bluuebunny/crossref_metadata_2025 huggingface dataset:
Design, Multi-Point Optimization and Analysis of Diffusive Stator Vanes to Enable Turbine Integration Into Rotating Detonation Engines
Authors: Grasa Sergio , Paniagua Guillermo
Published: September 2023

Pressure gain combustion is considered a possible path toward improved thermal cycle efficiency and reduced carbon emissions. However, ad-hoc turbine designs are required to maximize the thermodynamic potential; the new turbomachinery should be suited for the oscillations in flow conditions generated by the detonation combustor, which are very different from conventional gas turbines. This manuscript investigates the design, optimization, and analysis of diffusive stator vanes operating under large inlet flow angles in the high subsonic regime.

First, the design methodology is outlined, focusing on the geometric requirements to ingest high Mach number flow and the parametric modeling of the endwall and the 3D vane pressure and suction sides. Then, the impact of the inlet flow angle on the flow field and vane design is studied through a multi-point, multi-objective optimization with three different inlet angles, performed using steady Reynolds Averaged Navier Stokes simulations. Remarkable reductions in pressure loss and stator-induced rotor forcing are attained while maintaining an extensive operating envelope and a high amount of turning. Finally, several design guidelines are provided based on analyzing the optimized geometries and the comparison with those optimized for axial inflows.
Development of a Hybrid Tabulated-Virtual Chemistry Model for Pollutant Formation Prediction
Authors: Luu Tan-Phong , Darabiha Nasser , Fiorina Benoît
Published: September 2023

In the context of energy transition, numerical models that can accurately describe pollutant formation are needed for designing novel combustion chambers. However, due to the stiffness of the chemistry describing pollutants, modeling chemical kinetics is a challenging issue. Simplified models such as flamelet-based tabulated chemistry allow a good resolution of the heat release rate and the temperature profile but usually fail in predicting pollutants in practical burners. The virtual chemistry method, recently developed at the EM2C laboratory, is an alternative technique to the tabulated chemistry method. It consists of first building a learning database using detailed chemistry solutions. Then, properties of “virtual” species and reactions are optimized to target key variables of interest. In that way, two virtual schemes are obtained: a main scheme trained to retrieve the main properties of the flame such as temperature and the flame speed, and a satellite scheme trained to retrieve pollutant formation. Although the virtual chemistry method is substantially cheaper than detailed chemistry, further cost reduction can be achieved. To that end, an innovative hybrid method coupling flamelet-based tabulated chemistry for the flame structure and virtual satellite mechanism for pollutant prediction is presented. By tabulating the inputs of the satellite mechanism as a function of the mixture fraction and progress variable, the number of transported variables is reduced by a factor of two. By comparison with detailed chemistry, flamelet-based tabulation, and virtual chemistry, the hybrid model is validated through the simulations of 3 configurations: 1-D freely-propagating premixed flames, 2-D premixed slot burner, and 2-D flame-vortex interactions. Results show the good performances of this model.
Investigating the Suitability of Multi-Scroll Volutes for Improving Spanwise Incidence of Mixed Flow Turbine Rotors With Varying Blade Cone Angles in Automotive Turbocharging Applications
Authors: Martin Peter , Spence Stephen , Stuart Charles , Starke Andre , Leonard Thomas , Geron Marco
Published: September 2023

Targets to reduce fuel consumption and reduce CO2 emissions have been met using engine downsizing and turbocharging. In automotive applications, it is important that the turbocharger responds well to transient events and operates efficiently at both the design and off-design conditions. A Mixed Flow Turbine (MFT) is not constrained to a radial inlet blade angle allowing the peak efficiency to be shifted to a lower U/C_is, providing additional freedom to the designer. As the MFT leading edge varies in radius, the spanwise incidence angle also varies, leading to additional separation on the suction surface (SS) of the blade near the hub because of increasingly positive incidence, which is most noticeable at off-design conditions.

A multi-scroll volute was previously paired with a MFT with 45° blade cone angle (Λ) which generated non-uniform spanwise flow that improved efficiency at off-design at the cost of peak efficiency. The current study identified the range of blade cone angles that benefitted from a multi-scroll volute to reduce incidence at the hub region. A numerical investigation was conducted which determined the influence a multi-scroll volute can have on MFTs with varying levels of blade cone angle.

