Ricerc@Sapienza

Advanced modelling of electro-mechanical systems for energy harvesting (EH) and sensing is important to develop reliable self-powered autonomous electronic devices and for structural health monitoring (SHM). In this perspective, a novel computational approach is here proposed for both real-time and off-line parameter identification (PI). The system response is governed by a set of four partial differential equations (PDE) where the three displacement components and the electrical potential are the unknowns.

There is a growing interest in leaner burning internal combustion engines as an enabler for higher thermodynamic efficiency. The extension of knock-limited compression ratio and the increase in specific heat ratio with lean combustion are key factors for boosting efficiency. Under lean burning conditions, there is emerging evidence that certain fuels exhibit unusual heat release characteristics.

During the design phase of tunnel constructions in urban areas, advanced finite element models are a convenient tool to predict and reduce the impact of settlement on the surface infrastructure. These models can only deliver realistic results if, on the one hand, the level of detail of the model is high enough and, on the other hand, the model parameters have been well calibrated. Since soils are subjected to large parameter uncertainties, the determination of the model parameters is quite challenging.

Energy reduction can benefit from the improvement of energy efficiency in buildings. For this purpose, simulation models can be used both as diagnostic and prognostic tools, reproducing the behaviour of the real building as accurately as possible. High modelling accuracy can be achieved only through calibration. Two approaches can be adopted-manual or automatic. Manual calibration consists of an iterative trial and error procedure that requires high skill and expertise of the modeler.

The new approach of “enhanced artificial neural network” (EANN) is developed based on the new generated hybrid nanocomposite of F-MWCNTs–Fe 3 O 4 /EG which represents the Functionalized Multi Walled Carbon Nano Tubes together with Fe 3 O 4 nanoparticles, dispersed in ethylene glycol (EG) as the base fluid. Moreover, a new suitable sensitivity analysis is presented which involves a novel proposed method for the sensitivity analysis via ANNs. In this method, the sensitivity of the outputs predicted by means of an ANN to the inputs is calculated analytically rather than numerically.

In the current paper, the behavior of zinc oxide/SAE50 nano lubricant as a part of the new generation of coolants and lubricants is examined using response surface method (RSM). The data used in this study were viscosity at dissimilar volume concentrations (0-1.5%) and temperatures (5-50 °C) for dissimilar shear rate values. Therefore, sensitivity analysis based on variation of nanoparticle (NP) concentration and temperature was also implemented. The findings revealed that enhancing the volume fraction (φ) exacerbates the viscosity sensitivity to temperature.