Statistical properties of thermally expandable particles in soft-turbulence Rayleigh-Bénard convection

The dynamics of inertial particles in Rayleigh-Benard convection, where both particles and fluid exhibit thermal expansion, is studied using direct numerical simulations (DNS) in the soft-turbulence regime. We consider the effect of particles with a thermal expansion coefficient larger than that of the fluid, causing particles to become lighter than the fluid near the hot bottom plate and heavier than the fluid near the cold top plate.

Resource planning for aircraft refueling in airport parking area

This paper studies a scheduling problem application for the optimization of the employees used in aircrafts' refueling in a medium size airport. The problem is modelled as a particular resource leveling problem for which we provide a mixed integer mathematical formulation that we solve with CPLEX. The model allows to evaluate and analyse different scenarios that could be considered by the company in place of the current one in order to rearrange the available human resources used in refueling activity.

Numerical optimization of plasmid DNA delivery combined with hyaluronidase injection for electroporation protocol

Background and Objective: The paper focuses on the numerical strategies to optimize a plasmid DNA delivery protocol, which combines hyaluronidase and electroporation. Methods: A well-defined continuum mechanics model of muscle porosity and advanced numerical optimization strategies have been used, to propose a substantial improvement of a pre-existing experimental protocol of DNA transfer in mice. Our work suggests that a computational model might help in the definition of innovative therapeutic procedures, thanks to the fine tuning of all the involved experimental steps.

Discrete Eulerian model for population genetics and dynamics under flow

Marine species reproduce and compete while being advected by turbulent flows. It is largely unknown, both theoretically and experimentally, how population dynamics and genetics are changed by the presence of fluid flows. Discrete agent-based simulations in continuous space allow for accurate treatment of advection and number fluctuations, but can be computationally expensive for even modest organism densities. In this report, we propose an algorithm to overcome some of these challenges. We first provide a thorough validation of the algorithm in one and two dimensions without flow.

Machine learning agents to support efficent production management: Application to the Goliat's asset

GOLIAT is an offshore production field that spans from the subsea wells up to a complete process plant installed on a FPSO. Due to the comprehensive instrumentation installed on the plant, it is the perfect case study to test an innovative agent based software architecture able to support production management. The modularity and the scalability provided by the agent based architecture guarantees the applicability of the method to any part of the plant. Each agent is in charge of supervising a specific or a group of equipment and is fed by the real-time data coming from the field.