Doctoral School

Reading date: 06/05/2026

• HOUCHMAND, LAURA JO: Integrated assessment of passive rooftop strategies and photovoltaic on building energy demand and urban heat island effects under current and future Mediterranean climates
  
  Author: HOUCHMAND, LAURA JO
  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 ENVIRONMENTAL ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 08/04/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: GASSO DOMINGO, SANTIAGO | MACARULLA MARTÍ, MARCEL
  Thesis abstract: Climate change mitigation and adaptation are pressing challenges, particular in Mediterranean cities, where rising temperatures, intensifying urban heat island (UHI) effects, increasing cooling demand, and growing water scarcity intersect. Rooftops represent a critical interface for climate-responsive building transformation, as they offer substantial potential for passive strategies, such as cool roofs and green roofs, and active renewable technologies, particularly photovoltaic (PV) systems. Despite extensive research on rooftop technologies, four key knowledge gaps remain: (1) insufficient year-round assessment of the UHI impact of roof-mounted PV systems combined with passive roofing strategies; (2) limited integrated evaluation of green roofs and PV systems considering both building energy demand and urban climate implications; (3) lack of comparative analysis of the impact of passive roofing strategies under projected future Mediterranean climate scenarios on the buildings’ energy demand; and (4) inadequate quantification of the impacts of PV integration with passive roofing strategies on building energy demand under future climate change conditions. This doctoral research addresses these gaps through a structured, simulation-based methodological framework. The work begins with a targeted literature review to establish the scientific context of passive and active rooftop strategies in Mediterranean climates and to define the research questions. Building on these gaps, dynamic building energy simulations are conducted using DesignBuilder with EnergyPlus as the calculation engine. Barcelona (Csa climate classification) serves as the case study location. Current climate conditions are represented by high-resolution Typical Meteorological Year (TMY) data (1975–2021), while future conditions are assessed using morphed TMY files based on the IEA EBC Annex 80 methodology under RCP4.5 and RCP8.5 scenarios for mid-term (2047–2060) and long-term (2087–2100) horizons.A typical Mediterranean roof as basic roof (BR) serves as the reference case and is compared with passive strategies, including a cool roof (CR), a soil roof without vegetation (SR), and extensive green roofs under different irrigation regimes (EGR, EGRmin, EGRmax), as well as their integration with rooftop PV systems. Key performance indicators include convective heat fluxes from roof and PV surfaces (UHI contribution), conductive heat fluxes through the roof and annual heating and cooling demand (building energy demand), as well as PV electricity generation.The findings of this thesis highlight trade-offs between energy efficiency, urban heat, water use, and future climate impacts, emphasizing the need for climate-sensitive, typology-specific rooftop strategies.
  

Reading date: 08/05/2026

• BAREA SANCHEZ, GUILLEM: Wall-Bounded Supercritical Fluid Turbulence: Flow Topology & Modal Decomposition
  
  Author: BAREA SANCHEZ, 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 MECHANICAL, FLUIDS AND AEROSPACE ENGINEERING
  Department: Department of Mechanical Engineering (EM)
  Mode: Article-based thesis
  Deposit date: 13/04/2026
  Reading date: 08/05/2026
  Reading time: 12:30
  Reading place: Sala polivalent de l'edifici A, campus Diagonal-Besòs Escola d'Enginyeria de Barcelona Est (EEBE)
  Thesis director: JOFRE CRUANYES, LLUÍS
  Thesis abstract: This thesis investigates high-pressure transcritical wall-bounded channel turbulence, focusing on how strong thermophysical-property variations reorganize coherent motions and multiscale flow topologies. As motivated in Chapter 1, the work analyzes Direct Numerical Simulations (DNS) of nitrogen and carbon dioxide across bulk-pressure and wall-temperature pairs that traverse the pseudo-boiling region. To ensure cross-variable and cross-case comparability in these variable-density regimes, Chapter 2 establishes the analysis toolbox and a rigorous inner-product framework utilizing Favre-weighted and compressible-energy (Chu-type) norms.The investigation begins in Chapter 3 by characterizing the multiscale flow topology using invariants of the velocity-gradient tensor. Near the hot, supercritical gas-like wall, topology distributions shift toward outer-layer–like behavior even within the viscous sublayer, marked by an elevated prevalence of vortex-sheet events. Analysis of vorticity transport links these features to baroclinic-type production driven by strong density gradients. At scales comparable to the density-gradient thickness, sheet prevalence decreases and tube-like motions become prominent, highlighting how transcritical thermodynamics reshape the structural hierarchy relative to constant-property channels.To assess energetic coherence, Chapter 4 employs Proper Orthogonal Decomposition (POD) across CO2​ cases spanning laminar to turbulent regimes. Eigenvalue spectra demonstrate that while kinetic-energy-carrying motions admit compact representations, thermodynamic fields (temperature and specific isobaric heat capacity) are characterized by stronger intermittency and localization, requiring higher-dimensional subspaces. Cross-variable analyses reveal pronounced co-organization of hydrodynamic and thermodynamic modes inside pseudo-boiling layers, necessitating multi-variable states for accurate reduced-order descriptions.In the frequency domain, Spectral Proper Orthogonal Decomposition (SPOD) is utilized in Chapter 5 to reveal a coherent-structure scaffold for transcritical turbulence. A robust low-frequency band (f^​≈0.27) persists across operating conditions, manifesting as a wall-attached, streamwise-elongated sheet. Distinct from this universal hydrodynamic feature, a higher-frequency thermal branch appears conditionally at near-critical pressures and large wall-temperature differences, exhibiting compact structures and enhanced thermodynamic activity.Finally, the implications of these findings for modeling are synthesized in Chapter 6. The evidence supports Reduced Order Model (ROM) strategies that integrate thermodynamic variables into the state vector and respect the identified spectral separation. The thesis concludes by outlining pathways for integrating operator-based resolvent analyses with data-driven modes to develop physics-consistent ROMs for high-pressure real-fluid systems.
  

