Two aerodynamic technology streams promise a break-through in minimizing the energy consumption of flight: laminar wing technology and the significant increase of the aspect ratio of the wing. Both technologies require a significant change of the architecture of wing moveables for high-lift and flight control. At the leading edge, slats are no longer compatible with laminar wing technology, and classical flaps are no longer sufficient to provide sufficient load and deformation control due to the high slenderness and limited space for control actuation. The aim of the proposed session is to collect contributions from aerodynamics, aeroelastics and systems - as e.g. represented by the German Federal Aviation Research Programme project ULTIMATE - to the solution of the specific topics of integrating novel controls into high-aspect ratio laminar wings by simulating aerodynamics, aeroelastics, flight and system behaviors.

This Special Technology Session addresses critical industrial challenges around hydrogen deployment by bridging computational mechanics, measurements, and techno-economic reasoning. We invite contributions that quantify where hydrogen is technically and economically justified across heavy industry and transport, and that demonstrate validated models capable of guiding design and operations. Target topics include multiphysics modelling of electrolyzers and the operation of fuel cells; Hydrogen storage and transport; hydrogen effects on materials (embrittlement, fatigue, fracture); dispersion, combustion, and safety; as well as plant- and network-scale simulations integrating electricity markets and heat recovery. The session also emphasizes value network analysis across supply chains—linking component performance, reliability, and maintenance to system cost, lifecycle impacts, and its risks.

Recent advances have been made in numerical simulation [1] and optimisation [2] of neurostimulation implants. In particular, the advent of artificial intelligence methods has also enabled improvements and new useful approaches in this field [3]. This mini-symposium is aimed at presenting the latest research results in numerical simulation methods (finite elements, etc.) as well as artificial intelligence techniques (neural networks, global stochastic optimisation methods, etc.), for neurostimulation implants (cochlear implants, vestibular implants). References [1] M.A. Callejón-Leblic, M. Lazo-Maestre, A. Fratter, F. Ropero-Romero, S. Sánchez-Gómez, J. Reina-Tosina (2024), "A full-head model to investigate intra and extracochlear electric fields in cochlear implant stimulation", Physics in Medicine and Biology, 69 (15) (2024), Article 155010. [2] M. Hernández-Gil, Á. Ramos-de-Miguel, D. Greiner, D. Benítez, Á. Ramos-Macías, José M. Escobar (2025),"A computational model for multiobjective optimization of multipolar stimulation in cochlear implants: An enhanced focusing approach", Expert Systems with Applications, Volume 280, 25 June 2025, 127472. [3] G. Zhang, R. Chen, H. Ghorbani, W. Li, A. Minasyan, Y. Huang, S. Lin, M. Shao, "Artificial intelligence-enabled innovations in cochlear implant technology: Advancing auditory prosthetics for hearing restoration", Bioengineering & Translational Medicine (2025), 10.1002/btm2.10752.

Commercial CFD solvers widely used in turbomachinery rely on steady or unsteady Reynolds-averaged Navier–Stokes (RANS/URANS) equations. These approaches, based on temporal averaging, can provide satisfactory results for simple industrial cases. However, they are severely limited when simulating the highly compressible and unsteady flows of transonic turbomachinery, where flow separation, shock–boundary layer interaction, and a broad spectrum of scales must be captured. At the other end of the spectrum, high-fidelity methods such as large-eddy simulation (LES) or direct numerical simulation (DNS) offer superior accuracy but remain computationally prohibitive for engineering design. Recent advances in high-order discretization methods offer a promising path forward. Approaches such as discontinuous Galerkin schemes achieve higher accuracy with coarser meshes compared to second-order methods, enabling efficient resolution of multiple physical scales. When combined with turbulence-resolving strategies and robust shock-capturing techniques, still an active research frontier, these methods have the potential to deliver predictive simulations at a fraction of the cost of LES or DNS. The advent of exascale computing further expands these possibilities. Fully exploiting architectures capable of billions of operations per second requires advances in heterogeneous hardware utilization, energy efficiency, extreme parallelism, and scalability. Numerical algorithms and software frameworks must be carefully adapted to this paradigm to balance performance and data management on a scale. Typically, experimental data in turbomachinery is available only at discrete locations, though often with extremely fine temporal resolution—on the order of microseconds. Computational data, by contrast, can provide dense volumetric coverage of the flow field, but with greater uncertainty in representing transients. If the boundary conditions of both approaches are aligned and their uncertainties rigorously integrated, it becomes possible to construct a unified model that leverages the strengths of each, opening the possibility of new numerical accuracy. This session will highlight progress in advanced CFD methods for turbomachinery, together with their implementation on next-generation supercomputers. Contributions are encouraged on numerical schemes, high-performance computing strategies, experimental validation, and the integration of machine learning to improve turbulence modeling and

