Biblio

This paper is devoted to boundary control design for urban traffic described on a macroscopic scale. The state corresponds to vehicle density that evolves on a continuum two-dimensional domain that represents a continuous approximation of a urban network. Its parameters are interpolated as a function of distance to physical roads. The dynamics are governed by a new macroscopic multi-directional traffic model that encompasses a system of four coupled partial differential equations (PDE) each describing density evolution in one direction layer: North, East, West and South (NEWS). We analyse the class of desired states that the density governed by NEWS model can achieve. Then a boundary control is designed to drive congested traffic to an equilibrium with the minimal congestion level. The result is validated numerically using the real structure of Grenoble downtown (a city in France).

Numerical simulation of fluid flow is a significant research concern during the design process of a machine component that experiences fluid-structure interaction (FSI). State-of-the-art in traditional computational fluid dynamics (CFD) has made CFD reach a relative perfection level during the last couple of decades. However, the accuracy of CFD is highly dependent on mesh size; therefore, the computational cost depends on resolving the minor feature. The computational complexity grows even further when there are multiple physics and scales involved making the approach time-consuming. In contrast, machine learning (ML) has shown a highly encouraging capacity to forecast solutions for partial differential equations. A trained neural network has offered to make accurate approximations instantaneously compared with conventional simulation procedures. This study presents transient fluid flow prediction past a fully immersed body as an integral part of the ML-CFD project. MLCFD is a hybrid approach that involves initialising the CFD simulation domain with a solution forecasted by an ML model to achieve fast convergence in traditional CDF. Initial results are highly encouraging, and the entire time-based series of fluid patterns past the immersed structure is forecasted using a deep learning algorithm. Prepared results show a strong agreement compared with fluid flow simulation performed utilising CFD.