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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The aim of this webinar is to deal with the main criticisms related to acoustic simulation and noisesuppression 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.

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Overview and General Information:

Modern combat aircraft design is governed by signature reduction requirements, both in the electromagnetic and infrared spectra. At present it is commonly accepted to sacrifice other aspects of the design, such as aerodynamic and propulsion performance, to achieve low observability. Still, the final mission requirements might require a minimum trade-off according to the airframe mission (air dominance, surveillance, strike). In this webinar, the concept of signature reduction/control applied to combat aircraft will be discussed. Attendees will learn to quantify the performance characteristics attainable from different solutions. The basics of radar and infrared signature requirements (applied to aircraft), and their effect on final airframe shape will be analysed, considering the relative importance in current and future designs. The course will end with a trade-off analysis of some current designs.

Learning Objectives:

• Definition of survivability.
• Basic of Radar Cross Section and Infrared signature.
• Design requirements and major challenges in stealth airframe design.
• Trade-off considerations.

Target audience

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

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