Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.

Computational methods applied to undeciphered scripts from the Aegean and Cyprus: Linear A and Cypro-Minoan

The study of ancient, undeciphered scripts presents unique challenges, that depend both on the nature of the problem and on the peculiarities of each writing system. In this thesis, I present two computational approaches that are tailored to two different tasks and writing systems. The first of these methods is aimed at the decipherment of the Linear A afraction signs, in order to discover their numerical values. This is achieved with a combination of constraint programming, ad-hoc metrics and paleographic considerations. The second main contribution of this thesis regards the creation of an unsupervised deep learning model which uses drawings of signs from ancient writing system to learn to distinguish different graphemes in the vector space. This system, which is based on techniques used in the field of computer vision, is adapted to the study of ancient writing systems by incorporating information about sequences in the model, mirroring what is often done in natural language processing. In order to develop this model, the Cypriot Greek Syllabary is used as a target, since this is a deciphered writing system. Finally, this unsupervised model is adapted to the undeciphered Cypro-Minoan and it is used to answer open questions about this script. In particular, by reconstructing multiple allographs that are not agreed upon by paleographers, it supports the idea that Cypro-Minoan is a single script and not a collection of three script like it was proposed in the literature. These results on two different tasks shows that computational methods can be applied to undeciphered scripts, despite the relatively low amount of available data, paving the way for further advancement in paleography using these methods.

Numerical simulations of the spine for patient-specific models of severe scoliosis

In silico methods, such as musculoskeletal modelling, may aid the selection of the optimal surgical treatment for highly complex pathologies such as scoliosis. Many musculoskeletal models use a generic, simplified representation of the intervertebral joints, which are fundamental to the flexibility of the spine. Therefore, to model and simulate the spine, a suitable representation of the intervertebral joint is crucial. The aim of this PhD was to characterise specimen-specific models of the intervertebral joint for multi-body models from experimental datasets. First, the project investigated the characterisation of a specimen-specific lumped parameter model of the intervertebral joint from an experimental dataset of a four-vertebra lumbar spine segment. Specimen-specific stiffnesses were determined with an optimisation method. The sensitivity of the parameters to the joint pose was investigate. Results showed the stiffnesses and predicted motions were highly depended on both the joint pose. Following the first study, the method was reapplied to another dataset that included six complete lumbar spine segments under three different loading conditions. Specimen-specific uniform stiffnesses across joint levels and level-dependent stiffnesses were calculated by optimisation. Specimen-specific stiffness show high inter-specimen variability and were also specific to the loading condition. Level-dependent stiffnesses are necessary for accurate kinematic predictions and should be determined independently of one another. Secondly, a framework to create subject-specific musculoskeletal models of individuals with severe scoliosis was developed. This resulted in a robust codified pipeline for creating subject-specific, severely scoliotic spine models from CT data. In conclusion, this thesis showed that specimen-specific intervertebral joint stiffnesses were highly sensitive to joint pose definition and the importance of level-dependent optimisation. Further, an open-source codified pipeline to create patient-specific scoliotic spine models from CT data was released. These studies and this pipeline can facilitate the specimen-specific characterisation of the scoliotic intervertebral joint and its incorporation into scoliotic musculoskeletal spine models.

Numerical and physical modelling of axially loaded piles for offshore wind turbines

The scope of the thesis is to broaden the knowledge about axially loaded pipe piles, that can play as foundations for offshore wind turbines based on jacket structures. The goal of the work was pursued by interpreting experimental data on large-scale model piles and by developing numerical tools for the prediction of their monotonic response to tensile and compressive loads to failure. The availability of experimental results on large scale model piles produced in two different campaigns at Fraunhofer IWES (Hannover, Germany) represented the reference for the whole work. Data from CPTs, blow counts during installation and load-displacement curves allowed to develop considerations on the experimental results and comparison with empirical methods from literature, such as CPT-based methods and Load Transfer methods. The understanding of soil-structure interaction mechanisms has been involved in the study in order to better assess the mechanical response of the sand with the scope to help in developing predictive tools of the experiments. A lack of information on the response of Rohsand 3152 when in contact with steel was highlighted, so the necessity of better assessing its response was fulfilled with a comprehensive campaign of interface shear test. It was found how the response of the sand to ultimate conditions evolve with the roughness of the steel, which is a precious information to take account of when attempting the prediction of a pile capacity. Parallel to this topic, the work has developed a numerical modelling procedure that was validated on the available large-scale model piles at IWES. The modelling strategy is intended to build a FE model whose mechanical properties of the sand come from an interpretation of commonly available geotechnical tests. The results of the FE model were compared with other predictive tools currently used in the engineering practice.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.