When the MFT with large blade cone angle (Λ = 60°) was paired with a multi-scroll volute, the efficiency improved by 2.2%pts at design and 0.5%pts at off-design conditions. The incidence improved and the mass flow rate increased at the hub region. The MFT with smaller blade cone angle (Λ = 30°) had performance losses at both operating conditions because the multi-scroll volute increased incidence within the hub region, which reduced the peak efficiency by 1.3%pts. The off-design condition had an excessively positive incidence angle which was further increased at the hub region by the multi-scroll volute. This resulted in a 0.8%pts reduction in off-design efficiency. The multi-scroll volute concept was shown to offer efficiency improvements for MFTs with larger blade cone angles through better management of the non-uniform spanwise velocity distribution at rotor inlet.
Three-Dimensional Mode-I Fatigue Propagation of a Tangential Surface Crack in a Sub-Component Test Specimen Featuring the Disk Post of a Turboshaft Engine’s First-Stage High-Pressure Turbine
Authors: Yaren Mehmet Faruk , Bilgiç Ersin , Ayhan Ali Osman , Öztürk Umud Esat , Ertekin Şevket
Published: September 2023

Mode-I fatigue crack growth (FCG) simulations are performed for a tangential surface crack initiated at the highest-stress location of the bottom slot surface of a uniaxially-loaded sub-component test specimen. The specimen represents the disk post of a turboshaft engine’s first-stage high-pressure turbine. All three modes of the stress intensity factors (SIFs) along the growing crack fronts are calculated using three-dimensional (3-D) enriched finite elements. However, as the applied uniaxial loading mimics the centrifugal force of the assembled blades, as an approximation to the truly 3-D problem, the crack is initiated and grown in the corresponding tangential plane using only the mode-I SIF distributions in the employed crack growth law. Having computed the new coordinates of all crack front nodes in the original tangential plane, the parameters of the best-fit ellipse are calculated and the finite element model is updated accordingly to contain the incrementally updated new surface crack. The procedure is then, repeated until SIFs reach the fracture toughness. Using the SIFs from the described procedure, the FCG life is also calculated and compared to those obtained from empirical formula-based predictions using the radial and first-principal stress gradients along the possible directions of crack growth and the differences in the results are highlighted.
DLN Evo Combustion Technology Development for a High-Hydrogen Flexible F-Class Retrofit
Authors: Hughes Michael J. , Berry Jonathan D. , Zhao Wei , Crawley Brad , Onyima Travis , Feiz Homayoon , Paasche Elizabeth , Cretegny Laurent , Hong Seung-Hyuck (John) , Genova Tommy , Bower Hannah
Published: September 2023

GE Gas Power is nearing completion of technology development towards a retrofittable Dry Low NOx (DLN) combustion system for F-Class gas turbines. The “DLN Evo” combustor is envisioned to give existing power plants a path to decarbonize by significantly increasing their Hydrogen capability vs. current GE DLN systems. Hydrogen may be from any source be it electrolysis, reforming, ammonia, or other. Further attributes of the DLN Evo that may benefit operators include reduced min turndown load, reduced NOX emissions, reduced startup emissions, and increased fuel flexibility compared to today’s DLN 2.6+ based systems. This program builds on previous internal technology development programs, previous collaborations with the US Department of Energy, and the successfully fielded DLN 2.6e combustion system currently in operation on the latest GE HA class gas turbines. The DLN Evo combustor is the application of GE Gas Power’s latest experience and most advanced technology to F-Class as turbines.

This paper describes innovative approaches and technologies applied to create the new system. The Three Key Technology elements are the Compact Flames Micro Mixer (CFMM), Next Gen Axial Fuel Staging (NG-AFS) injectors, and unibody cooling that integrates with the Axial Fuel Stage (AFS). These technologies are, for the first time, applied to a can-annular system, and further advanced through extensive analytical simulation and screened via subscale testing at GE Research (GER). The full-scale system was manufactured and has completed a series of shakedown tests at GE Gas Power’s Gas Turbine Technology Lab (GTTL) in Greenville SC. Initial test results are summarized within.
Aerodynamic Design and Performance Optimization of a Centrifugal Fan Impeller
Authors: Avşar Gökhan , Ezertaş Ahmet Alper , Başkan Perçin Özge
Published: September 2023

