Structural health monitoring of storage tanks

In this thesis, Ph.D candidate presents a compact sensor node (SN) designed for long-term and real-time acoustic emission (AE) monitoring of above ground storage tanks (ASTs). Each SN exploits up to three inexpensive low-frequency sensors based on piezoelectric diaphragms for effective leakage detection, and it is capable by means of built-in Digital Signal Processing functionalities to process the acquired time waveforms extracting the AE features usually required by testing protocols. Alternatively, capability to plug three high frequency AE sensors to a SN for corrosion simulated phenomena detection is envisaged and demonstrated. Another innovative aspect that the Ph.D candidate presents in this work is an alternative mathematical model of corrosion location on the bottom of the AST. This approach implies considering the three-dimensional localization model versus the two-dimensional commonly used according to the literature. This approach is aimed at significant optimization in the number of sensors in relation to the standard approach for solving localization problems as well as to allow filtering the false AE events related to the condensate droplets from AST ceiling. The technological implementation of this concept required the solution of a number of technical problems, such as the precise time of arrival (ToA) signal estimation, vertical localization of the AE source and multilaration solution that were discussed in detail in this work. To validate the developed prototype, several experimental campaigns were organized that included the simulation of target phenomena both in laboratory conditions and on a real water storage tank. The presented test results demonstrate the successful application of the developed AE system both for simulated leaks and for corrosion processes on the tank bottom. Mathematical and technological algorithms for localization and characterization of AE signals implemented during the development of the prototype are also confirmed by the test results.

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies.

Nanostructured smart materials as functional components of medical devices

The field of medical devices has experienced, more than others, technological advances, developments and innovations, thanks to the rapidly expanding scientific knowledge and collaboration between different disciplines such as biology, engineering and materials science. The design of functional components can be achieved by exploiting composite materials based on nanostructured smart materials, that due to the inherent characteristics of single constituents develop unique properties that make them suitable for different applications preserving excellent mechanical proprieties. For instance, recent developments have focused on the fabrication of piezoelectric devices with multiple biomedical functions, as actuation and sensing functions in one component for monitoring pressure signals. The present Ph.D. Thesis aims at investigating nanostructured smart materials embedded into a polymeric matrix to obtain a composite material that can be used as a functional component for medical devices. (i) Nanostructured piezoelectric material with self-sensing capability was successfully manufactured by using ceramic (i.e. lead zirconate titanate (PZT)) and (ii) polymeric (i.e. poly(vinylidene fluoride-trifluoro ethylene (PVDF-TRFE)) piezoelectric materials. PZT nanofibers were obtained by sol-gel electrospinning starting from synthetized PZT precursor solution. Synthesis, sol-gel electrospinning process, and thermal treatment were accurately controlled to obtain PZT nanofibers dimensionally stable with densely packed grains in the perovskite phase. To guarantee the impact resistance of the laminate, the morphology and size of the hosting filler were accurately designed by increasing the surface area to volume ratio. Moreover, to solve the issue relative to the mechanical discrepancy between rigid electronic materials/soft human tissues/different material of the device (iii) a nanostructured flexible composite material based on a network of Poly-L-lactic acid (PLLA) made of curled nanofibers that present a tuneable mechanical response as a function of the applied stress was successful fabricated.

Effect of pulsed electromagnetic fields (pemfs) on muscle activity, tissue oxygenation and vo2 during exercise.

