Mixing dynamics in turbulent flows with high-density ratios.
Supervisors
Miguel Pérez Encinar (UC3M) & Alberto Vela Martín (UC3M)
Abstract
This project aims to advance the understanding and prediction of turbulent mixing in flows with varying density differences, ranging from uniform mixtures to highly contrasting cases such as hydrogen-air. High-precision pseudo-spectral simulations will be implemented and optimised on GPUs, generating large datasets of turbulent flows. These datasets will enable the characterization of concentration probability density functions (PDFs) across different scenarios, providing key insights into how density contrast affects mixing. Building on this foundation, probabilistic and machine-learning models will be developed to predict the likelihood of reaching concentrations of interest, enhancing the safety and sustainability of carbon-free energy technologies.
MIXTURB – Caracterización y modelado predictivo de mezclado transitorio en flujos turbulentos. Grant Number: PID2024-161656NA-I00 Funding Entity: AGENCIA ESTATAL DE INVESTIGACION (AEI)
Funding entity: Doctorado Industrial Agencia Estatal de Investigación
PhD Thesis
Guidance, Navigation, and Control of Spinning Electrodynamic Tethers Applied to On-Orbit Services.
Supervisors
Gonzalo Sánchez Arriaga (UC3M)
Abstract
The thesis improves how satellites are guided, navigated, and controlled during in-orbit servicing using spinning electrodynamic tethers. It follows a step-by-step approach that combines analytical modeling, software development, and numerical analysis to explain the main control elements, ending with hardware-in-the-loop testing.
Industrial PhD granted by Agencia Estatal de Investigación, carried out within PERSEiSpace (DIN2024-014115-2).
AI-Enhanced Flow Sensing for Control of Unsteady Flows.
Supervisors
Andrea Ianiro (UC3M) & Stefano Discetti (UC3M)
Abstract
Efficient flow sensing and control are crucial for enhancing aerodynamic performance and facilitating the development of high-efficiency, sustainable transportation systems. However, the time-varying and strongly nonlinear characteristics of complex unsteady flows present substantial challenges to achieving these goals. Recent advances in artificial intelligence and neuromorphic computing offer new pathways to address these limitations. The research will focus on the following key aspects. First, for typical unsteady flows such as dynamic stall and gust encounters, efficient flow dimensionality reduction and feature extraction will be achieved through manifold learning and multimodal learning techniques, aiming to uncover the intrinsic physics and low-dimensional representations of unsteady aerodynamics. Besides, under control actions, investigations of actuation manifolds and multi-actuator coordination control strategies will be conducted. Finally, within an event-driven asynchronous framework, new paradigms for event-based sensors and spiking neural network architectures will be explored, paving the way for practically deployable online flow sensing and control systems.
The project addresses the challenge of increasing the lifetime and reliability of electrospray thrusters for small satellites. These propulsion systems, based on ionic liquids, are highly efficient and scalable but face limitations due to performance degradation and short lifetime, particularly due to facility effects occurring during on-ground testing.
Industrial PhD granted by Comunity of Madrid and carried out within ienai SPACE
This PhD project focuses on the experimental study and modeling of electrospray propulsion systems based on ionic liquids. Through a series of characterization campaigns on both single emitters and emitter arrays, the project will investigate key aspects such as I-V behavior, emission modes, operational stability, and beam angular distribution. The goal is to develop a deeper understanding of emission dynamics across different configurations and extract insights useful for the design and optimization of scalable thruster architectures. As a complementary tool, machine learning techniques will be employed to build predictive models trained on experimental data, enabling interpretation and generalization of system behavior. The project aims to produce applicable knowledge for the development of next-generation high-precision space propulsion platforms.
Numerical simulation of ECR plasma thruster discharges, focusing on the understanding of the driving physics and the optimization of their design and operation.
Supervisors
Mario Merino Martínez (UC3M) & Eduardo Ahedo Galilea (UC3M)
Abstract
This research investigates Electron Cyclotron Resonance Thrusters (ECRTs) through hybrid Particle-in-Cell and fluid simulations. By modeling and analyzing discharge physics with HPC, it aims to uncover key plasma mechanisms, optimize performance, and guide next-generation thruster design.
Surrogate-Based Aerodynamic Shape Optimization under Noisy Functions.
