bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical Investigation of the Combustion Process in a Lean Burn Gas Turbine Fuel Injector
Authors: Rajput Amit , Divyansh Ayush , K. P. Shanmugadas 
Published: August 2024

Abstract
The present work investigates the aerodynamics and combustion characteristics of a novel fuel flexible lean burn fuel injector. Methane and hydrogen are considered as the fuel with different mixing ratios. Flame stability, temperature distribution, and species concentration are assessed to obtain the operational characteristics of the fuel injector. The injector assembly consists of pilot and main mixer blocks and each of them includes a set of axial and radial swirler assemblies. Considering the mixture as partially premixed, steady-state RANS turbulent combustion modelling is performed with standard k-epsilon model and steady diffusion flamelet model. Hydrogen is blended with methane with different mixing ratios ranging from 0–100%. Four geometrical configurations with pilot and main mixer fuel-air combination are considered with representative flow conditions. The pilot and main mixers generate individual recirculation zones and stable flame anchoring is observed for all cases. A uniform and symmetric temperature distribution is observed with Two Temperature peaks corresponding to main and pilot flame fronts. The hardware anchors the flame till 50% hydrogen concentration and any further addition resulted in an unstable flame.
A Systematic Approach to Sensor Selection for Gas Turbine Exhaust Gas Temperature Prediction
Authors: Xu Jiasen , Lou Fangyuan 
Published: August 2024

Abstract
The performance of a gas turbine deteriorates over time due to various primary flow component degradations such as fouling, erosion, corrosion, and foreign object damage. Exhaust gas temperature, commonly used for engine control, condition monitoring, fault detection, and maintenance decisions, effectively indicates the severity of engine performance degradation. Although EGT can be directly measured by placing a few probes at the exit of the turbine, EGT sensors themselves are subject to frequent failures, providing a fairly inaccurate indication of the gas turbine hot-section status. This paper proposes an active-subspace-based approach to sensor selection for EGT prediction based on active subspace approach. Instead of selecting a subset of parameters, the active subspace approach identifies a few primary directions along with EGT changes fastest. The method’s effectiveness is examined using the Advanced Geared Turbofan (AGTF30) engine, representing a 30,000 lbf thrust-class turbofan engine for civil aviation. All the numerical simulations are performed using the open-source Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) covering the operating conditions, including takeoff, climb, cruise, descent, and landing. Results show that the proposed method can effectively search for an optimal sensor suite for EGT prediction. The optimal suites yield reasonably good EGT predictions with majority predictions of 95% confidence interval and are less sensitive to input measurement uncertainties. Furthermore, intelligent selection of sensor suites is crucial for EGT prediction, and the inclusion of additional sensor counts can sometimes undermine the precision of the prediction.
A Roughness Prediction Method by Particle Deposition and its Effect on Flow and Heat Transfer in High-Pressure Turbine Cascade
Authors: Wang Zhenfei , Wang Zhiheng , Xi Guang 
Published: August 2024

Abstract
Particulate pollutants deposited on the blade surface alter the roughness and profile of the blade, leading to a decrease in the performance of the gas turbine. This paper proposes a method for predicting local roughness and integrates it with a critical velocity model to create a time-stepping calculation framework for particle deposition. This framework may be utilized to compute the alterations in flow and heat transfer caused by particle deposition. The framework was utilized to perform a steady-state numerical simulation on a high-pressure turbine cascade. The findings indicated that particles larger than 2 μm are primarily deposited on the pressure side, whereas smaller particles are mainly deposited on the suction side. As the diameter of the injected particles increases, the capture efficiency exhibits a trend of initially increasing and then decreasing, regardless of whether it is on the pressure or suction side. With the increase in operating time, particle deposition leads to a gradual rise in the surface roughness of the blade, although the extent of the increase gradually diminishes. Moreover, the roughness variations on the pressure side are more pronounced than those on the suction side. Changes in the blade surface roughness significantly impact the heat transfer coefficient on the blade surface. An increase in roughness at the blade’s leading edge results in a decrease in the heat transfer coefficient, while an increase in roughness elsewhere on the blade leads to an increase in the heat transfer coefficient. Furthermore, as operating time increases, the magnitude of the increase or decrease in the heat transfer coefficient gradually diminishes.
Mechanism Study of Stator Suction Slots to Inhibit the Boundary Layer Separation in a Single-Stage Axial Flow Compressor
Authors: Zhang Haoguang , Wang Hao , Feng Yiming , Xiao Jinhang , Chu Wuli 
Published: August 2024

