Sharif Digital Repository

According to the increasing amount of data exchanged among data centers, the need for speeding up and bandwidth and reduced power consumption has been increased. The information show that about 77% of the data is moved into the data centers. On the other hand, 10% of data center’s power consumption is used to data transmission. Improving the interconnection network of data centers can play an important role in reducing power consumption and speeding up. In recent years, optical interconnects have gained attention as a promising solution. Nevertheless, offering an all-optical and efficient architecture is an important issue. In this study, we intend to provide a multi-stage, all-optical... 

Today with the deployment of cloud computing and web applications; We need to have powerful datacenters with provisioning high bandwidth. Current data centers with electronic network interconnects, using excessive power to provisioning requisite bandwidth. Nevertheless, interconnecting networks in data centers isn’t in maximum efficiency and many components of them aren't used efficiently. So it is necessary to use an optical network with dynamic provisioning variable bandwidth and energy management. In this approach, our proposed architecture is designing topology with adopting dynamically for energy management in optical interconnect networks in data centers. To achieve this we can study... 

In this thesis, we studied the oscillation of neutrino flavors and the measurement of mixing parameters through the detection of cosmic neutrinos at neutrino telescopes. Most of the astrophysical explosions predict the emission of neutrino flux and kilometer-scale detectors are under construction for the observation of these neutrinos. Detection of astrophysical neutrinos opens a new window to understand the neutrino properties and also the properties of astrophysical sources.In this thesis first we review the phenomenology of neutrino flavor oscillation and characteristics of neutrino mass matrix. By introducing a new class of invariants (under general transformation of lepton fields bases)... 

Excitability is one of the most important characteristics of a neuron. In 1948, Hodgkin identified three different types of excitability of neurons. Excitability an all of its types can be observed in Hodgkin-Huxley model of neuronal dynamics (H-H model) as a four-dimensional system of differential equations and in at least two dimensional reductions of H-H type models. By applying the theory of dynamical systems (e.g. bifurcation theory), one can give a mathematical definition of excitability.Excitability of the neuron is equivalent to that the neuronal model is near a bifurcation through which the state of the system approaches to a stable limit cycle. In this thesis, a two-dimensional... 

Neutrino flavor oscillations are clear evidence of massive neutrinos. It has been for more than a decade that neutrino physics became one of active topics in the field of particle physics and various experiments started studying the flavor oscillation of neutrinos. Unfortunately, experiments studying the flavor oscillations are only sensitive to the mass square difference, and they are incapable of providing more information about the absolute mass scale of neutrinos. Additionally, sterile neutrinos having non-standard interactions are under vast investigations and studies. Determination of neutrino masses and their effective number is among the most challenging problems by which... 

Lobe blowers are positive displacement machines (PDM) that are widely used in various industries. The efficiency of lobe blowers depends on the clearance between fixed and moving parts. If the clearance between the parts increases, backflow occurs, which means that the flow returns from the high-pressure zone of the outlet to the low-pressure zone of the inlet, and this makes the blower not have the initial efficiency and work with a lower capacity than its design. This research is carried out with the aim of checking the feasibility of backflow detection using two condition monitoring methods, including ultrasonic and thermography techniques. The sample of this research includes seven... 

The prediction of Remaining Useful Life (RUL) has become a critical tool in enhancing the availability of rotating machinery. By knowing the remaining time before a bearing failure occurs during operation, significant reductions in unplanned production downtimes and associated costs can be achieved. In this study, by analyzing the time signals of vibration and ultrasonic data related to the bearing of a 4.1 MW electromotor from installation to replacement, and by investigating the behavior trends and correlation between extracted features, RMS has been ultimately identified as the health indicator in both vibration and ultrasonic techniques for monitoring the bearing’s condition. First,... 

Rotating machinery is a critical component in many industries, and its failure can lead to process interruptions and significant economic losses. Rolling bearings, as key elements of these machines, account for approximately 45–55% of failures. Therefore, timely detection of bearing faults plays a crucial role in reducing maintenance costs and improving safety. However, achieving timely fault detection is challenging due to weak signals and the presence of noise, which necessitates the use of advanced algorithms with strong diagnostic capabilities. Despite the considerable success of deep learning models in pattern recognition, their application to fault diagnosis of rotating machinery faces... 

Bearings play a critical role in the functionality of rotating equipment across various industries, accounting for approximately 50% of equipment failures due to bearing malfunctions. Accurate life prediction of bearings is essential not only for preventing unexpected breakdowns and subsequent damage but also for minimizing unnecessary replacements of functional bearings, which can lead to increased operational costs. With the rise of artificial intelligence, numerous predictive models have been developed; however, many of these require extensive datasets, which are often unavailable in industrial settings. Data collection is typically irregular, based on the sensitivity of the equipment,... 

Gas turbines' safe operation depends on the monitoring of performance thresholds and equipment limitations. This is accomplished through online data monitoring using sensors, with the collected data processed by monitoring software supplied by turbine manufacturers to extract information on operating conditions. The thresholds are usually set during manufacturing under standard conditions and saved in the control software. As the turbine ages and undergoes maintenance, these thresholds and operating conditions may change, requiring expert analysis to determine safe operating limits. Technological advancements have enabled the processing of operational data to establish patterns and create... 

Today, reliability assessment of production systems is crucial for ensuring continuous production and minimizing operational and maintenance (O&M) costs. One way to assess reliability at the system level is to create a reliability block diagram (RBD) and estimate the failure rates of equipment over their operational periods. This approach has been utilized in this thesis, focusing on assessment of the reliability of various units within the methanol plant at Khark Petrochemical Complex. In this context, understanding the condition of the equipment criticality is essential for constructing the reliability block diagram. The Crespo model and total criticality per risk (CTR) measure have been... 

Over the years, as machines have been invented, their maintenance and repair have consistently been regarded as critical concerns. Oil analysis techniques are an effective method for monitoring the condition of mechanical equipment. Since filters in these devices continually absorb and remove a significant portion of particles from the lubricant, the selection of appropriate filters is crucial. While filters positively affect machine performance, they can also negatively impact oil condition monitoring. To mitigate this negative effect and improve the predictive accuracy of equipment health monitoring, oil filter analysis was conducted. In this research, analytical ferrography tests were...