Gas Turbine Generator Research Articles

Metallurgical Investigation and Failure Analysis in GTD-111 Stage 2 Buckets From an Industrial F-Class Gas Turbine

Abstract A utility experienced cracking in multiple stage 2 buckets (S2Bs) in a large F-class power generation gas turbine after only 7348 h and 31 starts following a standard repair and rejuvenation heat treatment cycle. The blades were cast from a directionally solidified (DS) superalloy and the cracking was found in the shroud section of the blade in the Z-notch region. A detailed failure investigation was conducted to determine the failure mechanism and most likely contributing factors to root cause in terms of design, operation, fabrication, and metallurgy. Three blades, two with visible cracks and one without cracks, were subjected to nondestructive and destructive evaluation. Methods consisted of visual inspection, three-dimensional scanning, physical measurements, chemical analysis, optical metallography, LED surface topological evaluation, and scanning electron microscopy. The failure mechanism was determined to be due to creep with damage propagating along the grain boundaries in a high stress concentration area. Evaluation of precipitate structure and size also confirmed this area exhibited the highest temperature during operations. High temperature creep testing was also conducted in material from the two different casting houses to confirm metallurgical risk did not have a significant impact on creep performance. An evaluation of the operational profile of an entire fleet of GE 7FA units showed that the failed 7F.03 S2Bs were in the highest operating temperature base loaded units. The findings from this work will help better define key factors that influence the high temperature performance of nickel-base superalloys used in the hot section of industrial power generating turbines.

Impact of nozzle pressure ratio and convergent-divergent length on the exit Mach number of supersonic jet

PurposeThe purpose of the study is to find the effect of convergent and divergent section length on the exit flow characteristics. Converging-diverging (CD) nozzle design can be difficult because of the necessity for precise geometry and an understanding of compressible fluid flow dynamics. To obtain the ideal supersonic speeds, it is challenging to make sure that the flow chokes at the throat, where the Mach number approaches one and then expands appropriately in the diverging region. The design needs to take into consideration things like the relationship between the area and Mach number, the impact of various pressure ratios and the flow’s isentropic interactions.Design/methodology/approachAn ideal thrust production is achieved through the effective acceleration of exhaust gases through proper nozzle design. This paper numerically investigates impact of convergent, divergent length and nozzle pressure ratio on the exit Mach Number of CD nozzle supersonic jet. Exit Mach Number 1.6 convergent-divergent nozzle was used. In total, five cases were taken as the length of the both the convergent-divergent sections were modified with 50% of increment and decrement in its base length. At four different NPR, the analysis was carried out in over-expanded, correctly expanded and under-expanded conditions. The NPR used were 2, 3.2, 4 and 5.FindingsFrom the results, it is found that the convergent length linearly affects the exit Mach number, while the divergent length variation is not in order. Both the decreased and increased divergent length reduce the supersonic jet exit Mach number. The subsonic region is not majorly affected by the length. There is no rapid change in the flow properties whether the length is reduced or increased. Maximum of 2% to 3% variation is only noticed. On the contrary, a small change in supersonic region or divergent section makes major modification in the flow.Originality/valueTo achieve the desired Mach number, not only the area of the nozzle but also the length affects it. In terms of divergent angle and area ratio, only most of the studies on nozzle have been focused. This study aims to find the impact of convergent length and divergent length on the exit Mach number. This could be used in a wide range of applications, including laser cutting, thermal spraying, gas turbines for power generation, rocket and jet engines, supersonic wind tunnels and turbo chargers in automotive engineering, because of their capacity to accelerate fluids to supersonic speeds.

