6,460 research outputs found

    Fault diagnosis in aircraft fuel system components with machine learning algorithms

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    There is a high demand and interest in considering the social and environmental effects of the component’s lifespan. Aircraft are one of the most high-priced businesses that require the highest reliability and safety constraints. The complexity of aircraft systems designs also has advanced rapidly in the last decade. Consequently, fault detection, diagnosis and modification/ repair procedures are becoming more challenging. The presence of a fault within an aircraft system can result in changes to system performances and cause operational downtime or accidents in a worst-case scenario. The CBM method that predicts the state of the equipment based on data collected is widely used in aircraft MROs. CBM uses diagnostics and prognostics models to make decisions on appropriate maintenance actions based on the Remaining Useful Life (RUL) of the components. The aircraft fuel system is a crucial system of aircraft, even a minor failure in the fuel system can affect the aircraft's safety greatly. A failure in the fuel system that impacts the ability to deliver fuel to the engine will have an immediate effect on system performance and safety. There are very few diagnostic systems that monitor the health of the fuel system and even fewer that can contain detected faults. The fuel system is crucial for the operation of the aircraft, in case of failure, the fuel in the aircraft will become unusable/unavailable to reach the destination. It is necessary to develop fault detection of the aircraft fuel system. The future aircraft fuel system must have the function of fault detection. Through the information of sensors and Machine Learning Techniques, the aircraft fuel system’s fault type can be detected in a timely manner. This thesis discusses the application of a Data-driven technique to analyse the healthy and faulty data collected using the aircraft fuel system model, which is similar to Boeing-777. The data is collected is processed through Machine learning Techniques and the results are comparedPhD in Manufacturin

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Bio-inspired Surface Texture Fluid Drag Reduction using Large Eddy Simulation

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    Skin friction drag can be reduced through the application of bio-inspired riblet surfaces. Numerical simulations were performed using Large Eddy Simulation (LES) to investigate the effect of using riblets on reducing skin friction drag. In this study, three different riblet configurations were used; scalloped, sawtooth and a new design, hybrid, riblet. To validate the effect of using the proposed hybrid riblet design compared with other riblets used in the literature; before applying to complex geometries, they were initially applied to a flat plate in parallel arrangement. Results showed skin friction coefficient reduction of 14% using the proposed hybrid riblet. This reduction was 9.2 times and 1.2 times more compared to sawtooth and scalloped configurations, respectively. The hybrid riblet was then applied partially and fully to NACA 0012 airfoil. Skin friction coefficient reduction of 34.5% was obtained when the hybrid riblet fully applied on the airfoil surface. Furthermore, the Convergent-Divergent (C-D) arrangement was studied, where the riblets were placed fully on the NACA 0012 and aligned with a yaw angle with respect to the flow direction. The convergent lines are inspired by the sensory part of the shark skin, whereas the divergent lines or herringbone are found on the bird feather. The two different riblet configurations, sawtooth and hybrid were modeled with the C-D arrangement and the hybrid riblet with C-D arrangement contributed to higher skin friction coefficient reduction, 34.5%, than the sawtooth riblet shape, 26.75%. Moreover, the C-D arrangement was compared to the parallel arrangement and shown that the C-D arrangement increased the lift coefficient (cl) of the airfoil, the flow separation was delayed and the overall performance of the airfoil was enhanced

    Central-provincial Politics and Industrial Policy-making in the Electric Power Sector in China

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    In addition to the studies that provide meaningful insights into the complexity of technical and economic issues, increasing studies have focused on the political process of market transition in network industries such as the electric power sector. This dissertation studies the central–provincial interactions in industrial policy-making and implementation, and attempts to evaluate the roles of Chinese provinces in the market reform process of the electric power sector. Market reforms of this sector are used as an illustrative case because the new round of market reforms had achieved some significant breakthroughs in areas such as pricing reform and wholesale market trading. Other policy measures, such as the liberalization of the distribution market and cross-regional market-building, are still at a nascent stage and have only scored moderate progress. It is important to investigate why some policy areas make greater progress in market reforms than others. It is also interesting to examine the impacts of Chinese central-provincial politics on producing the different market reform outcomes. Guangdong and Xinjiang are two provinces being analyzed in this dissertation. The progress of market reforms in these two provinces showed similarities although the provinces are very different in terms of local conditions such as the stages of their economic development and energy structures. The actual reform can be understood as the outcomes of certain modes of interactions between the central and provincial actors in the context of their particular capabilities and preferences in different policy areas. This dissertation argues that market reform is more successful in policy areas where the central and provincial authorities are able to engage mainly in integrative negotiations than in areas where they engage mainly in distributive negotiations

