Simulated Gas Turbine Research Articles

Research on prediction algorithm of ship equipment heath condition

The prediction of ship equipment operation status is of great significance to ship equipment maintenance and ship navigation safety. How to use ship equipment operation data to predict ship equipment operation status is the most important part. Ship equipment operation status prediction is divided into equipment operation data feature extraction and prediction. And ship is a highly coupled system, so this article focuses on extracting ship equipment correlation to predict the operating status of ship equipment. In the research of this article, the UCI_CBMNPP dataset and UCI_CMHS dataset are used as the basic data set of this article, UCI_CBMNPP is generated from a complex gas turbine simulator, and UCI_CMHS is obtained through experiments on a hydraulic test bench. Analyze the characteristics of ship equipment operation data through two data sets. The improved dynamic time warping (DTW algorithm is a dynamic programming algorithm to calculate the similarity of two time series.) is used to extract the relationship between devices, this paper changes multivariate time series forecasting with graph neural networks (MTGNN: Multivariate Time Series Forecasting with Graph Neural Networks.) structure to establish predictive model, named ImDTW-MTGNN. Experiments demonstrated that ImDTW-MTGNN has achieved better results on the UCI_CBMNPP and UCI_CMHS.

Intelligent Combined Neural Network and Kernel Principal Component Analysis Tool for Engine Health Monitoring Purposes

An efficient maintenance plan is an important aspect for aeronautical companies to increase flight safety and decrease costs. Modern technologies are widely used in the Engine Health Monitoring (EHM) discipline to develop intelligent tools capable of monitoring the health status of engines. In this work, Artificial Neural Networks (ANNs) and in-detail Feed-Forward Neural Networks (FFNNs) were exploited in addition to a Kernel Principal Component Analysis (KPCA) to design an intelligent diagnostic tool capable of predicting the Performance Parameters (PPs) of the main components used as their health index. For this purpose, appropriate datasets containing information about degraded engines were generated using the Gas Turbine Simulation Program (GSP). Finally, the original datasets and the reduced datasets obtained after the application of KPCA to the original datasets were both used in the training and testing process of neural networks, and results were compared. The goal was to obtain a reliable intelligent tool useful for diagnostic purposes. The study showed that the degraded component detection and estimation of its performance achieved by using the hybrid KPCA–FFNNs were predicted with accurate and reliable performance, as demonstrated through detailed quantitative confusion matrix analysis.

Development of a combined Artificial Neural Network and Principal Component Analysis technique for Engine Health Monitoring

In aerospace sector, reliability is a crucial point. Modern technologies widely use Artificial Intelligence (AI) algorithms together with detections by sensors in order to design a health-based maintenance plan which stops an aircraft only when needed. In this work, an Engine Health Monitoring (EHM) system was developed by exploiting AI algorithms as Artificial Neural Networks (ANNs) trained to estimate a series of Performance Parameters (PPs) used as index of the health status of the main components constituting an engine. A neural network called Feed-Forward Neural Network (FFNN) in combination with a Principal Component Analysis (PCA) for feature reduction was used in this paper. The software Gas turbine Simulation Program (GSP) was used to generate a series of data containing information about engine performance under different flight conditions and compressor degradation levels. The datasets were subsequently used to train the neural networks to estimate the PPs of the degraded component. The final purpose of the present work is to develop an efficient diagnostic system useful to increase flight safety and decrease maintenance costs and fuel consumption.

Model-Based Dynamic Performance Simulation of a Microturbine Using Flight Test Data

Microturbines can be used not only in models and education but also to propel UAVs. However, their wider adoption is limited by their relatively low efficiency and durability. Validated simulation models are required to monitor their performance, improve their lifetime, and to design engine control systems. This study aims at developing a numerical model of a micro gas turbine intended for prediction and prognostics of engine performance. To build a reliable zero-dimensional model, the available compressor and turbine maps were scaled to the available test bench data with the least squares method, to meet the performance of the engine achieved during bench and flight tests. A steady-state aeroengine model was implemented in the Gas turbine Simulation Program (GSP) and was compared with experimental operating points. The selected flight data were then used as input for the transient engine model. The exhaust gas temperature (EGT) and fuel flow were chosen as the two key parameters to validate the model, comparing the numerical predicted values with the experimental ones. The observed difference between the model and the flight data was lower than 3% for both EGT and fuel flow.

