Preparation and Characterization of Bio-based PCM Microcapsules for Thermal Energy Storage

Phase change materials (PCM) have gained extensive attention in thermal energy storage applications. During their phase transition, the energy can be stored in or released from PCM. While changing from solid state to liquid state, PCM start to flow. Encapsulation is a widely-used technique to prevent PCM from migrating and reacting with their environment. In the first phase of the thesis, microencapsulation of 100% bio-based (organic) PCM microcapsules by solvent evaporation and oil-in-water emulsification was investigated under different conditions, resulting in optimal properties. For the second phase, the focus was on the studies of inorganic PCM, which have higher latent heat of melting, higher thermal conductivity and lower price than the organic PCM. However, their supercooling, phase segregation, and hydrophilic nature have caused major challenges to their applications. Tuning the thermal properties, including crystallization temperature and phase segregation of inorganic PCM by using bio-based nanoparticles and other additives was investigated in the second part of the research. Due to the hydrophilic nature of the inorganic PCM, a double emulsion (water in oil in water) solvent evaporation technique was explored for encapsulation with a polymeric shell. The challenges and strategies for supercooling prevention and encapsulating the hydrophilic PCM were investigated

Weakly-Supervised Multi-Task Learning for Audio-Visual Speaker Verification

In this paper, we present a methodology for achieving robust multimodal person representations optimized for open-set audio-visual speaker verification. Distance Metric Learning (DML) approaches have typically dominated this problem space, owing to strong performance on new and unseen classes. In our work, we explored multitask learning techniques to further boost performance of the DML approach and show that an auxiliary task with weak labels can increase the compactness of the learned speaker representation. We also extend the Generalized end-to-end loss (GE2E) to multimodal inputs and demonstrate that it can achieve competitive performance in an audio-visual space. Finally, we introduce a non-synchronous audio-visual sampling random strategy during training time that has shown to improve generalization. Our network achieves state of the art performance for speaker verification, reporting 0.244%, 0.252%, 0.441% Equal Error Rate (EER) on the three official trial lists of VoxCeleb1-O/E/H, which is to our knowledge, the best published results on VoxCeleb1-E and VoxCeleb1-H

Modeling and control of the starter motor and start-up phase for gas turbines

Improving the performance of industrial gas turbines has always been at the focus of attention of researchers and manufacturers. Nowadays, the operating environment of gas turbines has been transformed significantly respect to the very fast growth of renewable electricity generation where gas turbines should provide a safe, reliable, fast, and flexible transient operation to support their renewable partners. So, having a reliable tools to predict the transient behavior of the gas turbine is becoming more and more important. Regarding the response time and flexibility, improving the turbine performance during the start-up phase is an important issue that should be taken into account by the turbine manufacturers. To analyze the turbine performance during the start-up phase and to implement novel ideas so as to improve its performance, modeling, and simulation of an industrial gas turbine during cold start-up phase is investigated this article using an integrated modular approach. During this phase, a complex mechatronic system comprised of an asynchronous AC motor (electric starter), static frequency converter drive, and gas turbine exists. The start-up phase happens in this manner: first, the clutch transfers the torque generated by the electric starter to the gas turbine so that the turbine reaches a specific speed (cranking stage). Next, the turbine spends some time at this speed (purging stage), after which the turbine speed decreases, sparking stage begins, and the turbine enters the warm start-up phase. It is, however, possible that the start-up process fails at an intermediate stage. Such unsuccessful start-ups can be caused by turbine vibrations, the increase in the gradients of exhaust gases, or issues with fuel spray nozzles. If, for any reason, the turbine cannot reach the self-sustained speed and the speed falls below a certain threshold, the clutch engages once again with the turbine shaft and the start-up process is repeated. Consequently, when modeling the start-up phase, we face discontinuities in performance and a system with variable structure owing to the existence of clutch. Modeling the start-up phase, which happens to exist in many different fields including electric and mechanical application, brings about problems in numerical solutions (such as algebraic loop). Accordingly, this study attempts to benefit from the bond graph approach (as a powerful physical modeling approach) to model such a mechatronic system. The results confirm the effectiveness of the proposed approach in detailed performance prediction of the gas turbine in start-up phase

Diversity and Reliability in Erasure Networks: Rate Allocation, Coding, and Routing

