bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning Regression of Under-Expanded Hydrogen Jets
Authors: Cerbarano Davide , Tieghi Lorenzo , Delibra Giovanni , Minotti Stefano , Corsini Alessandro 
Published: August 2024

Abstract
The introduction of hydrogen-methane blends as fuel in gas turbines rises concerns on the capability of state-of-art ventilation systems to dilute possible fuel leaks in the enclosures. Traditional numerical methods to perform leak analysis are limited by the number of factors involved, i.e. location and direction of the leak, cross-section area, gas pressure in the pipelines, gas composition, and location of external objects. Hence, this arise the need for novel and fast tools capable for the accurate prediction of fuel dispersion in leak scenarios. To this extent, we propose a novel machine learning approach to model gas leaks. The model is trained on a dataset of numerical simulations accounting for several hydrogen/methane concentrations in the fuel, different storage to ambient pressure ratios at the leak section, and a set of cross-flow ventilation velocities. The architecture of the machine learning model is based on graph neural networks, to solve a node-level regression task predicting fuel concentration in space for different high pressure leak scenarios. The model shows a significant speed-up in predicting fuel dispersion with respect to conventional methodology (0.1 s vs 3.5 h) but the GPU memory requirements proved to be a problem when dealing with 3D domains.
Nature of Transonic Compressor Flow: Importance of 3D Athroat/Ainlet Part I: Subsonic Mach Numbers
Authors: Lefas Demetrios 
Published: August 2024

Abstract
A key problem in transonic compressor and fan design is that although a 3D description of the flow is necessary to correctly capture the shock, accounting for it during the sectional detailed design is difficult because the key driving design parameters are unknown. In this paper, it is shown that for inlet relative Mach numbers between 0.85 to 1.30, the pressure rise across the shock is primarily a function of the 3D streamtube area at the throat At over the inlet area A1. This key finding is based on three key transonic flow features, discussed in detail within this paper, being present together across a wide range of more than 3000 representative transonic compressor and fan designs published online: (https://whittle.digital/). Only the subsonic regime approaching a Mach number of unity is analysed in this paper, whilst the supersonic regime is analysed separately in Part II.
Moreover, it is shown that the effect of changes in the blade geometry, or the 3D streamtube height, on the transonic flow field is one of the same and can be explained simply by keeping track of the associated changes in At/A1. Surprisingly, the pre-shock Mach number at a given At/A1 is shown to be insensitive to the details of the blade surface geometry. Only geometric design choices made in the preliminary design phase, such as the maximum thickness and the inlet relative flow angles, are shown to have a second-order effect. These findings suggest, that the purpose of the sectional detailed design phase should be solely to make the desired changes in the real spanwise 3D At/A1.
The final part of the paper concerns itself with the level of fidelity necessary when calculating the spanwise 3D At/A1, for it to positively influence design; especially while approaching a Mach number of unity when small changes in At/A1 become increasingly important. A key conclusion is that not resolving the subtle changes in the 3D radial flow within the blade passage at the appropriate level of fidelity, especially at the early through/low multistage compressor design stage, could potentially mislead the transonic design process. As a result, this paper advocates the use of 3D CFD, even at this early design stage, for the rapid exploration of future compressor designs.
Optimisation of a Hydrogen-Fuelled Parametric Strut Injector Using an Automated Workflow CFD Method
Authors: Treleaven Nicholas C. W. , Fournier Guillaume J. J. , Leparoux Julien , Mercier Renaud 
Published: August 2024

Abstract
A strut injector for burning hydrogen has been optimised using an automated workflow system. The design process began with a parametric CAD model that allowed for various design features to be altered and various different designs to be generated from a given list of parameters. In order to choose the optimal set of injectors to cover the design space, an optimised design of experiments (DOE) method was used to automatically choose the parameters that best spanned the design space. One hundred candidate designs were chosen and a script used to generate a series of stereolithography (STL) files for each design. The STL files were then uploaded to a supercomputer for CFD analysis. For each of the 100 designs, a 4 step process was followed to generate the required data, this included a automated mesh generation step, field initialisation step, mesh adaptation step and finally an LES all within the YALES2 numerical framework. These 400 simulations were automated using an automatic workflow management process which limited the quantity of human intervention required and massively boosted productivity. In order to reduce the time required for post-processing and the amount of data required, the simulations relied heavily on a on-the-fly post-processing methodology which reduced the complex time-unsteady flow fields to a small number of quantified outputs of interest that measured the suitability of each design such as the pressure drop across the injector and the efficiency of the mixing process. At the conclusion of these simulations, automated scripts translated these outputs into a smaller set of parameters that could be used to compare each design and allow subsequent optimisation and surrogate modelling. Several surrogate modelling methods were attempted with mixed results however a simple classification methodology quickly identified the parameters of interest.
Experimental Study of Fuel Gas Leak in a Real Scale GT Enclosure
Authors: Ricco Alessandro , Lucherini Gabriele , Minotti Stefano , Azzini Francesco , Presotto Valentina , Vanghi Stefano 
Published: August 2024

