bluuebunny/crossref_metadata_2025_split huggingface dataset:
TPIV Experimental Investigation of Film Coolant-to-Mainstream Interaction From Shaped Cooling Holes With Various Inlet Geometries
Authors: Wang Hanlin , Wright Lesley M. 
Published: September 2023

Abstract
In this investigation, multiple sets of time-averaged Tomographic Particle Image Velocimetry (TPIV) measurements are completed for laid-back, fan-shaped film cooling holes with “racetrack” shaped inlets. Traditional 10-10-10, laid-back, fan-shaped holes are inclined 30° to the mainstream flow on a flat plate. The inlet cross-section varies from round (1:1) to horizontally elongated in a racetrack shape (2:1 and 4:1). The cross-sectional area and the outlet-to-inlet area ratio for all the geometries are held constant. The flat plate is installed in a low-speed wind tunnel with a mainstream turbulence intensity of Tu = 8% and an average velocity of 21.6m/s. The blowing ratios of the film jets range from M = 0.6 to M = 1.5 and the density ratio of the jet is DR = 1. The Reynolds Number of the cooling jet varies from 2600 to 8400. The results of the TPIV measurements are presented in velocity and vorticity iso-surface distributions volumetrically, as well as planar slices of velocity, vorticity, and Reynolds Stress distributions within the measurement volume. The characteristics of the flowfield are coupled with the detailed film cooling effectiveness distributions obtained from the pressure sensitive paint (PSP) technique. It can be noted from the results that the counter-rotating vortex pair (CRVP) generated by the 2:1 inlet is the closest to the surface and weakest in strength, likely caused by the minimum peak jet momentum of the three. The Reynolds stresses downstream of the 2:1 and 4:1 inlets are significantly lower than that downstream of the shaped hole with a round inlet. An inverse relation between volumetric turbulence accumulation, TA, and surface effectiveness, η, can be correlated for the blowing ratios considered. The turbulence accumulation term can thus be used to evaluate the performance of a film cooling hole design with flowfield data.
An Automated Gaussian Process Integrated With Bayesian Optimization Approach to Designing Spline-Based Pin-Fin Arrays
Authors: Dharmadhikari Susheel , Berdanier Reid A. , Thole Karen A. , Basak Amrita 
Published: September 2023

Abstract
Pin-fins are imperative in the cooling of turbine blades. The design of pin-fins, therefore, has seen significant research in the past. With the developments in metal additive manufacturing, novel design approaches toward complex geometries are now feasible. To that end, this article presents a Bayesian optimization approach for designing inline pins that can achieve low pressure loss. The pin-fin shape is defined using featurized (parametrized) piece-wise cubic splines in 2D. The complexity of the shape is dependent on the number of splines used for the analysis. From a method development perspective, the study is performed using three splines. Owing to this piece-wise modeling, a unique pin-fin design is defined using five features. After specifying the design, a computational fluid dynamics-based model is developed that computes the pressure drop during the flow. Bayesian optimization is carried out on a Gaussian processes-based surrogate to obtain an optimal combination of pin-fin features to minimize the pressure drop. The results show that the optimization tends to approach an aerodynamic design leading to low-pressure drop corroborating with the existing knowledge. Furthermore, multiple iterations of optimizations are conducted with varying degrees of input data. The results reveal that convergence to a similar optimal design is achieved with a minimum of just twenty-five initial design-of-experiments data points for the surrogate. Sensitivity analysis shows that the distance between the rows of the pin-fins is the most dominant feature influencing the pressure drop. In summary, the newly developed automated framework demonstrates remarkable capabilities in designing pin-fins with superior performance.
Effect of Insert Porosity on Combustion Instability in a Lean Premixed Combustor Analyzed by a POD-Based Phase Reconstruction Technique
Authors: Johnson Mitchell , James Ashley , Agrawal Ajay 
Published: September 2023