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

Accurate simulations of gas turbines’ dynamic performance are essential for improvements in their practical performance and advancements in sustainable energy production. This paper presents models with extremely accurate simulations for a real dual-fuel gas turbine using two state-of-the-art techniques of neural networks: the dynamic neural network and deep neural network. The dynamic neural network has been realized via a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), and the deep neural network has been based on a convolutional neural network (CNN). The outputs selected for simulations are: the output power, the exhausted temperature and the turbine speed or system frequency, whereas the inputs are the natural gas (NG) control valve, the pilot gas control valve and the compressor variables. The data-sets have been prepared in three essential formats for the training and validation of the networks: normalized data, standardized data and SI units’ data. Rigorous effort has been carried out for wide-range trials regarding tweaking the network structures and hyper-parameters, which leads to highly satisfactory results for both models (overall, the minimum recorded MSE in the training of the MISO NARX was 6.2626 × 10−9 and the maximum MSE that was recorded for the MISO CNN was 2.9210 × 10−4, for more than 15 h of GT operation). The results have shown a comparable satisfactory performance for both dynamic NARX ANN and the CNN with a slight superiority of NARX. It can be newly argued that the dynamic ANN is better than the deep learning ANN for the time-based performance simulation of gas turbines (GTs).

Oscillating Frequencies Generated by Combustion Oscillation in a Combustor Tube Fueled by Natural Gas and a Hydrogen Mixture

Abstract From previous combustion oscillation experiments using a simulated gas turbine combustor, oscillation frequencies around 350 Hz were measured in only natural gas-fired, and around 200 and 400 Hz were measured in the case of hydrogen-containing fuel. In this study, the axial gas column vibration mode was assumed, and the method to reproduce the change of oscillating frequency due to the difference of fuel was investigated. In the previous study, the temperature distribution in the combustor was divided into only two regions, and there were problems in terms of parameter estimation for modeling the flame dynamics. Therefore, the transfer matric method that incorporates a linear temperature gradient was employed. Also, the temperature distributions obtained from computational fluid dynamics, and experiments were reduced to one dimension to reproduce the difference in combustion characteristics due to the difference in fuel composition; four methods were proposed, the axial representative temperatures. The Nyquist plot method was used to calculate up to 10 combinations of resonant frequency and growth rate simultaneously. Furthermore, the oscillation frequency was determined in which the resonance frequency with the growth rate was maximum. As a result, the value of the oscillating frequency obtained differed depending on creating the representative temperature distribution.

The Effects of Axisymmetric Convergent Contouring and Blowing Ratio on Endwall Film Cooling and Vane Pressure Side Surface Phantom Cooling Performance

