Gas Turbine Test Research Articles

Transient Shutdown Cooling Simulation of a Gas Turbine Test Rig Configuration Under Ventilated Natural Convection

Abstract A transient simulation of shutdown cooling for a gas turbine test rig configuration under ventilated natural convection has been successfully demonstrated using a coupled aero-thermal approach. Large eddy simulation (LES) and finite element analysis (FEA) were employed for fluid domain computational fluid dynamics (CFD) and solid component thermal conduction simulation, respectively. Coupling between LES and FEA was achieved through a plugin communicator. The buoyancy-induced chimney effect under the axially ventilated natural convection is correctly reproduced. The hotter turbulent flow in the upper part of the annular path and the colder laminar-type air movement in the lower part of the annulus are appropriately captured. The heat transfer features in the annular passage are also faithfully replicated, with heat flux of the inner cylinder reaching its maximum and minimum at the bottom dead centre (BDC) and the top dead centre (TDC), respectively. Agreement with experimental measurements is good in terms of both temperature and heat flux, and the result of the transient simulation for the shutdown cooling is encouraging too. In addition, radiation is simulated in the FEA model based on the usual grey body assumptions and Lambert?s law for the coupled computation. It has been shown that at the high power (HP) condition, the radiation for the inner cylinder is approximately 11% of its convective heat flux counterpart. The importance of radiation is thus clearly revealed even for the present rig test case with a scaled-down temperature setup.

Evaluation of Noise-Reduction Techniques for Gas-Turbine Test Stands: A Preliminary Analysis

Emphasizing the importance of acoustic attenuation in maintaining compliance with stringent noise regulations and enhancing workplace safety, this analysis covers theoretical and practical aspects of prediction methods used for the development of sound attenuators for gas-turbine testing stands. This paper presents a preliminary analysis and evaluation of the improvement of the Embleton method for projecting a noise attenuator for industrial applications, especially for gas-turbine test stands. While primarily focusing on the static acoustic behavior of the attenuator, certain considerations were also made regarding flow conditions, Mach number-dependent attenuation, pressure drop, and self-generated noise aspects to provide a comprehensive perspective on applying a suitable evaluation method. The study investigates different calculation methods for the assessment of noise reduction for linear and staggered baffles applied on a scaled reduced model of an attenuator. Thus, the critical parameters and development requirements necessary for effective noise reduction in high-performance gas-turbine testing environments will be evaluated in a downscaled model. Key factors examined include the selection of design parameters and configurations from various topological options (single, double, and triple parallel baffles vs. double and triple staggered baffles). Advanced computational methods, like analytic and finite-element analysis (FEM), are used to predict acoustic performance and evaluate the prediction method. Experimental validation is performed to corroborate the simulation results, ensuring the reliability and efficiency of the attenuator. The results indicate that an improved prediction method led to a better design for a sound-attenuator module, which can significantly reduce noise levels without compromising the operational performance of the gas turbine inside a test cell.

Development and Testing of a Gas Turbine Test Rig Setup for Demonstrating New Aviation Propulsion Concepts

To further increase efficiency and to significantly reduce climate impact in the aviation sector, new propulsion concepts must be developed. As full electrification in mid- and long-range aviation is impractical due to the low gravimetric energy density of batteries, new approaches must be developed. Therefore, the so-called hybrid electric ground demonstrator (HeBo), equipped with a Rolls Royce M250-C20B gas turbine is set up. The test rig serves as a development platform for various new gas turbine-based propulsion concepts for aviation, such as hybrid electric concepts or a novel cycle concept with steam injection to the combustor, which are described in this paper. The main focus of the work is on the experimental setup and the commissioning of the baseline test rig. This will place the test rig in the context of current research activities and serve as reference for subsequent research results.

Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

Predicting emissions for gas turbines is critical for monitoring harmful pollutants being released into the atmosphere. In this study, we evaluate the performance of machine learning models for predicting emissions for gas turbines. We compared an existing predictive emissions model, a first-principles-based Chemical Kinetics model, against two machine learning models we developed based on the Self-Attention and Intersample Attention Transformer (SAINT) and eXtreme Gradient Boosting (XGBoost), with the aim to demonstrate the improved predictive performance of nitrogen oxides (NOx) and carbon monoxide (CO) using machine learning techniques and determine whether XGBoost or a deep learning model performs the best on a specific real-life gas turbine dataset. Our analysis utilises a Siemens Energy gas turbine test bed tabular dataset to train and validate the machine learning models. Additionally, we explore the trade-off between incorporating more features to enhance the model complexity, and the resulting presence of increased missing values in the dataset.