Abstract
Lean premixed (LPM) combustion is very effective at mitigating emissions but is vulnerable to strong thermoacoustic instabilities. A porous insert in the shape of an annular ring placed at the dump plane of the combustor has been proven to be an effective passive technique for mitigating these instabilities across a wide range of operating conditions. However, it is unclear if the change results from the insert geometry or porosity of the insert. In this study, swirl-stabilized LPM combustion is investigated for three configurations — without any insert, with a porous insert, and with a geometrically similar solid insert. Acoustics, flow, and heat release rate behavior of the three test geometries are investigated using diagnostics including dynamic pressure and acoustic probes, particle image velocimetry (PIV), and OH* chemiluminescence (OH*CL) imaging. Synchronized measurements at a fixed equivalence ratio were acquired at 40 kHz using sound probes and at 3.5 kHz using PIV and OH*CL. Results include time-series and spectral measurements of pressure, velocity, and OH*CL, and mode analysis by proper orthogonal decomposition (POD). In addition, the dynamics of the instability are investigated by high-resolution phase reconstructions of velocity and OH*CL data using a novel implementation of POD introduced in this work. Results show two different instability modes: a longitudinal instability for the solid insert case and a helical, precessing vortex driven instability for the no insert case. In both cases, the flow field and heat release rate oscillations are coupled to produce the instability. No such coupling or oscillations are observed for the porous insert case. These results ascertain the unique capabilities of the porous insert in protecting against instability from different, simultaneous driving mechanisms and demonstrate that the insert porosity and flow dynamics associated with it are the primary mitigating factors.
Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment
Authors: Tong Michael T. 
Published: September 2023

Abstract
Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.
The Impact of Multi-Scale Ceramic Matrix Composite Roughness on Heat Transfer and Boundary Layer Behavior
Authors: Wilkins Peter H. , Lynch Stephen P. , Thole Karen A. 
Published: September 2023

Abstract
Ceramic Matrix Composites (CMCs) can enable more efficient gas turbines relative to traditional nickel alloys resulting from enabling higher turbine entry temperatures that in turn benefit cycle performance. One negative effect of adding CMCs to the hot section is the introduction of a unique surface roughness due to the underlying weave topology. This surface roughness is generally at a macro scale compared with traditional turbine roughness such as deposits or erosion, which are well known to interact with the boundary layer development and increase convective heat transfer. In this study, scales representative of traditional turbine roughness in combination with macro scale weave roughness is investigated for convective heat transfer augmentation and boundary layer behavior. In addition to investigating the impact of the CMC roughness scales, 5 harness satin and twill weave patterns are studied to understand the differences between weaves. Heat transfer measurements are conducted in scaled up wind tunnel tests using a conjugate steady state analysis with freestream turbulence intensities of 0.5% and 24%. Boundary layer behavior is measured using Particle Image Velocimetry to capture cross-stream and streamwise planes. Compared to the 0° 5 harness satin surface, the twill surface has higher Stanton number augmentation, owing to the increased number and high density of flow facing features that disrupt the boundary layer. Additionally, the large-scale weave roughness and traditional small-scale turbine roughness act largely independent of one another.
A Proposed Framework for Minimizing Starts and Extending Maintenance Intervals Through Optimized Scheduling With Mixed Integer Programming
Authors: Allen Cody W. 
Published: September 2023

Abstract
In the Digital Age, many new tools have emerged through the use of advanced monitoring systems and relatively low cost, powerful computation. In particular, system Reliability increases have been achieved through various applications of remote monitoring. Mathematical optimization applied to scheduling has been used in many industries to lower costs successfully, generally as a planning tool. More recently, it has found application in day-to-day operational planning for the heavy industries of power generation and oil and gas, since computation time has decreased down to minutes instead of days. Combining this with the powerful improvements in statistical and machine learning forecasting, the intersection of these domains has resulted in operational optimization. Combining maintenance planning models into the operational optimization framework provides operators with the opportunity to extend maintenance intervals purely based on optimized scheduling, which costs nearly nothing compared with having to purchase new parts to achieve extended time between maintenance. When the application involves drivers utilizing carbon based fuels, it is often true that the optimized schedule results in reduced emissions compared with typical base-case operation, which in many locations can result in further cost reductions.
In this paper we propose a framework for scheduling machines at a particular location such that no machine exceeds a certain threshold, t¯, in running time. In particular, the solution attempts to push the facility-wide time between maintenance sessions to some operator defined limit higher than any individual unit’s required time between maintenance. The framework utilizes Mixed Integer Programming with partially stochastic demand forecasts to solve for optimal running schedules through Monte Carlo simulation. Optimality of a running schedule is based on multiple factors including ability to meet the demand forecast, number of starts required, start-reliability and possible over-fire of engines. The resulting set of solutions provide a likelihood of success in reaching a given site level extended time between maintenance. Operators can use this framework to determine how far the time between maintenance can be extended and determine the probability that a given extension can actually be realized at the physical site. This framework can also be used at managerial levels that consider personnel planning and future capital expenditures.
Physics-Informed Neural Network Modeling Approach for Mistuned Bladed Disks
Authors: Kelly Sean T. , Epureanu Bogdan I. 
Published: September 2023

