Gas Turbine Performance Research Articles

About the automation of procedures to assess the operation parameters of by-pass turbojet engines (BTJE)

During aircraft operation, the assessment of operation parameters with the different use of nomograms is completed. This approach is prevalent in the operation of aviation gas-turbine engines as part of aircraft power plants. In this case, for example, the assessment of engine operation parameters on the ground, depending on environmental conditions prior to start-up and run-up, is required. Reducing working hours of the aircraft power plants maintenance can be provided by the automation of the denoted procedures which purpose is the formation of the applicable list of values for assessed parameters while assigning available environmental conditions considering the required restrictions. To allow for the visualization and familiar conceptions of carried out assessments, it is advisable not to be limited to only documenting quantitative parameter estimations but also to provide them with graphical representations. The purpose of the article is not aimed at describing a complete algorithm of the engine operation parameters assessment but demonstrating a solution capability for the automation of the procedures for the engine operation parameters assessment on the ground as the task of search for a solution in the restricted conditions using the approximation 30 apparatus and piecewise approximations by the method of the smallest squares and the correlation criterion of significance. The examples of quantitative estimations for one of mass domestic engines are given. The described approach is applicable to a broad list of engines due to the common character of algorithms for the parameter assessment using nomograms. Software based on the tabular calculation processor from the LibreOffice package, Apache-licensed free software, is cross-platform.

Investigation of Regenerative Gas Turbine Performance under the Influence of Dynamic Atmospheric Conditions in India Using Energy and Exergy Analysis

Investigation of Regenerative Gas Turbine Performance under the Influence of Dynamic Atmospheric Conditions in India Using Energy and Exergy Analysis

Evaluation of Gas Turbine Performance in Power Plant with High-Pressure Fogging System

Evaluation of Gas Turbine Performance in Power Plant with High-Pressure Fogging System

Mistuning Identification of Blisks Using Mass Detuning and Influence Coefficients

Integrally bladed disks, also known as blisks, are important components in the operation of gas turbines. To study the dynamics of these structures, several computational techniques approximate blisks as tuned systems, where all blades have the same characteristics. In reality, manufactured blades have small variations from their intended design, thus creating so-called mistuned systems. Previous work has shown that such small variations can cause high response amplifications that can result in blisk structural failure. Therefore, it is essential to identify mistuning to ensure the safe operation of these systems. Several methods utilize cantilever blade frequencies in their procedure to model mistuning and calculate blisk vibration response. A previously developed mistuning identification method uses mass detuning to isolate the cantilever blade frequencies and a set of influence coefficients to quantify the blade-to-blade couplings. However, the mistuning is calculated with respect to an arbitrary mistuning value that later needs to be identified through a trial-and-error procedure that can affect the accuracy of the final mistuning values. This paper focuses on updating the method to eliminate this additive error that cannot precisely be determined. Furthermore, the updated procedure is applied to an as-manufactured blisk using experimental ping test results. An additional analysis is performed on the effect of adding multiple sets of influence coefficients to capture the blade-to-blade couplings more accurately.

Industrial gas turbine performance prediction and improvement – a case study

Abstract The gas turbines (GTs), model M3142R/GE MS 3002, equipping the natural gas compression and crude oil pumping stations of SONATRACH’s pipeline transport of hydrocarbons activity are robust despite their dilapidation and state of service, but mediocre in terms of energy efficiency and power output. According to the manufacturer, for temperatures ranging from 15 to 47 °C, the efficiency of these machines drops from 26.78 to 25.03 % and their power drops from 11.29 to 8.9191 MW. It should be noted, however, that the number of these turbines exceeds 80 units and that their operation dates from 1974. The intention of this paper is to improve the gas turbine performance, mainly the efficiency and shaft power, by an evaporative cooling process of the compressor intake air. Besides, it is proposed to lower the upper limit power in periods of high temperatures to reduce gas consumption. To achieve these objectives, a mathematical model was implemented under Matlab R16, which reproduced the real behavior of these machines. It follows that this simulation made it possible to highlight relevant gains in terms of power and efficiency of the order of 1.361 MW and 3.4 % at a temperature of 47 °C.