Numerical analysis of catalyst particle deposition characteristics in a flue gas turbine with an improved particle motion and deposition model

To more accurately understand and predict the deposition behavior of catalyst particles in the flue gas turbine cascade, test and numerical combined study is performed in this paper. Based on the systematic analysis of the deposition process and physical mechanism of the catalyst particles, the traditional DRW model, critical velocity particle deposition model and removal model were corrected with the user defined function custom function and validated with the actual deposition morphology. On this basis, the effects of the particle Stokes number and flue gas parameters on the particle deposition characteristics of the flue gas turbine cascade were detailed investigated. The results show that the revised DRW model, critical velocity and removal model can more accurately predict the deposition location and deposition rate of particles in the turbine cascade. With the increase in the Stokes number of particles, the average particle impact rate on the blade surface gradually increased, while the average deposition rate showed a trend of first increasing and then decreasing. The average deposition rate of particles in the rotor blade surface is roughly twice as high as that in the stator surface. With the increase of the flue gas expansion ratio, the deposition rate of particles less than 3 μm gradually increases, while the deposition rate of particles greater than 3 μm tends to decrease. In addition, the change in the flue gas expansion ratio has no obvious effect on the particle deposition distribution in different size ranges.

Research on Improved Deep Convolutional Generative Adversarial Networks for Insufficient Samples of Gas Turbine Rotor System Fault Diagnosis

In gas turbine rotor systems, an intelligent data-driven fault diagnosis method is an important means to monitor the health status of the gas turbine, and it is necessary to obtain sufficient fault data to train the intelligent diagnosis model. In the actual operation of a gas turbine, the collected gas turbine fault data are limited, and the small and imbalanced fault samples seriously affect the accuracy of the fault diagnosis method. Focusing on the imbalance of gas turbine fault data, an Improved Deep Convolutional Generative Adversarial Network (Improved DCGAN) suitable for gas turbine signals is proposed here, and a structural optimization of the generator and a gradient penalty improvement in the loss function are introduced to generate effective fault data and improve the classification accuracy. The experimental results of the gas turbine test bench demonstrate that the proposed method can generate effective fault samples as a supplementary set of fault samples to balance the dataset, effectively improve the fault classification and diagnosis performance of gas turbine rotors in the case of small samples, and provide an effective method for gas turbine fault diagnosis.

Protocol for selecting exemplary silicate deposit compositions for evaluating thermal and environmental barrier coatings

AbstractA protocol for selecting representative silicate compositions for comparative testing of gas turbine coating materials is presented. It begins with a curated dataset of compositions of engine deposits and naturally occurring siliceous debris including volcanic ashes, sands, and dusts. The compositions are first reduced to the five major oxides—those of Ca, Mg, Fe, Al, and Si—and then distilled further using principal component analysis and k‐means clustering. The process ultimately yields four classes of possible deposits with common chemical characteristics. Each class is represented by a composition centroid and a range in Ca:Si ratios. Key thermophysical properties of the possible deposits are calculated and related to the glass network connectivity, characterized by the Si:O ratio. Finally, deposits from each of these classes are compared in terms of their reactions with prototypical thermal and environmental barrier oxides, with due consideration of the effects of composition variations within each deposit class. The protocol is, in principle, adaptable to datasets compiled by OEMs and researchers in gas turbine coatings.

Enhancement of the HILOMOT Algorithm with Modified EM and Modified PSO Algorithms for Nonlinear Systems Identification

Developing a mathematical model has become an inevitable need in studies of all disciplines. With advancements in technology, there is an emerging need to develop complex mathematical models. System identification is a popular way of constructing mathematical models of highly complex processes when an analytical model is not feasible. One of the many model architectures of system identification is to utilize a Local Model Network (LMN). Hierarchical Local Model Tree (HILOMOT) is an iterative LMN training algorithm that uses the axis-oblique split method to divide the input space hierarchically. The split positions of the local models directly influence the accuracy of the entire model. However, finding the best split positions of the local models presents a nonlinear optimization problem. This paper presents an optimized HILOMOT algorithm with enhanced Expectation–Maximization (EM) and Particle Swarm Optimization (PSO) algorithms which includes the normalization parameter and utilizes the reduced-parameter vector. Finally, the performance of the improved HILOMOT algorithm is compared with the existing algorithm by modeling the NOx emission model of a gas turbine and multiple nonlinear test functions of different orders and structures.