Supervisors
Rauno Cavallaro (BSC-CNS) & Joaquim R. R. A. Martins (MDOLab – University of Michigan).
Tutor: Rodrigo Castellanos García de Blas (UC3M).
Abstract
The increasing computational power of HPC systems and advances in aerodynamic solvers enable larger, higher-fidelity aerodynamic optimization studies. However, integration with geometry modification workflows and black-box analysis functions (common in distributed industrial systems) introduces numerical noise and instabilities. These effects cause objective and constraint functions to become noisy or discontinuous, significantly reducing the efficiency and reliability of gradient-based algorithms. This thesis develops machine learning-aided hybrid optimization methods that retain gradient-based efficiency while accommodating non-differentiable functions. The methodology is designed for seamless HPC integration, incorporating parallelized surrogate-assisted approximations within gradient-based frameworks while optimizing resource utilization and adapting to queuing system constraints. Key objectives include designing hybrid optimization strategies that combine gradient-based methods with surrogate models and adaptive sampling to efficiently navigate noisy design spaces; implementing scalable parallelization schemes for surrogate-assisted gradient evaluation to fully utilize HPC resources; and demonstrating the methodology on aerodynamic shape optimization problems, including high-fidelity CFD with and without gradient sensitivity information, extending to multidisciplinary optimization workflows. The work explores robust gradient reconstruction strategies in the presence of noise, using machine learning models to both approximate function values and estimate derivatives in non-smooth regions. This dual-purpose application addresses approximation challenges and gradient estimation problems inherent in noisy optimization landscapes. The methodology will be benchmarked on canonical aerodynamic problems and validated on industrially relevant configurations where solver instability poses significant challenges. This research bridges the gap between traditional gradient-based optimization efficiency and the robustness required for real-world engineering applications, providing a practical framework for complex aerodynamic optimization in noisy computational environments.
Data-Driven Model-Based Approaches for Efficient Flow Control.
Supervisors
Rodrigo Castellanos García de Blas (UC3M) & Carlos Sanmiguel Vila (INTA).
Abstract
This PhD research explores advanced flow control for aerospace applications through a model-based reinforcement learning (RL) framework. It seeks to address challenges such as high system dimensionality and limited real-time observability by developing reduced-order models that capture essential flow dynamics. These models, combined with state estimation methods under noise and partial data, enable the training of efficient and robust RL control policies.
TIFON – Tecnologías Inteligentes para la Fabricación, el diseño y las Operaciones en entornos industriales PLEC2023-010251. This project has been granted by the CDTI, with the support of the Ministry of Science and Innovation, with file number MIG-20232039 through the TransMisiones 2023 initiative, within the scope of the “Programa Estatal para Catalizar la Innovación y el Liderazgo Empresarial del Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023” program.
Analysis of the interaction of a high energy plasma thruster plume with a downstream object.
Supervisors
Jiewei Zhou Zhu (UC3M) and Mario Merino Martínez (UC3M).
Abstract
This thesis will tackle the numerical modeling and simulation of plasma thruster plumes and their interaction with immersed bodies, applied to space debris removal with the ‘ion beam shepherd’ concept.
Aerodynamics, Stability and Control of bio-inspired flapping-wing vehicles.
Supervisors
Óscar Flores Arias (UC3M)
Abstract
The objective of this thesis is to build new knowledge on the field of unsteady aerodynamics, flight dynamics, stability and control of Micro-Air Vehicles (MAVs), to close the gap between current state-of-the art MAV design and real flight capabilities of natural fliers.
Design of Smart Geometry Variation Morphing Wings for Optimized Drone Performance.
Supervisors
Rodrigo Castellanos García de Blas (UC3M) & Carlos Sanmiguel Vila (INTA)
Abstract
This PhD research advances morphing wing technology for fixed-wing drones, aiming to optimize aerodynamic performance under diverse flight conditions. By integrating experimental methods, machine learning, and advanced sensing strategies, it develops real-time adaptive solutions to enhance efficiency, adaptability, and responsiveness.
TIFON – Tecnologías Inteligentes para la Fabricación, el diseño y las Operaciones en entornos industriales PLEC2023-010251. This project has been granted by the CDTI, with the support of the Ministry of Science and Innovation, with file number MIG-20232039 through the TransMisiones 2023 initiative, within the scope of the “Programa Estatal para Catalizar la Innovación y el Liderazgo Empresarial del Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023” program.