Abstract
In addressing the flow phenomenon that the starting point of boundary layer separation is located at the stator leading edge, three suction slots with axial positions of 15% Ca, 50% Ca and 85% Ca were designed on NASA Stage35. Four schemes were developed to investigate the effect of different suction slots on the boundary layer separation. The results show that four schemes improve the compressor total performance under the small and medium mass flow rate conditions. Suction-AB scheme, with double slot located at 15% Ca and 50% Ca, improves the total pressure ratio and isentropic efficiency by 0.89% and 1.83% under the near-stall condition, respectively. The internal flow field analysis shows that the slot-B, located at the 50% Ca, plays a key role in inhibiting the boundary layer separation. The airflow inside the separation area only needs to make a minimal directional turn to flow into the slot-B, making it difficult for the boundary layer separation to continue downstream, which is also the reason why the slot-C, located at 85% Ca, is unable to bring more improvements. However, the airflow into the slot-A, located at 15% Ca, requires a bigger directional turn, resulting in a lack of ability in slot-A to inhibit the boundary layer separation.
Tracking of Bristle Tip Deflections to Demonstrate Blow-Down in Brush Seals
Authors: Bowen Joshua P. , Bowsher Aaron A. , Crudgington Peter F. , Bull Sam , Sangan Carl M. , Scobie James A. 
Published: August 2024

Abstract
Sealing in gas turbines is paramount to overall performance and efficiency. Brush seals offer superior performance compared to other sealing solutions commonplace in modern turbomachinery. When subjected to a pressure load, a ring of flexible fine wire bristles — fitted at a lay angle to the radial plane — compact to resist the oncoming flow and deflect towards the rotor in a process known as blow-down. This study employs Digital Image Correlation to track individual bristle tips in three spatial axes throughout a large-scale brush seal test facility. This is the first-time direct measurements of blow-down throughout the bristle pack have been presented, providing a unique insight into the mechanical behaviour of brush seals.
Increased magnitudes of blow-down and axial bristle deflection were demonstrated in upstream bristle rows and at larger clearances. Analysis of these results in conjunction with the interrogation of the inter-bristle pressure field proved that blow-down is more prevalent for pressure relieving brush seals in comparison to conventional configurations. The reduction in the through-flow clearance area resulted in a significant enhancement in sealing performance for a clearance seal, highlighting a key advantage of the pressure relieving back plate design.
Synchronous Response of Rotor Components by Rotated Wavelet Probing
Authors: Iaffaldano Davide , Guida Roberto , Carassale Luigi 
Published: August 2024

Abstract
The analysis of the synchronous response of the rotor components can be carried out efficiently using time-frequency methods based on the continuous wavelet transform. The wavelet transform provides a measure that may be regarded as weighted averaging of the signal amplitude with the support of the wavelet. This averaging area is referred to as Heisenberg box and, for Gabor wavelet, has an elliptical shape. In the case of wavelet probing, the estimation of the amplitude and phase needs to squeeze in frequency direction the Heisenberg box (HB) for increasing the resolution. The limit of this technique arise when, in proximity of the resonance crossing, the HB of the wavelet intersects both the synchronous and non-synchronous response components. This interaction is particularly important when the dynamical system is time-variant due to the variation of the rotor speed.
This paper proposes the estimation of the synchronous response with a time-frequency method based on a wavelet whose HB is an ellipse arbitrarily rotated to increase the capability of separating the signal components. After the analytical definition of the wavelet, the proposed method is applied both to simulated and experimental data.
Flow Reconstruction in a Transonic Turbine Cascade Using Physics-Informed Neural Networks (PINNs)
Authors: McNichols Ezra O. , Juangphanich Paht , Hawke Mallory S. , Shoemaker Ethan J. , Poulson Mackinnon J. , Brandt Meghan E. , Bons Jeffrey P. 
Published: August 2024