Abstract To reduce the intensity of endwall secondary flow, the axisymmetric convergent contoured endwall is commonly designed in the first nozzle guide vane (NGV) passage of the real gas turbine engines. This endwall contouring can obviously alter flow field near endwall and affect the coolant flow through the upstream double-row discrete film holes. This leads to a significant influence on the endwall film cooling performance, vane surface phantom cooling, and vane passage aerodynamic performance. In this paper, a detailed numerical investigation on the endwall film cooling and vane pressure side surface phantom cooling was performed, at the simulated realistic gas turbine operating conditions (high inlet freestream turbulence level of 16%, exit Mach number of 0.85, and exit Reynolds number of 1.7 × 106). Based on a double coolant temperature model, a novel numerical method for the predictions of adiabatic wall film cooling effectiveness was proposed. This numerical method was validated by comparing the predicted results with experimental data of endwall Nusselt number, endwall film cooling effectiveness, and flow visualization near endwall. The results indicate that the present numerical method can accurately predict endwall thermal load distributions, endwall film cooling distributions, and vane surface phantom cooling distributions. The endwall heat transfer coefficient, endwall film cooling effectiveness, phantom cooling effectiveness of the vane pressure side surface, and total pressure loss coefficients (TPLC) were predicted and compared for two endwall contouring shapes (flat endwall and axisymmetric convergent contoured endwall) at three different blowing ratios (low blowing ratio of BR = 1.0, design blowing ratio of BR = 2.5, and high blowing ratio of BR = 3.5) with a constant density ratio of DR = 1.2, based on the present novel numerical method. Results show that the axisymmetric convergent endwall contouring leads to a slight enhancement (maximum enhancement level less than 20%) of endwall heat transfer in the entire vane passage (0 < x < 0.65Cx). The axisymmetric convergent endwall contouring has a significantly desired effect on endwall film cooling performance (maximum enhancement level of 67%), phantom cooling performance of the vane pressure side surface (maximum increase level approximately 100%), and aerodynamic loss (maximum reduction level of 1.45%) for all blowing ratio cases, but the benefit enhancement level is obviously affected by the blowing ratio values. This suggests that the optimum of endwall contouring shapes is an effective technical way to improve endwall film cooling performance and decrease the depletion of coolant; the coupled effects of the appropriate axisymmetric convergent endwall contouring and the optimum blowing ratio should be considered in the design process of advance endwall cooling schemes.

Cycle-Tempo Simulation of Ultra-Micro Gas Turbine Fueled by Producer Gas Resulting from Leaf Waste Gasification

Leaf waste has the potential to be converted into energy because of its high availability both in the world and Indonesia. Gasification is a conversion technology that can be used to convert leaves into producer gas. This gas can be used for various applications, one of which is using it as fuel for gas turbines, including ultra-micro gas ones, which are among the most popular micro generators of electric power at the time. To minimize the risk of failure in the experiment and cost, simulation is used. To simulate the performance of gas turbines, the thermodynamic analysis tool called Cycle-Tempo is used. In this study, Cycle-Tempo was used for the zero-dimensional thermodynamic simulation of an ultra-micro gas turbine operated using producer gas as fuel. Our research contributions are the simulation of an ultra-micro gas turbine at a lower power output of about 1 kWe and the use of producer gas from leaf waste gasification as fuel in a gas turbine. The aim of the simulation is to determine the influence of air-fuel ratio on compressor power, turbine power, generator power, thermal efficiency, turbine inlet temperature and turbine outlet temperature. The simulation was carried out on condition that the fuel flow rate of 0.005 kg/s is constant, the maximum air flow rate is 0.02705 kg/s, and the air-fuel ratio is in the range of 1.55 to 5.41. The leaf waste gasification was simulated before, by using an equilibrium constant to get the composition of producer gas. The producer gas that was used as fuel had the following molar fractions: about 22.62% of CO, 18.98% of H2, 3.28% of CH4, 10.67% of CO2 and 44.4% of N2. The simulation results show that an increase in air-fuel ratio resulted in turbine power increase from 1.23 kW to 1.94 kW. The generator power, thermal efficiency, turbine inlet temperature and turbine outlet temperature decreased respectively from 0.89 kWe to 0.77 kWe; 3.17% to 2.76%; 782 °C to 379 °C and 705°C to 304 °C. The maximums of the generator power and thermal efficiency of 0.89 kWe and 3.17%, respectively, were obtained at the 1.55 air-fuel ratio. The generator power and thermal efficiency are 0.8 kWe and 2.88%, respectively, with the 4.64 air-fuel ratio or 200% excess air. The result of the simulation matches that of the experiment described in the literature.

