Aeroderivative Gas Turbine Research Articles

Residual Stress Optimisation for Manufacturing of a Nozzle Guide Vane in Mar-M-509 by Laser Powder Bed Fusion

Abstract This paper delves into the potential of Additive Manufacturing (AM) technologies, focusing on the utilization of Laser Powder Bed Fusion (LPBF) to optimize the manufacturing process for Mar-M-509, a Cobalt-based superalloy, in aeroderivative industrial gas turbine nozzle guide vanes. While Mar-M-509 offers exceptional properties for high-temperature applications, the rapid cooling rates inherent in LPBF introduce significant residual stresses, leading to poor success rates below 50%. The study systematically addresses this challenge by proposing a comprehensive methodology to optimize LPBF parameters. By fine-tuning processing conditions, including elevating the powder bed and build chamber temperatures, the research achieved notable reductions in residual stresses by up to 55%. Computational simulations played a pivotal role in predicting deformations and thermal signatures, enabling proactive adjustments to process conditions, ultimately enhancing part quality and process reliability. Validation through successful printing with a yield exceeding 75% underscores the effectiveness of this approach. Moreover, the study suggests the broader applicability of these optimization strategies beyond Mar-M-509, paving the way for advancements in AM techniques across diverse materials and industrial sectors. This research not only presents a robust solution for mitigating residual stresses in LPBF-produced components but also showcases a proactive methodology that promises significant implications for additive manufacturing's future advancements and applicability.

A Comparative Study of Sustainable Energy Brayton Cycles in the Oil and Gas Industry. Part 1: Performance

Brayton cycles are open gas turbine cycles extensively used in civil aviation and the petrochemical industry because of their advantageous volume and weight characteristics. With the bulk of engine emissions associated, it is necessary to promote their environmentally-friendliness, including sound technical performance regularly. This research considers high bypass-low specific power plants in aviation and aero-derivative gas turbines combined-heat-and-power generation in the petrochemical industry. The investigation encompasses the comparative assessment of simple and advanced gas turbine cycle options including the component behaviour of the systems. This comprises the performance module. The research has contributed to understanding the technical performances of simple and advanced cycle helicopter engines, and aero-derivative industrial gas turbine cycles at design and off-design conditions. The simple cycles were modified for better fuel burn and thermal efficiency by using some additional components to form the advanced cycles. The helicopter engine investigated was converted to a small-scale aero-derivative industrial engine. Modeling the combined-heat-and-power performance of the small, medium, and large-scale aero-derivative industrial gas turbines was also implemented. The contribution also includes understanding the technical performances of both simple and advanced aero-derivative gas turbines combined heat-and-power at design and off-design A case study underlies the development and deployment of this model. The novelty is the conception of a tool for predicting the most preferred simple and advanced cycle aero-derivative engines combined heat-and-power generation in the petrochemical industry and the derivation of simple and advanced cycle small-scale aero-derivative industrial gas turbines from helicopter engines.

Robustness evaluation of acceleration-based early rub detection methodologies with real fluid-induced noise

The early detection of rotor-casing rub allows to prevent permanent damage and even catastrophic failure of rotating machines, saving maintenance costs and decreasing downtime. Very often, aeroderivative gas turbines can be monitored only with accelerometers on the casing, which provide signals that contain both stochastic and deterministic noise that masks the vibration measurements. Although real vibration data from turbomachines present a background noise that is neither white nor Gaussian, the rule of thumb is to evaluate the robustness of fault detection under white signal noise, which may lead to estimation errors of such robustness. Here we evaluate and compare the performance of several signal processing methodologies for very early rub detection on accelerometer signals under fluid-induced signal noise due to turbulent fluid excitation. The independence between the fluid-induced vibrations and the rub and unbalance vibration was proved, and experimental data of fluid-induced noise were added as signal noise to clean rub signals. The robustness and sensitivity of rub detection under fluid-induced noise were calculated and compared. This study proves the superiority of reassigned time–frequency analysis methods such as the Wavelet Synchrosqueezed Transform for very early rub detection as compared with traditional time–frequency analysis methods and with the Fourier Transform. To the authors’ knowledge, this is the first time that non-Gaussian fluid-induced signal noise is used to evaluate fault detection performance.