Flamelet Versus Detailed Chemistry Large Eddy Simulation for a Liquid-Fueled Gas Turbine Combustor: A Comparison of Accuracy and Computational Cost

Abstract A Honeywell liquid-fueled gas turbine test combustor at idle conditions is numerically investigated in simcenterstar-ccm+ version 2020.3. This work presents large eddy simulation (LES) results using both the flamelet generated manifold (FGM) and detailed chemistry combustion models. Both take advantage of a hybrid chemical (HyChem) mechanism which has previously demonstrated very good accuracy for real fuels such as Jet-A with only 47 species. The objective of this work is to investigate the ability of FGM and detailed chemistry modeling to capture pollutant formation in an aero-engine combustor. Comparisons for NOx, CO, unburned hydrocarbons (UHC), and soot are made, along with the radial temperature profile. To fully capture potential emissions, a soot moment model and Zeldovich NOx model are employed along with radiation. A comparison of results with and without chemistry acceleration techniques for detailed chemistry is included. Then, computational costs are assessed by comparing the performance and scalability of the simulations with each of the combustion models. It is found that the detailed chemistry case with clustering can reproduce nearly identical results to detailed chemistry without any acceleration if CO is added as a clustering variable. With the Lagrangian model settings chosen for this study, the detailed chemistry results compared more favorably with the experimental data than FGM; however, there is uncertainty in the secondary breakup parameters. Sensitivity of the results to a key parameter in the spray breakup model is provided for both FGM and complex chemistry (CC). By varying this breakup rate, the FGM case can predict CO, NOx, and UHC equally well. The smoke number, however, is predicted most accurately by CC. The cost for running detailed chemistry with clustering is found to be about four times that of FGM for this combustor and chemical mechanism.

Forced Response Excitation Due to the Stator Vanes of Two and Three Compressor Stages Away

Abstract State-of-the-art axial compressors of gas turbines employed in power generation plants and aero engines should have both high efficiency and small footprint. Thus, compressors are designed to have thin rotor blades and stator vanes with short axial distances. Recently, problems of high cycle fatigue (HCF) associated with forced response excitation have gradually increased as a result of these trends. Rotor blade fatigue can be caused not only by the wake and potential effect of the adjacent stator vane, but also by the stator vanes of two, three, or four compressor stages away. Thus, accurate prediction and suppression methods are necessary in the design process. In this study, the problem of rotor blade vibration caused by the stator vanes of two and three compressor stages away is studied. In the first part of the study, one-way fluid structure interaction (FSI) simulation is carried out. To validate the accuracy of the simulation, experiments are also conducted using a gas turbine test facility. It is found that one-way FSI simulation can accurately predict the order of the vibration level. In the second part of the study, a method of controlling the blade vibration is investigated by optimizing the clocking of the stator vanes. It is confirmed that the vibration amplitude can be effectively suppressed without reducing the performance. Through this study, ways to evaluate and control the rotor blade vibration are validated.

Recent advances in the thermal barrier coatings for extreme environments

Thermal barrier coatings (TBCs) are gaining tremendous research interest for protecting aircraft turbine blades, power plants, and other applications from high-temperature exposure. This opinion spotlights on those recent progresses that have directed to the use of TBCs for component life addition and, lately, as an essential part of advanced components design for high temperatures and related harsh environments, such as applications in the nuclear industries. Development of this coatings has been driven by results obtained from laboratory and industrial testing, corroborated by engine and gas-turbine testing and related high-temperature application experience. Factors that have led to the choice of current high-tech advanced coating fabrication methods–such as plasma spray deposition (PSP), electron-beam physical vapor deposition (EB-PVD), and electrophoretically fabricated thermal barrier coatings, have also been highlighted in current coating research and development. Here, we provide a short review on the present status of TBCs, their latest advances in fabrication and performance, drawbacks associated with them, as well as recommendations for their futuristic use in harsh high-temperature environments.

