Doctoral School

Reading date: 02/06/2026

• 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
  Programme: DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 08/04/2026
  Reading date: 02/06/2026
  Reading time: 12:00
  Reading place: UPC ESEIAATTerrassa, TR5, Aula 2.0a 226Carrer de Colom, 15, 08222 Terrassa, Barcelona
  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.
  

• PÉREZ QUINTANA, MARC: Multimodal Data Fusion for Multiple Object Tracking: A Reference Perception System for ADAS and AV Functions Validation
  
  Author: PÉREZ QUINTANA, MARC
  Programme: DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION
  Department: Institute of Robotics and Industrial Informatics (IRI)
  Mode: Normal
  Deposit date: 21/04/2026
  Reading date: 02/06/2026
  Reading time: 11:00
  Reading place: Sala d'Actes de la Facultat de Matemàtiques i Estadística (FME), Campus Diagonal Sud, Carrer de Pau Gargallo, 14, 08028 Barcelona
  Thesis director: AGUDO MARTÍNEZ, ANTONIO
  Thesis abstract: In 2021, 1.19 million people died from traffic accidents, which are the leading cause of death among people aged 5 to 29 years. Most of these accidents are caused by the driver; therefore, automated vehicles present a unique opportunity to reduce fatalities and improve road safety. But the large-scale deployment and adoption of safe automated vehicles require a robust validation procedure, including open-road tests, which are significantly more challenging than tests on proving grounds or in simulation, because there is no ground-truth data on the objects around the vehicle under test. This thesis studies how to build a reference perception system to enable the validation of automated vehicles and advanced driver assistance systems on the open road. We discuss object detection and present a clustering method to detect class-agnostic objects on point clouds. These detections can be projected to the image plane to be combined with image-based detections to improve robustness and add class information. We also present a method to exploit class prototypes, defined as the mean and covariance of the features of that class, to improve the performance of a learning-based object detector on point clouds. Object detections from different modalities can be combined in the presented multiple object tracking method, which works without assuming sensor synchronization and can be adapted to include object information from any source, such as vehicle-to-vehicle communications, and includes the handling of common errors that are understudied in the literature: Misclassifications and partial bounding box detections. These object detectors and multiple object trackers can be evaluated with a novel proposed methodology to evaluate perception systems focusing on safety, which we use to evaluate the effect on object detection performance of different weather conditions and the robustness layers included in recent testing protocols. We also present a pipeline to extract scenarios in standardized formats from recorded images and point clouds, that can be directly used by a simulator. Finally, we discuss how scenario extraction, combined with other tools, can help in the validation of automated vehicles on the open road.
  

Reading date: 03/06/2026

• GUZMÁN ALBIOL, MARC: An Exploration of Constraint Systems in Verifiable Computation
  
  Author: GUZMÁN ALBIOL, MARC
  Programme: DOCTORAL DEGREE IN NETWORK ENGINEERING
  Department: Department of Network Engineering (ENTEL)
  Mode: Normal
  Deposit date: 23/04/2026
  Reading date: 03/06/2026
  Reading time: 11:00
  Reading place: sala Aula Màster del C3 (Sala C3005
  Thesis director: MUÑOZ TAPIA, JOSE LUIS
  Thesis abstract: The accelerated adoption of digital services has highlighted the need for trust-minimized computation, where parties can verify the correctness of computations without re-executing them or revealing sensitive data. Zero-knowledge proof systems, including SNARKs and STARKs, provide cryptographic guarantees of correctness, privacy, and succinct verifiability, enabling applications in scalable blockchains, privacy-preserving identity systems, and verifiable federated learning.This thesis addresses key inefficiencies in constraint-based zero-knowledge proof systems at the arithmetization layer. The research focuses on two complementary problems: optimizing binary comparisons within Rank-1 Constraint Systems (R1CS), and extending the expressiveness of STARKs through an Extended Algebraic Intermediate Representation (eAIR).The first contribution presents a weighted accumulation method for implementing strict binary comparisons in R1CS. Traditional approaches generate a large number of constraints due to the lack of native comparison and control-flow operations in the R1CS model, forcing costly bit-by-bit decompositions and creating performance bottlenecks. The proposed weighted accumulation method significantly reduces constraint overhead without compromising system security or correctness, achieving substantial efficiency improvements over thelexicographic approach.The second contribution introduces the eSTARK protocol, which extends standard STARKs by enabling the concise handling of complex constraints such as lookups, permutations, and copy constraints. These operations are difficult to encode efficiently in standard AIR. The eSTARK protocol integrates vector commitment arguments and polynomial optimizations, providing a flexible and user-friendly framework for representing a broader class of computations without introducing unnecessary arithmetization overhead.Both contributions address practical limitations of current zero-knowledge proof systems. The first focuses on reducing constraint complexity for common operations, while the second expands the expressiveness of the proof system itself. Together, they demonstrate the importance of arithmetization-level optimizations for improving the efficiency and usability of zero-knowledge proofs.
  

