Modeling and optimization techniques for advanced vehicular networking

This thesis deals with optimization techniques and modeling of vehicular networks. Thanks to the models realized with the integer linear programming (ILP) and the heuristic ones, it was possible to study the performances in 5G networks for the vehicular. Thanks to Software-defined networking (SDN) and Network functions virtualization (NFV) paradigms it was possible to study the performances of different classes of service, such as the Ultra Reliable Low Latency Communications (URLLC) class and enhanced Mobile BroadBand (eMBB) class, and how the functional split can have positive effects on network resource management. Two different protection techniques have been studied: Shared Path Protection (SPP) and Dedicated Path Protection (DPP). Thanks to these different protections, it is possible to achieve different network reliability requirements, according to the needs of the end user. Finally, thanks to a simulator developed in Python, it was possible to study the dynamic allocation of resources in a 5G metro network. Through different provisioning algorithms and different dynamic resource management techniques, useful results have been obtained for understanding the needs in the vehicular networks that will exploit 5G. Finally, two models are shown for reconfiguring backup resources when using shared resource protection.

Performance comparison of C-V2X and WAVE protocols for vehicular to infrastructure communications: simulation of the highway use case

The objective of this thesis is the analysis and the study of the various access techniques for vehicular communications, in particular of the C-V2X and WAVE protocols. The simulator used to study the performance of the two protocols is called LTEV2Vsim and was developed by the CNI IEIIT for the study of V2V (Vehicle-to-Vehicle) communications. The changes I made allowed me to study the I2V (Infrastructure-to-Vehicle) scenario in highway areas and, with the results obtained, I made a comparison between the two protocols in the case of high vehicular density and low vehicular density, putting in relation to the PRR (packet reception ratio) and the cell size (RAW, awareness range). The final comparison allows to fully understand the possible performances of the two protocols and highlights the need for a protocol that allows to reach the minimum necessary requirements.

Virtual Calibration of CO2 and Pollutant Emissions of a High-Performance PHEV using Model-in-the-Loop Methodology

The study analyses the calibration process of a newly developed high-performance plug-in hybrid electric passenger car powertrain. The complexity of modern powertrains and the more and more restrictive regulations regarding pollutant emissions are the primary challenges for the calibration of a vehicle’s powertrain. In addition, the managers of OEM need to know as earlier as possible if the vehicle under development will meet the target technical features (emission included). This leads to the necessity for advanced calibration methodologies, in order to keep the development of the powertrain robust, time and cost effective. The suggested solution is the virtual calibration, that allows the tuning of control functions of a powertrain before having it built. The aim of this study is to calibrate virtually the hybrid control unit functions in order to optimize the pollutant emissions and the fuel consumption. Starting from the model of the conventional vehicle, the powertrain is then hybridized and integrated with emissions and aftertreatments models. After its validation, the hybrid control unit strategies are optimized using the Model-in-the-Loop testing methodology. The calibration activities will proceed thanks to the implementation of a Hardware-in-the-Loop environment, that will allow to test and calibrate the Engine and Transmission control units effectively, besides in a time and cost saving manner.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Modelling, control and testing of internal combustion engines to minimize consumption and pollutant emissions

The objective of the PhD thesis was to research technologies and strategies to reduce fuel consumption and pollutants emission produced by internal combustion engines. In order to meet this objective my activity was focused on the research of advanced controls based on cylinder pressure feedback. These types of control strategies were studied because they present promising results in terms of engine efficiency enhancement. In the PhD dissertation two study cases are presented. The first case is relative to a control strategy to be used at the test bench for the optimisation of the spark advance calibration of motorcycle Engine. The second case is relative to a control strategy to be used directly on board of mining engines with the objective or reducing the engine consumption and correct ageing effects. In both cases the strategies proved to be effective but their implementation required the use of specific toolchains for the measure of the cylinder pressure feedback that for a matter of cost makes feasible the strategy use only for applications: • At test bench • In small-markets like large off-road engines The major bottleneck that prevents the implementation of these strategies on mass production is the cost of cylinder pressure sensor. In order to tackle this issue, during the PhD research, the development of a low-cost sensor for the estimation of cylinder pressure was studied. The prototype was a piezo-electric washer designed to replace the standard spark-plug washer or high-pressure fuel injectors gasket. From the data analysis emerged the possibility to use the piezo-electric prototype signal to evaluate with accuracy several combustion metrics compatible for the implementation of advanced control strategies in on-board applications. Overall, the research shows that advanced combustion controls are feasible and beneficial, not only at the test bench or on stationary engines, but also in mass-produced engines.