• BLANCO CASARES, ANTONIO: A Numerical Framework for Solving Complex Flow Regimes with Continuous Galerkin
  
  Author: BLANCO CASARES, ANTONIO
  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 COMPUTATIONAL AND APPLIED PHYSICS
  Department: Department of Physics (FIS)
  Mode: Normal
  Deposit date: 18/02/2026
  Reading date: 08/05/2026
  Reading time: 10:15
  Reading place: Sala de Tesines, Edifici C1, Planta baixa, Aula 002, Escola de Camins, Campus Nord - UPC
  Thesis director: LEHMKUHL BARBA, ORIOL | MIRA MARTÍNEZ, DANIEL
  Thesis abstract: This thesis investigates a stable and high-order numerical formulation for solving a variety of flow problems using the continuous Galerkin method, including reacting, compressible, and incompressible flows. Reliable and accurate numerical tools for such problems are crucial in many real-world applications, ranging from aerodynamics and industrial processes to medical studies, where high-fidelity and computational efficiency are simultaneously required. Achieving stable solutions in advection-dominated problems, particularly on nonuniform meshes, remains a fundamental challenge.To address this, the thesis develops a stabilization strategy well-suited for high-order elements, such as the spectral ones, designed to effectively eliminate numerical instabilities while introducing minimal numerical dissipation to achieve a high accuracy solution. Based on recent literature, we have focused on stabilization methods based on gradient projection, which allows to detect the numerical fluctuations to effectively stabilize the system. The numerical stencil features a combination of a high- and low-order stabilization ruled by a smoothness sensor. High-order stabilization is applied in smooth regions to maintain physical profiles, while low-order stabilization is activated only near strong gradients to suppress non-physical oscillations. This methodology is formulated for a generic conservation law and tested across linear advection, scalar transport in reacting flows, compressible, and incompressible Navier–Stokes problems.The numerical results show that, for turbulent premixed combustion under the low-Mach assumption, the approach handles tabulated chemistry with the flamelet model, delivering a proper representation of the flame-front. For compressible flows, a Mach-number-based smoothness sensor has been developed, effectively handling complex flow features such as shock waves. For incompressible flows, the algorithm employs a fractional step method with well defined boundary conditions, maintaining the same stabilization principles inherent to this method. In all cases, the stabilization introduces diminishing numerical dissipation with increasing polynomial order, achieving optimal convergence under both mesh refinement and polynomial order increase.These developments contribute to practical, large-scale numerical simulations that balance computational efficiency with high-fidelity results, enabling accurate prediction of real-world physical phenomena. The proposed framework provides a robust and scalable tool for computational fluid dynamics applications, with potential impact across scientific research, and industrial modeling.
  

Reading date: 11/05/2026

• AGUILAR PLAZAOLA, JOSÉ AGUSTÍN: DATA-DRIVEN MODELLING, STATE ESTIMATION, CELL CONTROL AND MOTION PLANNING FOR PEM FUEL CELL-POWERED VEHICLES
  