Exhaust Temperature Prediction for Gas Turbine Performance Estimation by Using Deep Learning

Exhaust Temperature Prediction for Gas Turbine Performance Estimation by Using Deep Learning

Numerical study of the effect of pressure on the combustion characteristics of ammonia/coal-derived syngas mixture under gas turbine operating conditions

This study investigates the influence of pressure variation on combustion properties, including laminar burning velocity and ignition delay time, as well as emissions in gas turbine conditions. Based on numerical simulations and using Matlab, the impact of pressure on the relationship between laminar burning velocity and free radicals, as well as the relationship between ignition delay time and laminar burning velocity, were analyzed. The results of Matlab curve fitting suggest that the decrease in free radical concentration due to increased pressure is the main cause of the reduction in laminar burning velocity. The presence of reactions such as H + O2 = O + OH and all ammonia-related reactions results in a linear relationship between the laminar combustion velocity and max(H) or max(NH2 + H), which varies with pressure in mixed ammonia combustion. Moreover, the linear correlation coefficient between the laminar combustion velocity and free radicals is a function of pressure. Using a one-dimensional laminar premixed model and a zero-dimensional closed reactor model, this study has identified a power function relationship between the laminar combustion velocity and ignition delay time in synthetic gas mixed with ammonia combustion, where the coefficients and exponents of the power function are functions of pressure. Moreover, these two fitting relationships are independent of equivalence ratio and fuel ratio. Finally, the analysis of NO emissions indicates that the NO produced by chemical pathways affects the chemical equilibrium of NO produced by thermal pathways. Additionally, the reaction CO + OH = CO2 + H initially produces some NO in the thermal pathway·NH3 participates in reactions after H2 and CO have reacted. Lastly, it was found that the increase in pressure promotes the reaction NH + NO = N2O + H, which leads to a decrease in the molar fraction of NO with increasing pressure.

Prediction of Dry-Low Emission Gas Turbine Operating Range from Emission Concentration Using Semi-Supervised Learning.

Dry-Low Emission (DLE) technology significantly reduces the emissions from the gas turbine process by implementing the principle of lean pre-mixed combustion. The pre-mix ensures low nitrogen oxides (NOx) and carbon monoxide (CO) production by operating at a particular range using a tight control strategy. However, sudden disturbances and improper load planning may lead to frequent tripping due to frequency deviation and combustion instability. Therefore, this paper proposed a semi-supervised technique to predict the suitable operating range as a tripping prevention strategy and a guide for efficient load planning. The prediction technique is developed by hybridizing Extreme Gradient Boosting and K-Means algorithm using actual plant data. Based on the result, the proposed model can predict the combustion temperature, nitrogen oxides, and carbon monoxide concentration with an accuracy represented by R squared value of 0.9999, 0.9309, and 0.7109, which outperforms other algorithms such as decision tree, linear regression, support vector machine, and multilayer perceptron. Further, the model can identify DLE gas turbine operation regions and determine the optimum range the turbine can safely operate while maintaining lower emission production. The typical DLE gas turbine's operating range can operate safely is found at 744.68 °C -829.64 °C. The proposed technique can be used as a preventive maintenance strategy in many applications involving tight operating range control in mitigating tripping issues. Furthermore, the findings significantly contribute to power generation fields for better control strategies to ensure the reliable operation of DLE gas turbines.

MODELING AND SIMULATION OF AN OPERATING GAS TURBINE USING MODELICA LANGUAGE

This article uses Modelica to model and simulate the operating Gas Turbine (GT) in a combined cycle power plant in Ras-Djinet, Algeria. The modeling and simulation have been validated based on the data collected from this operating power plant. Details of GT modeling using Modelica language and ThermoPower library have been presented. Furthermore, the simulation results have been discussed in this article. The model has been examined in two different cases: the temperature effect and the reduction in fuel flow at a steady state. Besides, a comparison between the reel and simulation results for a different amount of fuel has been investigated. The accuracy of these simulations is noted and proven by the coherence of the simulation results with the experimental data collected from the power plant company.