Laser cladding and its potential to reduce particulate matter emissions from the automotive and locomotive sector

Laser Cladding (LC) is an emerging technology which is used both for coating applications as well as near-net shape fabrication. Despite its significant advantages, such as low dilution and metallurgical bond with the substrate, it still faces issues such as process control and repeatability, which restricts the extension to its applications. The following thesis evaluates the LC technology and tests its potential to be applied to reduce particulate matter emissions from the automotive and locomotive sector. The evaluation of LC technology was carried out for the deposition of multi-layer and multi-track coatings. 316L stainless steel coatings were deposited to study the minimisation of geometric distortions in thin-walled samples. Laser power, as well as scan strategy, were the main variables to achieve this goal. The use of constant power, reduction at successive layers, a control loop control system, and two different scan strategies were studied. The closed-loop control system was found to be practical only when coupled with the correct scan strategy for the deposition of thin walls. Three overlapped layers of aluminium bronze were deposited onto a structural steel pipe for multitrack coatings. The effect of laser power, scan speed and hatch distance on the final geometry of coating were studied independently, and a combined parameter was established to effectively control each geometrical characteristic (clad width, clad height and percentage of dilution). LC was then applied to coat commercial GCI brake discs with tool steel. The optical micrography showed that even with preheating, the cracks that originated from the substrate towards the coating were still present. The commercial brake discs emitted airborne particles whose concentration and size depended on the test conditions used for simulation in the laboratory. The contact of LC cladded wheel with rail emitted significantly less ultra-fine particles while maintaining the acceptable values of coefficient of friction.

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.

ICE Emissions Estimation through Machine-learning techniques to support real-world R&D experiments

The emissions estimation, both during homologation and standard driving, is one of the new challenges that automotive industries have to face. The new European and American regulation will allow a lower and lower quantity of Carbon Monoxide emission and will require that all the vehicles have to be able to monitor their own pollutants production. Since numerical models are too computationally expensive and approximated, new solutions based on Machine Learning are replacing standard techniques. In this project we considered a real V12 Internal Combustion Engine to propose a novel approach pushing Random Forests to generate meaningful prediction also in extreme cases (extrapolation, very high frequency peaks, noisy instrumentation etc.). The present work proposes also a data preprocessing pipeline for strongly unbalanced datasets and a reinterpretation of the regression problem as a classification problem in a logarithmic quantized domain. Results have been evaluated for two different models representing a pure interpolation scenario (more standard) and an extrapolation scenario, to test the out of bounds robustness of the model. The employed metrics take into account different aspects which can affect the homologation procedure, so the final analysis will focus on combining all the specific performances together to obtain the overall conclusions.

Long-term Prospects For The Environmental Profile Of Advanced Sugar Cane Ethanol.

This work assessed the environmental impacts of the production and use of 1 MJ of hydrous ethanol (E100) in Brazil in prospective scenarios (2020-2030), considering the deployment of technologies currently under development and better agricultural practices. The life cycle assessment technique was employed using the CML method for the life cycle impact assessment and the Monte Carlo method for the uncertainty analysis. Abiotic depletion, global warming, human toxicity, ecotoxicity, photochemical oxidation, acidification, and eutrophication were the environmental impacts categories analyzed. Results indicate that the proposed improvements (especially no-til farming-scenarios s2 and s4) would lead to environmental benefits in prospective scenarios compared to the current ethanol production (scenario s0). Combined first and second generation ethanol production (scenarios s3 and s4) would require less agricultural land but would not perform better than the projected first generation ethanol, although the uncertainties are relatively high. The best use of 1 ha of sugar cane was also assessed, considering the displacement of the conventional products by ethanol and electricity. No-til practices combined with the production of first generation ethanol and electricity (scenario s2) would lead to the largest mitigation effects for global warming and abiotic depletion. For the remaining categories, emissions would not be mitigated with the utilization of the sugar cane products. However, this conclusion is sensitive to the displaced electricity sources.