Assessing the impact of CH[formula omitted]/H[formula omitted] blends on the thermodynamic performance of aero-derivative gas turbine CHP configurations

To attain a net zero-carbon emissions energy system, the grid needs dispatchable and flexible power production capable of responding to wind and solar energy variability. Hydrogen-fired gas turbines could play a role in meeting this requirement in the electricity grid; however, research and development are needed to assess the impact of the fuel change on these systems. This study aims to determine the effect of fuel switching on the cycle performance of a typical aero-derivative gas turbine, ranging from 25-35MW when combined with various steam-based bottoming cycles using Aspen Plus thermodynamic simulations. The gas turbine cycle’s efficiency and net output power increased by about 2.26% and 5.24%, respectively, when fueled with hydrogen for the constant TIT control strategy compared with the base case. The other two control strategies—constant TOT and heat input—are also positive. The performance of the combined heat and power configurations, expressed by the fuel charged to power, is better for almost all the hydrogen-fired cases compared with the methane case. This implies that using hydrogen as fuel in the gas turbine and duct-firing requires less fuel to produce heat and electricity. Although hydrogen integration into the overall system may pose challenges with auxiliary equipment, storage, supply, and combustion, hydrogen as fuel shows promise in improving the performance of gas turbine combined heat and power units.

Thermodynamic analysis of the thermal and exergetic performance of a mixed gas-steam aero derivative gas turbine engine for power generation

A thermodynamic analysis is performed for an aero derivative gas turbine engine which utilizes steam injection to increase its efficiency. The target was to explore the performance of a high efficiency gas turbine unit for electric power generation without downstream Rankine cycle. A Rankine cycle for exhaust heat recovery is unattractive because of its large response time and cost of investment. The main purpose of this research was to develop a better understanding of how the optimal cycle efficiency is reached, when the steam for injection is generated by use of the turbine exhaust heat. The STIG cycle becomes attractive for grid stabilization because of its low CAPEX and small footprint and response time.A thermodynamic model has been developed to simulate the simple cycle gas turbine, steam generation and effects of steam injection. Reference input parameters for the model are taken for the GE LM6000 turbine as publicly available. The performance of the engine without steam injection as predicted by the model is compared with literature for validation and compares well.The performance of the STIG cycle as a function of operation parameter steam mass flow and design parameters pressure ratio and turbine inlet temperature is investigated and the optimal parameter settings determined. It is found that this type of cycle shows a very specific parameter setting for optimal efficiency. By using steam injection for the chosen turbine and its parameters an efficiency gain of around 11% points and an output power augmentation of 45% can be achieved.

Performance Analysis of WHR Systems for Marine Applications Based on sCO2 Gas Turbine and ORC

Waste heat recovery (WHR) can represent a solution to improve the efficiency of ships’ propulsion, helping to exceed stringent greenhouse gas emission limits. This is particularly suitable in the case of propulsion based on gas turbines due to their medium-high temperature level of the exhaust gases. This study analyzes the performance of a hybrid energy grid, in which the heat is recovered by the exhaust gases of an aeroderivative gas turbine, a GE LM2500+, when the bottoming system is a supercritical CO2 gas turbine. Given the issues and peculiarities related to the onboard installation, where size and weight are fundamental concerns, six WHR schemes have been analyzed. They span from the simple cycle to partial preheated and regenerative, to a cascade layout in which an ORC system receives thermal power by the sCO2 GT. The influence of the seawater temperature on the performance of the hybrid energy system has been also considered. The energetic and exergetic performance comparison of the different schemes has been carried out by using the commercial software Thermoflex. The results showed that an increase in overall performance by up to 29% can be obtained and that the increase in seawater temperature can lead to a decrease in the overall performance.

Utilizing artificial intelligence to develop an advanced compressor airfoil family for industrial, aero-derivative, and heavy-duty gas turbines

This paper describes the procedure for developing a new airfoil family. This airfoil family applies to heavy-duty, industrial, and aero-derivative gas turbine compressors ranging from subsonic to transonic flow regimes. The airfoil family is generated by filling a database with optimized airfoil geometries. This database is structured in six dimensions, called design space parameters, including inlet Mach number, inlet flow angle, outlet flow angle, axial velocity density ratio, maximum thickness to chord ratio, and solidity. This six-dimensional space includes all compressor blades used in stationary gas turbine compressors. Each set of these design space parameters is related to an optimal geometry produced by the optimization system. The optimization system includes a parametrized airfoil generator, an accurate, fast blade-to-blade flow solver, and an evolutionary optimization algorithm. Airfoils of different stationary gas turbine compressor types are investigated to cover the required design space. Four hundred thirty airfoils, denoted as reference airfoils, are used to define design space borders. Comparing the newly optimized airfoils with reference airfoils revealed superior performance throughout the entire design space. They incorporate these optimized airfoils into a surrogate model, resulting in a fast, optimized airfoil generator (airfoil family). The transonic rotor of the existing multistage compressor has been redesigned according to the developed airfoil family. 3D computational fluid dynamics showed a 2% efficiency improvement for optimized blade row over the original design. Integrating this airfoil family and a streamline curvature code as part of a compressor design system is the main application of this advanced airfoil family.