This study aims to optimize the impeller geometry of a backward-curved blade centrifugal fan in a helicopter oil cooler to improve the aerodynamic performance of the impeller. The study consists of three main parts: aerodynamic design and parametrization of the impeller, numerical analysis of impeller performance, and multi-objective design optimization to maximize fan static pressure and total to static isentropic efficiency. In the first part, a one-dimensional impeller design was performed using quasi-experimental methods and a direct optimization method with the multi-objective genetic algorithm, and the impeller geometry was parameterized through a commercial tool. The three-dimensional flow through the impeller was solved in the second part with a commercial computational fluid dynamics tool. The Reynolds-averaged Navier-Stokes equations were solved on a multi-block grid, and a second-order accurate finite volume method is employed. The most appropriate turbulence model and grid size were selected considering time, cost, and fidelity. In the third part, a sensitivity analysis was performed with the design-of-experiment method, and the parameters that affect the objective function most significantly were determined. A multi-objective design optimization based on a non-dominated sorting genetic algorithm was performed with the Kriging response surface method, and Pareto-optimal solutions were obtained. Results show that, at the design point, there is a 9.6% and 0.96% increase in the fan static pressure and total to static isentropic efficiency, respectively. Trained kriging response surface model predicted the fan static pressure and total to static isentropic efficiency with an error of 0.12% and 0.33%, respectively.
Prediction of Flow Separations in Turbine Center Frames Using Support Vector Machines and Random Forests
Authors: Staggl Marian , Sanz Wolfgang , Leitl Peter , Kurzthaler Maximilian , Pieringer Paul
Published: September 2023

The reduction of overall fuel consumption has become one of the main targets in developing new aero engines, where an increasing bypass ratio has proven to be an efficient way to achieve this goal. For widely spread two-spool configurations, this directly impacts the turbine center frame (TCF), located between the high- and low-pressure turbine. For higher bypass ratios, the component has to deal with bigger radial offsets and stronger curvatures, making the channel prone to flow separation and all the undesirable side effects. A large number of parameters and nonlinear interactions between them make it very challenging to define limits within the parametric space beyond which flow separations may occur. In order to investigate this behavior, a large data set of CFD calculations is created, comprising multiple geometrical and boundary-related parameters. The data set is used to train random forest classifiers (RFC) and support vector classifiers (SVC) to predict the appearance of flow separations for given parametric combinations. Both algorithms are tested on a separate data set and are compared in terms of accuracy. Further investigations are dedicated to the identification of the most crucial parameters. Finally, a linear SVC is used to derive an easily interpretable limit in parameter space, in order to avoid flow separations.
Research on Automatic Aerodynamic Performance Optimization for Flue Gas Turbine Blades Under Multiple Conditions
Authors: Yao He , Liu-Xi Cai , Jia-Wei Yao , Yan-Fang Hou , Yun Li
Published: September 2023

Flue gas turbine is one of the important equipment to realize waste heat reduction and energy recovery, which is widely used in electric power, petroleum, chemical industry and other fields. To address the problem of serious local flow loss in the rotor cascade and low aerodynamic performance due to unreasonable aerodynamic design of single-stage flue gas turbine, an automated multi-objective optimization platform is built for turbine blades redesign. The platform is based on the Metamodel of Optimal Prognosis (MOP) which combined with RANS solution techniques and parameter sensitivity analysis. Under the mass flow constraint, thickness distribution of rotor and angles of inlet on camber line are design variables, the optimization objective is maximum isentropic efficiency under multi-conditions. The study results show that the optimized blade efficiency is increased by about 0.38%, 0.35% and 0.45% under different conditions. Severe flow separation on suction side of rotor is alleviated. The aerodynamic performance of the turbine blade is better than that of the reference design. This parameter automatic optimization platform can realize the dimension reduction of design space, and provides a fast and accurate solution to the typical 3D multi-objectives and multi-conditions design problem of turbomachinery in engineering practice.
Techno-Economic Assessment of CO2 Based Power to Heat to Power Systems for Industrial Applications
Authors: Trevisan Silvia , Mehdi Shamsi Syed Safeer , Maccarini Simone , Barberis Stefano , Guedez Rafael
Published: September 2023