Precipitation-controlled grain boundary engineering in a cast & wrought Ni-based superalloy

Grain boundary engineering (GBE) describes the various approaches to control the fraction and composition of special boundaries in designing engineering materials. For example, complex thermo-mechanical processing routes are harnessed to increase the fraction of Σ3 twin boundaries, bringing about improvements to strength, toughness, and corrosion properties in austenitic stainless steels. However, high-temperature gas turbine engine components designed for the most demanding applications must be manufactured from Ni-based superalloys. To provide the required high-temperature strength, their microstructures are highly complex and consist of an austenitic γ-matrix, γ' precipitates, and grain boundary (GB) carbides, and/or borides. GBE approaches for Ni-based superalloys have been reported to reduce in-service GB cracking via targeted engineering of the GB microstructure. However, the same complex microstructures severely limit the processability of the most advanced Ni-based superalloys required to develop the next generation gas turbine engines.To tackle this challenge, we propose precipitation-controlled GBE for Ni-based superalloys with limited formability, via formation of GB serrations and precipitation upon slow cooling. We showcase our approach by achieving controlled GB precipitation of GB-γ' and M6C carbides in the cast & wrought Ni-based superalloy René 41 via a simple heat treatment. Ductility improvements are predicted via crystal plasticity modeling due to increased slip transmission compatibility. Instead of generating additional Σ3 twin boundaries only, these interfaces are directly incorporated into the GB network. It is shown that precipitation-controlled GBE is enabled by GB-γ' nucleating heterogeneously on M6C whereby competitive coarsening of GB-γ' provides the driving force. The fundamental mechanisms of our GBE approach are discussed and summarized in a qualitative microstructural model.

Joint peak power and carbon emission shaving in active distribution systems using carbon emission flow-based deep reinforcement learning

Distribution optimal power flow (D-OPF) with peak load shaving function is crucial for guaranteeing economical and reliable operations of active distribution grids with various distributed energy resources. However, conventional D-OPF methods reduce only the power operation cost without considering carbon emission reduction, which may lead to a slowdown in achieving global carbon neutrality. To resolve this issue, this study proposes a deep reinforcement learning (DRL)-assisted D-OPF framework realizing dual-peak shaving of power and carbon emission for low-carbon active distribution system operations based on the notion of carbon emission flow (CEF). The proposed framework aims to minimize the total power operation costs of substation and gas-turbine (GT) generators. It also aims to reduce the total carbon emission cost via mitigation of peak power and carbon emission in the CEF-based D-OPF framework with both power and carbon emission peak constraints. A key feature of the proposed framework is the adoption of the DRL method for the CEF-based D-OPF problem to determine economical and eco-friendly peaks of power and carbon emission under dynamically changing distribution system operations. Furthermore, a D-OPF optimization-based reward function for the DRL agent is designed to yield no constraint violations for the D-OPF problem during the agent’s training phase. Numerical examples conducted on the IEEE 33-node and IEEE 69-node distribution systems with GT generators, solar photovoltaic systems, and energy storage systems demonstrate that, in contrast with CEF-free and CEF-integrated optimization methods with fixed power and/or carbon emission peaks, the proposed method further reduces the total carbon emission and cost.

A novel model-based diagnostics for identifying component degradations in gas turbines for power generation

Improving the accuracy of gas turbine performance diagnosis is important for reducing maintenance costs. Conventional model-based diagnostics use either compressor map adaptation based on correction curves or compressor map scaling based on an assumed ratio of fouling factors. However, they usually do not reflect the characteristics of the actual gas turbine. In this study, adaptation of both the compressor and turbine maps was carried out. The ratio of fouling factors was determined by quantifying the actual fouling factors so that the novel diagnostics could be applied to any gas turbine. The novelty of the proposed diagnostics is that it identifies component degradations individually, particularly distinguishing between compressor and turbine degradations using only measurement data. To demonstrate the capabilities of the proposed diagnostics, the method was applied to a 180-MW-class gas turbine operated over a long period with off-line washing. According to the results, the compressor air flow rate before off-line washing was reduced by 3.1 kg/s. Consequently, the power and efficiency of the gas turbine decreased by 3.5 MW and 0.43%p, respectively. The diagnosis results after off-line washing showed that the air flow rate and efficiency of the compressor were fully recovered, but the turbine efficiency was still degraded by 0.49%p. As a result, it was found that the gas turbine power and efficiency were not recovered by 1.2 MW and 0.33%p. The results showed that the component degradations could be identified individually. The novel diagnostics is expected to be used in a variety of strategies to reduce maintenance costs.

Assessing Best Practices in Natural Gas Production and Emerging CO2 Capture Techniques to Minimize the Carbon Footprint of Electricity Generation.