Neural Nonlinear Autoregressive Model with Exogenous Input (NARX) for Turboshaft Aeroengine Fuel Control Unit Model

One of the most important parts of a turboshaft engine, which has a direct impact on the performance of the engine and, as a result, on the performance of the propulsion system, is the engine fuel control system. The traditional engine control system is a sensor-based control method, which uses measurable parameters to control engine performance. In this context, engine component degradation leads to a change in the relationship between the measurable parameters and the engine performance parameters, and thus an increase of control errors. In this work, a nonlinear model predictive control method for turboshaft direct fuel control is implemented to improve engine response ability also in presence of degraded conditions. The control objective of the proposed model is the prediction of the specific fuel consumption directly instead of the measurable parameters. In this way is possible decentralize controller functions and realize an intelligent engine with the development of a distributed control system. Artificial Neural Networks (ANN) are widely used as data-driven models for modelling of complex systems such as aeroengine performance. In this paper, two Nonlinear Autoregressive Neural Networks have been trained to predict the specific fuel consumption for several transient flight maneuvers. The data used for the ANN predictions have been estimated through the Gas Turbine Simulation Program. In particular the first ANN predicts the state variables based on flight conditions and the second one predicts the performance parameter based on the previous predicted variables. The results show a good approximation of the studied variables also in degraded conditions.

Influence of the geographical parameters on the performance of hybrid solar gas turbine

This study aims to investigate the influence of the geographical and climate parameters on the performance of the hybrid solar gas turbine with a pressurized air receiver. A number of sites located in South America (Chile, Bolivia, and Peru) and North Africa (Algeria and Libya) are considered. The geometric design parameters of the solar receiver and the tower are calculated using an in-house code. The layout and the optical performance of the heliostat field are carried out using SolarPILOT software. The simulation of the complete hybrid solar gas turbine is carried out using TRNSYS software. A 50 MWe hybrid solar gas turbine is chosen in this study. Results show that a hybrid solar gas turbine installed in North Africa performs better than that installed in South America. This is mainly due to the optical performance of the heliostat field, which are better in North Africa are than in South America. The highest annual optical efficiency of a solar field is observed at Bechar (Algeria) 56.8% while the lowest annual efficiency is observed at Antofagasta (Chile) 48.1%. The solar-to-electric efficiency at Atacama Desert is lower than in the Sahara Desert. Indeed, in Atacama region, the solar-to-electric efficiency varies from 17% at Antofagasta to about 18% in Arequipa while it is above 19% at Sabha and Bechar.

Micro Gas Turbine Small-Scale Effects in Range Extended Electric Vehicles

AbstractApplication of a range extender in an electric vehicle can reduce the battery bank size and extend the driving range on a need basis. A micro gas turbine offers high power density, fuel flexibility, a reliable thermal efficiency (with recuperation), and less raw exhaust gaseous emissions compared to an internal combustion engine. However, micro gas turbines also incur low component performances due to small-scale effects related to high viscous losses, heat transfer between hot and cold sections, and manufacturing and assembly constraints compared to their larger counterparts. In this paper, the micro gas turbine thermodynamic cycle has been designed in Gas Turbine Simulation Program (GSP) and evaluated in terms of the small-scale effects simultaneously with the battery bank energy and charging time analysis. The key objective is to demonstrate the effectiveness of a micro gas turbine in saving the weight of a range-extended electric vehicle while understanding the impact of small-scale effects on the battery bank energy and charging time. Results indicate that a relatively smaller 22-kWh battery bank can be utilized with prospects of cost-savings together with a 47-kW micro gas turbine range extender to achieve an average driving range of 100 km and a charging time of 30 min for the baseline electric vehicle. Furthermore, the compressor and turbine isentropic efficiencies are found to have a significant impact on the overall battery bank performance.