Renewables exploitation via hydrogen production in gas turbine test facilities: the ZEHTC Project

In order to pursue the goals of de-carbonization and renewable sources exploitation, an efficient, cost-effective and environmental friendly employment of energy systems is mandatory. In this context, the ERA-Net funded Project ZEHTC (Zero Emission Hydrogen Turbine Center) has been developed, aimed at the realization of a pilot plant consisting in the world first gas turbine test facility making use of the power produced during turbine tests – along with renewables (solar in particular) – for hydrogen production and batteries storage. The produced hydrogen will be locally used to run the gas turbine, thus reducing its environmental impact. The aim of this paper is to present the results of the first steps of the project, consisting in the definition of an optimized grid and in the development of a calculation model for the optimal energy systems design. To this respect, the typology of energy systems to be included into the grid (in addition to the current set-up of the existing turbine test center) will be presented. In addition, the mathematical models specifically developed for each component, as well as the developed operational logic of the entire grid, will be presented and discussed in order to set the optimal size of each systems.

The research on measurement and analysis method of gas turbine blade tip clearance based on empirical model

Blade tip clearance is one of the most important factors affecting the performance and safety of gas turbine. In recent decades, a variety of non-contact measurement methods of blade tip clearance have been developed and applied to aero engine and gas turbine test. However the research regarding the location and quantity of measurement sensors to ensure the most effective measurement, and how to fully analyze and utilize the test data is still rare. In this paper, a mathematical model of tip clearance has been developed. The model has been applied to study the required quantity and location of the sensors. Based on this model, an analysis method of the measured data is established, and the test data of a Dongfang Steam Turbine Co., Ltd. gas turbine is analyzed. The main factors that cause the non-uniform clearance are determined, and an optimization scheme is formed by this method. The results show that this model and method has great value for measurement scheme design, test data analysis and utilization.

Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring.

Transformation of an Aviation Turboshaft Engine into a Gas Turbine Experimental Facilities for Laboratory Testing

The article deals with the use of a small aviation turboshaft engine for laboratory purposes. This study describes its transformation into an experimental device for research and education. Various constructional, technological and controlling modifications and settings of the gas turbine test stand were carried out and tested on a stationary configuration. The stationary system can be used as a small backup power generator or as a drive unit for a compressor, pump, etc. The laboratory equipment can also be used for research into biofuels (fatty acid methyl esters and bioethanol) for use in aviation and energetics. New control systems, electronic elements and methods of measuring rotations, pressure and temperature are tested for educational and research purposes. The study includes a schematic description of modelling measurements and subsequent numerical evaluation of the thermodynamic characteristics of the cycle in an experimental gas turbine. The laboratory device presented here is, thanks to technological, material and thermodynamic research, suitable for educating and testing the knowledge of future aviation and mechanical engineers.

Design and setup of a low calorific SOFC off-gas combustion chamber for a pressurized MGT hybrid power plant test rig

A demonstrator system for a hybrid power plant is currently being built at DLR, designed for an electrical power output level of 30 kW. Since the very low energy dense exhaust gas of the fuel cell anode side represents the fuel for the combustion chamber in this application, a low calorific SOFC off-gas combustor was developed at DLR specifically for this use case. With thorough investigations on the atmospheric test rig, the expected operational range of the combustor was quantified in preceding works. Now, a novel machine design, including dilution air with an adjustable air split configuration is derived to validate the gathered information on the micro gas turbine test rig under pressurized machine conditions. This work explains the design of the combustion system and addresses the different design features specifically implemented for this use case. Since simplifications had to be made for the atmospheric combustor prototype, a significant positive influence on the operational envelope is expected with the transition to the machine configuration.

Investigation of Combustion of Emulated Biogas in a Gas Turbine Test Rig

Combustion of biogas in gas turbines is an interesting option for provision of renewable combined heat and power from biomass. Due to an increasing share of fluctuating renewable energies in the power grid (especially from wind and solar power), flexible power generation is of increasing importance. Additionally, with an increasing share of agricultural and municipal waste in biogas production, biogas composition is expected to be within a broader range. In this paper, the combustion of synthetic biogas (carbon dioxide and methane) in a combustion test rig with a swirl burner and a high pressure optical chamber is researched at different conditions. Results are compared to a CHEMKIN-PRO simulation using a detailed reaction mechanism. The results show that within the researched experimental matrix, stable biogas combustion for gas turbines can be achieved even with significantly changing gas composition and nominal power. Carbon dioxide concentration is varied from 0 to 60%. CO concentrations (normalized to 15% O2) in the flue gas do not change significantly with increasing carbon dioxide in the fuel gas and, for the researched conditions, stayed below 10 ppm. NOx concentration is below 10 ppm (normalized to 15% O2) for pure methane, and is further decreasing with increasing carbon dioxide share in the fuel gas, which is mainly due to changing reaction paths as reaction analysis showed. Thermal load of the combustor is varied from 100% to 20% for the reference gas composition. With decreasing thermal load, normalized carbon monoxide flue gas concentration is further reduced, while NOx concentrations are remaining at a similar level around 5 ppm (normalized to 15% O2).