We propose a Special Technology Session on the joint CFD-code developed by Airbus, ONERA and DLR and, in particular, we will present and demonstrate the advanced features of CODA compared to legacy methods.

Everybody knows the importance of reducing the risks of climate change. Reducing CO2 emissions and noise levels in aviation remains today and tomorrow a great challenge. Innovations in Research and Industry are crucial towards reaching climate neutrality for aviation and modern Transport by 2050. Now, halfway to the target, scientists and engineers are delivering results for a greener aviation in the fields of propulsion systems (1), aerostructures (2), aerodynamics (3) and disruptive aircraft configurations (4). These innovative technologies are candidates for integration into modern transport systems within the next two decades. A critical question for sustaining growth in the aviation sector is: are the current digital methods and tools in development including Artificial Intelligence (AI) sufficiently advanced for mastering new numerical methods and tools to provide solutions to new challenges contributing to a decarbonated environment? This STS (two hours format) will consist of Keynote lectures presenting innovative numerical solutions contributing to first priority challenges in climate neutrality in the fields of (1), (2), (3) and (4).

As the aerospace industry strives for unprecedented levels of efficiency, sustainability, and performance, traditional design and testing methodologies are proving insufficient. This session will highlight the latest breakthroughs in high-fidelity simulations and Multidisciplinary Design Optimization (MDO), which are now seamlessly integrated to create highly accurate and predictive digital twins of aircraft systems. We will explore how these simulations are enabling engineers to rapidly iterate on complex designs, from novel aerodynamic configurations and lightweight composite structures to advanced propulsion systems. The session welcomes presentations and discussions on a range of topics, including: 1) High-order schemes for fluid dynamics: e.g., finite-difference/element, Discontinuous Galerkin (DG), and Flux Reconstruction (FR) methods for more accurate and efficient CFD; 2) Aeroelastic analysis: e.g., with a focus on high-aspect-ratio and flexible wings, which are critical for enhancing aerodynamic efficiency; 3) High-fidelity structural analysis: e.g., material and geometrical nonlinear analysis to better predict the behavior of complex structures under various situations; 4) Multi-physics simulation: Covering complex interactions such as fluid-structure interaction (FSI) and combustion science, essential for integrated system analysis; 5) Data-driven methods: Utilizing reduced-order models (ROMs) and machine learning to accelerate the design cycle and manage massive datasets. Discussions will also address computational resource challenges and data-management needs in large-scale simulations. Designed for researchers and professionals at the intersection of aerospace engineering and computational science, this STS highlights the shift from physical prototyping toward virtual design paradigms—paving the way for innovative, sustainable aircraft development.

Additive Manufacturing (AM) technologies are undergoing exponential growth in many engineering fields, from aerospace to biomedical applications, from fashion to the food industry. AM technologies promise to revolutionize the world of manufacturing due to their capability to produce close-to-freeform components with structural and mechanical properties close to or even superior to those obtained using traditional manufacturing processes. In fact, thanks to the recent advancements in many AM technologies, manufacturing constraints are dramatically reduced, and the designer can finally focus on the intended application of the part rather than on its manufacturability. However, a widespread adoption of AM in many industrial sectors is yet hindered by the low reproducibility and standardization of the process. Therefore, the present Symposium aims at presenting and discussing the most recent efforts in the world of industry towards a deeper control and understanding of AM processes. The Symposium is meant to host contributions from industry, and it addresses, but is not limited to, the following topics: • Standards and certification • Process modelling • Material modelling • Metrology and sensing techniques • Uncertainty quantification • Benchmarking and Validation • Oli&Gas applications • Energy applications • Valves and pumps