Numerical Study of Combustion and Emission Characteristics for Hydrogen Mixed Fuel in the Methane-Fueled Gas Turbine Combustor

The aeroderivative gas turbine is widely used as it demonstrates many advantages. Adding hydrogen to natural gas fuels can improve the performance of combustion. Following this, the effects of hydrogen enrichment on combustion characteristics were analyzed in an aeroderivative gas turbine combustor using CFD simulations. The numerical model was validated with experimental results. The conditions of the constant mass flow rate and the constant energy input were studied. The results indicate that adding hydrogen reduced the fuel residues significantly (fuel mass at the combustion chamber outlet was reduced up to 60.9%). In addition, the discharge of C2H2 and other pollutants was reduced. Increasing the volume fraction of hydrogen in the fuel also reduced CO emissions at the constant energy input while increasing CO emissions at the constant fuel mass flow rate. An excess in the volume fraction of added hydrogen changed the combustion mode in the combustion chamber, resulting in fuel-rich combustion (at constant mass flow rate) and diffusion combustion (at constant input power). Hydrogen addition increased the pattern factor and NOx emissions at the outlet of the combustion chamber.

Effect of associated gas utilization on the creep life of gas turbines employed for power generation application

Utilizing associated gas (A-Gas) in gas turbines for power generation applications would greatly solve some of the acute power challenges Nigeria is faced with over the years. However, utilizing A-Gas to generate a given power demand, would necessitate the engine fired with associated gas to consume more fuel; thereby causing the engine to operate at elevated turbine entry temperatures, which may affect the creep life of the turbines. This study investigates the effect of associated gas utilization on the creep life of gas turbines. GasTurb details 5.1 was employed to model the A-Gas fuel, while GasTurb 11 was used to model and simulate the performance of the aero-derivative gas turbine engine fired with natural gas and associated gas separately. The input and output gas path parameters obtained from GasTurb simulations which include mass flow rate, Pressure ratio, turbine entry temperature, engine rotational speed, power output etc. were used as input to size the HP turbine Blade. Larson Miller parameter and blade metal temperatures obtained for different sections of the blade were used to estimate the time to failure of the HP turbine blade for the two conditions (blade of engine fired with natural gas and A-gas separately) investigated. The results show that the estimated time to failure of the HP turbine blades for engine fired with natural gas is 49,821.4 h as against 33,158 h for the one operated with A-Gas fuel.

Quantification of Autoignition Risk in Aeroderivative Gas Turbine Premixers Using Incompletely Stirred Reactor and Surrogate Modeling

Abstract The design and operation of premixers for gas turbines must deal with the possibility of relatively rare events causing dangerous autoignition (AI). Rare AI events may occur in the presence of fluctuations of operational parameters, such as temperature and fuel composition, and must be understood and predicted. This work presents a methodology based on incompletely stirred reactor (ISR) and surrogate modeling to increase efficiency and feasibility in premixer design optimization for rare events. For a representative premixer, a space-filling design is used to sample the variability of three influential operational parameters. An ISR is reconstructed and solved in a postprocessing fashion for each sample, leveraging a well-resolved computational fluid dynamics solution of the non-reacting flow inside the premixer. Via detailed chemistry and reduced computational costs, ISR tracks the evolution of AI precursors and temperature conditioned on a mixture fraction. Accurate surrogate models are then trained for selected AI metrics on all ISR samples. The final quantification of the AI probability is achieved by querying the surrogate models via Monte Carlo sampling of the random parameters. The approach is fast and reliable so that user-controllable, independent variables can be optimized to maximize system performance while observing a constraint on the allowable probability of AI.