Funding entity and Program: Instituto Nacional de Técnica Aeroespacial (INTA)
PhD Thesis
Physics-Informed Neural Operators for Aerodynamic Surrogate Modeling: Enhancing Generalization and Robustness in Aircraft Flow Prediction
Supervisors
Rodrigo Castellanos García de Blas (UC3M) & Carlos Sanmiguel Vila (INTA)
Abstract
My PhD thesis focuses on advancing Physics-Informed Machine Learning (PIML) for aerodynamic surrogate modeling. Traditional ML struggles with generalization and physical consistency in aerodynamics. By emedding physicis in ML models my work enhances accuracy, stability, and interpretability, reducing reliance on costly CFD simulations.
TIFON – Tecnologías Inteligentes para la Fabricación, el diseño y las Operaciones en entornos industriales PLEC2023-010251. This project has been granted by the CDTI, with the support of the Ministry of Science and Innovation, with file number MIG-20232039 through the TransMisiones 2023 initiative, within the scope of the “Programa Estatal para Catalizar la Innovación y el Liderazgo Empresarial del Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023” program.
Line of Sight Stabilization for Advance Satellite Pointing and Tracking Systems
Supervisors
Andrés Marcos Esteban (UC3M)
Abstract
Recent advancements in the capabilities of imaging systems and optical communications in satellites impose new requirements on avionics for new platforms. One of these requirements is to have pointing, acquisition, and tracking systems with greater stability and performance. Fine pointing requirements can be met with more complex AOCS systems, but these may not be justified due to cost and space constraints. While advanced hardware like magnetic bearings can reduce disturbances from reaction wheels, AOCS bandwidth limitations hinder compensation for disturbances from other subsystems. Alternatively, dedicated subsystems can improve pointing and reduce perturbations. Solutions include passive systems and active vibration control to minimize internal disturbances, with strategies targeting disturbance sources, structural paths, or payload interfaces. These can be combined with active pointing systems like gimbals and fast steering mirrors to meet stringent requirements, despite space and cost limitations.These systems must ensure proper performance in environments with disturbances of varied origin and nature, and their inclusion must be justified based on economic and practical feasibility criteria (especially in CubeSats). Given that this trend in requirements is expected to increase in the coming decades for astronomical missions, Earth observation, and optical-laser communications, this thesis will address the analysis and improvement of current proposed solutions to date in terms of hardware selection, system architecture, and advanced control algorithms for line-of-sight (Los) stabilization.
Plasma-wave interaction in Electrodeless Plasma Thrusters and its optimization
Supervisors
Mario Merino Martínez (UC3M)
Abstract
This PhD focuses on modeling and understanding physics of wave–plasma interactions in Electrodeless Plasma Thrusters (EPTs) using a 2D full-wave solver. Coupled with plasma simulations, it aims to optimize power deposition in EPTs and study helicon wave mode transitions in Helicon Plasma Thrusters.
ZARATHUSTRA – Revolutionizing advanced electrodeless plasma thrusters for space transportation.
Funding entity and Program: Von Karman Institute Doctoral Fellowship
PhD Thesis
Meshless CFD and Reduced Order Modeling of Cryogenic Sloshing in Conformal Tanks for Hydrogen Aviation
Supervisors
Miguel Alfonso Mendez (UC3M, von Karman Institute for Fluid Dynamics, Université Libre de Bruxelles).
Abstract
This PhD project is to explore advanced numerical simulation approaches to support the safe operation of hydrogen-powered aviation by addressing the critical phenomenon of propellant sloshing. Under the airplane acceleration, sloshing may significantly impact the vessel stability and the tank structural integrity, particularly concerning vibration-induced fatigue in thin reservoir tank shells. The cryogenic nature of liquid hydrogen adds additional complexity due to poorly understood pressure drop effects, particularly in non-canonical geometries. Although the Finite-Volume Volume of Fluid (VoF) method is widely adopted for isothermal sloshing simulations, its capability to handle heat and mass transfer processes accurately remains limited due to its diffuse interface modeling approach. Consequently, novel meshless numerical methods will be investigated to overcome these limitations.