MODELING AND SIMULATION OF AN OPERATING GAS TURBINE USING MODELICA LANGUAGE

This article uses Modelica to model and simulate the operating Gas Turbine (GT) in a combined cycle power plant in Ras-Djinet, Algeria. The modeling and simulation have been validated based on the data collected from this operating power plant. Details of GT modeling using Modelica language and ThermoPower library have been presented. Furthermore, the simulation results have been discussed in this article. The model has been examined in two different cases: the temperature effect and the reduction in fuel flow at a steady state. Besides, a comparison between the reel and simulation results for a different amount of fuel has been investigated. The accuracy of these simulations is noted and proven by the coherence of the simulation results with the experimental data collected from the power plant company.

Effect of blade surface cooling on a micro transonic axial compressor performance at low Reynolds number

Due to the high ratio of surface area to volume and the limitations of manufacturing precision, the impact of wall temperature and heat transfer on the aerodynamic performance of micro gas turbines is greater than that of traditional larger counterparts. Therefore, this study numerically investigates the effect of rotor wall cooling on the aerodynamic and thermal performance of a 1.5-stage transonic compressor at a low Reynolds number using a three-dimensional Reynolds averaged Navier-stokes (RANS) simulation.In this study, we use a volume model that takes into account the actual shaft work, and the main findings can be summarized as follows: At the optimal wall temperature, wall cooling could potentially raise the compressor performance, with the peak efficiency increasing by about 3.7% and the pressure ratio increasing by about 1.8%. Wall cooling reduces the dynamic viscosity and increases the wall shear stress, which delays the laminar separation transition process, promotes the reattachment, and shortens the laminar separation bubble, substantially decreasing separation losses. Meanwhile, the loss caused by vortex dissipation decreases. Wall cooling slightly improves the matching relationship between the rotor and the aft stator, and also reduces the flow separation losses of the stator. Wall cooling introduces additional thermal and viscous dissipation to the blade passages, which prevents the compressor’s aerodynamic performance from increasing continuously. The present study analyzes the separation transition process, the entropy production mechanism, and deeply explores the effect of wall cooling, which is meaningful for the aerodynamic and thermal design of advanced micro-compressors.

Long-Term Scenario Analysis of Electricity Supply and Demand in Iran: Time Series Analysis, Renewable Electricity Development, Energy Efficiency and Conservation

Electricity plays a vital role in the economic development and welfare of countries. Examining the electricity situation and defining scenarios for developing power plant infrastructure will help countries avoid misguided policies that incur high costs and reduce people’s welfare. In the present research, three scenarios from 2021–2040 have been defined for Iran’s electricity status. The first scenario continues the current trend and forecasts population, electricity consumption, and carbon dioxide emissions from power plants with ARIMA and single and triple exponential smoothing time series algorithms. As part of the second scenario, only non-hydro renewable resources will be used to increase the electricity supply. By ensuring the existence of potential, annual growth patterns have been defined, taking into account the renewable electricity generation achieved by successful nations. The third scenario involves integrating operating gas turbines into combined cycles in exchange for buyback contracts. Economically, this scenario calculates return on investment through an arrangement of various contracts for the seller company and fuel savings for the buyer.

Starting and windmilling simulations using compressor and turbine maps

Starting and windmilling simulations with a normal gas turbine performance program require extended compressor and turbine maps which include sub-idle corrected speeds down to say 5–10% of the design value. During such simulations certain specific phenomena which are insignificant in the normal operating range between idle and full power must be considered. For example, while starting a low bypass ratio mixed flow turbofan, flow reversal in the bypass duct can occur. This paper illustrates a general understanding of what happens from when the starter is activated to when stabilized idle operation is reached. Operating lines in the compressor and turbine maps are predicted depending on starter torque, starter power, burner light-up and starter cut-off speed. It is explained why knowing combustor efficiency precisely is not required for that. Simulating engine starting and windmilling is not a magical art. The laws of physics still apply at these somewhat exotic operating conditions.