PEMF are a medical and non-invasive therapy successfully used for clinical treatments of bone disease, due to the piezoelectric effect that improve bone mass and density, by the stimulation of osteoblastogenesis, with modulation of calcium storages and mineral metabolism. PEMF enhance tissue oxygenation, microcirculation and angiogenesis, in rats and cells erythrocytes, in cells-free assay. Such responses could be caused by a modulation of nitric oxide signal and interaction between PEMF and Ca2+/NO/cGMP/PKG signal. PEMF improve blood flow velocity of smallest vein without changing their diameter. PEMF therapy helpful in patients with diabetes, due to increased microcirculation trough enhance capillary blood velocity and diameter. We investigated the influence of stimulation on muscular activity, tissue oxygenation and pulmonary VO2, during exercise, on different intensity, as heavy or moderate, different subjects, as a athlete or sedentary, and different sport activity, as a cycling or weightlifting. In athletes, we observed a tendency for a greater change and a faster kinetic of HHb concentration. PEMF increased the velocity and the quantity of muscle O2 available, leading to accelerate the HHb kinetics. Stimulation induced a bulk muscle O2 availability and a greater muscle O2 extraction, leading to a reduced time delay of the HHb slow component. Stimulation increased the amplitude of muscle activity under different conditions, likely caused by the effect of PEMF on contraction mechanism of muscular fibers, by the change of membrane permeability and Ca2+ channel conduction. In athletes, we observed an increase of overall activity during warm-up. In sedentary people, stimulation increased the magnitude of muscle activity during moderate constant-load exercise and warm-up. In athletes and weightlifters, stimulation caused an increase of blood lactate concentration during exercise, confirming a possible influence of stimulation on muscle activity and on glycolytic metabolism of type-II muscular fibers.

Design and testing of innovative, artificial intelligence-based techniques to model and control the combustion process and to reduce the Co2 emissions in modern, high-performance, gasoline direct injection engines

This work deals with the development of calibration procedures and control systems to improve the performance and efficiency of modern spark ignition turbocharged engines. The algorithms developed are used to optimize and manage the spark advance and the air-to-fuel ratio to control the knock and the exhaust gas temperature at the turbine inlet. The described work falls within the activity that the research group started in the previous years with the industrial partner Ferrari S.p.a. . The first chapter deals with the development of a control-oriented engine simulator based on a neural network approach, with which the main combustion indexes can be simulated. The second chapter deals with the development of a procedure to calibrate offline the spark advance and the air-to-fuel ratio to run the engine under knock-limited conditions and with the maximum admissible exhaust gas temperature at the turbine inlet. This procedure is then converted into a model-based control system and validated with a Software in the Loop approach using the engine simulator developed in the first chapter. Finally, it is implemented in a rapid control prototyping hardware to manage the combustion in steady-state and transient operating conditions at the test bench. The third chapter deals with the study of an innovative and cheap sensor for the in-cylinder pressure measurement, which is a piezoelectric washer that can be installed between the spark plug and the engine head. The signal generated by this kind of sensor is studied, developing a specific algorithm to adjust the value of the knock index in real-time. Finally, with the engine simulator developed in the first chapter, it is demonstrated that the innovative sensor can be coupled with the control system described in the second chapter and that the performance obtained could be the same reachable with the standard in-cylinder pressure sensors.

Innovative photoresponsive materials & supramolecular structures

This thesis focuses on two main topics: photoresponsive azobenzene-based polymers and supramolecular systems generated by the self-assembly of lipophilic guanosines. In the first chapters describe innovative photoresponsive devices and materials capable of performing multiple roles in the field of soft robotics and energy conversion. Chapter 2 describes a device obtained by coupling a photoresponsive liquid-crystalline network and a piezoelectric polymer to convert visible light into electricity. Chapter 3 deals with a material that can assume different shapes when triggered by three different stimuli in different environments. Chapter 4 reports a highly performing artificial muscle that contracts when irradiated. The last two chapters report on supramolecular structures generated from functionalized guanosines dissolved in organic solvents. Chapter 6 illustrates the self-assembly into G-quadruplexes of 8- and 5’-functionalized guanosines in the absence of templating ions. Chapter 7 describes the supramolecular structure generated by the assembly of a lipophilic guanosine in the presence of silver cations. Chapter 6 is reproduced from an already published paper, while the other chapters are going to be submitted to different journals in a couple of months.

Development and testing of methodologies for the control and diagnosis of innovative combustion systems

In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.