Recently, erasure networks have received significant attention in the literature as they are used to model both wireless and wireline packet-switched networks. Many packet-switched data networks like wireless mesh networks, the Internet, and Peer-to-peer networks can be modeled as erasure networks. In any erasure network, path diversity works by setting up multiple parallel connections between the end points using the topological path redundancy of the network. Our analysis of diversity over erasure networks studies the problem of rate allocation (RA) across multiple independent paths, coding over erasure channels, and the trade-off between rate and diversity gain in three consecutive chapters. In the chapter 2, Forward Error Correction (FEC) is applied across multiple independent paths to enhance the end-to-end reliability. We prove that the probability of irrecoverable loss (P_E) decays exponentially with the number of paths. Furthermore, the RA problem across independent paths is studied. Our objective is to find the optimal RA, i.e. the allocation which minimizes P_E. Using memoization technique, a heuristic suboptimal algorithm with polynomial runtime is proposed for RA over a finite number of paths. This algorithm converges to the asymptotically optimal RA when the number of paths is large. For practical number of paths, the simulation results demonstrate the close-to-optimal performance of the proposed algorithm. Chapter 3 addresses the problem of lower-bounding the probability of error (PE) for any block code over an input-independent channel. We derive a lower-bound on PE for a general input-independent channel and find the necessary and sufficient condition to meet this bound with equality. The rest of this chapter applies this lower-bound to three special input-independent channels: erasure channel, super-symmetric Discrete Memoryless Channel (DMC), and q-ary symmetric DMC. It is proved that Maximum Distance Separable (MDS) codes achieve the minimum probability of error over any erasure channel (with or without memory). Chapter 4 addresses a fundamental trade-off between rate and diversity gain of an end-to-end connection in erasure networks. We prove that there exist general erasure networks for which any conventional routing strategy fails to achieve the optimum diversity-rate trade-off. However, for any general erasure graph, we show that there exists a linear network coding strategy which achieves the optimum diversity-rate trade-off. Unlike the previous works which suggest the potential benefit of linear network coding in the error-free multicast scenario (in terms of the achievable rate), our result demonstrates the benefit of linear network coding in the erasure single-source single-destination scenario (in terms of the diversity gain)

Dirac points with giant spin-orbit splitting in the electronic structure of two-dimensional transition-metal carbides

Two-dimensional (2D) materials, especially their most prominent member, graphene, have greatly influenced many scientific areas. Moreover, they have become a base for investigating the relativistic properties of condensed matter within the emerging field of Dirac physics. This has ignited an intense search for new materials where charge carriers behave as massless or massive Dirac fermions. Here, we theoretically show the existence of Dirac electrons in a series of 2D transition-metal carbides, known as MXenes. They possess twelve conical crossings in the 1st Brillouin zone with giant spin-orbit splitting. Our findings indicate that the 2D band structure of MXenes is protected against external perturbations and preserved even in multilayer phases. These results, together with the broad possibilities to engineer the properties of these materials phases, make Dirac MXenes a potential candidate for studying and developing novel Dirac-physics-based technologies.Comment: 4 figures and supplementar

Control requirements for future gas turbine-powered unmanned drones: JetQuads

The next generation of aerial robots will be utilized extensively in real-world applications for different purposes: Delivery, entertainment, inspection, health and safety, photography, search and rescue operations, fire detection, and use in hazardous and unreachable environments. Thus, dynamic modeling and control of drones will play a vital role in the growth phase of this cutting-edge technology. This paper presents a systematic approach for control mode identification of JetQuads (gas turbine-powered quads) that should be satisfied simultaneously to achieve a safe and optimal operation of the JetQuad. Using bond graphs as a powerful mechatronic tool, a modular model of a JetQuad including the gas turbine, electric starter, and the main body was developed and validated against publicly available data. Two practical scenarios for thrust variation as a function of time were defined to investigate the compatibility and robustness of the JetQuad. The simulation results of these scenarios confirmed the necessity of designing a compatibility control loop, a stability control loop, and physical limitation control loops for the safe and errorless operation of the system. A control structure with its associated control algorithm is also proposed to deal with future challenges in JetQuad control problems

Theoretical and experimental study of a micro jet engine start-up behaviour

Proper functioning of the start-up process in a micro jet engine is of great importance. This is due to the fact that the combustion chamber of such engine is so small and therefore, there is little time for fuel and air mixture to be present in the chamber. Hence, failed starts or repetitive attempts by the electric starter are very likely due to non-formation of the initial combustion. Also, the performance of the turbine start-up, impressively affects the limited life time of the micro jet engine. In this paper, an experimental study on the injection of compressed air during the start-up of a micro jet engine to improve its performance has been conducted. For this purpose, test components’ layout and a monitoring system are designed. The allowable pressure of the air injection has been calculated using both the engine dynamic model and experimental tests. The simulation results have also been compared and validated with the experimental test results. Finally, several tests were conducted to study the injection process of the compressed air form which, several results have been deduced including the reduction of maximum exhaust gas temperature (EGT) during the start-up, reduction of the start-up time interval and service lifetime enhancement. The method proposed in this paper is applicable on any micro jet engine with the similar structural form and starting process

A Scientometric Methodology Based on Co-Word Analysis in Gas Turbine Maintenance

Evaluation of scientific journals has a profound effect on the future of scientific research so that different institutes and countries can set appropriate goals and invest with less risk in various scientific fields. Accordingly, this article presents a new method based on a combination of co-word analysis and social network analysis to extract the hotspot topics. Using HistCite, NodeXL, and VOSviewer, then combining their results, the desired analysis is conducted for six time periods. Based on the bibliographic parameters in HistCite and by defining an index, the first five periods are selected such that both quantity and quality of articles in each period are maximum compared to other years, while the sixth time period contains the latest research. For each of the six periods, the co-word networks as created in VOSviewer are analyzed. Next, based on a combination of network centralities developed in NodeXL, the hotspot keywords are specified which are then validated and aggregated using the bibliographic parameters in HistCite. The results reveal five important time periods in gas turbine maintenance. The hotspot keywords obtained for the last period show that in recent years, some topics including gas turbine fault prognosis, neural network-based approaches, big data analysis, sensor fault diagnosis, blade availability, economic analysis and useful life estimation are prominent subjects in gas turbine maintenance