Abstract
This paper investigates the application of Physics-Informed Neural Networks (PINNs) for the analysis of turbine blades in a transonic cascade. The 2-D flow field in a transonic turbine cascade is reconstructed in three ways: the traditional forward approach (PINN not trained on experimental data), by training the PINN using discrete sets of experimentally measured pressure at midspan, and in the inverse sense where no inlet or outlet pressure boundary conditions are applied. Comparisons between the PINN solutions to measured data are made. This is repeated for three different turbine blades with distinct loading characteristics. Good agreement is shown between a CFD calculation of the CMC7 blade, and the PINN model trained with all data. The PINN is trained utilizing all available data, half the available data, data from only the leading edge region, and data from only the trailing edge region. The forward problem results deviate the most from experimental data but show promise. Solutions from the assisted training cases show that the PINN can reconstruct the flow field with acceptable accuracy when trained on measurements along the entire blade. In the inverse case, it is shown that to simultaneously achieve acceptable errors for inlet Mach number and outlet isentropic Mach number, the PINN must be trained on the static pressure data along the entire blade.
A Physics Informed Neural Network for Solving the Inverse Heat Transfer Problem in Gas Turbine Rotating Cavities
Authors: Puttock-Brown Mark R. , Mahesh Bindhu Goutham Krishna , Ashby Colin E. 
Published: August 2024

Abstract
Recently, Physics-Informed Neural Networks have displayed great potential in delivering swift and accurate solutions for inverse problems. Here, a Physics-Informed Neural Network (PINN) was used to solve the inverse heat conduction problem in the rotating cavities of a aeroengine high-pressure compressor internal air system. The neural network was designed to receive experimentally captured radially distributed temperature profiles as inputs and predict the associated surface heat fluxes. The correctness of these predicted heat fluxes are assessed by numerically solving the direct heat conduction equation using a 2D Finite-Element model, thereby recovering the original temperature profiles. A comparative analysis is conducted between the predicted temperature profiles and the initial inputs.
The physics informed neural network is trained using noise-free synthetic data, created from a range of radial temperature curve fit coefficients, and subsequently tested on noisy experimental data at engine representative conditions. The predicted temperature values exhibit some good agreement with their respective actual counterparts. Furthermore, the sensitivity of model hyperparameters are explored to showcase the capability of the proposed approach. The results show that Physics-Informed Neural Networks exhibit reduced susceptibility to experimental uncertainties when addressing inverse problems, in contrast to inverse solution methods, and offer a possible new approach for analysis of experimental data if trained on a sufficiently large dataset.
An Optimized and Reduced Chemical Kinetic Model for CFD Simulations of Hydrogen and Natural Gas Blends Combustion for Industrial Gas Turbines
Authors: Rahman Ramees K. , Kancherla Raghu , Vogel Gregory , Vasu Subith S. 
Published: August 2024