Estimation of Autoignition Propensity in Aeroderivative Gas Turbine Premixers Using Incompletely Stirred Reactor Network Modeling

Abstract The study of autoignition propensity in premixers for gas turbines is critical for their safe operation and design. Although premixers can be analyzed using reacting computational fluid dynamics (CFD) coupled with detailed autoignition chemical kinetics, it is essential to also develop methods with lower computational cost to be able to explore more geometries and operating conditions during the design process. This paper presents such an approach based on incompletely stirred reactor network (ISRN) modeling. This method uses a CFD solution of a nonreacting flow and subsequently estimates the spatial evolution of reacting scalars such as autoignition precursors and temperature conditioned on the mixture fraction, which is used to quantify autoignition propensity. The approach is intended as a “postprocessing” step, enabling the use of very complex chemical mechanisms and the study of many operating conditions. For a representative premixer of an aeroderivative gas turbine, results show that autoignition propensity can be reproduced with ISRN at highly reactive operating conditions featuring multi-stage autoignition of a dual fuel mixture. The ISRN computations are consequently analyzed to explore the evolution of reacting scalars and propose some autoignition metrics that combine mixing and chemical reaction to assist the design of premixers.

A Novel Integrated Control Method for an Aero-Derivative Gas Turbine of Power Generation

On account of the complexity of aero-derivative gas turbines and the much higher control requirements, it is significant and meaningful to design advanced controllers for obtaining the ideal control effect. In this paper, to improve the performance of the original controller of an aero-derivative gas turbine, a novel integrated control method is proposed by combining the original controller with a new neural network controller. It realizes the speed control by switching the two controllers during the operation process of the aero-derivative gas turbine. A tracking test and robustness test are conducted to assess the superiority of the novel integrated control method. The results show that in comparison with the original controller and the new neural network controller, the novel integrated control method has a much better speed tracking performance during the four tracking tests. When the model of the aero-derivative gas turbine changes with the ambient temperature and compressor performance degradation, the robustness of the novel integrated control method is also better than the other two controllers. Hence, the superiority of the novel integrated control method is validated.

Improving the sensitivity of early rub detection in rotating machines with an adaptive orthogonal filter

The early detection of rotor-casing rub helps to prevent permanent damage or catastrophic failure, saves maintenance costs and decreases the downtime of rotating machines. In aeroderivative gas turbines, rub detection is possible only with the use of casing sensors such as accelerometers. In previous works, the Wavelet Synchrosqueezed Transform (WSST) was proposed for very early detection of rub in aeroderivative gas turbines with accelerometer signals. In a quest for even better detection performances, this manuscript aims to design adaptive orthogonal wavelet filter banks for optimal rub feature separation from accelerometer signals. The outputs of the adaptive filter bank show improved feature separation between the no-rub and the rub regimes within a validation set from an experimental machine. Ultimately, it is demonstrated that the combination of the WSST and the adaptive orthogonal filter outperforms other methodologies for the early detection of rotor-casing rub with accelerometer signals, also when white noise is present in the data.

Machine Learning-Based Digital Twins Reduce Seasonal Remapping in Aeroderivative Gas Turbines

Abstract Aeroderivative gas turbines have their combustion set points adjusted periodically in a process known as remapping. Even turbines that perform well after remapping may produce unacceptable behavior when external conditions change. This article introduces a digital twin that uses real-time measurements of combustor acoustics and emissions in a machine learning model that tracks recent operating conditions. The digital twin is leveraged by an optimizer that select adjustments that allow the unit to maintain combustor dynamics and emissions in compliance without seasonal remapping. Results from a pilot site demonstrate that the proposed approach can allow a GE LM6000PD unit to operate for ten months without seasonal remapping while adjusting to changes in ambient temperature (4 − 38 °C) and to different fuel compositions.

Analysis of Auto-Ignition Chemistry in Aeroderivative Premixers at Engine Conditions

