Simulation and Control of an Autorefrigerated CSTR

, M.Sc. Thesis Sharif University of Technology Momen, Sajedeh (Author) ; (Shahrokhi, Mohammad (Supervisor)

Abstract

This thesis focuses on modeling, simulation and control of bulk polymerization of styrene in an autorefrigerated reactor where the generated heat is removed by heat of vaporization of the monomer. By means of two mathematical models for the kinetics of reaction, static and dynamic simulations were performed. For this purpose the equilibrium points were calculated using the steady state analysis, and then the reactor temperature was controlled at the unstable point using PI and adaptive generalized predictive controller (AGPC). In order to implement the GPC scheme a process model is required to provide the process output estimate over the prediction horizon. For this purpose an auto...

Design and Optimization of Channeled Hydrogel Scaffold Based on Extracellular Matrix of Heart Tissue with Oxygen Release Capability

, M.Sc. Thesis Sharif University of Technology Ghasemi, Sara (Author) ; Mashayekhan, Shohreh (Supervisor) ; Khorshidi, Sajedeh (Co-Supervisor)

Abstract

Despite the increase in the number of cardiovascular diseases worldwide, the number of new drugs to treat these diseases has been decreasing in the last decade. Current preclinical drug evaluation strategies, which use cell cultures and oversimplified animal models, cannot meet the growing demand for new and effective drugs. In the last decade, the development of microfluidic bioreactors and organ-on-chip systems to improve the drug screening process has been increasing significantly. These systems have shown many advantages over previous preclinical models. Despite all these advantages, keeping the oxygen concentration at the optimal physiological level in microfluidic systems has its own...

Particle trajectory study in submerged flows with baffles using v 2̄ - f and k -ε turbulence models

, Article 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, 7 January 2008 through 10 January 2008 ; 2008 ; 9781563479373 (ISBN) Mehdizadeh Momen, A ; Sherif, A ; Firoozabadi, B ; Sharif University of Technology

2008

Abstract

In this paper, the structure of a wall jet deflected by a baffle along with the trajectory of particles has been studied. This baffle is used to produce a stable deflected surface jet, thereby deflecting the high-velocity supercritical stream away from the bed to the surface. An elliptic relaxation turbulence model (v2̄ - f model) has been used to simulate this submerged flow. During the last few years, the v2̄ - f turbulence model has become increasingly popular due to its ability to account for near-wall damping without use of damping functions. In addition, it has been proven that the v2̄ - f model is superior to other RANS methods in many fluid flows where complex flow features are...

Evaluation of mechanical properties of multilayer graphyne-based structures as anode materials for lithium-ions batteries

, Article European Physical Journal Plus ; Volume 137, Issue 3 , 2022 ; 21905444 (ISSN) Momen, R ; Rezaee, R ; Azizi, B ; Rezaee, S ; Hou, H ; Ji, X ; Sharif University of Technology

Springer Science and Business Media Deutschland GmbH 2022

Abstract

Among various two-dimensional carbon allotropes, graphyne has received extensive research attention with outstanding physical features and excellent application prospects for energy storage systems, specifically lithium-ion batteries anode. The mechanical characteristic of the anode affects the performance and durability of the battery during charge/discharge cycles. Therefore, this research investigates the mechanical properties of α -, β -, and γ -graphyne multilayer configurations using the molecular dynamics (MD) simulation approach. The results demonstrate that the mechanical properties of multilayer graphyne do not significantly depend on carbon layers; however, as the layer numbers...

Brain Tumor Detection with Vision Transformers and Faster R-CNN

, M.Sc. Thesis Sharif University of Technology Momen Tayefeh, Mehrshad (Author) ; Hemmatyar, Ali Mohammad Afshin (Supervisor) ; Ghahramani Ghahramani, Amir Ali (Co-Supervisor)

Abstract

In the field of cancer diagnosis, particularly brain tumors, the priority is to achieve highly accurate tumor detection. Deep learning has shown remarkable potential in object detection tasks, making it a valuable tool for identifying brain tumors. We have proposed a new method for combining the strength of Faster R-CNN in detecting objects and the ability of Vision Transformer’s (Faster-VIT) ability to extract essential features. The proposed method significantly improves the accuracy and efficiency of brain tumor detection in MRI images. We have called the proposed combination Faster-VIT. To assess the effectiveness of the proposed method, we have utilized the Br35H dataset, comprising...