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"L'ora è confusa e noi come perduti la viviamo". Leggere Pier Paolo Pasolini quarant'anni dopo
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MAXIMIZING OVERALL RESULTS OF DRUG-ELUTING BEADS (DEB) PROCEDURES: PREDICTIVE CRITERIA OF DEB EMBOLIZATION IN UVEAL MELANOMA
No full textPERCUTANEOUS INTRAHEPATIC TRANSPLANTATION OF ISLETS AFTER KIDNEY AND ISLETS ALONE IN 34 TYPE 1 DIABETIC PATIENTS: TECHNICAL ASPECTS, COMPLICATIONS AND CLINICAL OUTCOME
No full textTRATTAMENTO PERCUTANEO ENDOVASCOLARE DI ANEURISMI DI ARTERIE VISCERALI (AAV) MEDIANTE STENT RICOPERTI
No full textDevelopment of a system magneto-thermal-hydraulics code for the modelling of nuclear fusion reactors
Full text linkThis doctoral research focuses on the critical problem of modeling and understanding Magnetohydrodynamic (MHD) phenomena in the Liquid Metal Breeding Blankets
(BB) within magnetic confinement fusion reactors. Conventional analytical and numerical methodologies have proven to be inadequate for capturing the complex inter-
actions and phenomena inherent to the extreme conditions present in fusion environments. To address this lacuna, the study presents the development, Verification and
Validation (V&V) of a novel Systems Thermal-Hydraulic (SYS-TH) computational tool, named RELAP5 Development for MagnetoHydroDynamics (REDMaHD).
The foundation for REDMaHD is the well-regarded RELAP5/Mod 3.3 code, which has been widely validated for thermo-fluid-dynamic assessments of fission reactors. Adapting this code for fusion applications has led to a software platform with comprehensive capabilities, including the computation of distributed MHD pressure losses in various channel configurations and the evaluation of localized MHD pressure drops due to factors like bends, cross-sectional changes, and electrical conductivity
discontinuities in the walls. The tool is also designed to work with fusion-relevant liquid metals, such as the lead-lithium eutectic alloy (PbLi) and sodium-potassium
(NaK), further expanding its applicability. An extensive V&V procedure was conducted to establish the reliability and accuracy of REDMaHD. This involved not only the validation of individual subroutines but also the comparison of the code output against existing numerical simulations and empirical data from three Test Blanket Modules (TBMs) including the LLCB, HCLL, and WCLL. The validation process has shown that REDMaHD predictions deviate only minimally, by approximately 1% to 10%, when compared to these benchmark results, thus confirming its fidelity in simulating key electromagnetic effects relevant to fusion reactor design. Although REDMaHD has proven to be an advanced and dependable tool, it is important to note the existing limitations. The code currently lacks specialized modules for certain electromagnetic coupling phenomena and three-dimensional MHD losses, which impacts its ability to predict mass flow distributions in multi-channel configurations. The study outlines the future work needed to resolve these issues, including the integration of additional modules that can handle the newly identified requirements. By providing an advanced numerical tool with verified capabilities, this thesis significantly contributes to ongoing research efforts in fusion technology, offering a robust computational platform for tackling a wide range of engineering challenges in the design and operational analysis of future fusion reactors
Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
No full textIn the paper, self-adapting models capable of reproducing time-dependent data with high
computational speed are investigated. The considered models are recurrent feed-forward
neural networks (RNNs) with one feedback loop in a recursive computational structure,
trained by using a back-propagation learning algorithm. The data used for both training
and testing the RNNs have been generated by means of a nonlinear physics-based model
for compressor dynamic simulation, which was calibrated on a multistage axialcentrifugal
small size compressor. The first step of the analysis is the selection of the
compressor maneuver to be used for optimizing RNN training. The subsequent step consists
in evaluating the most appropriate RNN structure (optimal number of neurons in the
hidden layer and number of outputs) and RNN proper delay time. Then, the robustness of
the model response towards measurement uncertainty is ascertained, by comparing the
performance of RNNs trained on data uncorrupted or corrupted with measurement errors
with respect to the simulation of data corrupted with measurement errors. Finally, the
best RNN model is tested on field data taken on the axial-centrifugal compressor on
which the physics-based model was calibrated, by comparing physics-based model and
RNN predictions against measured data. The comparison between RNN predictions and
measured data shows that the agreement can be considered acceptable for inlet pressure,
outlet pressure and outlet temperature, while errors are significant for inlet mass flow
rate
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