  Author: AGUILAR PLAZAOLA, JOSÉ AGUSTÍN
  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 AUTOMATIC CONTROL, ROBOTICS AND VISION
  Department: Institute of Robotics and Industrial Informatics (IRI)
  Mode: Normal
  Deposit date: 05/03/2026
  Reading date: 11/05/2026
  Reading time: 17:30
  Reading place: Aula 28.8, Av. Diagonal, 647, Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB), Campus Sud, 08028 Barcelona
  Thesis director: HUSAR, ATTILA PETER | ANDRADE CETTO, JUAN
  Thesis abstract: This doctoral thesis presents novel advances in the areas of modeling, state estimation, path planning, and control to improve energy efficiency and durability of the powertrain of autonomous robots and electric vehicles driven by proton exchange membrane fuel cells. The main objective of the present work is to design and implement algorithms that, based on a thorough knowledge of the systems in question, improve the characteristics and outperform the state-of-the-art methods. Special emphasis is placed on testing the developed algorithms, as much as possible, with dynamic experimental profile dataIn the area of proton-exchange membrane (PEM) fuel cell modelling, a computationally efficient physical model is proposed. Next, a model with a structure based on neural networks, built exclusively from data, is developed and validated. This model is framed within a new paradigm of machine learning, the computation by reservoirs. Subsequently, a hybrid model is built, combining both the physical model and the data-driven model by means of a fusion algorithm based on radial basis functions. The three models are tested with a set of dynamic experimental data, and it is shown how the proposed hybrid structure outperforms each of the individual models.In the area of state estimation, a particle filter is developed with the objective of estimating internal states (or parameters) of the fuel cell, taking into account the nonlinearity of the system and the uncertainty in its model. The algorithm is capable of estimating the internal variables of a nonlinear system with non-Gaussian probabilistic distribution. The algorithm is implemented to estimate the exchange current density of a fuel cell and is tested with two sets of experimental data, outperforming two state-of-the-art estimation algorithms. The exchange current density estimation is then used to fit an auto-regressive model and predict the evolution of the stack voltage in a durability experiment.In the area of PEM fuel cell control, an architecture composed of a high-level controller is proposed, which is in charge of calculating optimal temperature values with the objective of minimizing the degradation of the catalyst layer of the PEM fuel cell and at the same time maximizing its performance. These optimum values are then sent to the local controller of the fuel cell temperature regulation system. The proposed controller is based on the model predictive control paradigm; for this, a multiobjective cost function is designed, based on state-of-the-art models of the platinum degradation process that occurs during stack operation. The controller is validated in simulation tests and shown how it can adapt the temperature according to load conditions, optimizing the performance of the catalyst layer and minimizing its degradation.In the area of path planning, a new planning algorithm is developed taking into consideration the degradation mechanisms in the catalyst caused by the cell voltage profile. The developed algorithm is an extension of the A* algorithm, including new cost and heuristic functions based on the latest degradation models available in the literature. These functions incorporate penalties related to the expected voltage profile in the routes that are more detrimental to the catalyst integrity. Simulation tests are performed with different scenarios and the performance of the developed path planner is compared with the conventional A* algorithm.In the area of energy efficiency control, a controller is developed with the objective of including energy optimization in an adaptive cruise control module. Each part of the controller is designed, including the system model, the cost function, and the constraints. A series of simulation tests are performed to compare the performance between the energy-optimized adaptive cruise controller and the conventional one.
  

• NOURMOHAMMADI, FARZANEH: Deep Learning Driven Blocking Prediction in Elastic Optical Networks using Spatio-Temporal Hybrid Neural Network
  
  Author: NOURMOHAMMADI, FARZANEH
  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: 24/03/2026
  Reading date: 11/05/2026
  Reading time: 11:00
  Reading place: UPC Campus Nord D4-012 Sala de Juntes TSC
  Thesis director: COMELLAS COLOME, JAUME
  Thesis abstract: The explosive growth of bandwidth-intensive services such as ultra-high-definition video streaming, cloud gaming, and virtual reality is pushing optical transport networks toward greater flexibility and efficiency. Elastic Optical Networks (EONs) address these demands by allocating spectrum in variable-sized frequency slots rather than fixed-grid channels. However, the dynamic setup and teardown of heterogeneous lightpaths fragment the optical spectrum, often leading to connection blocking even when sufficient total capacity exists. Accurate early prediction of blocking events enables network controllers to trigger proactive spectrum defragmentation and resource reallocation, improving service continuity and network utilization.This dissertation investigates deep learning-based approaches for blocking prediction in EONs using network state information represented as spectrum occupancy matrices. First, we introduce one-dimensional and two-dimensional Convolutional Neural Networks (1D-CNN and 2D-CNN) to automatically extract spatial fragmentation patterns from simulated EON topologies. The proposed 2D-CNN achieves a prediction accuracy of 92.17\% and outperforms conventional machine learning baselines such as Support Vector Machines and k-Nearest Neighbors. Building on this, we propose hybrid spatio-temporal architectures that integrate convolutional feature extraction with Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) units to capture both spatial and temporal evolution of spectrum fragmentation. The CNN–BiLSTM model achieves 94.1\% prediction accuracy while maintaining reasonable computational complexity, and the CNN–LSTM model offers a favorable trade-off between accuracy and training speed.By unifying spatial and temporal deep learning techniques, this work provides a comprehensive framework for early and reliable blocking prediction in dynamic EON environments. The proposed models can be integrated into elastic optical network controllers to enable timely defragmentation actions, reducing blocking probability and improving overall network efficiency. The findings advance the application of artificial intelligence to optical networking and lay the foundation for future integration with intelligent routing, spectrum assignment, and network automation strategies.