Abstract
Natural gas is one of the common fuels that is used in gas turbines for power generation. Due to its interest in power generation and other applications, there are several chemical kinetic models available in the literature for natural gas. The power generation sector is moving towards decarbonization and there has been increased interest in replacing conventional fuels with fuels that produce less/zero carbon emissions like hydrogen. Pure hydrogen has a very wide flammability range and poses risks related to flashback and high thermal NOx production. Blending hydrogen with natural gas helps in having better control over the fuel properties. However, the literature lacks well-validated chemical kinetic models for the combustion of hydrogen blended natural gas for undilute mixtures at gas turbine-relevant conditions (8–16 bar). Hence, in this work, we develop a detailed chemical kinetic model for hydrogen blended natural gas and validate it with a wide range of experimental data for both dilute and undilute mixtures relevant to gas turbine operating conditions. We outline the strengths and weaknesses of the current mechanism to aid future users of our chemical kinetic mechanism. The detailed chemical kinetic mechanism is then reduced to two smaller versions (60 species and 44 species mechanisms) without significant loss in accuracy using Directed Relation Graph with Error Propagation (DRGEP). To improve the prediction for pure hydrogen combustion, while retaining all other predictive capabilities, an optimization is carried out for the most sensitive reactions for hydrogen combustion. The resultant mechanism can predict a wide range of experimental results with the least cumulative error.
Advancing AI Modeling for Prediction of Safety Parameters for Combustion of Hydrogen/Syngas/Natural Gas Mixtures
Authors: Bounaceur Roda , Glaude Pierre-Alexandre , Fournet René , Sirjean Baptiste , Montagne Pierre , Auvray Alexandre , Impellizzeri Eric , Biehler Pierre , Molière Michel 
Published: August 2024

Abstract
One promising way supporting efforts to decarbonize energy production is to hybridize gas turbine fuels, and in particular to make increasing use of hydrogen. The use of alternative decarbonated or renewable fuels will significantly modify safety parameters. New simulation tools need to be developed that can rapidly assess and forecast the safety characteristics of different reactive fluids. This paper focuses on the development of a learning model to estimate Auto Ignition Temperature and Auto Ignition Delay Times (AIT and AID) during the combustion of several mixtures of dihydrogen, natural gas, and syngas. A numerical procedure has been developed, which generates automatically a dataset of AIT covering a wide range of operating conditions. The dataset contains 50,000 points and has been simulated using the comprehensive combustion model from the literature developed at National University of Ireland at Galway (NUIG). In order to fit optimally all the hyperparameters of a neural network model, several methods of optimization have been tested and are discussed. The model developed is capable of predicting AIT for pressures between 1 and 50 atm, temperatures between 200°C and 700°C and equivalence ratio between 0.2 and 5. For both the training and testing datasets, the average value of the correlation coefficient was above 99.95%, the Mean Absolute Error (MAE) and the Mean Square Error (MSE) around 0.003 and lower than 1e−4, respectively. The final machine learning model shows a high degree of robustness in reproducing the results of the detailed model over all the parameter domain studied. It was used eventually for the analysis of a typical case study of the risk of auto-ignition in pipes when gases are fed to a gas turbine.
Validation of a Methodology to Assess the Flutter Limit Cycle Oscillation Amplitude of Low-Pressure Turbine Bladed-Disks - Part I: Mach Number Effects
Authors: Escudero Alvaro , Rodríguez-Blanco Salvador , Corral Roque 
Published: August 2024

Abstract
This paper presents a methodology to estimate the vibration amplitude of fluttering low-pressure turbine blades saturated due to friction effects. The study utilizes an analytical model that balances aerodynamic work and dry-friction work. The analytical predictions are compared against experimental results to validate the model. The first part of this paper focuses on the influence of the Mach number on the work balance between aerodynamic and mechanical components. It is observed that the vibration amplitude of low-pressure turbine rotor blades notably increases with higher Mach numbers. In addition, numerical simulations are employed to assess the influence of the Mach number on the critical damping ratio. The results demonstrate that an appropriate scaling of the critical damping ratio with the exit Mach number collapses all the damping versus inter-blade phase angle curves into a single curve. This finding validates the scaling of the aerodynamic damping for different pressure ratios. Unsteady pressure measurements were acquired, carefully post-processed to extract their flutter-induced peak components and presented in a nodal diameter by nodal diameter basis. The post-processed data were then used to characterize the vibration amplitude observed in the experiments. The trends of the measured unsteady pressure on the casing of a rotating rig and the proposed model with the Mach number for different shaft speeds are in good agreement. The vibration amplitude and the mean unsteady pressure increase with the Mach number, and exhibit a maximum with the shaft speed.
Centrifugal Compressor Performance and Flow Path Generation With Artificial Intelligence
Authors: Barannik Valentyn , Burlaka Maksym , Lysianskyi Bohdan , Moroz Leonid 
Published: August 2024