    Demand Response Applications for the Operation of Smart Natural Gas Systems

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    This chapter discusses different aspects related to the operation of natural gas systems in the framework of the new configuration of energy systems based on the smart grid concept. First of all, different experiences performed worldwide regarding the application of demand response principles to increase the efficiency and operability of natural gas networks are presented. Next, the characteristics of the natural gas system to be configured according to the smart grid architecture are discussed, including the necessary agents for the proper functioning of such infrastructure. After that, the current state of installation of gas smart meters in some European countries is presented, according to the massive rollout process promoted by the European Union. Barriers that prevent the full exploitation of demand response resources related to natural gas systems are presented in the next section. After that, technical constraints which may be solved by using demand response are presented. Finally, last tendencies related to the development of natural gas systems, such as the injection of hydrogen, are considered

    Design and development of a traction motor emulator using a three-phase bidirectional buck-boost AC-DC converter

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    An industrial drive testing, with a ???real-machine??? can pave way, for some serious issues to test-bench, motor, and the operator. A slight disturbance in control logic amid testing, can damage the physical machine or drive. Such dangerous testing conditions can be avoided by supplanting real motor with a power electronic converter based ???Motor Emulator??? (ME) test-bench system. The conventional ME comprises of two-stage three-phase AC-DC-AC conversion with first-stage AC-DC as emulator and second-stage DC-AC as regenerating unit. This two-stage power conversion, require independent control algorithm, burdening control complexity as well as the number of power electronic switches are quite significant. Therefore, to economize and downsize conventional multistage ME system, this research work experimentally validates a common-DC-bus-configured ME system with only the AC-DC regenerative emulator stage. A bidirectional three-phase AC-DC converter is proposed as the regenerative emulator converter in a common-DC-Bus-configured ME system. The Proposed converter???s operating principle along with mathematical design and control strategy are also presented. To validate the operation of the proposed converter as a common DC-bus-configured emulator, two permanent magnet synchronous motors (PMSM) of 7.5 kW and 2.0 kW are emulated and their simulation and experimental results are presented here. The proposed bi-directional converter inspired from classical buck-boost operation, requires just ten unidirectional IGBT switches preventing any circulating current in the system. The proposed converter also eliminates the regenerative converter stage in classical ME system. Also, the proposed common-DC-bus-configured ME system requires a single stage control unlike independent control in existing ME system. The proposed converter provides four-quadrant operation and emulation of motor under study. The dynamic model of PMSM motor is simulated on the MATLAB simulation platform and the Simulation results are compared with experimental results. From the simulation and experimental results, it is concluded that, with the presented control scheme, the proposed ME converter can be made to draw the same current as a real machine would have drawn, had it been driven by the same DUT. Since, the output current of proposed converter is fed back to DC bus, the input power source requirement is reduced, making the overall ME system more energy efficient

    Optimisation of Triboelectric Nanogenerator performance in vertical contact-separation mode

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    Triboelectric nanogenerator (TENG) is one of the most promising energy harvesters – a technology that uses repeated or reciprocating contact of suitably chosen materials to generate charge via the triboelectric effect (TE) and utilizes this as usable voltage and current. TENGs are attractive as they can continuously generate charge over a wide range of operating conditions and have several valuable advantages such as light weight, simple structure, low cost and high efficiency. Therefore, TENGs have been explored in a wide range of applications, including self-powered wearable electronics, powering electronics and even for harvesting ocean wave/wind energy. One of the major limitations of TENGs is their low power output (usually <500 W/m2). This thesis focuses of a few specific approaches to optimising TENG output performance. This thesis begins by presenting a solution to this challenge by optimizing a low permittivity substrate beneath the tribo-contact layer. The open circuit voltage is found to increase by a factor of 1.3 in moving from PET to the lower permittivity PTFE. TENG performance is also believed to depend on contact force, but the origin of the dependence had not previously been explored. Herein, we show that this behaviour results from a contact force dependent real contact area Ar as governed by surface roughness. The open circuit voltage Voc, short circuit current Isc and Ar for a TENG were found to increase with contact force/pressure. Critically, Voc and Isc saturate at the same contact pressure as Ar suggesting that electrical output follows the same evolution as Ar. Assuming that tribo charges can only transfer across the interface at areas of real contact, it follows that an increasing Ar with contact pressure should produce a corresponding increase in the electrical output. These results underline the importance of accounting for real contact area in TENG design, as well as the distinction between real and nominal contact area in tribo-charge density definition. High-performance ferroelectricassisted TENGs (Fe-TENGs) are developed using electrospun fibrous surfaces based on P(VDFTrFE) with dispersed BaTiO3 (BTO) nanofillers in either cubic (CBTO) or tetragonal (TBTO) form in this thesis. TENGs with three types of tribo-negative surface were investigated and output increased progressively. Critically, P(VDF-TrFE)/TBTO produced higher output than P(VDFTrFE)/ CBTO even though permittivity is nearly identical. Thus, it is shown that BTO fillers boost output, not just by increasing permittivity, but also by enhancing the crystallinity and amount of the β-phase (as TBTO produced a more crystalline β-phase present in greater amounts)

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
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