On the Behavior of the Start and Stop System in European Real Driving Emissions Tests and Its Effect on Greenhouse and Tailpipe Emissions

The Start/Stop (S/S) system is a technology that switches off the engine without the intervention of the driver when the vehicle is stopped. The goal of this device is to eliminate the consumption of fuel associated with the idling of the engine and, consequently, save carbon dioxide (CO2) and pollutant emissions. However, its effectiveness is related to the percentage of the total driving time with the vehicle stopped. Moreover, even if the S/S system is installed and the vehicle is stopped, the S/S system can be inhibited by the condition of the vehicle like, for example, a too low state of charge of the battery. This investigation evaluates the actual effect of S/S on tailpipe gaseous emissions in Real Driving Emissions tests compliant with the new European Regulations (E-RDE). The investigation is based on data from on-road and on-track RDE tests performed with a Portable Emission Measurement System on a diesel sports utility vehicle (SUV). From the analysis of these data, the reduction of emission guaranteed by the S/S system was found to be quite lower than the potential in the New European Driving Cycle (NEDC) test due to the limited activation of the S/S system in real driving tests. Moreover, the analysis put into evidence that the saving associated with the S/S could be counterbalanced by the engine restart especially if the stop time is shorter than a certain threshold

Corrigendum to "Scaling laws of diffusion and time intermittency generated by coherent structures in atmospheric turbulence" published in Nonlin. Processes Geophys., 19, 113–126, 2012

No abstract available

Scaling laws of diffusion and time intermittency generated by coherent structures in atmospheric turbulence

We investigate the time intermittency of turbulent transport associated with the birth-death of self-organized coherent structures in the atmospheric boundary layer. We apply a threshold analysis on the increments of turbulent fluctuations to extract sequences of rapid acceleration events, which is a marker of the transition between self-organized structures. <br><br> The inter-event time distributions show a power-law decay ψ(τ) ~ 1/τ<sup><i>μ</i></sup>, with a strong dependence of the power-law index <i>μ</i> on the threshold. <br><br> A recently developed method based on the application of event-driven walking rules to generate different diffusion processes is applied to the experimental event sequences. At variance with the power-law index μ estimated from the inter-event time distributions, the diffusion scaling <i>H</i>, defined by ⟨ <i>X</i><sup>2</sup>⟩ ~ <i>t</i><sup>2<i>H</i></sup>, is independent from the threshold. <br><br> From the analysis of the diffusion scaling it can also be inferred the presence of different kind of events, i.e. genuinely transition events and spurious events, which all contribute to the diffusion process but over different time scales. The great advantage of event-driven diffusion lies in the ability of separating different regimes of the scaling <i>H</i>. In fact, the greatest <i>H</i>, corresponding to the most anomalous diffusion process, emerges in the long time range, whereas the smallest <i>H</i> can be seen in the short time range if the time resolution of the data is sufficiently accurate. <br><br> The estimated diffusion scaling is also robust under the change of the definition of turbulent fluctuations and, under the assumption of statistically independent events, it corresponds to a self-similar point process with a well-defined power-law index <i>μ</i><sub><i>D</i></sub> ~ 2.1, where <i>D</i> denotes that <i>μ</i><sub><i>D</i></sub> is derived from the diffusion scaling. We argue that this renewal point process can be associated to birth and death of coherent structures and to turbulent transport near the ground, where the contribution of turbulent coherent structures becomes dominant

Characterizing memory in atmospheric time series

Atmospheric dynamics originates persistent and/or intermittent structures spanning over several spatial and temporal scales. The dynamical instabilities trigger abrupt transitions between these meteorological structures. An approach based on the theory of renewal processes is proposed to describe these critical transition events. An alternative statistical analysis to qualitatively estimate the memory content of atmospheric time series is reviewed and an application to turbulence data in the Atmospheric Boundary Layer is illustrated. The connection between the proposed analysis and the assumption of a local flux-gradient relationship is also discussed

Fractal dimension of a liquid flows predicted coupling an Eulerian-Lagrangian approach with a Level-Set method

The fractal dimension of a liquid column is a crucial parameter in several models describing the main features of the primary break-up occurring at the interface of a liquid phase surrounded by the gas-flow. In this work, the deformation of the liquid phase has been numerically studied. The gas-phase is computed as a continuum in an Eulerian frame while the liquid phase is discretized in droplets Lagrangian tracked and coupled via the momentum equation with the surrounding gas flow. The interface is transported by the flow field generated because of the particle forcing and it is numerically computed using the Level-Set method. Finally, the fractal dimension of the interface is locally estimated and used as criterion for the model of the primary breakup.Comment: 6 pages, 6 figure

A Combined Optimization Method for Common Rail Diesel Engines

The optimization method proposed in the present study consists of a multi-objective genetic algorithm combined with an experimental investigation carried out on a test bench, by using a DI Diesel engine. The genetic algorithm selects the injection parameters for each operating condition whereas the output measured by the experimental apparatus determines the fitness in the optimization process. The genetic algorithm creates a random population, which evolves combining the genetic code of the most capable individuals of the previous generation. Each individual of the population is represented by a set of parameters codified with a binary string. The evolution is performed using the operators of crossover, mutation and elitist reproduction. This genetic algorithm allows competitive fitness functions to be optimized with a single optimization process. For the determination of the overall fitness function the concept of Pareto optimality has been implemented. In this work, the input variables used for the optimization method are injection parameters like start of pilot and main injection, injection pressure and duration. The engine used is a FIAT 1929 cc DI diesel engine, in which the traditional injection system has been replaced by a common rail high pressure injection system. The competitive fitness functions were determined based on the measured values of fuel consumption, emissions levels (i.e. NOx, soot, CO, CO2, HC); combustion noise and overall engine noise, for each operating conditions. The optimization was performed for different engine speed and torque conditions typical of the EC driving cycles