Abstract
Centrifugal compressors are widely used turbomachines employed across various fields as components of systems, such as being part of a turbocharger in an internal combustion engine or a refrigeration cycle. As a result of system analysis, the compressor parameters are determined with 0D or 1D solvers. Simultaneously, the compressor performance, as well as other components, influence the results of system analysis. The system analysis starts with compressor performance assumptions that could be taken from a prototype or based on the engineer’s experience and prior domain knowledge. If the new design deviates significantly from the prototype, its performance assumptions diverge from the actual compressor performance, necessitating numerous iterations to align system and component parameters with overall system requirements and constraints. Therefore, the design and development projects of modern centrifugal compressors are lengthy and very expensive.
The research described in this paper aims to develop highly accurate single-stage centrifugal compressor performance models coupled with flow path geometry generation models leveraging artificial intelligence (AI) technology. These models offer the potential to circumvent expensive design-build-test iterations, thereby substantially reducing the time and cost of developing modern centrifugal compressors.
In this paper, the various configurations of centrifugal compressors are considered, along with a description of the configurations of interest. The selection of input and output variables that provide sufficient information about compressor design with the respective ranges is justified. There is a detailed description of automated workflows for centrifugal compressor design and performance data generation for neural network training. The approaches for data preprocessing that enable high-accuracy predictions are provided, and the peculiarities of applying the AI technology developed by the authors for centrifugal compressors are discussed. An analysis of the trained model accuracy as well as the technique for quantitative assessment of prediction reliability is provided. The utilization of the centrifugal compressor AI model integrated into a gas turbine engine simulation environment is demonstrated.
Evaluation of Cold Flow Test Conditions and Test Fluid Selection for a 60-kW Radial Inflow Supercritical CO2 Turbine
Authors: Hoque Syed Jiaul , Gupta Shrey Sahai , Kumar Pramod 
Published: August 2024

Abstract
Indian Institute of Science, Bangalore, India, is developing a supercritical CO2 power block. The power block features a 60-kW single-stage, unshrouded, vaneless radial inflow turbine designed to operate at 55,000 rpm. A cold flow test rig is being built to test the turbine assembly under reduced operating conditions before integrating it into the power block. The cold flow test must mimic the performance of the actual design condition and should be able to operate at design speed. This paper uses similarity method to determine the operating conditions and appropriate test fluid for the cold flow test.
Four candidate test fluids are considered: air, subcritical CO2, Sulfur hexafluoride, and R134a. A lower turbine inlet pressure is recommended for the cold test to minimize capital costs and ensure safety. However, challenges are encountered to match all similarity parameters at lower turbine inlet pressures. The similarity parameters matching improves as inlet pressure increases. The maximum inlet pressure for R134a and SF6 is limited by their saturation pressures, and they require speeds below 20,000 rpm. Subcritical CO2 and air necessitate higher inlet pressures of ∼ 40–60 bar and operating speeds of 35,000 and 49,300 rpm, respectively, to ensure similarity.
Computational fluid dynamic (CFD) simulations are employed to compare the velocity distribution, flow structures, entropy generation, loss distribution, and efficiency between the design and test conditions. The findings highlight air as the best-suited test fluid, offering the best aerodynamic match. The turbine can be tested with air up to full speed without choking. Furthermore, air as test fluid eliminates the need for a discharge storage tank as it can be directly released into the atmosphere.