Quantificação de fluxos de mercúrio gasoso na interface solo/atmosfera utilizando câmara de fluxo dinâmica: aplicação na bacia do Rio Negro

Gaseous mercury sampling conditions were optimized and a dynamic flux chamber was used to measure the air/surface exchange of mercury in some areas of the Negro river basin with different vegetal coverings. At the two forest sites (flooding and non-flooding), low mercury fluxes were observed: maximum of 3 pmol m-2 h-1 - day and minimum of -1 pmol m-2 h-1 - night. At the deforested site, the mercury fluxes were higher and always positive: maximum of 26 pmol m-2 h-1 - day and 17 pmol m-2 h-1 - night. Our results showed that deforestation could be responsible for significantly increasing soil Hg emissions, mainly because of the high soil temperatures reached at deforested sites.

Efeito da adição de cinza da casca de arroz em misturas cimento-casca de arroz

The rice husk and its ash are abundant and renewable and can be used to obtain alternative building materials. An increase in the consumption of such waste could help minimize the environmental problems from their improper disposal. This study aimed to evaluate the use of ashes as a cargo mineral (filler). However, the rice husk chemically interferes in the conduct of the based cement mixtures. Thus, different mixes cement-rice husk with and without the addition of ash were evaluated in order to highlight the influence of its components (husk; ash), which could otherwise be excluded or be underestimated. Cylindrical samples (test of simple compression and traction by diametrical compression) and samples extracted from manufactured pressed board (test of bending and parallel compression to the surface), were used to evaluate the behavior of different mixtures of components (rice hush; RHA - rice husk ahs). The results of the mechanical tests showed, in general, there is not a statistical difference between the mixtures, which are associated with the chemical suppressive effect of the rice husk ash. The mixture of rice husk of 10 mm, with an addition of 35% of the rice husk ash, is notable for allowing the highest consumption of rice husk and rice husk ash, to reduce 25% the consumption of cement and to allow the storage (without emissions to the atmosphere), around 1.9 ton of CO2 per ton of cement consumed, thus contributing to the reduction of CO2 emissions, which can stimulate rural constructions under an ecological point of view.

Efeitos de dietas contendo Leucaena leucocephala e Saccharomyces cerevisiae sobre a fermentação ruminal e a emissão de gás metano em bovinos

Este trabalho foi realizado com o objetivo de avaliar os efeitos do uso de leucena e levedura em dietas para bovinos sobre o metabolismo ruminal, incluindo o pH e as produções de ácido graxos voláteis (AGV), amônia e gás metano. Quatro bovinos machos com 800 kg e fistulados no rúmen foram mantidos em quadrado latino 4 × 4, em arranjo fatorial 2 × 2, composto de dois níveis de leucena (20 e 50% MS) e feno de capim coast-cross na presença ou ausência de levedura. Não houve influência das dietas nos valores médios de pH (média 6,82) e nas concentrações de amônia no rúmen, que variaram de 18 a 21 mg/100 mL. Houve interação entre níveis de leucena e levedura na concentração total de AGV. As dietas não diferiram quanto à concentração de ácido acético, mas os animais alimentados com a dieta com 50% de leucena e contendo levedura apresentaram maiores concentrações médias de ácido propiônico (média 19,14 mM). A emissão de metano reduziu em12,3% em relação à mesma dieta sem levedura e em 17,2% quando os animais foram alimentados com 20% de leucena com levedura. Verificou-se efeito associativo de leucena, quando fornecida em alto nível na dieta (50% MS), e levedura na redução da emissão de metano e na melhoria no padrão de fermentação no rúmen, o que pode reduzir as perdas de energia e melhorar eficiência energética do animal.