The industrial sector is a major source of wealth, producing about one-quarter of the global gross product. However, industry is also a major emitter of CO2 and it represents a key challenge towards achieving the worldwide CO2 emission reduction targets. Nowadays, about 22% of the overall energy demand is heating for the industrial sector, generating about 40% of the global CO2 emissions. Additionally, 30% of the final energy demand of the industrial sector is electricity. Solutions to decarbonize the industrial sector are needed. This work presents the techno-economic assessment of four different molten salts based power-to-heat-to-heat and power solutions aiming at decarbonizing the industrial sector, requiring medium temperature heat. The systems are studied under different electric markets. Dispatch strategies and system sizing are identified to ensure optimal techno-economic performance. The main performance indicators investigated are the levelized cost of heat and electricity (LCoH and LCoE), the operational expenditure, and the attainable savings with respect to alternative business as usual solutions. The results highlight that the proposed system can be cost-competitive, particularly in markets characterized by low electricity prices and high daily price fluctuations, such as Finland. In these locations LCoE as low as 100 €/MWh and LCoH lower than 55 €/MWh can be attained by the base system configuration. The introduction of high temperature heat pumps can provide further LCoH reduction of about 50%. This study sets the ground for further power-to-heat-to-heat and power techno-economic investigations addressing different industrial sectors and identifies main system design strategies.
Thermodynamic Assessment of a Combined Cycle Gas Turbine With Exhaust Gas Recirculation Under Part-Load Operation Toward Carbon Capture Penalty Reduction
Authors: Verhaeghe Antoine , Mendoza Morales Maria Jose , Blondeau Julien , Demeyer Frederiek , Bricteux Laurent , De Paepe Ward
Published: September 2023

Performing Exhaust Gas Recirculation (EGR) is a solution to reduce the negative impact of applying Post-Combustion Carbon Capture (PCCC) on Combined Cycle Gas Turbine (CCGT) power plant. However, knowing that CCGTs will operate most of the time under part-load conditions, to back-up renewable production, the impact of using EGR during part-load CCGT operation is still unclear. Therefore, the objective of this work is to investigate different operating strategies for the application of EGR under full and part-load CCGT operations. To this end, an Aspen Plus model of the CCGT has been built and validated using data from Thermoflow, to which an EGR loop has been added. The PCCC unit is not modelled in this work and will be addressed in future work. The results show that keeping the turbine inlet temperature constant is the operating strategy that maximizes CCGT performance when EGR is applied. In contrast, working with a constant turbine exhaust temperature degrades performance. Therefore, the load control strategy currently used for heavy-duty gas turbines, which follows a fixed turbine exhaust temperature profile, will induce a performance reduction when EGR is applied. A strategy based on a turbine inlet temperature, rather than an exhaust temperature setpoint, would improve performance. Finally, EGR provides flue gas characteristics more favourable to carbon capture in terms of size, performance, and flexibility.
Experimental 3D Analysis of Flame Propagation in a Lean-Premixed Swirl Burner Operated With Hydrogen
Authors: Flebbe Saskia Martina , Tartsch Simon , Sattelmayer Thomas
Published: September 2023

Highly reactive fuels increase the flashback propensity in stationary gas turbines when operated under lean premixed conditions. This leads to undesired events such as flame propagation and flame anchoring. In this study, the flame propagation as well as the propagation to anchoring transition is experimentally investigated for a premixed burner with centerbody and fuel injection in the swirler. The burner is operated with 100% hydrogen as fuel under atmospheric pressure. The preheat temperature corresponds to engine conditions. The flame behavior at different bulk flow velocities is analyzed by means of OH*-chemiluminescence high speed imaging. A 360° optical measurement technique was established to identify the flame propagation to anchoring transition characteristics. The experimental results reveal that the flame propagation through the burners mixing tube exhibits three phases; (1) transition from unconfined to confined state, (2) upstream propagation of the flame up to an intermediate stabilization within the mixing tube and, (3) flame propagation to anchoring transition. Depending on the bulk flow velocity, the three phases take place independently of each other at/along the centerbody or at/along the mixing tube boundary layer. Two modes of flame structure are observed during the flame propagation to anchoring transition for the propagation along the centerbody. Furthermore, the 360° view imaging data reveals that the flame shows a symmetrical propagation behavior for the propagation along the centerbody and an asymmetry of the flame when propagating along the mixing tube wall.
LES Modeling of High-Lift High-Work LPT Blades: Part II—Validation and Application
Authors: Kerestes Jared , Marks Christopher , Clark John , Wolff Mitch , Ni Ron-Ho , Fletcher Nathan
Published: September 2023