Abstract The minimization of auto-ignition risk is critical to the design of premixers of high power aeroderivative gas turbines as an increased use of highly reactive future fuels (for example, hydrogen or higher hydrocarbons) is anticipated. Safety factors based on ignition delays of homogeneous mixtures are generally used to guide the choice of a residence time for a given premixer. However, auto-ignition chemistry under aeroderivative conditions is fast (0.5–2 ms) and can be initiated within typical premixer residence times. The analysis of what takes place in this short period necessarily involves the study of low-temperature auto-ignition precursor chemistry, but precursors can change with fuel and local reactivity. Chemical explosive modes (CEMs) are a natural alternative to study this as they can provide a measure for auto-ignition risk by considering the whole thermochemical state in the framework of an eigenvalue problem. When transport effects are included by coupling the evolution of the chemical explosive modes to turbulence, it is possible to obtain a measure of spatial auto-ignition risk where both chemical (e.g., ignition delay) and aerodynamic (e.g., local residence time) influences are unified. In this article, we describe a method that couples large eddy simulation (LES) to newly developed, reduced auto-ignition chemical kinetics to study auto-ignition precursors in an example premixer representative of real life geometric complexity. A blend of pure methane and di-methyl ether (DME), a common fuel used for experimental auto-ignition studies, was transported using the reduced mechanism (38 species/238 reactions) under engine conditions at increasing levels of DME concentrations until exothermic auto-ignition kernels were formed. The resolution of species profiles was ensured by using a thickened flame model where dynamic thickening was carried out with a flame sensor modified to work with multistage heat release. This paper is outlined as follows: First, a reduced mechanism is constructed and validated for modeling methane as well as DME auto-ignition. Second, sensitivity analysis is used to show the need for chemical explosive modes. Third, the thickened flame model modifications are described and then applied to an example premixer at 25 bar/890 K preheat. The chemical explosive mode analysis closely follows the large thermochemical changes in the premixer as a function of DME concentrations and identifies where the premixer is sensitive and flame anchoring is likely to occur.

Scaling of an Aviation Hydrogen Micromix Injector Design for Industrial GT Combustion Applications

Decarbonising the energy grid through renewable energy requires a grid firming technology to harmonize supply and demand. Hydrogen-fired gas turbine power plants offer a closed loop by burning green hydrogen produced with excess power from renewable energy. Conventional dry low NOx (DLN) combustors have been optimized for strict emission limits. A higher flame temperature of hydrogen drives higher NOx emissions and faster flame speed alters the combustion behavior significantly. Micromix combustion offers potential for low NOx emissions and optimized conditions for hydrogen combustion. Many small channels, so-called airgates, accelerate the airflow followed by a jet-in-crossflow injection of hydrogen. This leads to short-diffusion flames following the principle of maximized mixing intensity and minimized mixing scales. This paper shows the challenges and the potential of an economical micromix application for an aero-derivative industrial gas turbine with a high-pressure ratio. A technology transfer based on the micromix combustion research in the ENABLEH2 project is carried out. The driving parameter for ground use adaption is an increased fuel orifice diameter from 0.3 mm to 1.0 mm to reduce cost and complexity. Increasing the fuel supply mass flow leads to larger flames and higher emissions. The impact was studied through RANS simulation and trends for key design parameters were shown. Increased velocity in the airgates leads to a higher pressure drop and reduced emissions through faster mixing. Altering the penetration depth shows potential for emission reduction without compromising on pressure loss. Two improved designs are found, and their performance is discussed.

An Ensemble of Recurrent Neural Networks for Real Time Performance Modeling of Three-Spool Aero-Derivative Gas Turbine Engine

Abstract Gas turbine is a complex system operating in nonstationary operation conditions for which traditional model-based modeling approaches have poor generalization capabilities. To address this, an investigation of a novel data driven neural networks based model approach for a three-spool aero-derivative gas turbine engine (ADGTE) for power generation during its loading and unloading conditions is reported in this paper. For this purpose, a nonlinear autoregressive network with exogenous inputs (NARX) is used to develop this model in matlab environment using operational closed-loop data collected from Siemens (SGT-A65) ADGTE. Inspired by the way biological neural networks process information and by their structure which changes depending on their function, multiple-input single-output (MISO) NARX models with different configurations were used to represent each of the ADGTE output parameters with the same input parameters. First, data preprocessing and estimation of the order of these MISO models were performed. Next, a computer program code was developed to perform a comparative study and to select the best NARX model configuration, which can represent the system dynamics. Usage of a single neural network to represent each of the system output parameters may not be able to provide an accurate prediction for unseen data and as a consequence provides poor generalization. To overcome this problem, an ensemble of MISO NARX models is used to represent each output parameter. The major challenge of the ensemble generation is to decide how to combine results produced by the ensemble's components. In this paper, a novel hybrid dynamic weighting method (HDWM) is proposed. The verification of this method was performed by comparing its performance with three of the most popular basic methods for ensemble integration: basic ensemble method (BEM), median rule, and dynamic weighting method (DWM). Finally, the generated ensembles of MISO NARX models for each output parameter were evaluated using unseen data (testing data). The simulation results based on datasets consisting for experimental data as well as data provided by Siemens high fidelity thermodynamic transient simulation program show improvement in accuracy and robustness by using the proposed modeling approach.

