Analysis and prediction of fretting fatigue life.

Fretting fatigue is a fatigue damage process that occurs when two surfaces in contact with each other are subjected to relative micro-slip, causing a reduced fatigue life with respect to the plain fatigue case. Fretting has been now studied deeply for over 50 years, but still no univocal design approach has been universally accepted. This thesis presents a method for predicting the fretting fatigue life of materials based on the material specific fatigue parameters. To validate the method, a set of fretting fatigue experimental tests have been run, using a newly designed specimen. FE analyses of the tests were also run and the SWT parameter was retrieved and it was found to be useful to successfully identify which samples failed. Finally, S-N curves were retrieved by using two different fatigue life predicting methods (CoffinManson and Jahed-Varvani). The two different methods were compared with the experimental results and it was found that the Jahed-Varvani method gave accurate results in terms of fretting fatigue life.

Environmental assessment of industrial chemical and primary sector processes from a life cycle perspective

During the PhD program in chemistry at the University of Bologna, the environmental sustainability of some industrial processes was studied through the application of the LCA methodology. The efforts were focused on the study of processes under development, in order to assess their environmental impacts to guide their transfer on an industrial scale. Processes that could meet the principles of Green Chemistry have been selected and their environmental benefits have been evaluated through a holistic approach. The use of renewable sources was assessed through the study of terephthalic acid production from biomass (which showed that only the use of waste can provide an environmental benefit) and a new process for biogas upgrading (whose potential is to act as a carbon capture technology). Furthermore, the basis for the development of a new methodology for the prediction of the environmental impact of ionic liquids has been laid. It has already shown good qualities in identifying impact trends, but further research on it is needed to obtain a more reliable and usable model. In the context of sustainable development that will not only be sector-specific, the environmental performance of some processes linked to the primary production sector has also been evaluated. The impacts of some organic farming practices in the wine production were analysed, the use of the Cereal Unit parameter was proposed as a functional unit for the comparison of different crop rotations, and the carbon footprint of school canteen meals was calculated. The results of the analyses confirm that sustainability in the industrial production sector should be assessed from a life cycle perspective, in order to consider all the flows involved during the different phases. In particular, it is necessary that environmental assessments adopt a cradle-to-gate approach, to avoid shifting the environmental burden from one phase to another.

Monitoring and Prediction of Thermal Emergencies in High Performance Computing Systems

Modern scientific discoveries are driven by an unsatisfiable demand for computational resources. High-Performance Computing (HPC) systems are an aggregation of computing power to deliver considerably higher performance than one typical desktop computer can provide, to solve large problems in science, engineering, or business. An HPC room in the datacenter is a complex controlled environment that hosts thousands of computing nodes that consume electrical power in the range of megawatts, which gets completely transformed into heat. Although a datacenter contains sophisticated cooling systems, our studies indicate quantitative evidence of thermal bottlenecks in real-life production workload, showing the presence of significant spatial and temporal thermal and power heterogeneity. Therefore minor thermal issues/anomalies can potentially start a chain of events that leads to an unbalance between the amount of heat generated by the computing nodes and the heat removed by the cooling system originating thermal hazards. Although thermal anomalies are rare events, anomaly detection/prediction in time is vital to avoid IT and facility equipment damage and outage of the datacenter, with severe societal and business losses. For this reason, automated approaches to detect thermal anomalies in datacenters have considerable potential. This thesis analyzed and characterized the power and thermal characteristics of a Tier0 datacenter (CINECA) during production and under abnormal thermal conditions. Then, a Deep Learning (DL)-powered thermal hazard prediction framework is proposed. The proposed models are validated against real thermal hazard events reported for the studied HPC cluster while in production. This thesis is the first empirical study of thermal anomaly detection and prediction techniques of a real large-scale HPC system to the best of my knowledge. For this thesis, I used a large-scale dataset, monitoring data of tens of thousands of sensors for around 24 months with a data collection rate of around 20 seconds.

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.