This research is guided by several core questions: -Can meshless particle-based methods (SPH, MPS, GFDM) accurately model isothermal sloshing in complex geometries? -Can simulation results from meshless methods be used to establish effective Lagrangian modal decomposition techniques? -What potential do meshless methods hold for developing intrusive and non-intrusive Reduced Order Models (ROMs)? -Can isothermal Lagrangian CFD/ROM frameworks be effectively extended to account for non- isothermal effects? -Can Lagrangian ROMs serve as frameworks to support the design of passive and active sloshing mitigation strategies?
Funding entity and Program: PIPF – Realization on INTA
PhD Thesis
Aerodynamic Shape Optimization using Reinforcement Learning
Supervisors
Rodrigo Castellanos García de Blas (UC3M) & Esther Andrés Pérez (INTA)
Abstract
This research explores Reinforcement Learning (RL) for aerodynamic shape optimization, integrating AI with CFD to enhance airfoils, propellers and wings performance. By developing a geometry generator and RL-based optimization framework, this study enables real-time aerodynamic adaptation, improving efficiency and reducing costs.
Aerodynamic Design of Propellers for Distributed and Hybrid-Electric Propulsion
Supervisors
Andrea Cini (UC3M) and Rodrigo Castellanos (UC3M).
Abstract
This thesis focuses on optimizing propeller aerodynamics for distributed and hybrid-electric propulsion using surrogate modeling and simulations. It extends to powerplant integration, addressing turbomachinery design and optimization. The goal is an MDO framework for sustainable, efficient, low-emission engine design.
ODE4HERA (Open Digital Environment for Hybrid-Electric Regional Architecture)
Grant Number: 101140510
Funding entity: EUROPEAN COMMISSION RESEARCH EXECUTIVE AGENCY
Hardware-in-the-Loop for Active Flow Control in Aerodynamics
Supervisors
Rodrigo Castellanos García de Blas (UC3M) & Francisco Barranco (UGR)
Abstract
This PhD research focuses in hardware-in-the-loop control with active flow control, combining aerospace and CS expertise. As a software engineer at Avidyne, I develop advanced avionics systems. My work integrates real-time control, simulation, and automation to enhance aerodynamic performance and aviation safety.
The proposed PhD research aims to advance the design, modeling, and control of electrodynamic tether (EDT) systems to enable propellant‐free orbital maneuvers and in‐orbit services. The proposed research plans to develop dynamic models for spinning EDTs with different degrees of fidelity with emphasis on configurations where the tether spins outside the orbital plane, a configuration that is expected to maximize the changes in key orbital elements. To achieve these objectives, analytical analysis and advanced numerical studies will be combined. An important aspect of this work is the development of a GNC tool designed to be integrated into the BETsMA v2.0 software, contributing to its upcoming version v3.0. This tool will model complex tether dynamics, capture realistic orbital perturbations, and incorporate adaptive control algorithms to optimize the EDT spin plane orientation, current regulation, and the reel-in/reel-out capability.
The thesis will be developed in the framework of the project E.T.COMPACT, founded by the European Innovation Council through the Pathfinder program.
Funding entity and Program: AEI – Proyecto de generación de conocimiento 2022
PhD Thesis
Active flow control for aeronautical applications
Supervisors
Carlos Sanmiguel Vila (INTA) & Marco Raiola (UC3M)
Abstract
Martin’s research focuses on developing control strategies for turbulent jets in aerospace applications. By combining physics-based modeling with data-driven approaches, the goal is to regulate free and impinging jets, improving efficiency, reducing noise, and enhancing aerodynamic performance.
Grant Number: PID2022-138314NB-I00
Funding entity: AGENCIA ESTATAL DE INVESTIGACION (AEI)
Tecnologías Avanzadas de Test, Ensamblaje y Encapsulado de Circuitos Integrados Electrónicos y Fotónicos
Supervisors
Guillermo Carpintero (UC3M), Daniel Gallego (LeapWave)
Abstract
The objective of this thesis is to develop the necessary techniques and knowledge to adapt ultra-wideband interconnection technology, thereby creating a platform for chip integration and interconnection capable of operating at frequencies of at least 100 GHz. Upon completion of the project, the following will be demonstrated:
Commercial Viability: A significant portion of the project’s effort will be directed towards engaging key stakeholders within the semiconductor industry during the design phase, evaluating results (including gauging interest based on the outcomes obtained), and identifying a viable business proposal plan.
