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In the paper some methods are presented, with the corresponding practical examples, related to MonteCarlo (MC) techniques for thermal model/test correlation purposes. The MonteCarlo techniques applied to model correlation are intended to be used as an alternative to empirical ‘manual’ correlation techniques, gradients methods, matrix methods based on least square fit minimization. First of all, Design Of Experiments (DoE) tools are used to determine the model response to uncertain parameters and the confidence level of such a response. A sensitivity map is built, allowing the design of the test to maximize the response of the system to the uncertain parameters. Techniques derived from the extreme statistics are used to extrapolate data beyond test limits, with a sufficient confidence in the queue behaviour.

Transport activities contribute significantly to the air pollution and its impact on emissions is a key element in the evaluation of any transport policy or plan. Calculation of emissions has therefore gained institutional importance in the European Community. To obtain emission factors several methods make use of only vehicle mean velocity, which can be easily obtained by vehicle flow and density in the road. Recently in ARTEMIS project by Rapone et al. (2005–2007) a meso scale emission model, named KEM (Kinematic Emission Model), able to calculate emission factor has been developed. This model is based on a new statistical methodology, capable to consider more attributes than the simple mean speed to characterize driving behaviour. An interesting approach to determine the exact mix of driving cycles is represented by the use of microscopic traffic simulation models that could be used to avoid the very expensive costs of experimental campaigns needed to obtain real driving cycle.

Transport activities contribute significantly to air pollution. For this reason any policy or plan, carried out by administration or institution, requires the assessment of its impact on the emissions. To assess the overall pollutant production from transport, it is necessary to calculate emission factors. For this aim several methods exist which only use the average speed of the traffic stream, which can be theoretically obtained by vehicles flow and density on the road. Recently, a new statistical approach has been developed capable to consider more attributes than the simple mean speed to characterize driving behaviour, not only in the determination of driving cycles but also in the emission modelling. In this context, a meso scale emission model, named KEM, Kinematic Emission Model, able to calculate emission factor was developed. However, it is necessary to consider that the input to this model is, in any case, the driving cycle.

We present a framework for the robust optimization of the heat flux distribution for an anti-ice electro-thermal ice protection system (AI-ETIPS) and iced airfoil performance analysis under uncertain conditions. The considered uncertainty regards a lack of knowledge concerning the characteristics of the cloud i.e. the liquid water content and the median volume diameter of water droplets, and the accuracy of measuring devices i.e., the static temperature probe, uncertain parameters are modeled as uniform random variables. A forward uncertainty propagation analysis is carried out using a Monte Carlo approach. The optimization framework relies on a gradient-free algorithm (Mesh Adaptive Direct Search) and three different problem formulations are considered in this work. Two bi-objective deterministic optimizations aim to minimize power consumption and either minimize ice formations or the iced airfoil drag coefficient.

The dynamics and evolution of turbulent structures inside an engine-like geometry are investigated by means of Large Eddy Simulation. A simplified configuration consisting of a flat-top cylinder head with a fixed, axis-centered valve and low-speed piston has been simulated by the finite volume CFD code OpenFOAM®; the standard version of the software has been extended to include the compressible WALE subgrid-scale model, models for the generation of synthetic turbulence, some improvements to the mesh motion strategy and algorithms for LES data post-processing. In order to study both the initial transient and the quasi- steady operating conditions, ten complete engine cycles have been simulated. Phase and spatial averages have been performed over cycles three to ten in order to extract first and second moment of velocity; these quantities have then been used to validate the numerical procedure by comparison against experimental data.

Metallic open-cell foams have proven to be valuable for many engineering applications. Their success is mainly related to mechanical strength, low density, high specific surface, good thermal exchange, low flow resistance and sound absorption properties. The present work aims to investigate three principal aspects of real foams: the geometrical characterization, the flow regime characterization, the effects of the pore size and the porosity on the pressure drop. The first aspect is very important, since the geometrical properties depend on other parameters, such as porosity, cell/pore size and specific surface. A statistical evaluation of the cell size of a foam sample is necessary to define both its geometrical characteristics and the flow pattern at a given input velocity. To this purpose, a procedure which statistically computes the number of cells and pores with a given size has been implemented in order to obtain the diameter distribution.