topological states. The understanding of the origin of these correlated states could help unravel the physics of highly correlated flat band systems which could potentially provide key technological developments. The main objective of this thesis is to study magic-angle twisted bilayer graphene (MATBG) by creating monolithic gate-defined Josephson junctions. By exploiting the rich phase space of the material, we can create a Josephson junction by independently tuning the superconductor and the weak link state. Studying the Josephson effect is a first step towards understanding fundamental properties of a superconductor, such as its order parameter. First, we have optimized the fabrication of these gate-defined junctions made of all van der Waals materials. We have made double-graphite-gated hBN encapsulated MATBG devices where the top gate is split into two parts via nanolithography techniques. This configuration allows to independently control the three regions of the Josephson junction (superconductor, weak-link and superconductor). Then, we have studied the gate-defined Josephson junctions via low-temperature transport measurements. After demonstrating the Josephson effect in the fabricated devices, we focus on the behavior of one of these junctions in great detail. In particular, we have observed an unconventional behavior when the weak link of the junction is set close to the correlated insulator at half-filling of the hole-side flatband. We have observed a phase shifted Fraunhofer pattern with a pronounced magnetic hysteresis, characteristic of magnetic Josephson junctions. To understand the origin of the signals, we have performed a critical current distribution Fourier analysis as well as a tight binding calculation of a MATBG Josephson junction. Our theoretical calculations with a valley polarized state as the weak link can explain the key signatures observed in the experiment. Lastly, the combination of magnetization and its current-induced magnetization switching has allowed us to realize a programmable zero-field superconducting diode.Finally, we have shown the flexibility of these devices by studying a MATBG p-n junction under light illumination. We have studied the relaxation dynamics of hot electrons using time and frequency-resolved photovoltage measurements. The measurements have revealed an ultrafast cooling in MATBG compared to Bernal-bilayer from room temperature down to 5 K. The enhanced cooling in MATBG can be explained by the presence of the moiré pattern and corresponding mini-Brillouin zone. In summary, we have demonstrated that by integrating various MATBG states within a single device, we can gain a deeper insight into the system's properties and can engineer innovative, complex hybrid structures, such as magnetic Josephson junctions and superconducting diodes.
  

DOCTORAL DEGREE IN POLYMERS AND BIOPOLYMERS

• FERRERES CABANES, GUILLEM: Hybrid metal-organic nanoparticles for antimicrobial applications
  
  Author: FERRERES CABANES, GUILLEM
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN POLYMERS AND BIOPOLYMERS
  Department: Department of Chemical Engineering (EQ)
  Mode: Normal
  Deposit date: 03/04/2024
  Deposit END date: 16/04/2024
  Thesis director: TZANOV, TZANKO | TORRENT BURGUES, JUAN
  Committee:
       PRESIDENT: PASHKULEVA, IVA HRISTOVA
       SECRETARI: GARRIGA SOLE, PERE
       VOCAL: VASSILEVA, ELENA
  Thesis abstract: Antimicrobial resistance (AMR) is a global health concern, which leads to increased morbidity and mortality, huge economic burden to the healthcare systems and potentially untreatable infections. Due to the inappropriate use of antibiotics, the natural adaptation has been accelerated and bacteria have developed multiple ways to degrade, alter, or expel drug molecules. Besides these resistance mechanisms, bacteria can adhere to surfaces and grow as biofilms ¿ organised assemblies of surface-bound cells, enclosed in a self-produced extracellular polymer matrix (EPM). The EPM holds the pathogens together, enables adhesion to surfaces, and enhances the tolerance to host immune responses and antibiotics compared to free-floating cells. Metal nanoparticles (NPs) have been suggested as a potential solution to fight resistant bacteria due to their strong antimicrobial activity and versatile mechanisms of action. However, inherent toxicity towards mammalian cells and large variation of physical properties are challenges that preclude the clinical application of such materials. In this thesis, metal NPs have been combined with different biomolecules for enhanced biocompatibility, increased antimicrobial efficacy, and enabling new functionalities to mitigate AMR.The first part of the thesis describes the formation of Ag NPs using bioactive macromolecules to produce multifunctional nanostructures. First, the matrix-degrading enzyme (MDE) ¿-amylase was used to reduce Ag(I), yielding NPs with antimicrobial and biofilm-degrading activity towards both gram-positive and gram-negative bacteria. Then, chitosan-Ag NPs were decorated with the quorum-quenching enzyme (QQE) acylase I, which combination was able to kill Pseudomonas aeruginosa, hinder biofilm formation, and inhibit bacterial quorum sensing (QS) based on acyl homoserine lactones (AHLs). Finally, adipic acid dihydrazide (ADH) was grafted on hyaluronic acid (HA) and used to form Ag NPs. The modified polymer (HA-ADH) played a crucial role in the interaction of the NPs with bacterial membranes, assessed using Langmuir isotherms, and reduced the toxicity of Ag towards human cells. In the second part of the thesis, HA-ADH and epigallocatechin gallate (EGCG) were used to produce nanostructured complexes with a scarcely studied antimicrobial Co(II). On one hand, Co(II) formed a complex with the biopolymer, which complex was transformed to antimicrobial nanogels (NGs) using an ultrasonic approach. On the other hand, incubation of EGCG with Co(II) yielded nanostructured metal-phenolic networks (MPN). These cobalt-containing NPs were active towards both gram-positive and gram-negative bacteria, and were able to inhibit biofilm formation due to the capacity of ECGC to disrupt QS. The last chapter of the thesis validates the use of the novel nanomaterials for antimicrobial functionalisation of medical devices. Coating of contact lenses with NGs hindered bacterial colonisation and unspecific absorption of proteins without affecting the optical properties and comfort of the material. Inclusion of MPN NPs in thiolated hyaluronic acid (THA) hydrogels endowed these materials with properties promoting efficient chronic wound treatment. The antibiotic-free hydrogels were able to control the main factors of wound chronicity by inhibiting the activity of deleterious wound enzymes, scavenging reactive oxidative species, and demonstrating pronounced antimicrobial activity, resulting in similar to commercial products wound management efficacy confirmed in vivo.
  