Reading date: 04/06/2026

• LEZECK, HENDRICK: Application of essential oils microcapsules on the fabric surface to get antibacterial properties.
  
  Author: LEZECK, HENDRICK
  Programme: DOCTORAL DEGREE IN TEXTILE AND PAPER ENGINEERING
  Department: Department of Engineering Graphics and Design (DEGD)
  Mode: Normal
  Deposit date: 28/04/2026
  Reading date: 04/06/2026
  Reading time: 15:00
  Reading place: INTEXTER Conference Room
  Thesis director: LIS ARIAS, MANUEL JOSÉ
  Thesis abstract: Essential oils (E.O.) are widely used in traditional medicine, pharmacy, food, and cosmetic applications due to their natural origin, biodegradability, and broad antimicrobial activity. Despite these advantages, their high volatility and hydrophobic nature significantly limit their direct application in textile substrates. In recent years, the increasing demand for sustainable, functional textiles has driven research to integrate bioactive compounds into fabrics while preserving their efficacy and durability.This doctoral thesis investigates the application of essential oil microcapsules onto textile substrates as a strategy to overcome the intrinsic limitations of E.O. and to impart bio-functional properties to fabrics. By employing microencapsulation techniques, this work aims to enhance the stability, retention, and controlled release of essential oils on textiles, enabling the development of sustainable materials with long-lasting bioactive performance.
  

• MASCLANS SERRAT, NÚRIA: Scientific Machine Learning in Turbulent Flows: Observability, Reconstruction & Acceleration
  
  Author: MASCLANS SERRAT, NÚRIA
  Programme: DOCTORAL DEGREE IN MECHANICAL, FLUIDS AND AEROSPACE ENGINEERING
  Department: Department of Mechanical Engineering (EM)
  Mode: Normal
  Deposit date: 06/05/2026
  Reading date: 04/06/2026
  Reading time: 11:30
  Reading place: Sala polivalent de l'edifici A (EEBE) del Campus Diagonal-Besòs.
  Thesis director: JOFRE CRUANYES, LLUÍS
  Thesis abstract: The analysis and design of engineering systems governed by wall-bounded turbulent supercritical fluid flows are fundamentally constrained by two distinct barriers: the intrinsic limitations of optical diagnostics in resolving scalar thermodynamic fields under extreme fluid regimes, and the prohibitive computational cost required to achieve fully converged flow statistics in high-fidelity simulations. This thesis addresses these challenges by developing advanced scientific machine learning (SciML) frameworks that rigorously integrate physical domain knowledge into deep learning architectures.To overcome the experimental observability gap inherent to high-pressure transcritical flows, this work first proposes a novel thermophysics-informed neural network (TINN). By embedding the real-gas equation of state directly into the network's optimization loss function as a soft constraint, while enforcing physical boundary conditions through the hard-constrained network architecture, this framework successfully reconstructs hidden thermodynamic state variables, specifically density and temperature, exclusively from available kinematic velocity data. This methodology provides a reliable, non-intrusive alternative to overcome the severe optical distortions that traditionally limit quantitative scalar measurements in supercritical fluid experiments. To address the computational burden of temporal numerical integration, the thesis introduces a paradigm shift in turbulence simulation by adapting deep reinforcement learning (DRL) to accelerate the convergence of flow statistics. Specifically, a Reynolds eigenspace perturbation (REP)-based DRL methodology is formulated. In this approach, a distributed multi-agent DRL framework acts as an active flow control system, iteratively interacting with the numerical solver to optimize the instantaneous flow trajectory. Initially established and validated on a reduced one-dimensional turbulence (ODT) model, the agents apply mathematically constrained perturbations directly to the eigenspace of the Reynolds stress tensor. This strict structural constraint ensures that all dynamic modifications to the Reynolds stress magnitude, shape, and orientation maintain rigorous physical realizability. This hybrid DRL-CFD methodology is subsequently scaled to fully resolved, three-dimensional direct numerical simulations (DNS) of turbulent channel flows. By overcoming complex software engineering barriers to achieve a low-latency, in-memory coupling between the dynamic Python-based REP-DRL framework and a massively parallelized C++ CFD solver, the implementation dynamically manipulates the instantaneous flow fields. While the CFD simulation operates within a statistically stationary state, this active flow control framework steers the system to achieve statistical convergence in a significantly reduced integration time.Collectively, this thesis demonstrates that embedding physical laws, thermodynamic equations, and structural constraints into machine learning algorithms transforms them from passive data interpolators into scalable, physically consistent frameworks capable of both recovering hidden flow physics and actively accelerating numerical simulations. The proposed methodologies establish a foundational pathway towards bridging the gap between experimental measurement limitations and computational feasibility, thereby facilitating both the fundamental study and the practical engineering design of complex turbulent flows.