The present project is linked to the military and civilian context of active flow control. It aims at studying the reduction in viscous drag by air injection for naval hydrodynamic applications. Air injection inside a boundary layer developing along ship hulls makes possible to decrease the local friction coefficient by more than 80%. It is then expected to increase the velocity of surface ships and reduce their fuel consumption. Actually the physical mechanisms are not completely understood and for a better control and generalization of the process, modelling the gas-liquid phase interactions is required. This project intends to develop numerical and modelling tools to predict air injection drag reduction, based on the collaboration between three partners: GIP French Naval Academy/IRENav, IMFT and LML with both numerical and experimental methods. In this project, the mechanisms that govern air injection drag reduction will be analyzed in details in the case of a two-phase flow developing along an horizontal flat plate including different states of the flow: bubbly flow and air cavity, both steady and unsteady cavities. Systematic tests of influent parameters will be conducted. For the bubble drag reduction, the part of bubble induced compressibility effect and the part of bubble-turbulence interaction effect will be investigated. In particular, the dispersion of the bubbles by the large scales of the liquid turbulence, the bubble induced change in the turbulence distribution of the liquid in the very near wall region and the bubble induced change in the spatial distribution of the wall friction will be considered. For that purpose, it requires the development of original experimental methods, suited to two-phase flows. The numerical framework is based on the eulerian calculation of the liquid flow, taking care of the coupling between the bubbles and the liquid flow for all the scales of the turbulence, the bubbles being tracked by a lagrangian approach. The influence of both the bubble size and the void fraction on the near wall turbulence and on the wall shear stress will be analyzed. For air cavity drag reduction, air injection induced modifications in the physical characteristic of the gas-liquid mixture (density, viscosity) and their consequence on both the viscous drag and the stability of the cavity will be investigated. The approach will be based on a homogeneous flow assumption. In particular, compressibility effects will be analyzed. A particular attention will be paid to the 2D and 3D study of the closure region of unsteady cavities. To achieve this, experimental and numerical methods, developed for the analysis of unsteady cavitating flows, will be adapted to the analysis of this ventilated flow. The change in the viscous drag and stability of the cavity due the variation of the external flow conditions (direction, intensity and fluctuations) will be examined. As a summary, the analyses of the physical mechanisms responsible for air injection drag reduction will lead to a discrimination of the different numerical tools, according to their ability to predict the viscous drag, as a function of the gas-liquid coupling level and the turbulence scale resolution. Systematic tests of influent parameters and the development of models will be useful for the hulls’ design of eco and fast ship.

Maritime environment is experiencing a growing activity, which has led to the use of new services for localization of vessels such as the Automatic Identification System (AIS), which allows real-time surveillance of maritime traffic and provides aids to navigation. Recent works have shown that falsification of AIS messages was possible, and therefore could lead to illegals actions and new maritime risks. This way, some ships have been hijacked without the knowledge of their crew or surveillance centers. DéAIS project proposes a methodological approach for modelling, analysing and detecting these new maritime risks. The objective is to detect when if ship’s AIS reports have been falsified (or spoofed). For this purpose, real-time AIS information is analysed and compared to historical, expected or predicted information. This problem will be undertaken by an academic partner, leader in risk management and maritime mobility, which have already been part of many projects in this domain.

The quality of the air we breathe is a central concern of individuals living in urban and suburban areas. Millions of people are exposed every day to air pollution at high levels. The impact of such pollution on the human health is extremely alarming. Particularly, WHO and IARC have classified air pollution, including fine particles, as certain carcinogenic. Understanding the totality of exposures to air pollutants over the course of our daily life is a key concern to reduce the risk of some major diseases. However, the ability to acquire high-quality, relevant, and useful individual’s exposure data is challenging. Currently available air pollution fixed station networks allow to only account for background air pollution and less frequently proximity air pollution from road traffic. As a result, the measurements made through this kind of network typically provide the average exposure to air pollution in a specific geographical zone. In particular, they fall short to quantify the real individual’s exposure with respect to his/her indoor/outdoor daily life activities in different settings, such as transport, work, dwellings, etc. Nowadays, an increasing number of wearable and lightweight environmental sensors have emerged, enabling a continuum measurement of the real personal exposure anywhere at anytime. Such an evolution has been the main enabler of providing new solutions for data acquisition, namely community-based participatory sensing where citizens contribute data to the system with the purpose of sharing events of interest within the community. This technology has recently gained a great interest among the actors of environmental science in public, associative, and private sectors, while stimulating a wide range of research projects worldwide. Building on top of such a technology evolution, Polluscope aims at bringing together experts from environmental, metrology, epidemiological, and data sciences while providing methodologies, techniques, and tools – expected to drastically change the way individual’s exposure and exposure variability are measured, perceived, and evaluated. Such measurements will not only consider gaseous pollutants (Ozone, NO2), but also particulates (via particulate matter and black carbon) and those typical of indoor environments (VOC) – providing a representative overview of the air pollution. Gaining such enriched insights into individual’s exposure will contribute towards reducing individual risks of some diseases by changing their behavior. This will end up in a solid, invaluable, and vital societal impact namely, saving life and improving the individual well-being. To achieve these objectives, a novel infrastructure for real individual’s exposure data acquisition, processing, and analysis will be develope. For this to be done, several scientific and technical challenges come into the picture. The data are collected at a high frequency and might be massive and noisy. Therefore, the system must be able to process them efficiently, while taking into account both their velocity and their uncertainty. More importantly, it has to offer microenvironment and user’s activity recognition, through integration with external spatiotemporal resources. An efficient data collection and analysis will provide an insightful knowledge on individual’s exposure over his/her daily life activities, and will enable conducting analytical queries, novel risk assessment modeling, mining and comparing profiles of pollution exposures, and so on. Therefore, it is evident that a robust, efficient, and powerful data science technology is crucial. Lastly, Polluscope will be evaluated under real-world use cases. Several type of population will be targeted by the data acquisition campaign. Both diseased and healthy subjects will be involved to conduct an epidemiological study relating air pollution exposure to health on the one hand, and volunteer participants for the crowd sensing on the other hand.