The Model Based Systems Engineering (MBSE) technology had been well applied in aviation industry. This STS will focus on recent practices and achievements of MBSE technology in the design of low-altitude air vehicles and low-altitude system of systems. The topics consist of and are not restricted to: Application of MBSE Technology in Design of Low-Altitude Air Vehicle at Conceptual Design Synthesis Stage, Low-Altitude Air Vehicle System Design, Low-Altitude System of Systems Design and Optimization, MBSE-Driven Exploration of Logical Architecture Design Spaces and Trade-off Analysis of Multiple Solutions, and Design Technologies of Low-Altitude Spectrum Monitoring System Based on MBSE.

The topics include but not restricted to: eVTOL configuration, rotor and propulsion system, eVTOL aerodynamic performance, simulation and optimization, eVTOL applications.

The rapid expansion of the low-altitude economy and the widespread adoption of unmanned aerial vehicles (UAVs) present unprecedented opportunities and challenges across industries such as logistics, surveillance, agriculture, and urban air mobility. This mini-symposium explores the transformative role of artificial intelligence (AI) in addressing key computational and operational aspects of UAV systems and low-altitude operations. Topics of interest include but are not limited to: AI-driven aerodynamic modelling and optimization, machine learning-enhanced flight control and autonomy, computer vision for navigation and obstacle avoidance, and data-centric approaches for air traffic management and swarm coordination. Additionally, the session will cover AI applications in predictive maintenance, mission planning, and regulatory compliance, emphasizing the integration of computational mechanics with AI techniques to enhance safety, efficiency, and scalability. We welcome contributions that demonstrate novel methodologies, case studies, and interdisciplinary approaches leveraging AI to advance the future of low-altitude ecosystems and UAV technologies.

The rapid advancement of computational materials science and the emergence of deep learning technologies are transforming the landscape of materials discovery, design, and optimization. This session focuses on the integration of physics-based simulations, high-throughput computations, and data-driven approaches to accelerate innovation in materials research. A particular emphasis will be placed on modeling of materials microstructures, including their generation, evolution, and optimization, as well as the theoretical modeling of diverse functional materials beyond structural mechanics. These include functionally graded materials (FGMs), thermoelectric materials, metal-organic frameworks (MOFs), and piezoelectric materials, where the interplay between structure and electronic functionality is critical. The related crystal structures modeling and phonon engineering are also depicted. Topics of interest include, but are not limited to: • Theoretical modeling and design of functional materials such as functionally graded materials (FGM), thermoelectric materials, and metal-organic frameworks (MOFs). • Microstructure modeling and optimization using mathematical models and AI-assisted techniques • Applications of deep learning in microstructure-property relationships, defect prediction, and synthesis planning • Autonomous materials discovery using active learning and reinforcement learning In this session, through interdisciplinary dialogue, we aim to define the next-generation paradigm of materials research empowered by advanced computation and deep learning methodologies.

Data driven methods under the Artificial Intelligence (AI) umbrella and its associated subset Machine Learning (ML) have seen extensive development in recent years. This is mainly due to a number of powerful opensource codes democratising the use Deep Neural Network (DNN), and increase in computing powers with new GPU and CPU chips. The AI methods although originally developed to replicate the human brain, once trained, are shown to be able to be to learn highly complex relationships in engineering datasets. The ability to apply the AI methods to physical problems, ranges from analytical, to numerical as well as experimental fluid mechanics. The new AI/ML methods is claimed to cut simulation times from days/hours to near instant and improves efficiency. However, these methods often require a large amount of training data, to produce accurate results for the highly non-linear fluid mechanics problems, nearly all the results published in the literature relating to high-dimensional test cases have used large number of simulations often much higher than even “classical” surrogate models used in the industrial optimisation workflows. In this STS, we would like to review and discuss novel methods that are used to address the aforementioned inefficiencies, aiming to accelerate CFD simulations used to design turbomachinery components like multi-stage Fan, Compressor and Turbine components in a fraction of the cost and time associated with direct design using high-fidelity analysis.