Abstract
Due to the demand for lighter aircraft and increased engine efficiency, bladed disks manufactured as a single component (i.e., blisks) have seen increased interest for use in modern turbomachinery, particularly in aerospace applications. However, due to a lack of friction interfaces like those present in bladed disks with inserted blades, blisks contain low intrinsic damping leading to increased vibration amplitudes and stress concentrations in operation. Stress concentrations are further exacerbated due to energy localization from inherent random variations in material properties and geometry, known as mistuning. As such, modeling mistuned blisk dynamics is of utmost importance for blisk design and maintaining structural integrity in harsh operating conditions. Due to the high dimensionality of finite-element blisk models particularly in industry settings, reduced-order models are often necessary to simulate mistuned blisk dynamics with computational efficiency. Reduced-order modeling of blisks has recently been dominated by purely physics-based methods. However, with the increase in experimental data availability and improved computational resources, data-driven methods have seen increased interest. Recently, the authors developed a data-driven approach to model mistuned blisk dynamics with traveling-wave excitation similar to that experienced in operation. This approach was expanded to maintain analytically derived linear and nonlinear relationships via a single physics-informed neural network while maintaining a sector-level viewpoint. Because physical response data from blades is used directly, unlike previous physics-based methods this approach can easily incorporate experimental data such as data measured in bench or operating conditions such as with blade tip timing. Previously, this approach was validated only considering an isolated first mode family with excitations targeting blade-dominated modes. Here, we illustrate the application of this method for a veering region in which multiple disk and blade-dominated modes participate from higher mode families including both bending and torsional modes. Additional considerations for practical application of this approach are discussed, along with further studies to illustrate robustness and generalizability. Validation is shown using a high-fidelity finite-element blisk model considering traveling-wave excitations targeting higher blade and disk-dominated modes in a veering region. For the veering region considered, physical response data from as few as two degrees of freedom per blade tip are needed to achieve high prediction accuracy for test mistuning patterns not used for model training.
Aerodynamic Performance in a Turbine Cascade With Large Tip Clearance and Casing-Side Injection
Authors: Hong Dunam , Choi Minsuk , Kim Myungho , Song Seung Jin 
Published: September 2023

Abstract
This study investigates how the casing-side coolant injection alters the flow field and loss in linear turbine cascade with tip clearance size similar to the incoming boundary layer thickness. The tested tip clearance is of the incoming full boundary layer thickness (at x/Cx = −0.5), or 1.35% of span. Without injection, tip clearance vortex is the only dominant downstream flow feature that contributes to loss. Other secondary flow features, such as passage vortex and trailing shed vortex, are hardly observable. When the coolant is injected with a mass flowrate of 1.5% of the main flow and a swirl equivalent to 70% of the rotor tip tangential speed, the boundary layer downstream of the coolant injection slot thickens, and the tip clearance size relative to the boundary layer thickness decreases to 45%, generating a horseshoe vortex. In addition, incidence near the casing is decreased. The horseshoe vortex develops into the passage vortex, and multiple slot exit vortices combining with the passage vortex strengthens the passage vortex. Furthermore, coolant injection reduces the tip loading, and the axial location of the maximum tip loading shifts downstream by 0.05Cx. Hence, the loss associated with the tip clearance vortex is slightly reduced. However, due to the significant increases in the losses associated with the passage vortex and trailing shed vortex, the total mass-averaged loss of the upper half span increases by 18.5%, and the mixed-out loss by 8.2%.
Development of an Artificial Intelligence Model to Predict Combustion Properties, With a Focus on Auto-Ignition Delay
Authors: Bounaceur Roda , Heymes Romain , Glaude Pierre-Alexandre , Sirjean Baptiste , Fournet René , Montagne Pierre , Auvray Alexandre , Impellizzeri Eric , Biehler Pierre , Picard Alexandre , Prieur-Garrouste Bastien , Molière Michel 
Published: September 2023