Natural gas (NG) is expected to provide a substantial portion of electricity generation in many jurisdictions for the foreseeable future. Postcombustion carbon capture and storage (CCS) effectively abates direct CO2 emissions; however, indirect NG supply chain emissions in most jurisdictions are incompatible with climate change mitigation goals. This life cycle assessment evaluates specific opportunities to reduce the carbon footprint of combined cycle gas turbine (CCGT) generation with CCS using existing low-emission NG production practices, technologies, and processes combined with emerging CCS techniques to achieve high CO2 capture rates and mitigate startup emissions. We find baseload life cycle greenhouse gas (GHG) emission intensity ranges from 22 to 62 kgCO2e/MWh for 95-98.5% CO2 capture, within the range of published estimates for wind and photovoltaic power and considerably below prior estimates of CCGT with CCS. Low-emission NG production practices reduce other environmental impacts, which are dominated by combustion-related air pollution. We also show how interim solvent storage can effectively mitigate emissions from CCGT start/stop cycles. This work highlights the importance of mitigating both CO2 and methane emissions from NG supply chains and proposes a more nuanced discussion regarding the potential contribution of NG to the future energy supply. A surrogate model is provided to estimate life cycle GHG emissions for CCGT with CCS and user-input parameters.

Evaluation of Key Performance Factors and Recommendation of Optimization Strategies of a Power Generation Company

This paper presents the analysis of key performance indicators and some effective improvement strategies of four gas turbine generators (GTG) of Nigeria's power plant company for four years (2019-2022). The investigation used the NERC/IEEE Standard 762 (2006) generator performance indices amongst other calculated key performance indices to evaluate the collected data. The research methodology was done through the collection of data using questionnaires, operational records, and plant data sheets recorded by operators in the power station and data analysis using Excel software, and then constructive optimization techniques were recommended for each key performance factor. From the result obtained, the operational performance shows an average energy generation of approximately 389.71 GWh. The equipment availability factor averaged 54.08%, indicating moderate reliability. The energy availability factor was notably lower, averaging 11.99%, suggesting significant room for improvement. The capacity factor averaged 9.39%, while the plant use factor was relatively high at 80.59%, demonstrating efficient operational usage. The load factor was low, with an average of 0.10, pointing to potential underutilization of capacity. The shortfall in performance levels is attributed to less plant availability due to overdue overhauling of some units resulting in frequent breakdowns/failures, obsolete technology, aging plant equipment, instability of the national grid system, and disruption in gas supply among others. The recommended strategic techniques are designed to address the specific challenges associated with each factor, thereby enhancing the overall operational performance of the power plant and other power generation companies for maximum productivity.

Simplified dynamic modeling and Fuzzy gain scheduled PID control of woodward-governor controlled grid-interactive twin-shaft gas turbine plants

Biomass-integrated gas turbines for electricity generation are competitive with coal and nuclear energy production. Gas turbine-based power generation system looks to be an excellent solution amid the emerging environmental conditions and the increased energy crisis. This paper presents a simplified dynamic analysis model for biomass-based, Woodward governor-controlled, twin-shaft heavy-duty gas turbine (HDGT) plants, alongside an intelligent controller for stability enhancement. Grid-connected HDGTs can become unstable under load disturbances, potentially causing system shutdowns. A simplified model for twin-shaft gas turbines, rated from 18.2 MW to 106.7 MW, has been identified based on control characteristics during startup. The speed controller is dominant, while the acceleration controller is only active at startup, and the temperature controller's effect is minimal during normal operation. This model suits all dynamic studies of twin-shaft gas turbines, regardless of varying power ratings and rotor time constants. For improving the grid stability, a Takagi-Sugeno-Kang (TSK) fuzzy gain scheduling PID controller has been proposed in this work and its behavior is validated with 5001M, 7001Ea, and 9001Ea models. Step response of fuzzy PID controller under load disturbances and set-point variations are compared with fixed gain PID controllers tuned by Ziegler-Nichols (ZN) and performance index-based tuning using the typical gas turbine model. Further the controller behavior is validated with field test-based model parameters as derived from the real-world combustion turbines used in Alaskan Railbelt system. Extensive research simulation and validation results reveal that the fuzzy self-tuning PID controller showed superior adaptability for grid-interactive twin-shaft HDGTs under load variations and set-point variations. Time domain specifications and the performance indices confirms that the proposed fuzzy tuned PID controller ensure reliable and stable operation for the energy management in grid-operative mode. The proposed simplified model and intelligent control logic enable various dynamic studies in grid-connected environments for both simple cycle and combined cycle operations.

