Courses

Composites’ design cannot use extensions of the methodologies adopted for metals. Such a strategy may lead to oversizing, let alone the risks arising from a wrong design. Composites are more complex material systems than metals due to their multiscale nature. Brittle orthotropic fibers, ductile isotropic matrices, and soft cores coexist.  Such complexity leads to challenging predictive models, e.g., to detect composite structures’ damage and failure mechanisms, which is still far from reliable predictions via virtual models and needs high computational costs, precluding structural engineering calculations. Uncertainties in the models lead to safety factors and tests. Therefore, the full spectrum of composites’ advantages is not exploitable, and costly experimental tests are necessary.

This webinar reviews the mechanical behavior of composites and highlights modeling challenges. It highlights limitations of standard finite element techniques and presents approaches to improving solutions. It also introduces structural theories and detailed methods for developing them. Numerical examples concerning linear analysis, failure index evaluations, and structural dynamics provide guidelines on the proper modeling. Extensions to nonlinear analysis, process simulation, and multiscale analyses are also presented.

Learning objectives:

• Mechanical behavior of composite structures
• Review of theories of structures
• Structural theories for composites
• 1D and 2D finite elements
• Guidelines for modeling
• Basics of multifield analysis, multiscale modeling, failure analyses, and process simulation

Target audience: doctoral students, non-academic professionals, and undergraduate students.

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Overview and General Information:

Dynamic testing is essential in the design and qualification of aerospace hardware. It serves diverse purposes, from characterizing dynamic responses at different system levels to evaluating hardware performance under operational loads. Vibration testing is fundamental for correlating and updating numerical models used in aerospace product design and validation. Environmental dynamic testing aims at replicating real conditions, such as in-flight or transport scenarios, in laboratory settings to ensure systems will perform as expected throughout their operational lifetime. With the emergence of disruptive aerospace systems and increasingly competitive markets, the industry demands innovations in practices that have remained mostly unchanged for decades.

This webinar presents recent advances in dynamic testing of aerospace hardware. It provides an overview of the topic and explores key innovations that address industry demands. The presentation covers various testing practices including vibration control, multi-axis testing, shock testing, virtual shaker technology, ground vibration testing, and flight testing. Additionally, it describes how digital twins can improve and de-risk test campaigns in these applications.

Learning Objectives:

• Overview of dynamic testing practices of aerospace hardware
• Vibration testing in shaker platforms: current practices and new trends
• Testing of aircraft structures: from GVT to flight testing
• The role of Digital Twins in dynamic testing

Target audience

Master’s students, doctoral students, academic and non-academic professionals

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The course offers a thorough overview on the emerging topic of deep learning for computer vision and egomotion estimation. Such technique is extremely useful to carry out the task of spacecraft relative navigation in proximity operations, e.g. during planetary approaches, landing or rendezvous. The course is intended to provide a solid theoretical knowledge together with the exploration of the latest state-of-the-art techniques.

The seminar is organized in lectures, demos and small workshops. Lectures are passive (from the student’s point of view). Demo sessions are used to demonstrate and run state-of-the-art implementation of some algorithms. Workshop sessions are used to get acquainted with the most famous CV and DL libraries (openCV, pyTorch, etc.).

Learning objectives: Introduction to Computer Vision, Introduction to Deep Learning, Convolutional Neural Networks, Multi-view geometry and Relative Navigation

Target audience: doctoral and master students, non-academic professionals.

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Spacecraft attitude equations are usually given by nonlinear equations. However, spacecraft attitude control laws are often designed using a linear approximation of those equations about an operating condition. Thus, the effectiveness of the control laws can be guaranteed only for attitude angles and angular velocities close to the operating condition. There are occasions when the spacecraft motion involves attitude angles and angular velocities that are far from the operating condition. For those motions, the full nonlinear attitude equations must be used for evaluating the effectiveness of the control laws. This course presents the design of attitude control laws for two typical spacecraft operations along with basic tools that are useful to validate the design with nonlinear attitude equations.

Learning objectives: Learning control laws for spacecraft detumbling and spacecraft attitude regulation. Learning mathematical tools for validating attitude control laws using nonlinear attitude equations.

Target audience: doctoral and master students, non-academic professionals.

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Overview and General Information:

The aim of this webinar is to deal with the main criticisms related to acoustic simulation and noise suppression in the aerospace sector. This objective is achieved by initially introducing and discussing the state-of-the-art methods and technologies that are relevant to this field. Subsequently, the fundamentals of analytical (Transfer Matrix Method) and numerical (Wave Finite Element Method) approaches are illustrated, which constitute powerful and efficient techniques to estimate the absorption and transmission properties of a sound package. Lastly, some innovative acoustic meta-material configurations are presented, based on a periodic pattern of porous unit cells, whose main homogenization models are defined and discussed too. These topics address different applications not only in the aerospace industry, but more generally in transportation (automotive, railway), energy and civil engineering sectors, where both weight and space, as well as vibroacoustic comfort, still remain as critical issues.