Cities are important application scenarios for eVTOL, and noise is a crucial factor in determining the environment-friendliness of eVTOL. Firstly,eVTOLs adopt typically a distributed propulsion layout, utilizing various combinations of multiple rotors and tiltrotors to realize flight in different states such as takeoff, transition, cruise and landing. The flow mechanism and noise generation mechanism are complex, and many sound sources pose issues with both external noise and cabin noise; Secondly, eVTOL take off and fly in cities, and the takeoff and landing points are close to residential and commercial areas. Noise of eVTOL does not undergo long-distance attenuation and directly affects the surrounding environment, bringing potential noise pollution.Overall, noise pollution in low altitude environments is one of key factors limiting the large-scale application of eVTOL. This STS will conduct specialized academic exchanges on the numerical simulation methods of eVTOL noise sources and propagation characteristics, experimental verification methods, etc. We look forward to experts and scholars from universities, research institutions, and industries around the world, such as the European Union and China, sharing research results and exploring future technological developments.

Sustainable aviation is a major challenge that requires technology developments in many different areas. One important area is the reduction of green-house gas (GHG) emissions, which is directly related to aircraft operations and energy efficiency. One of the key components for improving aircraft efficiency is drag reduction, and increased wing aspect ratio is a key enabler for that. Therefore this STS will focus on technologies for design and development of high aspect ratio (HAR) wings for short- and medium range (SMR) aircraft. This category of aircraft is responsible for a major contribution in aviation GHG emissions and is therefore important to address. At the same time, these aircraft have high-tech wings with advanced aerodynamics, optimized structures and complex integration of primary and secondary flight controls. Such technology developments are being pursued in the Clean Aviation UP Wing project (2023-2026, [1]). The further improvement of these high-tech wings requires advanced modelling, innovative computational methods and design tools for all required technology areas. In particular, increasing the wing aspect ratio will require special attention for load control, for which combined numerical-experimental investigations are being completed in the UP Wing project. The STS will invite papers on the design, modelling, analysis, testing, validation, manufacturing and assembly of all the relevant technologies that are involved in the development of these advanced high-aspect ratio wings.

The session focuses on the more recent results in the fourth and last year of the project EFACA (Environmentally Friendly Aviation for All Classes of Aircraft). After an introductory overview of the EFACA project (Paper 1), it goes into more detail at the five levels of the project: (i) Level 1: Laboratory and test bench demonstration to TRL3 of 3 critical technologies, namely: (Paper 2) A gearbox combining power from a gas turbine and electric motor to drive a 700 HP propeller; (Paper 3) An hydrogen fuel cell with novel phase change cooling to boost efficiency and operability; (Paper 4) A complete liquid hydrogen fuel system, comprising cryogenic storage and transport, vaporizer and combustor; (ii) Level 2: Status and prospects of two technologies: (Paper 5) Advances in battery power for emissions free flight of small aircraft; (Paper 6) Alternative sustainable aviation fuels for carbon neutral flight of large long-range airliners; (iii) Level 3: Preliminary design of two aircraft types: (Paper 7) A propeller regional airliner (PRA) with 1000km range with 80 passengers using hybrid turboelectric propulsion combining turboprop and hydrogen fuel cell in the ATR72-600 class; (Paper 8) A liquid hydrogen fuelled jet liner (LHJ) with 2000km range with 150 passengers in the A 220/320 class; (iv) Level 4: Assessment of the current and future status of (Paper 9) Airport Noise and (Paper 10) Local and Global Emissions of all types (CO2, NOx, Sulphur, Particles and Contrails), including the benefits of the 2 EFACA designs: PRA/LHJ compared with current ATR72-600/A320neo; (v) Level 5: Roadmap for greening of aviation (Paper 11), relative to ACARE FlightPath 2050, EU FIT 55 for 2035 and ICAO NetZero 2050 targets, bearing in mind the benefit of new technologies and time scale for replacement of older by cleaner aircraft.