Experimental characterization of the acoustic response of cavity-backed perforated plates to control thermo-acoustic instabilities in gas turbines

Abstract Prediction and control of thermoacoustic instabilities is a major challenge in the development of modern power generation gas turbines and aeroengines. Such instabilities arise from the coupling between flame dynamics and combustor acoustic modes, resulting in severe oscillations that can lead to premature aging of combustor components and structural damage. In many combustors, passive dampers are implemented to increase the acoustic energy dissipation of the system and prevent the onset of these harmful flame-acoustic interactions. In the present study, passive damping systems based on a cavity-backed perforated plate are experimentally analyzed, with a focus on studying the impact of bias flow on the reflection coefficient over a wide range of frequencies. Tests are carried out on two cavity-backed perforated plates characterized by the same porosity but a different number of holes 25 and 49, namely P25 and P49, respectively. It is observed that, for a given plate geometry, a higher bias flow leads to an increase in the plates absorption capacity over a wider range of frequency. This is more pronounced in the P 49 plate configuration. For both tested configurations, comparing the experimental results with Scarpato model proposed in the literature [1], a good match it is observed only for low values of bias flow. The model instead is not able to correctly capture the behavior of the damping systems when higher dissipation is reached.

Diagnosis of GHG Emissions in an Offshore Oil and Gas Production Facility

This work presents a diagnosis of greenhouse gas (GHG) emissions for floating production storage and offloading (FPSO) platforms for oil and gas production offshore, using calculation methodologies from the American Petroleum Institute (API) and U.S. Environmental Protection Agency (EPA). To carry out this analysis, design data of an FPSO platform is used for the GHG emissions estimation, considering operations under steady conditions and oil and gas processing system simulations in the Aspen HYSYS® software. The main direct emission sources of GHG are identified, including the main combustion processes (gas turbines for electric generation and gas turbine-driven CO2 compressors), flaring and venting, as well as fugitive emissions. The study assesses a high CO2 content in molar composition of the associated gas, an important factor that is considered in estimating fugitive emissions during the processes of primary separation and main gas compression. The resulting information indicates that, on average, 95% of total emissions are produced by combustion sources. In the latest production stages of the oil and gas field, it consumes 2 times more energy and emits 2.3 times CO2 in terms of produced hydrocarbons. This diagnosis provides a baseline and starting point for the implementation of energy efficiency measures and/or carbon capture and storage (CCS) technologies on the FPSO in order to reduce CO2 and CH4 emissions, as well as identify the major sources of emissions in the production process.

Development of Detailed and Reduced Chemical Kinetic Models for Ammonia Gas Turbines

Abstract The power generation sector has been recently moving toward decarbonization, and there is an increased interest in replacing conventional fossil fuels with fuels that produce reduced/zero carbon emissions. One such fuel is ammonia (NH3). However, ammonia is hard to ignite, has a low flame speed, and produces a significantly large amount of nitrogen oxide (NOx) emissions. Hence, using 100% ammonia as fuel in gas turbines requires significant modifications and the development of novel combustors. Blending hydrogen with ammonia, however, helps in having better control over the combustion properties. For example, a 70%/30% mixture of NH3/H2 mixture has a flame speed comparable to natural gas. Before utilizing hydrogen-blended ammonia in an actual gas turbine combustor, thorough simulation studies are required to evaluate its performance, possible hazards, and emissions. The literature lacks well-validated chemical kinetic models for the combustion of hydrogen-blended ammonia for undiluted mixtures at gas turbine-relevant conditions (∼20 bar). Most models available in the literature have been developed for ammonia extremely diluted in diluents such as argon or nitrogen. Hence, in this work, we develop a detailed chemical kinetic model for hydrogen-blended ammonia combustion and validate it with a wide range of experimental data for both dilute and undiluted mixtures relevant to gas turbine operating conditions. We outline the strengths and weaknesses of the current mechanism to aid future users of our chemical kinetic mechanism. The detailed chemical kinetic mechanism was reduced to a smaller version (32 species mechanism) without significant loss in accuracy using the directed relation graph with error propagation (DRGEP) and full species sensitivity analysis. The resultant mechanism can predict a wide range of experimental results with the least cumulative error and will be a valuable tool in computational fluid dynamics (CFD) simulations that will enable the development of gas turbines for zero-carbon power generation.