Off-line and on-line optimization of the energy management strategy in a Hybrid Electric Helicopter for urban air-mobility

This study addresses the optimization of the energy flows in a Hybrid Electric Helicopter for air-taxi operation in order to minimize the fuel consumption on four typical missions (defined in terms of power and altitude profiles) and contemporarily allowing electric back-up operation at any time during the mission. The proposed parallel hybrid electric powertrain includes a turboshaft engine, two electric machines, a lithium ion battery and all necessary control systems. The powertrain is modeled with an empirical but comprehensive approach in order to make possible the combination with numerical optimization algorithms. In particular, the battery is modeled with an electric equivalent circuit model that includes the effect of battery aging. The fuel consumption of the turboshaft engines is calculated with a mathematical function of power request, altitude and Mach number whose coefficients are fitted by means of comparison with the commercial code Gas-turbine Simulation Program (GSP). The optimization of the energy management of the hybrid powertrain is performed in two steps. The first one was the application of Dynamic Programming in order to obtain the optimal usage of the battery for a given mission (target values) and to provide insights into how to develop a suitable on-line optimizer to be applied during the real operation of the rotorcraft. For this second step analysis, the authors developed a version of the Equivalent Consumption Minimization Strategy opportunely adapted to the specific case of an aerial vehicle with turboshaft engine, in particular taking in to account the desired state of charge and the actual state of health of the battery. After an off-line optimization of its parameters with a multi-objective approach, the on-line optimizer guaranteed results similar to the target values and allowed a reduction of the fuel burn ranging between 10% and 22% with respect to using only the thermal engine to power the rotor shaft and without any need to adapt the parameters in case of aged battery.

Optimal Energy Management of a Hybrid Electric Helicopter for Urban Air-Mobility

In this study, a hybrid electric helicopter for air-taxi operations is considered. The drivetrain includes a turboshaft engine and two electric machines, fed by a lithium ion battery. In previous works, some of the authors developed a simple but thorough modelling approach for the electric path of the power system that was validated by means of experimental data from literature. In this investigation simulation results from the Gas-turbine Simulation Program (GSP) commercial environment were used to develop and validate an off design model for the turboshaft. The second innovative contribution of the paper is the application of Dynamic Programming to four different missions of the helicopter to quantify the fuel saving potentiality of hybridization and also as benchmark for future online control strategies. A constraint was considered in the investigation to allow at any time the electric backup in case of engine failure. The results of DPM showed that it is possible to obtain a reduction of fuel burn from 12% to 24% (with respect to using only the engine to move the rotor) depending on the specification of the mission and the state of health of the battery. Moreover, it was proved that charging the battery on board is not necessary in this kind of application.

Dynamic simulation of gas turbines via feature similarity-based transfer learning

Since gas turbine plays a key role in electricity power generating, the requirements on the safety and reliability of this classical thermal system are becoming gradually strict. With a large amount of renewable energy being integrated into the power grid, the request of deep peak load regulation for satisfying the varying demand of users and maintaining the stability of the whole power grid leads to more unstable working conditions of gas turbines. The startup, shutdown, and load fluctuation are dominating the operating condition of gas turbines. Hence simulating and analyzing the dynamic behavior of the engines under such instable working conditions are important in improving their design, operation, and maintenance. However, conventional dynamic simulation methods based on the physic differential equations is unable to tackle the uncertainty and noise when faced with variant real-world operations. Although data-driven simulating methods, to some extent, can mitigate the problem, it is impossible to perform simulations with insufficient data. To tackle the issue, a novel transfer learning framework is proposed to transfer the knowledge from the physics equation domain to the real-world application domain to compensate for the lack of data. A strong dynamic operating data set with steep slope signals is created based on physics equations and then a feature similarity-based learning model with an encoder and a decoder is built and trained to achieve feature adaptive knowledge transferring. The simulation accuracy is significantly increased by 24.6% and the predicting error reduced by 63.6% compared with the baseline model. Moreover, compared with the other classical transfer learning modes, the method proposed has the best simulating performance on field testing data set. Furthermore, the effect study on the hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning knowledge from the physical theory domain or from the real-world operation domain.

