Search

In this paper we suggest to innovatively use scaling laws and more specifically Universal Multifractals (UM) to analyse simulated surface runoff and compare the retrieved scaling features with the rainfall ones. The methodology is tested on a 3 km 2 semi-urbanised with a steep slope study area located in the Paris area along the Bièvre River. First Multi-Hydro, a fully distributed model is validated on this catchment for four rainfall events measured with the help of a C-band radar. The uncertainty associated with small scale unmeasured rainfall, i.e. occurring below the 1 km × 1 km × 5 min observation scale, is quantified with the help of stochastic downscaled rainfall fields. It is rather significant for simulated flow and more limited on overland water depth for these rainfall events. Overland depth is found to exhibit a scaling behaviour over small scales (10 m–80 m) which can be related to fractal features of the sewer network. No direct and obvious dependency between the overland depth multifractal features (quality of the scaling and UM parameters) and the rainfall ones was found. [ABSTRACT FROM AUTHOR]

Peak power consumption is becoming an increasing problem worldwide and particularly in France during the late afternoon winter periods. This paper describes the experimental and numerical studies carried out to investigate the potential of a PCM (phase change material) – Air heat exchanger, conceived for load shifting purposes. The objective is to possess the necessary tools for the development of advanced control strategies taking into account various factors: peak power reduction, thermal comfort and indoor air quality. The experimental approach involved the construction of a heat exchanger coupled to an experimental cell. The heat storage system is composed of a set of PCM (paraffin) plates, embedded in the exchanger and integrated in a ventilation system. In parallel, a numerical model was developed using the apparent heat capacity approach and the finite differences method. Furthermore, a series of characterization tests was conducted and the experimental data were used for model calibration and validation. The model was then coupled with a building simulation program, presenting good agreement between the integrated model prediction and the experimental data. Finally, a preliminary control strategy of the test cell is presented along with results demonstrating the capabilities of such system for peak load management. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study dealing with the risk for heavy vehicles in ramps. Two approaches are used. On one hand, statistics are applied on several accidents databases to detect if ramps are more risky for heavy vehicles and to define a critical value for longitudinal slope. χ 2 test confirmed the risk in ramps and statistical analysis proved that a longitudinal slope superior to 3.2% represents a higher risk for heavy vehicles. On another hand, numerical simulations allow defining the speed profile in ramps for two types of heavy vehicles (tractor semi-trailer and 2-axles rigid body) and different loads. The simulations showed that heavy vehicles must drive more than 1000 m on ramps to reach their minimum speed. Moreover, when the slope is superior to 3.2%, tractor semi-trailer presents a strong decrease of their speed until 50 km/h. This situation represents a high risk of collision with other road users which drive at 80–90 km/h. Thus, both methods led to the determination of a risky configuration for heavy vehicles: ramps with a length superior to 1000 m and a slope superior to 3.2%. An application of this research work concerns design methods and guidelines. Indeed, this study provides threshold values than can be used by engineers to make mandatory specific planning like a lane for slow vehicles. [ABSTRACT FROM AUTHOR]

The COVID-19 virus outbreak has affected most of the world in 2020. This paper deals with artificial intelligence (AI) methods that can address the problem of predicting scale, dynamics and sensitivity of the outbreak to preventive actions undertaken with a view to combatting the epidemic. In our study, we developed a cellular automata (CA) model for simulating the COVID-19 disease spreading. The enhanced infectious disease dynamics S E I R (Susceptible, Exposed, Infectious, and Recovered) model was applied to estimate the epidemic trends in Poland, France, and Spain. We introduced new parameters into the simulation framework which reflect the statistically confirmed dependencies such as age-dependent death probability, a different definition of the contact rate and enhanced parameters reflecting population mobility. To estimate key epidemiological measures and to predict possible dynamics of the disease, we juxtaposed crucial CA framework parameters to the reported COVID-19 values, e.g. length of infection, mortality rates and the reproduction number. Moreover, we used real population density and age structures of the studied epidemic populations. The model presented allows for the examination of the effectiveness of preventive actions and their impact on the spreading rate and the duration of the disease. It also shows the influence of structure and behavior of the populations studied on key epidemic parameters, such as mortality and infection rates. Although our results are critically dependent on the assumptions underpinning our model and there is considerable uncertainty associated with the outbreaks at such an early epidemic stage, the obtained simulation results seem to be in general agreement with the observed behavior of the real COVID-19 disease, and our numerical framework can be effectively used to analyze the dynamics and efficacy of epidemic containment methods. • Cellular Automata include density population of Poland, France, Spain in SEIR model. • We analyze influence of parametric adjustment for modeling pandemic prevention. • The earlier reduction of personal contacts the faster reduction of infections number. • Age dependent mortality rates are explored to reflect the real features of COVID-19. • Our approach allows for modeling of COVID-19 presumable dynamics. [ABSTRACT FROM AUTHOR]