Ali Naseri
PhD Programme: Nanoscience, Materials and Chemical Engineering
Research group: COMPLEXS – Molecular Simulation
Supervisor: Josep Bonet Avalos
Bio
Ali Naseri received a bachelor's in mechanical engineering from the University of Mohaghegh Ardabili in Iran. He obtained a master's degree in structural engineering in 2013 in Iran. After five years working in the industry, he started a new adventure at the University of Sapienza in Rome, Italy. He got his master's degree in mechanical engineering in 2021. During his academic career, Ali participated in multiple projects, including the development of machine learning on wall function. He became experienced in numerical methods, CFD applications, and machine learning algorithms. His master's thesis was about gas turbine cooling methods, which is the most critical issue in gas turbines and jet engines. Besides his academic education, Ali has industrial experience in research projects in fluid dynamics and fluid machinery.
Project: Dynamics of colloidal and microscopically structured systems with Lagrangian hydrodynamic method
Today, nanoscience has spread the knowledge of structures and materials in comparison to just a few years ago. Mesoscopic domains are the most spectacular advances, to build by self-assembly thermodynamically stable structures, which is helpful in engineering, biotechnology, and medicine. Mesoscopic heat transport is of vital importance in miniaturized systems, such as molecular motors, and microelectronic devices, but also in reactive fronts and interfaces. Large temperature gradients may induce strong couplings between non-equilibrium processes which are of paramount importance in the understanding and modeling of energy transfer at the nano-scale. The most important usage is in the dynamics of microscale fluids, given the boom in designs for microfluidic applications, among others. However, the explicit simulation of the fluid in a micrometric volume system (1 μm3) is not feasible in case of the presence of a large number of particles. Therefore, it is necessary to develop the so-called mesoscopic algorithms, in which the particles contain large numbers of physical molecules and take into account the presence of relevant thermal fluctuations on that small scale. Simulation methods, such as Dissipative Particle Dynamics with energy conservation (DPDE), are suitable for the analysis of momentum transport and heat flow at the microscale, as they can describe the effect of thermal fluctuations in the field dynamics. DPDE was introduced by the URV team and has been recently extensively applied. The subject of this thesis will be the development and application of algorithms such as DPDE for systems with the presence of colloids and structured materials in the scale of interest of the simulation. In particular, the development of algorithms for very viscous systems is intended. To simulate small viscous systems, the main problem lies in the fact that the momentum is transferred fast as compared to the time the particles take to diffuse. The ratio of these two effects is commonly referred to as the Schmidt number, which is rather large in common fluids like water (Sc≈400). Thus, the direct application of the original DPDE, or either SPH or the fluctuating counterpart SDPD, is computationally very expensive, as the resolution of the momentum relaxation limits the size of the time-steps that can be applied. The objective of this thesis is to construct a fluctuating particle-based (Lagrangian) method, with the capabilities of DPDE, to describe viscous fluids at the microscale and mesoscale, with low computational cost, and yet able to reproduce the dominating hydrodynamic interactions for small systems with large Sc numbers. The targeted applications are colloidal suspensions of different types, but the intended simulation scheme has wide application to biological fluids, being blood its paramount representative.
Outreach activities
- European Researchers' Night 2023: "¿Por qué vuelan los aviones?".