Gas Turbine Performance Monitoring and Operation Challenges: A Review

Gas turbines efficiently produce high amounts of electrical power hence they have been widely deployed as dependable power generators. It has been detected that the performance of gas turbines is a function of plenty of operational parameters and environmental variables. The impacts of those variables on the said performance can be mitigated using powerful monitoring techniques. Thus, extra maintenance costs, component defect costs, and manpower costs can be illuminated. This paper has enlisted the factors impacting gas turbine efficiency. It has also reviewed multiple monitoring solutions for the said impacting factors, It has been concluded that all types of sensors have ignored errors in their work, which may exacerbate the problems of malfunctions in gas turbines due to the critical environment in which they operate (heat, fumes, etc.); however, the machine learning-based monitoring systems excel in addressing such problems. The most cost-effective and accurate monitoring task can be achieved by using machine learning and deep learning tools.

The Design and Demonstrative Analysis of the Gas Turbine Operation’s Simulator

The Gas Turbine operation was investigated with a view to evolving a system designed to provide a realistic imitation of the controls and operation of a Gas Turbine, used for training purposes. Operator Training Simulator has been widely adopted by many industries being a computer simulation which attempts to model a real-life plant so that it can be studied. A well trained and skilled operator is key in increasing power plant safety and productivity. Therefore, enabling quality training for operators is becoming more important as they need to handle increased load of information and duties whereas the lack of training is a major reason for inadequate performance. By changing variables in the simulator, predictions are made about the behaviour of the engine. It is a tool to virtually investigate the behaviour of the system while in operation. This work becomes indispensable because it is prohibitively expensive or simply too dangerous to allow trainees use the real equipment in a power plant. The Gas Turbine operation’s simulator is born from Object Oriented Programming, employing key programming languages. The simulator design focused on specific tasks in the operation of the Gas Turbine which include; startup, synchronization and monitoring of vital parameters like vibration, temperature, pressure, and angle of the Inlet Guide Vane. The statuses of various valves, pumps and motors as well as the Performance of actuators and the response of concatenated components are also being tracked. The simulator was found to effectively mimic a real plant life. With this simulator, trainee operators in Gas Turbine can spend time learning valuable lessons in a "safe" virtual environment yet living a lifelike experience. This will go a long way in minimizing operators’ error in GT power plants, thereby curtailing power outages and conserving power plant components.

Modeling an Evaporative Cooling System to Improve Gas Turbines Performance

Abstract Evaporative cooling systems are commonly used in thermoelectric plants to cool the air at gas turbines inlet, improving the performance of these engines. Normally, the evaporative cooling is modeled as adiabatic saturation and, in this case, the water-air equilibrium temperature depends only on the atmospheric air properties. However, other factors such as the water temperature that supplies the equipment and the ratio between the mass flow rates of water and air, also affect the equilibrium conditions of these systems. This work presents three methodologies to calculate the air temperature in equilibrium state, considering all the factors mentioned. The methodologies were implemented in a computer program written in fortran. In all cases tested, the results obtained by the three models showed high convergence. As an example, for 70 different sets of inputs, the absolute and relative differences of the results were below 0.3236 °C and 1.2480%, respectively. A statistical study, also on this sample of results, revealed that, for a confidence level of 99%, the hypothesis of the equivalence between the methods cannot be rejected.

An FV-EE model to predict lean blowout limits for gas turbine combustors with different structures and sprays

The occurrence of Lean Blowout (LBO) is a disadvantage that endangers a stable operation of gas turbines. A determination of LBO limits is essential in the design of gas turbine combustors. A semiempirical model is one of the most widely used methods to predict LBO limits. Among the existing semiempirical models for predicting LBO limits, Lefebvre’s LBO model and the Flame Volume (FV) model are particularly suitable for gas turbine combustors. On the basis of Lefebvre’s and FV models, the concept of effective evaporation efficiency is introduced in this paper, and a Flame Volume-Evaporation Efficiency (FV-EE) model is derived and validated. LBO experiments are carried out in a model combustor with 23 different structures and 10 different sprays. The prediction uncertainty of the FV-EE model is less than ± 13% for all of these 33 structures and sprays, compared with ± 50% for the FV model and ± 60% for Lefebvre’s model. Furthermore, the prediction uncertainty of the FV-EE model is also less than ± 13% for other combustors from available literature.