Technological Feasibility: This will be achieved through the design, fabrication, and characterization of demonstrators that experimentally validate competitive performance compared to any existing potential technology. This validation will focus on parameters such as bandwidth (with a minimum target of 100 GHz) and efficiency (with a maximum insertion loss of 3 dB per connection). Additionally, the maximum achievable channel capacity will be investigated, allowing an assessment of performance in terms of energy efficiency per transmitted bit.
Funding entity and Program: PIPF UC3M funded by Project F4CLIM and REFMAP
PhD Thesis
Aircraft Trajectory Optimization and Sustainable Aviation Fuel for Climate Impact mitigation
Supervisors
Manuel Soler (UC3M)
Abstract
Sustainability has become one of the primary objectives for the aviation industry as it faces increasing pressure to reduce its environmental footprint. With growing awareness of the broader climate impacts of aviation beyond CO₂ emissions, numerous studies have emerged that investigate the production and implications of non-CO₂ emissions, such as nitrogen oxides (NOx) and contrail formation. These studies are essential for the development of robust climate models that can accurately quantify the full environmental impact of air travel. However, despite progress in understanding these emissions, there remains a gap in applying these models to evaluate the economic and operational impacts on key industry players, particularly airlines. The aim of this research is to bridge that gap by utilizing advanced climate models to assess the economic and operational effects of climate-conscious decision-making on airlines. Specifically, this study seeks to develop a climate cost function, integrating environmental impact alongside traditional airline priorities such as operational cost, fuel consumption, and flight time. Novel techniques bridging optimal control and artificial intelligence will be used along with state of the art climate models.
Airframe Design and Manufacturing enhanced by Artificial Intelligence.
Supervisors
Andrea Cini (UC3M)
Abstract
This research focuses on developing an innovative multi-fidelity design framework to optimize the structural design of airframes for the next generation of environmentally friendly and quiet aircraft. The project leverages artificial intelligence (AI) and machine learning (ML) to enhance the accuracy of structural sizing tools, which are crucial for optimizing the internal structure and mass distribution of airframes. Various fidelity levels and optimization strategies will be explored to strike a balance between precision and computational efficiency. The research also aims to utilize advanced composite materials and multifunctional structures to minimize structural weight. Incorporating high-fidelity GFEM will further refine the design by accounting for detailed structural elements like assembly techniques and joints. The project ultimately seeks to contribute to sustainable aviation by integrating advanced materials and AI-driven optimizations into airframe design.
TIFON – Tecnologías Inteligentes para la Fabricación, el diseño y las Operaciones en entornos industriales PLEC2023-010251. This project has been granted by the CDTI, with the support of the Ministry of Science and Innovation, with file number MIG-20232039 through the TransMisiones 2023 initiative, within the scope of the “Programa Estatal para Catalizar la Innovación y el Liderazgo Empresarial del Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023” program.
Multi-Fidelity Surrogate Models for High Dimensional Aerodynamic Data
Supervisors
Rodrigo Castellanos García de Blas (UC3M) and Esther Andrés Pérez (INTA)
Abstract
This thesis aims to develop advanced multifidelity models for precise aerodynamic prediction in transonic regimes. By strategically integrating high-fidelity data, whether from experimental measurements or high-resolution simulations, with low-fidelity CFD results, this research seeks to produce surrogate models that significantly enhance prediction accuracy, empowering more refined aircraft design and performance evaluation.