Weekend/weekday differences in concentrations of ozone, nox, and non-methane hydrocarbon in the metropolitan area of São Paulo

Ozone and inhalable particulate matter are the major air pollutants in the Metropolitan Area of São Paulo, Brazil, a region that has more than 19 million inhabitants and approximately 7 million registered vehicles. Proximity of roadways, adjacent land use, and local circulation are just some of the factors that can affect the results of monitoring of pollutant concentrations. The so-called weekend effect (higher ozone concentrations on weekends than on weekdays) might be related to the fact that concentrations of ozone precursors, such as nitrogen oxides (NOx) and Non Methane-Hydrocarbon (NMHC), are relatively lower on weekends. This phenomenon has been reported in some areas of the United States since the 1970s. The differences between the concentrations of ozone in period of weekend and weekday, were obtained from analysis of data hourly average of CETESB for 2004, studied the precursors to the formation of troposphere ozone, the meteorological variables and traffic profile for RMSP. Because of the proximity to sources of emissions from the station Pinheiros showed higher concentrations of NO and NO² and greater variations to the periods weekend and weekday. With fewer vehicles circulating during the weekend, and consequently less emission of pollutants, it has cleaner air and less concentration of NO and NO², there is the ideal setting to the formation of troposphere ozone, despite the lower concentration of NO². The proximity with the source emissions, aided by the increased availability of solar radiation and the presence of ozone precursors, were factors conditions for the occurrence of weekend effect.

Ambiental volatile organic compounds in the megacity of São Paulo

In order to characterize the composition of the main urban air organic compounds in the megacity of Sao Paulo, analysis of samples collected during the winter of 2003 downtown was carried out. The samplings were performed on the roof of a building in the commercial center of São Paulo. Hydrocarbons and carbonyls compounds were collected on August 4, 5 and 6. Comparing to previous data, the concentration of hydrocarbons presented no decrease in the concentration, except for the aldehydes, which decreased when compared to previous data. Among the HCs species analyzed, the highest concentrations observed were those of toluene (7.5 ± 3.4 ppbv), n-decane (3.2 ± 2.0 ppbv), benzene (2.7 ± 1.4 ppbv) and 1,3,5-trimethylbenzene (2.2 ± 1.5 ppbv).

Medidas de audição de pais de indivíduos com deficiência auditiva de herança autossômica recessiva

TEMA: avaliação audiológica de pais de indivíduos com perda auditiva de herança autossômica recessiva. OBJETIVO: estudar o perfil audiológico de pais de indivíduos com perda auditiva, de herança autossômica recessiva, inferida pela história familial ou por testes moleculares que detectaram mutação no gene GJB2, responsável por codificar a Conexina 26. MÉTODO: 36 indivíduos entre 30 e 60 anos foram avaliados e divididos em dois grupos: grupo controle, sem queixas auditivas e sem história familiar de deficiência auditiva, e grupo de estudos composto por pais heterozigotos em relação a genes de surdez de herança autossômica recessiva inespecífica ou portadores heterozigotos de mutação no gene da Conexina 26. Todos foram submetidos à audiometria tonal liminar (0,25kHz a 8), audiometria de altas freqüências (9kHz a 20) e emissões otoacústicas produtos de distorção (EOAPD). RESULTADOS: houve diferenças significativas na amplitude das EOAPD nas freqüências 1001 e 1501Hz entre os grupos, sendo maior a amplitude no grupo controle. Não houve diferença significativa entre os grupos para os limiares tonais de 0,25 a 20KHz. CONCLUSÃO: as EOAPD foram mais eficazes, em comparação com a audiometria tonal liminar, para detectar diferenças auditivas entre os grupos. Mais pesquisas são necessárias para verificar a confiabilidade destes dados.