Abstract
As part of NovaLT16 100% H2 product release, a new ventilation system was designed to achieve hydrogen dilution within the gas turbine (GT) package according to safety criteria. In this context, an experimental study of fuel gas leak in a real GT enclosure was performed to have valuable reference, due to a lack of literature experience with this kind of application.
This paper describes test bench setup and requirements to validate the ventilation design as part of NovaLT16 100% H2 test campaign. The test is representative of a typical gas leak from the fuel gas line inside the GT package leading to a target %LFL (Lower Flammability Limit) at the ventilation outlet defined according to gas detection control.
During the test, the ventilation is kept in operation while the GT is turned off to avoid any potential source of ignition. The gas leak is generated releasing natural gas into the GT package via a set of orifices with sonic flow. For safety reasons, natural gas is chosen instead of pure hydrogen, being equally indicative of a gas release phenomenon. The simulated flow rates are representative of real possible leakages that may occur due to damage to connections or flanges along the fuel supply line. Multiple gas detectors both within the package and in the ventilation outlet duct provide a visualization of the dynamic evolution of the gas cloud both in terms of concentration and location.
The test confirms the gas detection system capability to identify presence of local gas leaks preventing the occurrence of hazardous scenarios. Moreover, acquired data are employed to tune numerical models that are used to design ventilation and gas detection systems with a higher level of confidence.
Investigating the Performance of a Small Horizontal Axis Wind Turbines (HAWT) Using Toroidal Blades
Authors: Ezz Ahmed , El Baz Ahmed M. R. 
Published: August 2024

Abstract
Optimizing the aerodynamic design of small-scale horizontal-axis wind turbine (HAWT) blades is essential for enhancing annual energy production, performance, and efficiency. This study employs Computational Fluid Dynamics (CFD) with the ANSYS FLUENT solver to investigate the aerodynamic behavior of a newly designed multi-bladed HAWT, specifically introducing toroidal blades. The primary objectives include understanding the aerodynamics of two and three-bladed toroidal rotors, improving HAWT performance by increasing the power coefficient (Cp) and identifying the optimal looping ratio (C) for elliptic toroidal blade shape. The research involves a detailed comparison between toroidal and conventional turbine blades, utilizing Blade Element Momentum (BEM) theory and a NACA 4412 airfoil for blade sections. The geometry characteristics, such as chord length and twist, are carefully calculated along the blade. A 3D geometric domain is created and appropriately meshed for the rotor. Analysis is conducted on a model representing the rotor at a constant wind speed of 7 m/s and various Tip Speed Ratios (TSR). A parametric study for the toroidal blade focuses on the shape ratio (C), determining the optimum ratio as 0.25. The results reveal a notable 14% increase in the power coefficient (Cp) for the three-bladed toroidal blade, calculated based on the optimum TSR for both basic and toroidal blades. The findings suggest that toroidal blades can effectively enhance small HAWT performance, especially at lower TSR. Overall, this research highlights the potential of toroidal blades to improve the power coefficients of small-scale HAWTs.
Topology Optimization of Fuel Cells: an Innovative Approach for Sustainable Aviation
Authors: Casari Nicola , Di Caterino Antonio , Pietropaoli Marco 
Published: August 2024

Abstract
The imperative to meet COP 21 targets underscores the pressing need to reduce our dependence on fossil fuel-based technologies. Among the array of viable alternatives, hydrogen stands out as a compelling candidate, with applications spanning from ground vehicles to energy production and even the most challenging sectors to decarbonize, like aviation.
Hydrogen’s versatility and scalability render it a subject of growing interest across various industries, but the rapid market penetration of hydrogen-related solutions is hampered by some shortcomings in the performances of the applications. For instance, the current maximum efficiency of fuel cells hovers around 60%, leaving significant room for improvement.
This paper shows how topology optimization can help designers and manufacturers of fuel cells understand and improve some of the crucial features of these devices. For example, the application of topology optimization can increase the uniformity in reactant delivery, improve the cooling system and reduce the pressure losses in the manifolds. These aspects are crucial for improving the energy density of these products, increasing also their efficiency and reliability. The resulting geometries show how, with the support of topology optimization, fuel cells will be soon ready for real-world aviation applications.
Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes
Authors: Mirhashemi Arman , Juangphanich Paht , Miki Kenji 
Published: August 2024