Funding entity and Program: Von Karman Institute for Fluid Dynamics
PhD Thesis
Digital Twinning and Control for the Thermal Management of Cryogenic Tanks
Supervisors
Miguel Alfonso Mendez (UC3M) and Associate Professor at the von Karman Institute for Fluid Dynamics
Abstract
Liquid cryogenic propellants, such as liquid hydrogen (LH2), liquid oxygen (LOx), and liquid methane (LCH4), are at the forefront of space exploration and stand as critical prospects in the industrial transition toward sustainable solutions in the hard-to-abate sectors (e.g., aviation, maritime, and heavy-duty transportation), as they offer the potential to reduce greenhouse gas emissions. Nevertheless, the cryogenic nature of such fuels constitutes an engineering challenge in light of efficient storage. Their low saturation temperature results in a significant temperature difference from the environment, leading to irreversible heat leakage through the storage tanks. Consequently, the heat leakage partially evaporates the cryogen, producing significant boil-off gas, leading to pressure buildup, thermal stratification, abnormal propellant conditions for engine usage, and fuel loss. Critically, considering how thermally sensitive these fuels are, it is unmistakable that the pathway toward the future passes through their efficient thermal management. Our current research framework focuses on characterizing and optimizing the thermal management of cryogenic fuel through novel active control strategies. The overall objective is to maintain the system at peak performance (e.g., homogeneous liquid thermal field or nominal pressure level) under static and dynamic (sloshing) operating conditions. Throughout this project, we will develop distinct laboratory-scale prototypes that closely replicate real-world engineering scenarios in cryogenic fluid management for space and aviation applications, ensuring a thorough approach. To achieve this, we will employ surrogate cryogenic and non-cryogenic fluids and implement a digital twinning and control framework, leveraging an online learning environment to enhance controllability and responsiveness. The long-term vision is to deploy the developed and validated reinforcement twinning framework on a large-scale engineering system, aiming for potential zero-boil-off opportunities.
RE-TWIST (REinforcement TWInning SysTems) project
HASTA (Hydrogen Aircraft Sloshing Tank Advancement) project
The work of this thesis focuses on the study of plasma discharge in high-power Hall effect thrusters (HETs), ranging from 20 to 100 kW. In particular, the aim is to address the design and analysis of multi-channel prototypes, a non-conventional configuration that is currently in the experimental phase and is of great interest for future missions due to its versatility in operating over a wide power range and its optimal power-to-weight ratio. A simulation code will be developed, implementing a hybrid plasma transport model that will use a fluid model for the electron population and a kinetic particle model for the heavy species (ions and neutrals). The starting point will be HYPHEN, an already existing and well-established code, although limited to the study of conventional configurations mainly due to the use of a magnetic grid in the electron fluid model. This unstructured grid, although it limits the numerical diffusion of anisotropic electrons, requires an ad-hoc process for its generation and complicates the analysis of complex and larger geometries, which demand greater control over the number and quality of the grid cells to optimize computation times and limit interpolation errors. Therefore, a structured cylindrical grid will be implemented, possible numerical issues will be studied, and algorithms will be proposed to solve them. Once the code is developed, several studies will be carried out. First, results of the discharge with the magnetic and cylindrical grids will be compared. This will allow, on the one hand, the evaluation of numerical errors associated with interpolation and the reconstruction of boundary variables, as well as the comparison of computation times, which will reveal possible algorithmic improvements. On the other hand, the comparison will also allow for the evaluation of numerical diffusion errors in the treatment of magnetized electrons. Second, starting from virtual prototypes designed based on literature references, key aspects of the plasma discharge physics and the operation of these prototypes will be studied, such as the effect of magnetic topology, plasma interaction between channels, operation with alternate channels, cathode coupling with multiple channels, and the issue of current neutralization in the plume. The lifetime (thermal loads and erosion) and performance (losses and efficiencies) will also be analyzed over a wide operating range with various propellants.
Project HEEP Grant number: PID2022-140035OB-I00 Funding Entity: Agencia Estatal de Investigación
Design Manufacturing, characterisation and certification of composite laminates with unconventional stacking sequences
Supervisors
Andrea Cini (UC3M)
Abstract
Composites allow designers to tailor structures at the material level, assigning, not only the material distribution, but changing ply material, orientation, shape, number and stacking sequence to locally obtain therequired mechanical properties. The large number of variables, however, increases the complexity ofthe design process fo:n;ingdesigners to preselect stacking sequences (quasi-isotropic materials) and applytraditional methods for metal structures (black metal design). The suggested projecthas the objective of overcoming current composite design limitation by enabling effective non-blackmetal design and aeroe1astic tailoring. Manufacturing, and certification constraints will be exerted without limiting the design space by rules and analytical models to take into account industrialisation and feasibility aspects.
