Real-Time Engine Diagnostic and Control Based on Acoustic Emissions Analysis

Engine developers are putting more and more emphasis on the research of maximum thermal and mechanical efficiency in the recent years. Research advances have proven the effectiveness of downsized, turbocharged and direct injection concepts, applied to gasoline combustion systems, to reduce the overall fuel consumption while respecting exhaust emissions limits. These new technologies require more complex engine control units. The sound emitted from a mechanical system encloses many information related to its operating condition and it can be used for control and diagnostic purposes. The thesis shows how the functions carried out from different and specific sensors usually present on-board, can be executed, at the same time, using only one multifunction sensor based on low-cost microphone technology. A theoretical background about sound and signal processing is provided in chapter 1. In modern turbocharged downsized GDI engines, the achievement of maximum thermal efficiency is precluded by the occurrence of knock. Knock emits an unmistakable sound perceived by the human ear like a clink. In chapter 2, the possibility of using this characteristic sound for knock control propose, starting from first experimental assessment tests, to the implementation in a real, production-type engine control unit will be shown. Chapter 3 focus is on misfire detection. Putting emphasis on the low frequency domain of the engine sound spectrum, features related to each combustion cycle of each cylinder can be identified and isolated. An innovative approach to misfire detection, which presents the advantage of not being affected by the road and driveline conditions is introduced. A preliminary study of air path leak detection techniques based on acoustic emissions analysis has been developed, and the first experimental results are shown in chapter 4. Finally, in chapter 5, an innovative detection methodology, based on engine vibration analysis, that can provide useful information about combustion phase is reported.

Contribution of rare damaging variants in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a heterogeneous and highly heritable neurodevelopmental disorder with a complex genetic architecture, consisting of a combination of common low-risk and more penetrant rare variants. This PhD project aimed to explore the contribution of rare variants in ASD susceptibility through NGS approaches in a cohort of 106 ASD families including 125 ASD individuals. Firstly, I explored the contribution of inherited rare variants towards the ASD phenotype in a girl with a maternally inherited pathogenic NRXN1 deletion. Whole exome sequencing of the trio family identified an increased burden of deleterious variants in the proband that could modulate the CNV penetrance and determine the disease development. In the second part of the project, I investigated the role of rare variants emerging from whole genome sequencing in ASD aetiology. To properly manage and analyse sequencing data, a robust and efficient variant filtering and prioritization pipeline was developed, and by its application a stringent set of rare recessive-acting and ultra-rare variants was obtained. As a first follow-up, I performed a preliminary analysis on de novo variants, identifying the most likely deleterious variants and highlighting candidate genes for further analyses. In the third part of the project, considering the well-established involvement of calcium signalling in the molecular bases of ASD, I investigated the role of rare variants in voltage-gated calcium channels genes, that mainly regulate intracellular calcium concentration, and whose alterations have been correlated with enhanced ASD risk. Specifically, I functionally tested the effect of rare damaging variants identified in CACNA1H, showing that CACNA1H variation may be involved in ASD development by additively combining with other high risk variants. This project highlights the challenges in the analysis and interpretation of variants from NGS analysis in ASD, and underlines the importance of a comprehensive assessment of the genomic landscape of ASD individuals.