Experimental Study of Direct Water Injection Effect on NOx Reduction from The Gas Fuel

Direct Water Injection (DWI) is commonly used in many nitrogen oxides (NOx) emissions control applications due to its effect to reduce the adiabatic flame temperature. In this paper an experimental test rig is designed to study the effect of water injection spray inside a simulated gas turbine combustor from the gas fuel. The practical work introduced by the chemical reaction methodology followed by the experiment which was presented and discussed carefully. Results are obtained in term of the exhaust gas temperature and different injection parameters including position, direction and fuel mass flow rate on the nitrogen oxide emission value in PPM (Parts per Million) at different conditions. The results showed that the best water injection effect was obtained at 45° degree inside the primary air zone. Injection location has a major effect on the NOx reduction as the best injected location is the Primary air zone compared with the direct fuel nozzle tip due to the increase of the water droplets residence time inside the combustor and perform a vortex that will affect the reduction of exhaust gas temperature and NOx emission respectively. The huge impact was observed at LPG (Liquefied Petroleum gas) flowrate 2.7L/min and water to fuel ratio about 0.4 as the NOx value was decreased about 73% from almost 381 PPM to 73 PPM. The chemical reaction arrangement order methodology presented good agreement with the experimental results at different fuel flow rate and equivalence ratio. The chemical Reaction equations were implemented to calculate the different adiabatic flame temperatures which is experimentally known as the exhaust gas temperature and impacted directly the NOx emission results.

Spray Flame Characteristics of Bio-Derived Fuels in a Simulated Gas Turbine Burner

Abstract Fuel injection plays an important role in liquid-fueled gas turbine combustion. The strong interdependence of liquid breakup and atomization, turbulent dispersion of these droplets, droplet evaporation, and fuel–air mixing make the spray modeling an extremely challenging task. The physical processes are even more difficult to predict for alternative fuels with different thermophysical properties. In this study, spray flames of unheated and preheated vegetable oil (VO) produced by an air-blast (AB) atomizer in a swirl stabilized combustor are investigated experimentally. Phase Doppler particle analyzer (PDPA) is used to measure the instantaneous diameter and axial velocity of droplets at different axial and radial locations in both flames. Experiments are conducted at an equivalence ratio of 0.79 and atomizing air to liquid ratio by a mass of 2.5 to obtain stable VO flames. Radial profiles of mean axial velocity and Sauter mean diameter (SMD) are presented to show the effect of fuel preheating. Joint probability density functions (joint PDF) are presented to show the correlation between droplet diameter and axial velocity. Results are analyzed to show that both sprays exhibit self-similar droplet diameter distributions at different axial and radial locations when normalized properly. Thus, the vast amount of PDPA data in the spray can be reduced to simple distribution functions. A method to reconstruct the joint PDF from experimentally determined distribution functions is presented. We envision that the joint PDF approach outlined in this study could be implemented in high-fidelity computational fluid dynamic models to improve spray predictions in future studies.

Data regarding dynamic performance predictions of an aeroengine.

The design of aeroengine real-time control systems needs the implementation of machine learning based techniques. The lack of in-flight aeroengine performance data is a limit for the researchers interested in the development of these prediction algorithms. Dynamic aeroengine models can be used to overcome this lack.This data article presents data regarding the performance of a turbojet that were predicted by the dynamic engine model that was built using the Gas turbine Simulation Program (GSP) software.The data were also used to implement an Artificial Neural Network (ANN) that predicts the in-flight aeroengine performance, such as the Exhaust Gas Temperature (EGT).The Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network was used. The neural network predictions have been also given as dataset of the present article.The data presented here are related to the article entitled “MultiGene Genetic Programming - Artificial Neural Networks approach for dynamic performance prediction of an aeroengine” [1].

Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines

In this article, the adaptive neuro-fuzzy inference system (ANFIS) and multiconfiguration gas-turbines are used to predict the optimal gas-turbine operating parameters. The principle formulations of gas-turbine configurations with various operating conditions are introduced in detail. The effects of different parameters have been analyzed to select the optimum gas-turbine configuration. The adopted ANFIS model has five inputs, namely, isentropic turbine efficiency (Teff), isentropic compressor efficiency (Ceff), ambient temperature (T1), pressure ratio (rp), and turbine inlet temperature (TIT), as well as three outputs, fuel consumption, power output, and thermal efficiency. Both actual reported information, from Baiji Gas-Turbines of Iraq, and simulated data were utilized with the ANFIS model. The results show that, at an isentropic compressor efficiency of 100% and turbine inlet temperature of 1900 K, the peak thermal efficiency amounts to 63% and 375 MW of power resulted, which was the peak value of the power output. Furthermore, at an isentropic compressor efficiency of 100% and a pressure ratio of 30, a peak specific fuel consumption amount of 0.033 kg/kWh was obtained. The predicted results reveal that the proposed model determines the operating conditions that strongly influence the performance of the gas-turbine. In addition, the predicted results of the simulated regenerative gas-turbine (RGT) and ANFIS model were satisfactory compared to that of the foregoing Baiji Gas-Turbines.

Inquisition on the Combined Effects of Ambient Temperature and Relative Humidity on The Performance of a Uniform Speed Single Shaft Gas Turbine in Tropical Monsoon Climate, using GPAL

This paper investigates the combined effects of Ambient Temperature and Relative Humidity on the performance of a uniform speed single shaft Gas Turbine, sited in Tropical Monsoon climate. A single shaft gas turbine simulator (known as GPAL) from Gas path Analysis ltd was employed. The City of Portharcourt, Nigeria, was chosen to represent the tropical monsoon climate, with its climatic data of monthly ambient temperature and relative humidity obtained from Koppen. With parameters like speed, reference power, inlet and exhaust losses kept constant, the ambient temperature and relative humidity were continually varied according to their climatic values. Each time, the performance of the gas turbine was simulated and parameters such as; Efficiency, Turbine power and Net power output, Turbine inlet Temperature and Exhaust Gas Temperature, as well as Specific fuel consumption were monitored. The environmental impact of the gas turbine was equally assessed in terms of Carbon (IV) Oxide (CO2) emission in Tonnes/day and in Kg/MWhr, NOX emission and Carbon Monoxide (CO) emission. The results of the study indicate that it is most efficient and productive to operate the gas turbine in Portharcourt in the months of January and December whereas it is least efficient in the month of April. Whereas CO emission was relatively low and uniform throughout the year, the highest specific fuel consumption was recorded in April.

An Inquisition on the Combined Effects of Ambient Temperature and Relative Humidity on The Performance of a Uniform Speed Single Shaft Gas Turbine in Tropical Monsoon Climate, using GPAL

This paper investigates the combined effects of Ambient Temperature and Relative Humidity on the performance of a uniform speed single shaft Gas Turbine, sited in Tropical Monsoon climate. A single shaft gas turbine simulator (known as GPAL) from Gas path Analysis ltd was employed. The City of Portharcourt, Nigeria, was chosen to represent the tropical monsoon climate, with its climatic data of monthly ambient temperature and relative humidity obtained from Koppen. With parameters like speed, reference power, inlet and exhaust losses kept constant, the ambient temperature and relative humidity were continually varied according to their climatic values. Each time, the performance of the gas turbine was simulated and parameters such as; Efficiency, Turbine power and Net power output, Turbine inlet Temperature and Exhaust Gas Temperature, as well as Specific fuel consumption were monitored. The environmental impact of the gas turbine was equally assessed in terms of Carbon (IV) Oxide (CO2) emission in Tonnes/day and in Kg/MWhr, NOX emission and Carbon Monoxide (CO) emission. The results of the study indicate that it is most efficient and productive to operate the gas turbine in Portharcourt in the months of January and December whereas it is least efficient in the month of April. Whereas CO emission was relatively low and uniform throughout the year, the highest specific fuel consumption was recorded in April.