Design, Testing, and Performance Impact of Exhaust Diffusers in Aero-Derivative Gas Turbines for Mechanical Drive Applications

Abstract The growing penetration of renewables calls for power generation and mechanical drive gas turbine (GT) capable of quickly adjusting production and operate at part load. Aero-derivative engine architectures leverage the large experience from aircraft propulsion, have small footprint, high performance, availability, and maintainability. Aircraft engines adjust power with fuel rate and shaft speed that go hand in hand. Mechanical drive engines need to change the delivered power by keeping the shaft speed under control to guarantee the operation of the driven equipment (an LNG compressor or an electric generator). Hence, the power turbine exhaust may deliver velocity and angle profiles that put the discharge diffuser in severe off-design with flow separations, high kinetic losses, and cycle performance shortfall. This paper describes Baker Hughes a GE company experience in the computational fluid dynamics (CFD) assisted design and similitude scale-down testing of aero-derivative hot-end drive exhaust diffusers in multiple operating points. The diffuser inlet conditions reproduce power turbine exit profiles by using swirl vanes and perforated plates, the design of which is heavily CFD assisted. Predictions match measurements in terms of pressure recovery, kinetic losses, and exhaust velocity profiles. Different data postprocessing and averaging are considered to properly factor in the diffuser losses into the overall turbine performance.

Nonlinear generalized predictive controller based on ensemble of NARX models for industrial gas turbine engine

New design and operation of modern gas turbine engines (GTEs) are becoming more and more complex where several limitations and control modes should be fulfilled at the same time to accomplish a safe and ideal performance for the engine. For this purpose, a constrained multi-input multi-output (MIMO) non-linear model predictive controller (NMPC) based on neural network model is designed to fulfill the control requirements of a Siemens SGT-A65 three-spool aero-derivative gas turbine engine (ADGTE) used for power generation. However, the implementation of NMPC in real time has two challenges: Firstly, the design of an accurate non-linear model, which can run many times faster than real time. Secondly, the usage of a rapid and reliable optimization algorithm to solve the optimization problem in real time. To solve these issues, the constrained MIMO NMPC is created based on the generalized predictive control (GPC) algorithm as a result of its clarity, ease of use, and capacity to deal with problems in one algorithm. In addition, seven ensembles of eight multi-input single-output (MISO) non-linear autoregressive network with exogenous inputs (NARX) models are used as a base model for the GPC controller to predict the future process outputs. Estimation of free and forced responses of the GPC based on the neural network (NN) model of the plant each sampling time without performing instantaneous linearization is proposed in this study, which reduces the NMPC optimization problem to a linear optimization problem at each sampling step. In addition, the Hildreth's quadratic programming algorithm is used to solve the quadratic optimization problem within the NMPC controller, which offers ease of use and reliability in real time applications. To demonstrate the performance of the NNGPC controller developed in this study, we have compared the performance of the neural network generalized predictive control (NNGPC) controller to the existing controller of the SGT-A65 engine. The simulation results show that the NNGPC has demonstrated output responses with less oscillatory behavior and smoother control actions to the sudden variation in the electric load disturbance than those observed in the existing min-max controller. However, the min-max controller has faster response than that of the NNGPC controller.

A new correlation for estimating the gas turbine cost

The recent changes in energy scenario with rising attention to decarbonization, introduction of new technology and renewable source have led to design power plant with the lowest values of cost of energy, investment payback period, CO2 emission, especially in cogeneration and combined cycle plants. The cost of a gas turbine, an industrial key intellectual property value, represents a large portion of total plant capital cost. In fact, the correlations used to determine this cost, represent an important part in the optimization of power plant studied. In this work, a new cost correlation has been determined using gas turbine data presents into GTW handbook. The overall correlation, dependently only on gas turbine output power, used in a lot of scientific studies is here actualized, calculating new split-power and new coefficients for Heavy Duty and Aeroderivative gas turbine. Therefore, a more complete and detailed correlation is developed, introducing additional parameters, such as thermodynamic efficiency, pressure ratio, exhaust temperature and exhaust mass flow rate, whose values are available on gas turbine datasheet. The new correlation here proposed reflects better real gas turbine configurations and costs.