Abstract
This work presents procedures for implementing machine learning methods into existing algorithms for multi-hole probe calibration and data reduction. It demonstrates that using artificial neural networks (ANNs) can decrease the amount of calibration data needed to achieve a specific calibration uncertainty by over 50%, while also significantly reducing data reduction times. Instead of surface fitting methods, ANNs are employed. Initially, directional calibration coefficients related to flow angles are computed based on pressure measurements, and then these flow angles serve as input parameters for subsequent ANNs to iteratively define Mach number, static pressure, and total pressure. In an alternative approach, new calibration coefficients directly relate pressure measurements from the five-hole probe to the quantities of interest, thereby eliminating the need for iterative algorithms used in conventional surface fitting methods. This method offers several advantages: an average increase of less than 1% in calibration uncertainty for flow angles and a significant reduction in data reduction times to a few seconds on average. Additionally, the methodology is confirmed to avoid both over- and under-fitting.
Operation Planning for Hydrogen Co-Combustion in Combined-Cycle Power Plants
Authors: Hübel Moritz , Rohde Daniel , Gottelt Friedrich , Harper James , Pezzini Paolo , Noble David R. 
Published: August 2024

Abstract
Hydrogen Co-Combustion is seen as an important measure to reduce carbon dioxide emissions of gas combined-cycle power plants. Besides technical challenges, the addition of a new fuel introduces a need for more advanced economic planning, considering new and existing technical constraints as well as different types of legal incentives or constraints to reduce carbon emissions or increase hydrogen blending in gas-turbine combined cycle plants. The paper will demonstrate a gradient-based dynamic optimization routine that is applied to a natural gas-fired combined cycle power plant model with hydrogen co-combustion and real-world boundary conditions. It is used to investigate different legal boundary conditions such as reducing CO2 emission rights or prescribing minimum hydrogen-blend rates and study the effects produced energy, CO2 emissions and the operating result (EBITDA). Therefore, it provides guidance for plant operators, defining the right technological development for a given plant site, e.g. investing in increasing technical feasible hydrogen blending capabilities beyond the minimum legal requirements, planning hydrogen storage capacity for onsite production with electrolyzer or securing continuous hydrogen delivery with other methods, e.g. pipelines, tube-trailers, or ship delivery. In addition, the paper reveals how market participants will operate with variant legal boundary conditions. The findings can be used to validate suggested rules, e.g. EPA suggestion to introduce minimum hydrogen blend rates, the European emission trading systems penalizing CO2 emissions or cooperate sustainability targets enforcing CO2 reduction paths and how these will affect a plant site and resulting consequences for the electricity system.
Gas Path Fault Diagnosis Method Under Small Samples With Inter-Class Imbalance
Authors: Wang Kun , He Ai , Liu Yuan , Xiao Tingyu , Chen Zhixiong , Hu Zhongzhi 
Published: August 2024

Abstract
Current data-driven methods for gas path fault diagnosis in aero-engines often require extensive fault sample sets, which are challenging and expensive to obtain in practice. Additionally, even with limited samples, these methods face issues of inter-class imbalance, including imbalances in normal and fault data quantities, imbalances in the quantities of different fault classes, and imbalances in fault severity. Additionally, current methods for simulating gas path faults solely focus on the degradation of gas path component efficiency, neglecting the non-linear alterations in component characteristics caused by faults and the interconnected effects between components. Therefore, we propose to use a transfer learning-based variational autoencoder (TL-VAE) approach to generate fault samples and optimize the accuracy of gas path fault diagnosis. First, we train the VAE using normal engine operation samples. Then, by incorporating transfer learning, we retrain the VAE using a small number of fault samples by fine-tuning certain weights of VAE. This allows us to combine the operational state features from the source domain with the fault features from the target domain, fitting the distribution of the measured parameters of the faulty engine. This enables the TL-VAE to function as a generator of fault samples. Subsequent fault diagnosis strategies rely on mature classification methods and the generated samples, including Softmax and SVM classifiers. We validate the effectiveness and superiority of the proposed method through simulation. The experimental results demonstrate that the proposed method significantly improves fault diagnosis results with limited samples. Especially within the coverage of the generated samples, fault diagnosis accuracy (FDA) of the Softmax and SVM classifiers significantly improved from 73.3% and 66.5%, respectively, to a perfect 100% after employing the proposed approach. The FDA of Softmax and SVM classifiers with the proposed method is improved from 77.9% and 80.9% to 100% compared to other typical data generation methods.
A Keyphasor-Free Traveling Waves Analysis Method Based on Blade Tip Timing and Experimental Validation
Authors: Meng Yiming , Xiao Zhicheng , Chai Pengfei , Wang Shang , Zhu Xiaocheng , Ouyang Hua 
Published: August 2024