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

• MAJORAL RAMONEDA, MARC: A Flexible System-on-Chip FPGA Architecture for Prototyping Experimental GNSS Receivers
  
  Author: MAJORAL RAMONEDA, MARC
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
  Department: Department of Signal Theory and Communications (TSC)
  Mode: Normal
  Deposit date: 03/04/2024
  Deposit END date: 16/04/2024
  Thesis director: FERNANDEZ PRADES, CARLOS | ARRIBAS LÁZARO, JAVIER
  Committee:
       PRESIDENT: CAPARRA, GIANLUCA
       SECRETARI: BARTZOUDIS, NIKOLAOS
       VOCAL: FONT BACH, JOSEP ORIOL
       VOCAL NO PRESENCIAL: VILÀ VALLS, JORDI
  Thesis abstract: The rapid evolution in satellite navigation technology (GNSS) requires advanced prototyping tools for exploring new signals and developing innovative systems. Prototyping is essential in the design and development process, as it allows researchers to test and refine their ideas before implementing them on a large scale.Prototyping using commercial GNSS receivers poses several challenges. Currently, these receivers are primarily based on application-specific integrated circuits (ASICs), which are characterized by low power consumption, compact dimensions, and low cost, but offer limited flexibility. Although some commercial devices incorporate software-defined radio (SDR) techniques, they often contain proprietary code that restricts reconfiguration through an application programming interface (API) established by the manufacturer.GNSS receivers based on free and open-source software have become very valuable resources in the field of research and development, especially in satellite navigation. These receivers are highly valued for their adaptability and flexibility, allowing researchers to tailor the software to specific experimental needs or develop new signal processing algorithms. However, software-defined receivers tend to be less energy-efficient compared to hardware-based receivers, as they operate on general-purpose processors, which are not optimized for low power consumption.This thesis focuses on the design and development of a low-cost architecture for prototyping experimental GNSS receivers, based on System-on-Chip Field Programmable Gate Arrays (SoC FPGAs). This architecture overcomes the limitations of commercial GNSS receivers in terms of adaptability, flexibility, and reprogramming capacity, and offers improved energy efficiency compared to software-based receivers that rely on general-purpose processors. The strategy consists of combining the versatility of software-defined radio with the intensive parallelism and optimized energy consumption of programmable logic devices, providing the best of both worlds. This fusion allows the development of compact, portable GNSS receivers, thus facilitating the prototyping of embedded devices suitable for field testing. In addition, the GNSS processing core is based on a free and open-source software implementation, which provides detailed access to the signal processing chain and allows unrestricted exploration and modification of the algorithms used.This thesis also presents a design methodology for the development of new prototypes and new GNSS signal processing algorithms based on the proposed SoC FPGA architecture. This methodology places special emphasis on code reuse, a key aspect for reducing development costs and time.The practical applications of this architecture have been demonstrated through three prototypes: a GNSS receiver for low Earth orbit (LEO), a GNSS signal repeater, and a high-sensitivity GNSS receiver.The innovative approach presented in this thesis facilitates the development of experimental prototypes of flexible and portable GNSS receivers and signal generators, suitable for both laboratory experiments and field testing.
  

