Sharif Digital Repository

In this project dynamic effect of changes in gas turbine load on power plant, reforming and Co2 capture section is investigate. The reason for study of effects change of gas turbine load is that gas turbine determines the performance of the other parts in combined cycle including gas cycle and change of gas turbine load can alter the load and the performance of other parts in the power plant and in other process such as production of synthesis gas and carbon dioxide separation unit. One Autothermal reactor and eight sewgs reactor are utilized to produce gas turbine fuel which is mainly consist of Hydrogen. this part of thesis is to be implemented in the gPROMS from process system enterprise... 

The main objective of this thesis is to investigate the scaling laws of electrical breakdown and the devices that use it as the main current-passage mechanism. To achieve this goal, first, the physical phenomena involved in electrical breakdown and the mechanisms that cause electrical breakdown in different environments have been investigated. It is determined which phenomena become more important with down scaling and which phenomena lose their importance, and how the electrical breakdown mechanism changes based on the importance of these phenomena. In the second step, spark gap switches that use electrical breakdown as the main current-passage mechanism have been introduced, and dependence... 

Additive manufacturing (AM) has gained significant attention in recent years as a novel approach to fabricate three-dimensional parts. Its advantages, including high speed, low-cost production for small-scale batches, and greater design freedom, have made it a competitive alternative to traditional manufacturing methods. However, the application of AM for components subjected to fretting fatigue requires careful evaluation. Fretting fatigue is a major cause of failure in turbines, particularly at the contact interface between turbine blade roots and disk. In this study, the influence of AM and post-processing treatments on the fretting fatigue life of Ti-6Al-4V alloy was investigated. Five... 

In this project Visbreaker furnace was modeled and optimized. For this goal, Lobo & Evans method and refinery data was used. Effect of parameters such as, excess air, stack height, decoking period, burn out and fuel heat value were investigated. Among optimization method, genetic algorithm and MITA (Musical Instrument Tempering Algorithm) were used to minimizing cost function of furnace. Also based on Zone method, profiles of properties of process fluid were obtained. Finally fuzzy logic was used for furnace behavior identification  

Existing face forgery detection methods mainly focus on certain features of images, such as features related to image noise, local textures or frequency statistics of images for forgery detection. This makes the extracted representations and the final decision depend on the data in the database and makes it difficult to detect forgery with unknown manipulation methods. Solving this challenge, which is called the generalization challenge in artificial intelligence literature, has become the main goal of researchers in this field. In this thesis, the focus is on extracting effective features for success in forgery detection and preventing the performance of the forgery detection network from... 

One of the most important problems in machine learning is investigating the performance of the learning algorithms, and especially deep neural networks, on adversarial examples, which are generated by imperceptibly perturbing input images, so that cause the model make a wrong prediction. Not only this line of research is important for making deep neural networks dependable, but also can help with understanding the fundamental limitations of deep neural networks, and the nature of their operation, which can in turn provide researchers with valuable insights into artificial intelligence. In this research work, we have tried to approach the topic with a mainly theoretical mindset. The method we... 

In recent years, deep learning has experienced rapid and remarkable advancements, demonstrating exceptional performance in various applications such as computer vision, natural language processing, and autonomous vehicles. These models are continually evolving and expanding, yet they harbor inherent vulnerabilities that prevent complete trust in their reliability. One of the most critical issues is their susceptibility to adversarial attacks, where a clean image is subtly manipulated to create an adversarial example. Adversarial examples, generated by adding imperceptible perturbations to input data, can easily mislead deep learning models into making highly confident yet incorrect... 

Wind energy technologies guarantee low pollution and operational costs. Using a DFIG and a fractionally rated power electronics converter gives variable speed operation with a low cost drive train. As energy policy organizations have allocated a considerable quota of wind energy generation to offshore wind farms, the absence of slip rings and brushes in the brushless DFIG (BDFIG) is an advantage for offshore wind turbines where maintenance is vital and expensive. With increasing wind power penetration in power systems, grid code requirements are an important consideration for the ride-through capability of wind farms through voltage dips, particularly for multi- MW wind turbine generators.... 

Deep learning is one of the most powerful branches of machine learning, demonstrating exceptional performance in tasks such as image and speech recognition with the help of neural networks. However, deep learning networks are highly vulnerable to small and intentional changes in input data, known as adversarial perturbations. These perturbations, despite having a significant impact on the model's output, are usually imperceptible to humans and can pose serious security challenges in sensitive areas such as autonomous vehicles and medical diagnostics. To counter these perturbations, extensive research has been conducted, most of which has utilized a single deep learning network to create a...