Mr. Daniel Aláez

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Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 November 2022 in Simulation Modelling Practice and Theory

Journal Article

VTOL UAV digital twin for take-off, hovering and landing in different wind conditions

Daniel Aláez

X. Olaz

M. Prieto

J. Villadangos

J.J. Astrain

30 December 2023 in SoftwareX

Journal Article

UAVradio: Radio link path loss estimation for UAVs

Daniel Aláez

Mikel Celaya-Echarri

Leyre Azpilicueta

Jesús Villadangos

18 April 2023 in Expert Systems with Applications

Journal Article

Quadcopter neural controller for take-off and landing in windy environments

16 December 2022 in Information

Journal Article

HIL Flight Simulator for VTOL-UAV Pilot Training Using X-Plane

Daniel Aláez

Xabier Olaz

Manuel Prieto

Pablo De Porcellinis Pascau

Jesus Villadangos

With the increasing popularity of vertical take-off and landing unmanned aerial vehicles (VTOL UAVs), a new problem arises: pilot training. Most conventional pilot training simulators are designed for full-scale aircrafts, while most UAV simulators are just focused on conceptual testing and design validation. The X-Plane flight simulator was extended to include new functionalities such as complex wind dynamics, ground effect, and accurate real-time weather. A commercial HIL flight controller was coupled with a VTOL convertiplane UAV model to provide realistic flight control. A real flight case scenario was tested in simulation to show the importance of including an accurate wind model. The result is a complete simulation environment that has been successfully deployed for pilot training of the Marvin aircraft manufactured by FuVeX.

31 July 2024 in IEEE Transactions on Industrial Informatics

Journal Article

On Constructing Efficient UAV Aerodynamic Surrogate Models for Digital Twins

Aerodynamic modeling and optimization for unmanned aerial vehicles (UAVs) are complex and computationally intensive tasks. Surrogate models have emerged as a powerful tool for increasing efficiency in the aircraft design and optimization process. We review and evaluate some modeling techniques, such as artificial neural networks and support vector regression, showing that Gaussian process regression generally provides a well-performing solution to this type of problem. We propose an active learning algorithm based on the relevance factor, that combines bias estimated from nearest-neighbor Euclidean distance and variance, to achieve higher accuracy with fewer compuational fluid dynamics (CFD) simulations. The obtained performance is evaluated using four 2-D test functions and an experimental CFD case, indicating that the proposed active learning approach outperforms classical random sampling techniques. Thanks to this architecture, the development process of a new commercial UAV can be significantly streamlined by expediting the testing phase through the use of DTs modeled more efficiently.

23 February 2025 in Applied Sciences

Journal Article

Towards a Heat-Resistant Tethered Micro-Aerial Vehicle for Structure Fire Sensing

The collapse of structures during firefighter intervention is one of the greatest risks that firefighters must face when entering buildings. To reduce these risks, situational awareness is key. Although many advances have already been developed in wildland and outdoor fires, there is still room for improvement in structure fires. The development of a heat-resistant micro-aerial vehicle for indoor fires poses a series of challenges such as component cooling, battery management, and protection from impacts. In this paper, a heat-resistant tethered micro-aerial vehicle is designed, modeled through thermal analysis, and successfully tested in real-world conditions. This platform has been equipped with a micro-sized thermal sensing camera and first-person-view (FPV) camera, optimized for thermal management, to allow for situational awareness in structure fires.

About Me

Dr. Daniel Alaez works as an assistant lecturer and doctoral project researcher within the Distributed Systems research group at the Department of Statistics, Computer Science and Mathematics, Universidad Pública de Navarra (UPNA), Pamplona, Spain, and is affiliated with the Institute of Smart Cities research centre. He received his Bachelor’s degree in Aerospace Engineering in 2020 and his Master’s degree in Aeronautical Engineering in 2022 from the University of León, Spain, where he obtained an extraordinary academic award for his master‑level research work on digital‑model‑oriented engineering projects. He completed his PhD in Science and Industrial Technologies at UPNA in 2024, with his doctoral thesis focusing on digital twin development for VTOL UAVs. Beyond research activities, he delivers university teaching for multiple computer‑related undergraduate courses, including software architecture, advanced programming, and operating systems. His core research interests cover unmanned aerial vehicles, digital twin systems, flight simulation, aerodynamic modeling, and distributed embedded systems.

Mr. Daniel Aláez

Mr. Daniel Aláez

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About Me