Optimizing thermal performance in internal passage cooling with extended rib: Applying response surface method with artificial neural networks

Efficient cooling technology is crucial for high-temperature gas turbines which are significant applications in thermal processes particularly in industries such as power generation aerospace and marine propulsion. Enhancing cooling efficiency in these systems not only improves energy utilization but also plays a critical role in reducing fuel consumption increasing operational reliability and minimizing environmental impacts. This research focuses on optimizing the thermal performance of internal passage cooling channels with extended ribs using a combination of the response surface method and artificial neural networks. By varying the extended rib length and rib width the study aims to enhance heat transfer and minimize flow loss leading to improvements in overall system efficiency. Latin Hypercube Sampling is employed to strategically select variables and automated numerical simulations are conducted to perform a thorough parametric analysis. A steady-state k-ω shear stress transport turbulence model is applied in the simulations and the numerical results are validated against experimental data ensuring accuracy. The combined response surface method–artificial neural network approach integrated with TensorFlow-based modeling allows for the prediction of heat transfer and friction factor ultimately generating a comprehensive response surface to identify the optimal design point. Applying the optimized design leads to a 23.99 % improvement in the thermal performance factor compared to the reference case. The findings of this study highlight a significant enhancement in the thermal performance of the cooling channel structure particularly in high heat flux environments. This research provides valuable insights into the design of more efficient thermal management systems for gas turbines and has broader implications for other high-temperature thermal processes. The proposed optimization methodology offers a reliable approach for improving cooling technologies contributing to the development of the next generation of energy-efficient high-performance gas turbines and other industrial applications where effective thermal management is essential.

A dual-standby power source configuration scheme for micro grid in Pelagic Island

Realizing clean energy supply is important for the sustainable development of pelagic island. The volatility and randomness features of renewable energy generation may reduce power supply reliability, thus backup units is necessary in micro-grid in pelagic island. This paper proposed a dual-standby configuration scheme based on Sequence Operation Theory to achieve reliable power standby. Firstly, characteristics of standby units, micro gas turbine and diesel generator, are analyzed. Next, the probability distribution function models of wind, photovoltaic and load demand in pelagic island are established, and the reliability index of power shortage probability is calculated by Sequence Operation Theory. Then, the two-stage dual-standby control scheme to make full use of the complementary standby units is introduced and object function to minimize equivalent annual cost on the premise of satisfying probabilistic reliability index is modeled. Hybrid integer particle swarm algorithm is used to solve the problem. Finally, the proposed model for dual-standby configuration is applied to a pelagic island. The simulation results show that the proposed method can meet the load demand in both daily and extreme weather and achieve superior economy.

Life Cycle Assessment of Natural Gas Production in Java Sea-East Java Province, Indonesia

Energy products including natural gas play a very important role in the context of global economic, industrial, and infrastructure development. However, it is undeniable that the production process from raw material extraction to ready-to-use products has an impact on the environment. This study was conducted to evaluate the environmental impact of natural gas production in the Kepodang field, East Java Province, Indonesia. The next objective is to analyze the magnitude and significance of the impact or hotspot analysis. The characterization results for 4 main categories show: Global Warming Potential 70.02 kg CO₂ eq, Ozone Layer Depletion 1.11E-06 kg CFC-11 eq, Acidification Potential 0.65 mol H+ eq, Marine Eutrophication 0.30 kg N eq, and Freshwater Eutrophication 2.17E-03kg P eq. The results also show that throughout the life cycle of this product, the largest contributor to environmental impacts is in the downstream phase, especially the user gas metering unit process. This is due to the 200 km of pipelines for product transportation from the Central Processing Platform (CPP) to the user gate. Pipeline parameters (Ecoinvent database) refer to long-distance pipelines with high capacity. In addition, hotspots were also identified in the core phase: gas measurement and chromatography, gas turbine generators, and flare gas.