The research activities will include design, manufacturing, characterisation and certification of composite material with unconventional stacking sequences for mechanical performance enhancement and aeroelastic tailoring. The concurrent definition of material design and manufacturing process considering their mutual constraints is a cost effective and time efficient way to develop optimised manufacturable solutions reducing the risks of non-compliances, redesign iterations and process plan modifications. Airworthiness authorities prescribe a list of safety requirements to obtain flight certification. Safety regulation compliance must be demonstrated by means oflarge and expensive testing campaign consisting of aeroelastic flight tests and structural building blocktestpyramids. Considering certification requirements since the early material design phase is necessary to smooth the product development phase and shorten the time-to-market. The so-called desi1m for certification approach in fact limits the amount of redesign actions on mature components needed to ensure airworthiness. The project aims at embedding a design for certification approach by testing and simulating compliance with structural integrity requirements.
TIFON – Artificial Intelligence for Design and Manufacturing (AIDme) PLEC2023-010251. This project has been granted by the CDTI, with the support of the Ministry of Science and Innovation, with file number MIG-20232039 through the TransMisiones 2023 initiative, within the scope of the “Programa Estatal para Catalizar la Innovación y el Liderazgo Empresarial del Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023” program.
Disrupting the Skies: Formation Flight for Aviation Sustainability
Supervisors
María Cerezo Magaña (UC3M) and Ricardo Gázquez Torres (UC3M, Statistics Department)
Abstract
Formation flight offers a path toward sustainable ATM strategies, yet non-CO2 impact mitigation and network-scale benefits remain understudied. This thesis aims to model non-CO2 emissions and contrail formation in formation flight, assessing benefits and challenges for implementation.
Establishment of environmental performance indicators for aviation from an air navigation service provider (ANSP) perspective
Supervisors
Manuel Fernando Soler Arnedo (UC3M) & María Cerezo Magaña (UC3M)
Abstract
The aviation sector faces the challenge of reducing its environmental impact not only in terms of CO2 emissions, but also in terms of other greenhouse gases (GHGs) and non-CO2 effects such as condensation trails and cirrus cloud formation. The previous article, ‘Data-driven Methodology to Characterise CO2 Emission Discrepancies Between Actual and Optimum Operations’, presents a data-driven methodology to estimate and compare inefficiencies in fuel consumption and CO2 emissions, with a focus on the perspective of Air Navigation Service Providers (ANSPs): ABSTRACT: This paper presents a data-driven methodology for estimating and comparing fuel consumption inefficiencies and CO2 emissions in the aviation sector, with a focus on improving environmental sustainability from the Air Navigation Service Provider (ANSP) perspective and contributing to decarbonization goals by emphasizing the establishment of new performance indicators for assessing the environmental impact of ANSPs. The methodology involves predicting the fuel consumption of actual flight trajectories from publicly available historical surveillance and weather data and comparing it to that of each flights respective performance optimal trajectory using a multivariate regression factor weighting analysis. A case study of Airbus A320 flights between LAX and SFO is used to demonstrate the methodology’s effectiveness, including the development of a representative performance indicator for this aircraft type and flight route. The results show a significant relationship between predictor variables such as differences in altitude, distance, time, and wind between actual and optimal trajectories, with differences in distance, which ANSPs can control, being identified as the most influential factor. The methodology has the potential to enhance aviation efficiency and reduce CO2 emissions by providing a framework for evaluating the impact of ANSPs on environmental performance and offering insights for optimizing operational trajectories. This PhD project proposes to extend this methodology to develop environmental performance indicators that comprehensively address all GHG and non-CO2 impacts. It aims to provide a reference framework that will enable ANSPs to improve their environmental sustainability and contribute more effectively to the decarbonisation objectives of the sector.
Development of Enhanced Lower-Order Finite Element (FE) Approaches for Rapid Structural Design and Multifidelity Optimization in Aerodynamic-Structural/Aeroelastic Systems
Supervisors
Andrea Cini (UC3M) & Rauno Cavallaro (BSC-CNS)
Abstract
This research project focuses on the enhancement of Finite Element approaches for faster, more accurate structural design in aerospace. Multifidelity optimization will combine high- and low-fidelity models to improve performance, offering innovative tools for aerospace design.
TIFON – Tecnologías Inteligentes para la Fabricación, el diseño y las Operaciones en entornos industriales PLEC2023-010251. This project has been granted by the CDTI, with the support of the Ministry of Science and Innovation, with file number MIG-20232039 through the TransMisiones 2023 initiative, within the scope of the “Programa Estatal para Catalizar la Innovación y el Liderazgo Empresarial del Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023” program.
PhD in Aerospace Engineering UC3M 