The exhaust system of the light-duty diesel engine has been evaluated as a potential environment for a mechanical energy recovery system for powering an IoT (Internet of Things) remote sensor. Temperature, pressure, gas speed, mass flow rate have been measured in order to characterize the exhaust gas. At any engine point explored, thermal energy is by far the most dominant portion of the exhaust energy, followed by the pressure energy and lastly kinetic energy is the smallest fraction of the exhaust energy. A piezoelectric flexible device has been tested as a possible candidate as an energy harvester converting the kinetic energy of the exhaust gas flow, with a promising amount of electrical energy generated in the order of microjoules for an urban or extra-urban circuit.

The Aircraft uptime is getting increasingly important as the transport solutions become more complex and the transport industry seeks new ways of being competitive. To reach this objective, traditional Fleet Management systems are gradually extended with new features to improve reliability and then provide better maintenance planning. Main goal of this work is the development of iterative algorithms based on Artificial Intelligence to define the engine removal plan and its maintenance work, optimizing engine availability at the customer and maintenance costs, as well as obtaining a procurement plan of integrated parts with planning of interventions and implementation of a maintenance strategy. In order to reach this goal, Machine Learning has been applied on a workshop dataset with the aim to optimize warehouse spare parts number, costs and lead-time. This dataset consists of the repair history of a specific engine type, from several years and several fleets, and contains information like repair claim, engine working time, forensic evidences and general information about processed spare parts. Using these data as input, several Machine Learning models have been built in order to predict the repair state of each spare part for a better warehouse handling. A multi-label classification approach has been used in order to build and train, for each spare part, a Machine Learning model that predicts the part repair state as a multiclass classifier does. Mainly, each classifier is requested to predict the repair state (classified as “Efficient”, “Repaired” or “Replaced”) of the corresponding part, starting from two variables: the repairing claim and the engine working time. Then, global results have been evaluated using the Confusion Matrix, from which Accuracy, Precision, Recall and F1-Score metrics are retrieved, in order to analyse the cost of incorrect prediction. These metrics are calculated for each spare part related model on test sets and, then, a final single performance value is obtained by averaging results. In this way, three Machine Learning models (Naïve Bayes, Logistic Regression and Random Forest classifiers) are applied and results are compared. Naïve Bayes and Logistic Regression, that are fully probabilistic methods, have best global performances with an accuracy value of almost 80%, making the models being correct most of the times.

Bio-hydrogen from sustainable biomass (i.e. agro-industrial residues) gasification can play a relevant role in the hydrogen economy, providing constant hydrogen from renewable sources. Nowadays, most hydrogen production systems integrate one or more water-gas shift (WGS) units to maximize the hydrogen yield that, however, needs additional syngas treatments, investment and operational costs. Besides, different electricity inputs are needed along the process to power the compression of raw syngas, shifted syngas, and pure hydrogen to the desired pressure. This common process integration with WGS generates a kind of off-gas from the hydrogen separation unit whose composition may or may not be suitable for power production, depending on the operating conditions of the gasification unit. In this regard, this work proposes a different approach in which no WGS reactors are involved and the off-gas is used to generate heat and power to provide the energy input needed by the system. In particular, the authors tested the bio-syngas and the corresponding off-gas in a 4-cylinders, spark ignition natural gas internal combustion engine operated in cogeneration mode with the aim to analyse the effect of removing the hydrogen from the original bio-syngas on mechanical/electric and thermal power, on fuel efficiency and CO2 specific emission.

Predictive maintenance is the latest frontier in the management and maintenance of many industrial assets, including aeroengines. Made possible by last decades advances in monitoring equipment and machine learning algorithms, it permits individual-based maintenance schedules, on the basis of performance monitoring and estimates resulting from the application of diagnostic and prognostic techniques, whether on ground or real time. Predictive maintenance results in operational cost reduction and asset usage optimization, if compared with traditional maintenance strategies, which instead may suffer from unanticipated failure or unnecessary maintenance and therefore higher operational costs. In the study, Remaining Useful Life (RUL) estimates will be carried out for different turbofan engines, based on historical individual and fleet data made available by the Prognostics Center of Excellence at NASA. The design of Prognostics and Health Management (PHM) algorithms requires at first an analysis of available data to identify which of them is effectively related to equipment degradation and hence could be useful in determining future system evolution and predicting failure. In particular, RUL prediction of test engines suffering from high pressure compressor fault with exponential degradation trend has been carried out with both regression and Artificial Neural Networks (ANNs). In turn, different regression models and neural network architectures have been compared, namely tree regression with different levels of tree depth, Gaussian Process Regression (GPR) with different kernel functions and Multilayer Perceptron (MLP) with one to three hidden layers and varying number of nodes. The objective is to demonstrate the capability of such machine learning algorithms to predict engine failure and thus their importance in supporting predictive maintenance planning, and to evaluate the quality of results in relation to the algorithm structure. Results show comparable performance in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of predicted with respect to actual RUL, in particular predictions obtained through recourse to multilayer perceptron reveal to be the most accurate, with a RMSE of 17.38 and a MAE of 12.50.

In a previous work, the effectiveness of late pilot injection on improving combustion behaviour – in terms of fuel conversion efficiency and pollutant emission levels – in a diesel/natural gas dual-fuel engine was assessed. Then, an additional set of experiments was performed, aiming at speeding up the combustion process possibly without penalizing NOx levels. Therefore, hydrogen was added to natural gas in a percentage equal to 10%. Results show that hydrogen addition has a significant effect on the combustion development specially during the early stage of combustion: ignition delay is shortened and combustion centre is advanced, while the combustion duration increases when pilot injection timing is set to conventional values, while remains basically unchanged for late timings. Fuel conversion efficiency is only slightly penalized when hydrogen is added. Moreover, it was confirmed that, in general, combustion strategy with late pilot injection timing does not penalize fuel conversion efficiency; indeed, in some cases, it actually increases. Concerning regulated emission levels, it is again proven that late pilot injection does not penalize pollutant production: the hydrocarbons and carbon monoxide reduce as pilot injection is delayed, probably due to the higher temperatures reached into the cylinder during most part of the expansion stroke. Moreover, adding hydrogen always reduces their levels. Concerning NOx, they are drastically reduced delaying pilot injection; as expected, hydrogen addition promotes NOx formation, but the increase, evident with conventional pilot injection timings, becomes marginal with late injection strategy. Therefore, combustion strategy performance with late pilot injection in dual-fuel diesel/natural gas combustion conditions can be further improved with 10% hydrogen addition to natural gas.