Thermodynamic analysis and optimization of the semi-closed oxy-combustion supercritical CO2 power cycle with blending hydrogen into the natural gas/syngas

The semi-closed oxy-combustion supercritical CO2 (sCO2) cycle is regarded as a promising power cycle featuring the combined advantages of high efficiency and nearly 100 % CO2 capture. Blending hydrogen into fuels has attracted significant attention in the research of gas turbine and internal combustion engine power generation as it contributes to economy-wide decarbonization and bolsters the broader energy sources, especially green hydrogen. The present work carried out the thermodynamic analysis of a semi-closed sCO2 cycle to investigate how the hydrogen content in natural gas or syngas affects the operating parameters and performance. The results show that the energy efficiency decreases with increasing hydrogen content in natural gas whereas the maximum efficiency exists for the syngas with 60 % H2. The key factor is the increasing H2O content in the flue gas caused by adding hydrogen in fuels enhances both the heat-work conversion in the turbine and the heat transfer in the regenerator. The optimal turbine inlet temperature is around 1150 °C, which is the same as the basic cycle. The optimal turbine inlet pressure for natural gas and natural gas with 40 % H2 is both 28 MPa, while the optimal turbine inlet pressure decreases with hydrogen content in syngas. For all the fuels, the optimal turbine outlet pressure becomes lower for the higher hydrogen content. The maximum efficiencies obtained for natural gas, natural gas with 40 % H2, and syngas with 60 % H2 are 56.08 %, 55.52 % and 56.15 %. Finally, it is found that the recompression cycle and the multiple combustor arrangement (reheat cycle) cannot be the effective configuration for the semi-closed oxy-combustion sCO2 power cycle. The heat from the sCO2 recompression conflicts with the compression heat of the ASU, reducing the energy efficiency by 0.27 %, 0.34 %, and 1.23 % for the three fuels, respectively. The limitation of the regenerator allowable temperature requires the reheat cycle to either reduce the turbine inlet temperature or reduce the turbine outlet pressure, decreasing the energy efficiency for natural gas by 1.83 % and 6.38 % due to the reduced turbine output power or significantly increasing compression power.

Investigation on the Influence of Thermal Inertia on the Dynamic Characteristics of a Gas Turbine

In mini-grids and marine-isolated grids, power generation gas turbines are subjected to rapid start-up, shutdown, and acceleration/deceleration. This sudden load change can pose a significant impact on the power grid, severely affecting the operational characteristics of gas turbines. To understand the dynamic characteristics of the gas turbine in the transitional processes, this testing takes twin-shaft medium-sized power generation gas turbines as the test object, and goes through the process of startup, acceleration, deceleration, acceleration, shutdown in one hour, and repeats this process 40 times continuously. With fuel flow as the control parameter and power turbine outlet temperature and high-pressure turbine speed as the controlled parameters, the parameter response rate of the gas turbine under various transition processes is analyzed and the effect of thermal inertia on the gas turbine mass temperature as well as speed is studied. Research findings: During the transition processes, the gas temperature exhibited an axial gradient distribution in the channel. In both the acceleration and deceleration processes, the working fluid temperature gradually decreased along the flow direction. And thermal inertia posed different extents of impact on the dynamic characteristics of the gas turbine under different transitional processes. In the same transition process, the impacts of thermal inertia on the response speeds of temperature and rotational speed varied. The results of this study help to more accurately predict the operating state of the gas turbine during the transition process and lay the foundation for the dynamic simulation model of the non-adiabatic gas turbine.

