Space Systems Resilience Engineering and Global System Reliability Optimisation Under Imprecision and Epistemic Uncertainty

The paper introduces the concept of design for resilience in the context of space systems engineering and proposes a method to account for imprecision and epistemic uncertainty. Resilience can be seen as the ability of a system to adjust its functioning prior to, during, or following changes and disturbances, so that it can sustain required operations under both expected and unexpected conditions. Mathematically speaking this translates into the attribute of a dynamical system (or time dependent system) to be simultaneously robust and reliable. However, the quantification of robustness and reliability in the early stage of the design of a space systems is generally affected by uncertainty that is epistemic in nature. As the design evolves from Phase A down to phase E, the level of epistemic uncertainty is expected to decrease but still a level of variability can exist in the expected operational conditions and system requirements. The paper proposes a representation of a complex space system using the so called Evidence Network Models (ENM): a non-directed (unlike Bayesian network models) network of interconnected nodes where each node represents a subsystem with associated epistemic uncertainty on system performance and failure probability. Once the reliability and uncertainty on the performance of the spacecraft are quantified, a design optimisation process is applied to improve resilience and performance. The method is finally applied to an example of preliminary design of a small satellite in Low Earth Orbit (LEO). The spacecraft is divided in 5 subsystems, AOCS, TTC, OBDH, Power and Payload. The payload is a simple camera acquiring images at scheduled times. The assumption is that each component has multiple functionalities and both the performance of the component and the reliability associated to each functionality are affected by a level of imprecision. The overall performance indicator is the sum of the performance indicators of all the components

Algorithms for design optimization of chemistry of hard magnetic alloys using experimental data

A multi-dimensional random number generation algorithm was used to distribute chemical concentrations of each of the alloying elements in the candidate alloys as uniformly as possible while maintaining the prescribed bounds on the minimum and maximum allowable values for the concentration of each of the alloying elements. The generated candidate alloy compositions were then examined for phase equilibria and associated magnetic properties using a thermodynamic database in the desired temperature range. These initial candidate alloys were manufactured, synthesized and tested for desired properties. Then, the experimentally obtained values of the properties were fitted with a multi-dimensional response surface. The desired properties were treated as objectives and were extremized simultaneously by utilizing a multi-objective optimization algorithm that optimized the concentrations of each of the alloying elements. This task was also performed by another conceptually different response surface and optimization algorithm for double-checking the results. A few of the best predicted Pareto optimal alloy compositions were then manufactured, synthesized and tested to evaluate their macroscopic properties. Several of these Pareto optimized alloys outperformed most of the candidate alloys on most of the objectives. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of the alloys. A sensitivity analysis of each of the alloying elements was also performed to determine which of the alloying elements contributes the least to the desired macroscopic properties of the alloy. These elements can then be replaced with other candidate alloying elements such as not-so-rare earth elements

Self-Organizing Maps for Pattern Recognition in Design of Alloys

A combined experimental\u2013computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasirandom number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as selforganizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process

Nuovo progetto del ricevitore SXL per l’antenna parabolica dell’ IRA di NOTO (SR)

Con il seguente lavoro vengono descritte le varie attività e azioni intraprese per riprogettare ricevitore SXL per l’antenna di Noto. Il nuovo progetto del ricevitore riguarderà in particolar modo la parte elettronica di trattamento del segnale raccolto dai tre feed systems. Nel nuovo progetto, inoltre, saranno introdotte alcune variazioni rispetto alla configurazione originale in particolar modo sul sistema di instradamento dei segnali verso il back-end dove verranno utilizzati dei link in fibra ottica anziché la classica discesa in cavo coassiale. Il progetto ha seguito le linee guida di “massima semplicità” e “massima robustezza” (anche da un punto di vista delle sovratensioni causate da scariche atmosferiche)

Contribution of Genetic Background, Traditional Risk Factors, and HIV-Related Factors to Coronary Artery Disease Events in HIV-Positive Persons

We show in human immunodeficiency virus-positive persons that the coronary artery disease effect of an unfavorable genetic background is comparable to previous studies in the general population, and comparable in size to traditional risk factors and antiretroviral regimens known to increase cardiovascular ris

The high-frequency upgrade of the Sardinia Radio Telescope

We present the status of the Sardinia Radio Telescope (SRT) and its forthcoming update planned in the next few years. The post-process scenario of the upgraded infrastructure will allow the national and international scientific community to use the SRT for the study of the Universe at high radio frequencies (up to 116 GHz), both in single dish and in interferometric mode. A telescope like SRT, operating at high frequencies, represents a unique resource for the scientific community. The telescope will be ideal for mapping quickly and with relatively high angular resolution extended radio emissions characterized by low surface brightness. It will also be essential for spectroscopic and polarimetric studies of both Galactic and extragalactic radio sources. With the use of the interferometric technique, SRT and the other Italian antennas (Medicina and Noto) will operate within the national and international radiotelescope network, allowing astronomers to obtain images of radio sources at very high angular resolution

Status of the High-Frequency Upgrade of the Sardinia Radio Telescope

The Sardinia Radio Telescope is going through a major upgrade aimed at observing the universe at up to 116 GHz. A budget of 18.700.000 E has been awarded to the Italian National Institute of Astrophysics to acquire new state-of-the-art receivers, back-end, and high-performance computing, to develop a sophisticated metrology system and to upgrade the infrastructure and laboratories. This contribution draws the status of the whole project at eight months from the end of the funding scheme planned for August 2022

AI tools in the design process of industrial products

Abstract. The definition of Artificial Intelligence that can be found on the pages of Wikipedia is the intelligence exhibited by machines or software which clearly has a rather broad and vague meaning that in many circumstances has been misunderstood. I would therefore focus more on Artificial Intelligence Tools, i.e. the spectrum of mathematical procedure that can be used to gain, explore and exploit knowledge during a design process. To gain knowledge means to probe design opportunities in a systematic way in order to collect sufficient data to be able to understand and predict product behaviour. To explore knowledge means to be able to drive automatically through the design options using optimization techniques. To exploit knowledge means to be able to take rational decisions about the configuration of a product to be produced. All these actions can be performed by means of software components based on AI-related tools: Neural networks, Evolutionary Computing, Classifier Systems just to name a few. In the development of decision support software for design optimization there is not one technique that would prevail but a blending of tools, including more traditional mathematical algorithms, that contribute to the finding of the best design configuration. In this presentation a selection of industrial application form transportation industry to consumer goods will be used to showcase the use of AItools in daily design activity while possible future needs will be identified by looking at the opportunity offered by collaborative environments