Learning Objectives:

• Aircraft noise: methods and technologies
• Transfer Matrix and Wave Finite Element Methods in acoustics
• Acoustic characterization of porous meta-materials

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Overview and General Information:

Virtual Reality applications have immense potential and applications in numerous smart manufacturing and advanced/precision manufacturing applications for design, prototyping, simulation, and training. Extended reality (XR) is a blanket term that is used to refer to multiple technologies such as virtual reality, augmented reality and mixed reality. In this webinar, many VR frameworks that were designed for additive manufacturing and smart manufacturing applications including specialized areas such as pharmaceutical manufacturing will be demonstrated.  For optimal user experience, careful consideration to the human sensory stimuli (visual, auditory, haptic, etc.) is inevitable. The various modes range from fully immersive to semi or partially immersive experiences as well as features that involve juxtaposing real world and virtual objects. Considering the wide range of options available, besides selecting the optimal mode (VR/AR/MR) for specific application and audience, it is vital to design and deliver XR experiences in manufacturing for optimal user experience with reduce cognitive load and higher engagement. This webinar will demonstrate the successfully implemented smart and digital manufacturing applications with due consideration to the above UI/HCI factors.

Learning Objectives:

The webinar  will discuss in detail various aspects including:

• Specialized digital and smart manufacturing applications
• Distinctions between VR, AR, MR, and desktop VR
• Common tools & techniques for VR/AR application development
• Hardware and software considerations and limitations

Target audience

Doctoral and post-graduate students, aerospace and defence industry professionals, and military officers.

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Overview and General Information:

Modern combat aircraft design is governed by a careful balance of aerodynamic and propulsion performance, which are pushed to the limit. The blending of different specifications, often in opposition, is one of the most complex tasks in Aeronautical Engineering, especially with the requirement to integrate the now mandatory requirements of signature reduction requirements. Attendees will learn to quantify combat aircraft performance, and how different machines can be compared and relatively classified as superior/inferior. The short course will also explore common airframe solutions to achieve specific performances.

Learning Objectives:

• Kinematics performance analysis.
• Energy Manoeuvrability and Specific Excess Power theories.
• Tactical engagements analysis (Within Visual Range Engagemets).
• Introduction to combat aircraft aerodynamics.

Target audience

Doctoral and post-graduate students, aerospace and defence industry professionals, and military officers.

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Spacecraft attitude equations are usually given by nonlinear equations. However, spacecraft attitude control laws are often designed using a linear approximation of those equations about an operating condition. Thus, the effectiveness of the control laws can be guaranteed only for attitude angles and angular velocities close to the operating condition. There are occasions when the spacecraft motion involves attitude angles and angular velocities that are far from the operating condition. For those motions, the full nonlinear attitude equations must be used for evaluating the effectiveness of the control laws. This course presents the design of attitude control laws for two typical spacecraft operations along with basic tools that are useful to validate the design with nonlinear attitude equations.

Learning objectives: Learning control laws for spacecraft detumbling and spacecraft attitude regulation. Learning mathematical tools for validating attitude control laws using nonlinear attitude equations.

Target audience: doctoral and master students, non-academic professionals.

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Space instrumentation encounters challenges due to the hostile space environment, resource constraints, communication delays, and the demand for precision. Navigating extreme temperatures, radiation, and vacuum conditions necessitates robust designs. Additionally, stringent limitations on power, weight, and budget pose further hurdles, demanding optimal instrument performance within constrained parameters. Moreover, technological aspects and the need for precision and accuracy in measurements present ongoing challenges. Miniaturization enables the development of small, yet powerful instruments, while advanced imaging technologies enhance the resolution of captured data. For example, the development of the MarsTEM temperature sensor for Mars, the JANUS COver Mechanism (COM)  for JUICE mission and the new VENOM astrobiology experiment will be described and challenges presented.

Learning objectives: designing a space instrument: functionality, efficiency, testing and normative aspects.

Target audience: doctoral students, non-academic professionals, and undergraduate students.

Dates and time: 31 October – from 10:30 to 12:30

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Overview and General Information:

Problems of interest in science and engineering are often multi-physics, with complexities stemming from the interactions of various mechanisms, and inherent uncertainties and variabilities. In an industrial setting, we frequently aim to conduct optimization tasks in such complex and high-dimensional domains. For example, the 3D printing of thermoplastics involves heat and mass transfers, where the material undergoes thermo-chemical and thermo-mechanical changes along with several phase transformations. Evaluating the performance of the material under such conditions is challenging due to the complexities of the underlying multi-physics problem, as well as noise/errors in measurements, process uncertainties, and material variabilities. To evaluate or conduct optimization tasks, current practices often rely on methods such as Design of Experiments (DoE), and/or numerical methods.

In the recent decade, the application of data-driven and machine learning (ML) methods has also been explored with varying degrees of success. However, ML methods have been shown to suffer from a variety of shortcomings, including brittleness outside of their training zones. More recently, different families of data-driven methods have evolved to address the complexities of such multi-physics problems, including theory-guided machine learning (TGML), also referred to as scientific AI or physics-informed ML. TGML represents a merger between science-based methods, including finite element (FE) analysis, and ML techniques to overcome the challenges associated with theory-agnostic ML methods in physical domains.

Learning Objectives:

This course aims to introduce participants to TGML and its applications. First, a short overview of theory-agnostic ML methods, including Neural Networks (NN) for large datasets and Gaussian Process Regression (GPR) for small datasets, will be given. Simple engineering applications including heat transfer will be demonstrated. Next, TGML and its notable techniques will be introduced, with examples provided. A combination of experimental data and numerical data will be used to train ML models. Python programming with built-in libraries will be employed to develop ML codes during the course. Participants can follow the instructor to develop codes using Python on their own machines. Python codes and example datasets will be provided as well.

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