Optimal transient power sharing method for shipboard MVDC System based on segmented variable step dynamic programming

Supplying power for large pulsed power load with shipboard medium voltage direct current integrated power system has continued to be a challenge. The energy storage device configured in the system can effectively mitigate the power impact on grid and gas turbine gensets, but it also complicates the system operation, requiring advanced power sharing and control strategies for support. In this paper, the control framework of system configured with hybrid energy storage is presented, the objective function to quantify transient power disturbance on gas turbine genset is established, and the segmented variable step dynamic programming method for system transient power sharing is innovatively proposed and employed to derive the optimal transient power-sharing scheme for the rectangular and triangular large pulsed power load conditions. Compared with the conventional approach through MATLAB/Simulink simulation, the scheme derived from segmented variable step dynamic programming demonstrates enhanced utilization of the transient power compensation capability of hybrid energy storage, and leads to a reduction in the power regulation pressure on gas turbine gensets. In the assumption of two typical operating processes, as the results of the power impacts，the transient regulation rate of the genset rotor is reduced from 10.4 % to 9.9 % and from 13.7 % to 8.6 %, respectively, the fatigue damage of shaft system is decreased by 53 % and 94.4 %, respectively, and the maximum inlet relative temperature of gas generator turbine is reduced by 0.015 and 0.139, respectively.

Effects of molten silicate reactivity on high temperature erosion behavior of plasma sprayed Yb2Si2O7-based EBCs

Particulate/debris damage caused by ingestion of calcium magnesium aluminosilicates (CMAS) hinders the durability of environmental barrier coatings (EBCs) used to protect SiC-based ceramic matrix composite components in next generation gas turbine engines. Similarly, ingestion of any debris in the engine can lead to mechanical damage and recession of coatings due to particulate erosion. Investigating particulate interactions at relevant engine conditions is crucial for determining limiting mechanisms in the operating lifetime of EBCs. This study assessed the effects of extrinsic phase formation and microstructural changes due to CMAS interactions on the erosion durability of Yb2Si2O7-based EBCs in a laboratory-scale combustion facility. CMAS exposures and erosion testing were carried out at 1316°C. Using 60 μm Al2O3 particles as the erodent material, the effects of CMAS loading on erosion durability at various impingement angles were evaluated.

New Technique to develop Intelligent High reliable & Efficient Unmanned Power Generation Modular

Primary aim of this research project is to develop Modular Generator Total Power Output: 1 MW or greater, integrated with High density diesel (NATO F76) generator. This modular comprises of Generators, Fuel Cells (MTBF), Gas Turbine Generators. Note that novelty will be sought on how to configure installation of multiple power units to allow for easy access for maintenance, quick removal and replacement as well as optimize operating life via controls. This proposed power modular will Operate in a marine environment condition such as salt air ingestion, ships motion in high sea states, shock, vibration. Note that novelty will be pursued on how to best optimize design to combine multiple power unit connections to a single ship interface.[1] Key words. Novelty, Corrective Maintenance and Preventive maintenance and Intelligent module.

Emissions reduction in LNG production facilities through emerging post-combustion carbon capture and storage technologies

The liquefied natural gas (LNG) sector relies extensively on gas turbines for on-site power generation and to drive refrigeration compressors. They provide an efficient and reliable energy source for LNG operations in remote locations. With upcoming changes to the Safeguard Mechanism in Australia, there is an increased focus on Scope 1 emissions, so management and targeted reduction are a priority to reduce liabilities. Despite technology advancements, gas turbines remain one of the largest sources of carbon dioxide (CO2) emissions in LNG production facilities. Also, in Australia, many facilities are not grid-connected, meaning that displacement of Scope 1 emissions through renewable electricity sourcing is a challenge. To develop deeper cuts in Scope 1 emissions, one solution is to deploy post-combustion carbon capture and storage (CCS) on existing gas turbines, with the potential to reduce associated emissions by over 90%. Globally, a number of LNG facilities are already deploying CCS to permanently store reservoir CO2 from the LNG processing trains. Several Australian LNG facilities are located in close proximity to potential CO2 storage reservoirs, meaning that a coordinated approach to both reservoir and post-combustion CO2 should be considered when sizing pipelines, shipping systems and storage wells to optimise and leverage the required investment. This paper outlines the technological, economic and policy aspects of integrating both reservoir post-combustion CCS on LNG facilities, including barriers and opportunities for deployment. It will discuss new developments and enablers for CCS to be more widely deployed at LNG production sites.