Abstract
Vibration-induced high-cycle fatigue of blades is a prevalent issue in turbomachinery. Blade Tip Timing (BTT) emerges as a non-intrusive, promising method for assessing blade vibration. Despite its potential, BTT’s inherent measurement limitations often result in the under-sampling of individual blade data. Assuming that the bladed-disk assembly vibrates in nodal diameter modes, traveling wave analysis (TWA) can overcome the limitations of under-sampling and identify the nodal diameter modes associated with the frequency components. Traditional TWA methods rely on a keyphasor or once-per-revolution (OPR) sensor to provide accurate reference signals. However, such a sensor could be damaged during operation or challenging to install due to limited space in field tests. In this paper, a new keyphasor-free TWA (KF-TWA) method is introduced, which is capable of measuring the order and nodal diameter of nodal diameter vibrations with as few as two sensors. Theoretical derivations reveal the method’s resilience to the steady-state displacement of blades. The feasibility and effectiveness of the KF-TWA method are validated through numerical experiments and compressor tests. In the numerical experiments, blade detuning and nodal diameter vibration responses are simulated, leading to the development of a criterion for identifying nodal diameter vibrations. A compressor test rig, equipped with a 64-keyphasor disk and a sufficient number of circumferentially distributed sensors, is designed to provide blade vibration benchmark. In the experiments, the KF-TWA method successfully identified nodal diameter vibrations with amplitudes as low as 0.014 mm, consistently yielding identical results across multiple sensor pairs, which demonstrates a higher robustness compared to the traditional TWA method. The results presented offer valuable insights into the engineering application of this technology.
High Temperature Industrial-Scale CO2 Heat Pumps: Thermodynamic Analysis and Pilot-Scale Testing
Authors: Held Timothy J. , Miller Jason D. , Mallinak Jason A. , Magyar Luke 
Published: August 2024

Abstract
Electrification of heat-generating processes is a key means to decarbonize industrial emissions, and heat pumps significantly improve the efficiency of electrified heat. Carbon dioxide is one of the oldest known heat pump working fluids, but its use is presently limited to low temperature (&lt; 120 °C) applications and, when operating in a transcritical mode, for heating liquid water and similar single-phase materials. Conventional subcritical HFC or HFO refrigerants also have temperature limitations due to thermal degradation and are subject to eventual phase-out. Finally, most smaller heat pump systems use oil-lubricated positive displacement compressors, which also impose temperature limitations due to the thermal stability of the entrained oil in the refrigerant.
By taking advantage of oil-free, turbomachinery-based equipment and using CO2 as the refrigerant, the temperature limitations of existing heat pump solutions can be eliminated. Novel cycle architectures, partially derived from sCO2 power cycle concepts, have been developed that significantly improve the performance of CO2 heat pumps relative to conventional vapor compression architectures, both for single-phase material heating and for medium pressure steam generation. Cycle simulations of these new heat pump cycles that cover a wide range of conditions and applications have been completed. Pilot-scale (&lt; 50 kWth) demonstration systems have been built and tested, and their measured performance provides validating data for the simulated results.
Numerical Investigation of Lean Hydrogen-Air Flame Stabilization Regimes Using Large-Eddy Simulations
Authors: Verma Ishan , Yadav Rakesh , Orsino Stefano , Shrivastava Sourabh , Zhang Yuxin , K. Vasudevarao , Sukumar Vijayaraj 
Published: August 2024

Abstract
High-fidelity large-eddy simulations (LES) of turbulent combustion in a typical H2-air flame have been conducted in this work. The simulations are based on a laboratory-scale coaxial dual-swirl injector in which fuel and air are injected separately. Based on the thermal power, at a given equivalence ratio, two flame archetypes are observed in the experiments: a flame anchored to the injector (at ∼4 kW) and an aerodynamically stabilized flame (at ∼10 kW). LES allows the scrutiny of the flame structure and stabilization mechanisms seen with each archetype. The LES is first validated on a non-reacting configuration using Particle Image Velocimetry (PIV), followed by extending the modeling formulation to account for combustion. The modeling formulations successfully retrieve the two regimes, and the stabilization mechanism is investigated further by comparing the LES results with PIV and OH* chemiluminescence images. The mean velocity field for both operating conditions suggests the existence of a strong central recirculation zone (CRZ), which penetrates along the central axis in the injector nozzles, leading to a radial deviation of the central hydrogen jet. As a result, CRZ favors the stabilization of one mode over the other. In one mode, the flame anchors over the lip of the injector and exists along the mixing layer between hydrogen and swirling air jets. In the latter mode, the flame is aerodynamically lifted and stabilizes in the inner shear layer between CRZ and exiting swirling air jets.
Further, LES analysis suggests that the flame is diffusion-dominated in the anchored regime, while the lifted regime has partial premixing and diffusion fronts. Another aspect of numerical investigations is to predict the NOx generated for each operating point. The combustion model formulation is further extended to account for NOx by using the extended Zeldovich mechanism. Finally, various approaches are compared for computational efficiency to predict different NOx levels for each operating condition. In general, LES results successfully predict the flame stabilization regimes along with NOx.