DOCTORAL DEGREE IN SUSTAINABILITY

• CAICEDO MAFLA, MARÍA ANGÉLICA: Design of bike networks adaptive to heterogeneous demands and the needs of social groups. Case study: Bike mobility networks for the cities of Quito and Guayaquil, Ecuador.
  
  Author: CAICEDO MAFLA, MARÍA ANGÉLICA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SUSTAINABILITY
  Department: University Research Institute for Sustainability Science and Technology (IS.UPC)
  Mode: Normal
  Deposit date: 11/04/2024
  Deposit END date: 24/04/2024
  Thesis director: ESTRADA ROMEU, MIGUEL ANGEL | MAYORGA CÁRDENAS, MIGUEL YURY
  Committee:
       PRESIDENT: DE OÑA, ROCÍO
       SECRETARI: MARTÍNEZ DÍAZ, MARGARITA
       VOCAL: MOURA BERODIA, JOSE LUIS
  Thesis abstract: Bike¿s recognition as a vital form of urban transportation underscores its capacity to enhance mobility, improve quality of life, and address several urban challenges such as air pollution, traffic congestion, and greenhouse gas emissions. This thesis explores bike¿s potential, emphasizing the importance of developing infrastructure, involving the community in mobility planning, and promoting policies to maximize its benefits.The study employs a dual-pronged approach to investigate the complexities of integrating cycling into urban transportation systems. It applies a sociological perspective to identify and assess barriers to bike use and their relationship with urban characteristics. Using ordered probit models, factors such as road insecurity, linked to the lack of adequate bike infrastructure, and topography, are highlighted. Concurrently, an engineering perspective guides the design of cycling networks to cater to varied demand, user types (differentiated by bike ownership and vehicle type), and topographies, reflecting real-world conditions. The optimal bike network results from minimizing the general system costs, including both agency and user costs. For flat terrains, continuous approximation techniques optimize network efficiency and accessibility, considering the heterogeneous demand and various user types, based on bike ownership and travel chains. In contrast, for cities with varied topographies, discrete approaches incorporate topographical elements into the model. Network performance and structure are evaluated based on two route selection criteria based on vehicle type: minimizing energy for traditional bike users and minimizing time for e-bike users. This engineering perspective aims to develop cycling networks that are practical and responsive to the diverse needs of urban dwellers.The methods are empirically validated through case studies in Quito and Guayaquil, Ecuador, showcasing their efficacy in developing adaptive bike networks tailored to diverse urban contexts, thereby significantly enhancing bike mobility across varied settings.The study's findings indicate that the network's layout, including lane spacing and station locations, is primarily influenced by the concentration of trip origins and destinations rather than topography. However, topography does affect route selection, which in turn influences flow distribution and infrastructure utilization. Moreover, the variation in trip distribution across different user types has a minimal impact on the network's lane configuration but significantly affects the number of bike-sharing stations and fleet size. The necessity for a safety stock at each station leads to an oversized fleet, increasing agency costs. Despite being an individual mode of transport, the study highlights that bike-sharing systems benefit from economies of scale. As demand increases and becomes more concentrated, the cost per user decreases, resulting in denser lane networks and improved network efficiency. E-bikes emerge as a viable solution for overcoming topographical barriers, offering a significant advantage in areas with steep slopes. For instance, where users of traditional bikes might need to walk, thereby increasing overall journey times, e-bike users experience reduced travel times and physical exertion, making e-bikes efficient in urban contexts with varied topographies.Future research directions and policy recommendations are proposed, highlighting the importance of a holistic and adaptive approach to bike mobility planning. This includes integrating diverse weather-related variables, exploring other personal mobility vehicles (PMVs), and employing robust datasets to inform sustainable urban transport strategies.