1,296 research outputs found

    Electromagnetic Analysis of Transients in the ITER PF Superconducting Joints

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    Il progetto ITER (International Thermonuclear Experimental Reactor) prevede lo sviluppo e la realizzazione di un prototipo di reattore a fusione nuceare, con lo scopo di ottenere dati sperimentali sulla fisica del plasma e verificare la stabilità e l’affidabilità di componenti ad alto contenuto tecnologico che operano in condizioni estreme di temperatura, corrente e campo magnetico. La reazione di fusione nucleare avviene in una corrente di plasma confinata in una camera toroidale attraverso il campo magnetico prodotto da bobine superconduttive sottoposte ad alto campo magnetico (fino a 12 T) e alte correnti (fino a 100 kA), mantenute a temperature criogeniche (inferiori a 5 K) tramite una corrente di elio supercritico. I componenti del sistema magnetico di ITER devono essere adeguatamente progettati e testati per verificarne l’affidabilità durante tutta la vita utile della macchina, evitando costose operazioni di riparazione in caso di guasto; dato l’elevato costo di ogni test è necessario sviluppare modelli numerici per simulare il comportamento dei componenti in diverse condizioni operative. In questo lavoro si analizzano, tramite due differenti modelli numerici, la resistenza elettrica in corrente continua (DC Resistance) e le perdite di energia in corrente alternata (AC Losses) del giunto superconduttivo che connette due avvolgimenti (Double Pancakes) della bobina inferiore del sistema magnetico per la generazione del campo poloiodale (Poloidal Field Coil), atto al controllo della posizione verticale della corrente di plasma. La validazione dei codici è basata sull’analisi sperimentale del campione PFJEU1 testato presso la SULTAN facility (Villigen, Switzerland) in Ottobre 2016

    Integration of Instrumentation and Computer Modelling to Understand and therefore Better Design and Represent the Rock bolt Support Behaviour

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    A safe and efficient ground control system is one of the most vital components of an underground mine’s operations. Current rockbolt ground reinforcement design methods do not consider the actual in-situ behaviour of the rockbolt. Instrumented rock bolts can be used to understand the actual rockbolt response under different loading conditions. Work done in this thesis aims to integrate the actual in-situ response of rockbolt with improved numerical modelling procedures for designing better ground support

    Explainable Artificial Intelligence for Mechanics: Physics-Explaining Neural Networks for Constitutive Models

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    (Artificial) neural networks have become increasingly popular in mechanics and materials sciences to accelerate computations with model order reduction techniques and as universal models for a wide variety of materials. However, the major disadvantage of neural networks remains: their numerous parameters are challenging to interpret and explain. Thus, neural networks are often labeled as black boxes, and their results often elude human interpretation. The new and active field of physics-informed neural networks attempts to mitigate this disadvantage by designing deep neural networks on the basis of mechanical knowledge. By using this a priori knowledge, deeper and more complex neural networks became feasible, since the mechanical assumptions can be explained. However, the internal reasoning and explanation of neural network parameters remain mysterious. Complementary to the physics-informed approach, we propose a first step towards a physics-explaining approach, which interprets neural networks trained on mechanical data a posteriori. This proof-of-concept explainable artificial intelligence approach aims at elucidating the black box of neural networks and their high-dimensional representations. Therein, the principal component analysis decorrelates the distributed representations in cell states of RNNs and allows the comparison to known and fundamental functions. The novel approach is supported by a systematic hyperparameter search strategy that identifies the best neural network architectures and training parameters. The findings of three case studies on fundamental constitutive models (hyperelasticity, elastoplasticity, and viscoelasticity) imply that the proposed strategy can help identify numerical and analytical closed-form solutions to characterize new materials

    Cyber Threat Intelligence based Holistic Risk Quantification and Management

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