Experimental studies have been carried out to establish the possibility of using vibratory machining technology through shock-wave transmission for oxide coating preparation on aluminum-alloyed machine components and also to discuss the technological possibilities of applying vibration mechanochemical solid lubricant coatings based on MoS 2 to improve the surface quality and performance properties of machine component parts. The coating characteristics are determined by measuring and comparing certain tribological properties of the samples before processing, after normal coating, and after vibratory coating process. A deeper study with a scanning microscope was made by comparing result of normal and vibratory coating. The vibratory coating shows a reduction of grain sizes, a regular orientation of the grain, and a dense grain structure leading to the formation of a thin layer covered by a film orientated parallel to the surface of friction giving an imparted surface finish. The reduction of microroughness is also accompanied with good performances in terms of increasing in wear resistance and decreasing in coefficient of friction. This reflects the presence of complex influence of mechanical and chemical components in the formation of coating on superficial layers during lower shock-wave vibration giving at the end structured ameliorated state of surface that leads to an increase in the part lifespan and equally shows technological opportunities that can be used to improve surface quality and performance properties of machine component parts.

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We present a simple methodology to design curved shell finite elements based on Nzengwa-Tagne’s shell equations. The element has three degrees of freedom at each node. The displacements field of the element satisfies the exact requirement of rigid body modes in a ‘shifted-Lagrange’ polynomial basis. The element is based on independent strain assumption insofar as it is allowed by the compatibility equations. The element developed herein is first validated on analysis of benchmark problems involving a standard shell with simply supported edges. Examples illustrating the accuracy improvement are included in the analysis. It showed that reasonably accurate results were obtained even when using fewer elements compared to other shell elements. The element is then used to analyse spherical roof structures. The distribution of the various components of deflection is obtained. Furthermore, the effect of introducing concentrated load on a cylindrical clamped ends structure is investigated. It is found that the CSFE3-sh element considered is a very good candidate for the analysis of general shell structures in engineering practice in which the ratio h/R ranges between 1/1000 and 2/5.

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This work investigates the effect of low frequency vibratory processing for cleaning and washing various machine components parts from rusts and old paints deposits. The experimental investigation was carried out with special prepared samples that were weighted and exposed to paints and rust contaminants. These samples were treated in universal horizontal vibration machine UVHM 4 × 10 with different combination of instrumental processing medium, process fluid, machine amplitude and frequency of oscillations. They were periodically reweighted after processing and compared to etalon with control of quantity of dust that have been removed, sample cleanliness and also other functional parameters. Statistical analysis has been used to characterize ongoing process and full factorial analysis to establish experimental parameters dependency. The result is showing the complex dependence of samples cleanliness to each processing parameters like processing time, amplitude of oscillations, frequency of oscillations, process fluid parameters, instrumental medium, etc. Between this parameters although the most important successively the amplitude of oscillations, the frequency of oscillations the processing medium and the processing fluid depending to his considered composition, the optimal processing time can be reach only by complex combination of all this parameters every of them carry an amplify coefficient. Low frequency oscillations can be used to monitor and optimize washing and cleaning operations of paints and rusts contaminations. That guarantees process automation, its effectiveness for a large industrial application.

The objective of this work is to improve the combustion management in W18V50DF dual fuel engine by determining for a desired power, the optimal values of the parameters of pressures and subsequently to map them in real time based on a power set point. The interest was mainly focused in pressure parameters, other being considered as constant. Two methods have been used, namely mathematical modeling and learning by neural networks. The results show that, in the beginning mathematical modeling result helps to monitor the ongoing process and with longer learning period the result with neural network become better and significant due to the adaptation to the reality. Furthermore, the neural network method improves significantly in the long term the rationalization of fuel consumption in such a system in order to significantly reduce the carbon dioxide emission rate. Finally, work has proved that for an immediate result mathematical model can be used but without robustness on the control process, this is obtained by a neural network. But this approach requires a good data base and long learning time.

Maintaining the quality of breathing air in urban and industrial areas is one of the biggest challenges faced by humanity in the modern era. Diesel engines, as one of the main providers of energy supply for modern equipment and transport, are also unfortunately contributing highly to the deterioration of air quality. A recent research path on the limitation of diesel engine emissions is the use of alternative fuel from vegetable or animal fats or oil called biodiesel. Although the use of biodiesel has proven its efficiency in reducing emissions, it remains a problem to maintain the engine’s efficiency when shifting to biodiesel, especially due to its injection and atomization properties; most of the recent research focused on improving biodiesel fuel quality by blending it with traditional diesel fuel, but few works can be found on the regulation or control of diesel engine process when shifting to 100% biodiesel fuel (B100). This work proposes a fuel control strategy and methodology based on diesel engine operating data obtained from an experimentally designed rate of injection model (ROI) at different injection pressures and a jet and spray droplet distribution validated a two-zone model. Results show that B100 gives a higher amount of about 8% of injected fuel, a longer jet penetration of about 20 mm higher at 100 MPa injection pressure, a wider cone angle, and about a 40% increase of coarseness of the jet distribution. The experimental and numerical-based control strategy provides interacting relationships between B100 properties and specific engine features where actions shall be made to keep the engine’s efficiency when the shift is made; meanwhile, the algorithm provides a hierarchical step-by-step correcting procedure taking into account the possible degradation that could occur from the use of B100 in diesel engines.

The work carried out in this paper focused on "Simulation of Performance and Emissions Related Parameters in A Thermal Engine using A Deep Learning Approach". The goal of this work is to develop a neural network model of a thermal engine and to make a prediction of parameters related to engine management and directly impacting pollutant emissions and fuel consumption. The novelty of this work is the use of a particular type of neural network to learn long sequence of data, obtained from a running engine, and to predict internal parameters related to engine performance and emissions with a good precision. For it, data were taken from an experimentation engine, the L15B7 1.5 L, a gasoline engine with direct lateral injection from the manufacturer Honda, fitted to Honda Civic vehicles. These data made it possible to make maps of its operation. These maps enabled the calibration of a Simulink model of a thermal engine. Through a system identification approach, the temporal response of the motor was estimated and made it possible to develop a database that was used for training the LSTM artificial neural network. The work carried out showed that the learning phase of the neural network proceeded consistently (overall decrease in cost functions) and converged towards a value of RMSE = 1.09 better than those observed in the literature. The resulting neural engine model made it possible to predict several variables (fuel mass flow rate and pollutant mass flow rates) with acceptable residual errors. These results reveal that the neural model obtained correctly predicts the said variables and can, therefore, be used in closed-loop simulations of the operation of a vehicle or for a context of simulation of the operation of the engine.