Advances in numerical methods together with the availability of high-performance computers (HPC) allow to address highly complex design problems and challenging multi-disciplinary optimisation issues. The integration of artificial intelligence (AI) and of machine learning features will enhance this development. Significant advances in adjoint methods have enabled efficient and accurate computation of shape gradients for high-dimensional and high-fidelity aerodynamic shape optimisation problems [1]. A shape optimisation of functional surfaces presents a multifaceted challenge, characterized by numerous geometric, functional, and performance constraints. This is particularly evident in vehicle design, where modern simulation processes have to address a wide spectrum of requirements [2]. In this STS, different novel methods will be discussed, which are aiming to accelerate CFD simulations used to design aeronautics components like aircraft wings, profile shape and other aeronautics or industry components associated with direct design using high-fidelity analysis.

The integration of Artificial Intelligence (AI) and Fluid Dynamics is opening new frontiers in computational modeling and simulation. This Special Technology Session (STS) is dedicated to exploring cutting-edge methodologies that leverage data-driven approaches to enhance, accelerate, and redefine traditional fluid mechanics analyses. We invite contributions on a wide range of topics, including but not limited to: AI-augmented turbulence modeling and closure strategies, physics-informed neural networks (PINNs) for forward and inverse problems, deep learning techniques for flow field reconstruction and super-resolution, and machine learning-enabled reduced-order modeling for real-time simulation and control. Additional areas of interest encompass AI-empowered aerodynamic shape optimization, data-enhanced experimental methods, and uncertainty quantification in multi-scale flow systems. A key focus will be placed on hybrid frameworks that integrate physical knowledge with data-driven insights to improve predictive accuracy, computational efficiency, and design innovation. We welcome submissions presenting novel algorithms, rigorous validations, and impactful applications that demonstrate the transformative role of intelligent computational methods in fields such as aeronautics, aerospace propulsion, renewable energy, environmental flows, and biomedical engineering.

The present STS at ECCOMAS 2025 will include five contributions concerning novel wing morphing, able to drastically increase the aerodynamic performances leading to a considerable fuel’s consumption decrease and noise sources reduction. Emphasis will be attributed in the efficiency of multiscale electrical actuations with increased DoF over strategic areas of the lifting structures, able to attenuate harmful instabilities and optimise the fluid-structure interaction system by means of AI. These objectives are inscribed in the HORIZON-EIC-2023-2027-PATHFINDER Project N° 101129952 – BEALIVE, "Bioinspired Electroactive multiscale Aeronautical Live skin, http://horizon-europe-bealive.eu/ . The presentations will analyse the morphing effects on the fluid-structure interaction, beneficially manipulating the surrounding turbulence towards simultaneous drag reduction, increase of lift and noise sources attenuation. The new morphing designs ensure a considerable energy decrease for the propulsion, beneficial for all sources of renewal energy. The presentations will demonstrate through well focused experiments on morphing wing prototypes and High-Fidelity numerical approaches, the effects of Travelling Waves (TW) and Standing Waves (SW) produced by the “live-skin”, as well as by other novel morphing concepts thanks to new generation of smart actuators. These designs are able to produce optimal interfacial layers interacting with the coherent and chaotic turbulence structures and applying deformation of strategic parts of the wing. The topic of this session prepares future wing design for aeronautics industrial applications aiming at saving energy and at reducing the pollution through these new, multiple-degrees-of freedom morphing concepts, enabling a considerable reduction of emissions, reaching ACARE’s European Commission targets (DG MOVE/ DG RTD, (https://data.europa.eu/doi/10.2777/15458) Flightpath 2050 that include a 80% reduction in CO2 emissions per passenger kilometers to support the Air Transport Action Group target: “Carbon-neutral growth starting 2020 and 90% or practically zero CO2 emission reduction by 2050”, as well as a 90% reduction in NOx emissions and noise emissions reduced by 65%..