Application of an Experimentally Validated High-dimensional Surrogate Model to Performance Prediction of a CHP Unit

This research study mainly presents the development of a data-driven surrogate model as a baseline for efficient and intelligent control and monitoring of a micro gas turbine driven combined heat and power system. Micro gas turbines are considered to be an efficient alternative to costly generation and transmission of electricity, especially in remote areas and cogeneration applications where effective tools for remote monitoring and diagnostics would be required for the realization of distributed generation. Given the extensive data obtained during the experimental tests from our gas turbine test rig, an accurate surrogate model is developed in this paper. The predictability of the metamodel is validated against data that is not used during modeling process. A sensitivity analysis is also performed to investigate the dependency between initially selected input and output parameters. The performance of concluding mathematical model is then compared with previously developed and validated optimum artificial neural network. The developed model does not require detailed and exact component characteristics, making it applicable for monitoring and control in a sophisticated smart grid.

Detailed Examination of a Modified Two-Stage Micro Gas Turbine Combustor

Jet-stabilized combustion is a promising technology for fuel flexible, reliable, highly efficient combustion systems. The aim of this work is a reduction of NOx emissions of a previously published two-stage micro gas turbine (MGT) combustor (Zanger et al., 2015, “Experimental Investigation of the Combustion Characteristics of a Double-Staged FLOX-Based Combustor on an Atmospheric and a Micro Gas Turbine Test Rig,” ASME Paper No. GT2015-42313 and Schwärzle et al., 2016, “Detailed Examination of Two-Stage Micro Gas Turbine Combustor,” ASME Paper No. GT2016-57730), where the pilot stage (PS) of the combustor was identified as the main contributor to NOx emissions. The geometry optimization was carried out regarding the shape of the pilot dome and the interface between PS and main stage (MS) in order to prevent the formation of high-temperature recirculation zones. Both stages have been run separately to allow a detailed understanding of the flame stabilization within the combustor, its range of stable combustion, the interaction between both stages, and the influence of the modified geometry. All experiments were conducted at atmospheric pressure and an air preheat temperature of 650 °C. The flame was analyzed in terms of shape, length, and lift-off height, using OH* chemiluminescence (OH-CL) images. Emission measurements for NOx, CO, and unburned hydrocarbons (UHC) emissions were carried out. At a global air number of λ = 2, a fuel split variation was carried out from 0 (only PS) to 1 (only MS). The modification of the geometry leads to a decrease in NOx and CO emissions throughout the fuel split variation in comparison with the previous design. Regarding CO emissions, the PS operations are beneficial for a fuel split above 0.8. The local maximum in NOx emissions observed for the previous combustor design at a fuel split of 0.78 was not apparent for the modified design. NOx emissions were increasing, when the local air number of the PS was below the global air number. In order to evaluate the influence of the modified design on the flow field and identify the origin of the emission reduction compared to the previous design, unsteady Reynolds-averaged Navier–Stokes simulations were carried out for both geometries at fuel splits of 0.93 and 0.78, respectively, using the DLR (German Aerospace Center) in-house code turbulent heat release extension of the tau code (theta) with the k–ω shear stress transport turbulence model and the DRM22 (Kazakov and Frenklach, 1995, “DRM22,” University of California at Berkeley, Berkeley, CA, accessed Sept. 21, 2017, http://www.me.berkeley.edu/drm/) detailed reaction mechanism. The numerical results showed a strong influence of the recirculation zones on the PS reaction zone.

The process of gas-dynamic design of pneumatic braking system using the baseline compressor

The article presents the results of work on the design of the air brake for testing of industrial gas turbine engines with free turbine. Designing of the air brake was performed on the basis of existing units using the program CFD - simulation Numeca FineTurbo. During the design the air brake arrangement was determined, which allows to utilize the required power to the shaft of the free turbine, increases stall margin of the air brake by waisting of the meridional flow channel. It was also made designing of the outlet guide vane to remove the residual twist. Unified nozzle also was designed to provide the air brake work at necessary points on the characteristic.