Premixed combustion and emission characteristics of methane diluted with ammonia under F-class gas turbine relevant operating condition

Ammonia has been used on a small scale in other industrial equipment, such as gas turbines, as a carbon-free fuel. However, ammonia fuel suffers disadvantages such as high ignition temperature, low flame velocity and high NOx emissions. Doping with ammonia using a more reactive fuel, such as methane, can solve the above problems. Therefore, under the relevant operating conditions of the gas turbine (T = 723 K, p = 16.5 atm), the effect of ammonia content on the combustion and emission characteristics of laminar premixed methane flames was numerically investigated. This research uses the PREMIX code from ANSYS CHEMKIN-PRO 2020 and Okafor chemical kinetic mechanisms and provides a reference for our subsequent analysis of gas turbine operating conditions. Firstly, the emission data of major pollutants under different ammonia content (XNH3 = 0–1.0) and equivalent ratio (Φ = .6–1.4) were calculated. Then, the laminar premixed flame structure is analyzed under the lean fuel conditions associated with gas turbines (Φ = .6, .8). Finally, the effect of ammonia addition on the chemical reaction path of NO and CO emission was studied. The results show that ammonia/methane mixture fuel is more suitable for combustion at .6 < Φ < .8 under high temperature and pressure. High ammonia content (XNH3 > .6) and low equivalent ratio can reduce NO and CO emissions. The molar fractions of H, O, and OH radicals and flame temperature decreased with the increase in ammonia content. In addition, high temperature and high pressure conditions and ammonia content greatly influence the reaction path of NO and CO production. The increase in pressure resulted in a change in the primary reaction that produced NO. In conclusion, this study guides reducing the emission of NO and CO from lean side of gas turbine plants.

Statistical Rule Extraction for Gas Turbine Trip Prediction

Abstract Gas turbine trip is an operational event that arises when undesirable operating conditions are approached or exceeded, and predicting its onset is a largely unexplored area. The application of novel artificial intelligence methods to this problem is interesting both from the computer science and the engineering point of view, and the results may be relevant in both the academia and the industry. In this paper, we consider data gathered from a fleet of Siemens industrial gas turbines in operation that includes several thermodynamic variables observed during a long period of operation. To assess the possibility of predicting trip events, we first apply a new, systematic statistical analysis to identify the most important variables, then we use a novel machine learning technique known as temporal decision tree, which differs from canonical decision tree because it allows a native treatment of the temporal component, and has an elegant logical interpretation that eases the posthoc validation of the results. Finally, we use the learned models to extract statistical rules. As a result, we are able to select the five most informative variables, build a predictive model with an average accuracy of 73%, and extract several rules. To our knowledge, this is the first attempt to use such an approach not only in the gas turbine field but also in the whole industry domain.

Development Trend of Cooling Technology for Turbine Blades at Super-High Temperature of above 2000 K

Aeroengines and heavy-duty gas turbines are the core power equipment in the field of national defense and energy. Their research and development (R&D) level and manufacturing level represent the status of a country’s heavy industry in the world. The common cooling technologies of turbine blades including impingement cooling, film cooling, effusion cooling, layer cooling, pin fin cooling, and rough ribs were introduced in this paper. With the continuous improvement of the efficiency and performance of aeroengines and gas turbines, the turbine inlet temperature increases gradually every year; turbine blades will be exposed to higher gas temperatures in the future as gas temperatures break 2000 K. In order to ensure the safe operation of turbine blades under severe super-high temperature working conditions, cooling technology must be developed emphatically. This paper first reviews the research status of turbine blade cooling technology and points out future research focuses. The development trends of next-generation turbine blade cooling technology for above 2000 K temperature are summarized from several aspects: the innovative excavation of high-efficiency composite cooling configuration, multi-objective cooperative cooling structure and optimization design based on 3D printing, composite cooling structure design and optimization based on an artificial intelligence algorithm, tapping the cooling potential of new cooling media and heat pipes, integrated thermal protection with new thermal insulators, and the application of low-resistance and high-efficiency surface dimple cooling. The summary of this paper can provide a reference for the researchers of turbine blade cooling technology.