Abstract
Hydrogen-compatible gas turbines are one way to decarbonize electricity production. However, burning and handling hydrogen is not trivial because of its high reactivity and tendency to detonate. Mandatory safety parameters, such as auto-ignition delay times, can be estimated thanks to predictive detailed kinetic models, but with significant calculation times that limit coupling with fluid mechanic codes. An auto-ignition prediction tool was developed based on an artificial intelligence (AI) model for fast computations and an implementation into an explosion model. A dataset of ignition delay times was generated automatically using a recent detailed kinetic model from National University of Galway (NUIG) selected from the literature. Generated data covers a wide operating range and different compositions of fuels. Clustering problems in sample points were avoided by a quasi-random Sobol sequence, which covers uniformly the entire input parameter space. The different algorithms were trained, cross-validated and tested using a database of more than 70’000 ignitions cases of Natural Gas/Hydrogen blends calculated with the full kinetic model by using a common split of 70/30 for training, testing. The AI model shows a high degree of robustness. For both the training and testing datasets, the average value of the correlation coefficient was above 99.91%, the Mean Absolute Error (MAE) and the Mean Square Error (MSE) around 0.03 and lower than 0.04 respectively. Tests showed the robustness of the AI model outside the ranges of pressure, temperature, and equivalence ratio of the data set. A deterioration is however observed with increasing amounts of large alkanes in the natural gas.
Experimental Evaluation of Phi-Scaling Validity for Local Temperature Predictions of Turbine Components Under Real Engine Conditions
Authors: Wimmer Thomas , Mick York 
Published: September 2023

Abstract
A Siemens Energy Large Gas Turbine was fully instrumented to quantify operation point parameters and to validate turbine components temperature predictions using thermocouples measurements. The engine was operated under real-grid conditions and has also undergone specific testing operation modes over a several months long period. An automated approach was used to detect steady-state operation from the collected data and the average for each sensor signal was calculated for these periods for data reduction. The acquired metal temperature readings have been non-dimensionalized using the ϕ-approach. The non-dimensional temperature data was then categorized by several engine conditions. The standard deviation of the non-dimensional temperatures for the different categories was calculated. The constant ϕ-scaling approach was assessed for its accuracy and validity range. Additionally, a more elaborate ϕ-scaling approach was used to predict metal temperatures using the experimental data which are compared to the measured metal temperatures. A comparison of the different ϕ-scaling approaches in terms of accuracy is presented as well as the uncertainty posed by the approaches for different engine conditions. The conducted analysis shows the range of operating conditions and locations for which the ϕ-scaling approach delivers sufficient accuracy to avoid full detailed component analysis and allows the quantification of the underlying uncertainty.
Influence of Tantalum and Cobalt on the Weldability of New High Gamma Prime Nickel Based Superalloys for Repair and 3D AM of Turbine Engine Components
Authors: Gontcharov Alexandre , Louden Paul , Brochu Mathieu 
Published: September 2023

Abstract
The propensity of high gamma prime Ni-based superalloys to weld cracking is examined with emphasis on the influence of Ta and Co on weldability. This study focuses on the behavior of LW4280 and LW4280W superalloys containing 16 wt.% Co and 32 wt.% Co respectively. The weldability of experimental compositions of LW4280 with Ta ranging between 0.6 and 6.5 wt.% were also studied. LW4280W alloy, with significantly higher Co fraction, showed a reduced propensity to liquation and stress-strain cracking, improving weldability. However, tensile strength of welds above 982°C (1800°F) was reduced relative to LW4280. The optimal level of Ta in LW4280 was found to be in the middle of the range examined, allowing sound laser DED and GTAW welds to be produced at ambient temperature. Higher or lower levels of Ta resulted in weld cracking produced by GTAW-MA. Possible mechanisms by which Ta and Co levels impact weldability are discussed.
Hydrogen and Gas Turbines – A Rational Approach
Authors: Gülen S. Can , Singh Raj , Banerjee Pritam 
Published: September 2023

Abstract
Hydrogen, as an energy vector, has been receiving significant attention as a prime actor in decarbonization of a wide variety of industry sectors including electricity, chemicals, steel, and cement. In order to be a true decarbonizing agent, H2 must be produced without concomitant generation of carbon dioxide (CO2). Presently, this is only possible via energy-intensive water electrolysis (green H2) using zero-carbon resources such as wind, solar PV, and nuclear power (pink H2). Worldwide, the bulk of H2 production for industrial use is via steam-methane reforming (SMR) with natural gas feedstock, which generates significant amount of CO2 (gray H2). This can be alleviated by combining SMR with carbon capture utilization and sequestration (CCUS), which is labeled as blue H2.
In this paper, utilization of H2 in gas turbine power plants will be investigated for identification of the most promising combination of production, storage, transportation, and power generation. Gasification of coal and/or other “dirty” fuels is shown to be a more sensible way of producing blue H2. Detailed process simulation and performance models will be used to identify the technology challenges as realistically as possible. The objective is to provide a concise and comparative assessment of decarbonization opportunities free of over-optimistic assumptions to help facilitate a realistic understanding of the impact of hydrogen economy on the electricity sector.