Over the years, computational fluids dynamics (CFD) has matured to such a state so as to be indispensable in turbine design. In the past two decades, significant advances in turbine design have been made through the use of CFD — in particular, through the use of Reynolds-Averaged Navier-Stokes (RANS) simulations. Currently, turbine design is RANS-driven; however, significant gains in performance and efficiency are becoming more difficult to achieve using RANS. For this reason, the turbomachinery CFD community is moving toward Large-Eddy Simulations (LES). In the design of low-pressure turbine (LPT) blades, LES is particularly beneficial owing to its ability to capture accurately both transition and separation. In this paper, LES is used to characterize a new family of high-lift high-work LPT blades — designated the LXFHW-LS family — designed at the U.S. Air Force Research Laboratory (AFRL). LES simulations are conducted in accordance with the methodology outlined in Part I of this paper. The purpose of this paper is twofold: 1) to use LES to predict the performance of the LXFHW-LS family and compare to measurements in a low-speed linear cascade and, in doing so, 2) to illustrate how LES may be used in LPT design as it evolves from RANS-driven to LES-driven. For each blade in the family, the loading distribution and loss coefficient are computed for sixteen separate Reynolds numbers. Computational results are validated using detailed experimental measurements from a low-speed linear cascade wind tunnel. Experimental measurements include blade loading distributions, total pressure loss, and particle image velocimetry (PIV) measurements. In comparing computational and experimental results, particular emphasis is placed on the evolution of the suction surface (SS) separation bubble (if one exists). A variety of techniques are used to visualize the extent of the separation bubble, including machine learning.
Increasing the Scattering Coefficient by Modifying the Microstructure of the Thermal Barrier Coatings
Authors: Wang Yao , Hsu Pei-feng
Published: September 2023

Thermal barrier coatings (TBCs) are widely used to protect hot components in gas turbine engines. With higher turbine inlet temperature, the thermal radiative transfer becomes important in the overall heat transfer in the coating layer. The microstructure, including pore size distribution and pore morphology, has the primary impact to radiative properties in TBCs. Modifying the microstructure is an effective method to change the radiative heat transfer and the overall heat transfer in the TBC. In this study, a new approach is developed to modify the microstructure of air plasma spayed topcoat by mixing the fugitive polyester powder into the base 8 wt.% yttria-stabilized zirconia (8YSZ) powder. By changing the polyester powder size and the volumetric mixing ratio of polyester vs. 8YSZ, 8YSZ coating films of various microstructure are fabricated. The directional-hemispherical transmittance and reflectance are measured at the wavelength range of 1.4 to 2.6 μm under the room temperature. The scattering coefficient and the absorption coefficient are reduced by using a hybrid method combing the Kubelka-Munk four-flux method and the discrete ordinates method over the wavelength range. Using image processing tools on the SEM images of the 8YSZ films’ cross sections, the morphology-based pore size distribution (MPSD) is obtained. The results show that mixing polyester into 8YSZ powder is a very effective way to modify the microstructure of the topcoat, which, in turn, can increase the scattering coefficient.
Performance, Emissions, and Decarbonization of an Industrial Gas Turbine Operated With Hydrotreated Vegetable Oil
Authors: Runyon Jon , James Stuart , Kadam Tanmay , Ofir Barak , Graham David
Published: September 2023

As part of Uniper’s plans for its 22.5 GW of installed power-generating capacity in Europe to be carbon neutral by 2035, a Kraftwerk Union/Siemens V93.0 gas turbine (GT) in Malmö, Sweden was operated with hydrotreated vegetable oil (HVO) as a low-carbon replacement for gas oil in July 2021. An extensive feasibility study was first undertaken to understand the potential impacts of replacing gas oil with HVO in this GT. This included a fuel analysis, flame temperature modelling to predict the impact on NOx emissions, and a detailed hazard identification study for the short-duration trial. During the two-day demonstration, baseline GT performance and accredited emissions were first measured using the existing gas oil. HVO was subsequently used in all operating conditions including start-up, full load, part load, and shut-down. Accredited emissions of NOx, CO, SO2, and dust were measured to allow direct comparison between fuels. When operating with HVO, all required performance targets were achieved, including an onload fuel switch from HVO to gas oil. Direct flame imaging through a silo combustor sight-glass was used to observe the HVO start-up ignition process and to allow for a flame intensity comparison between fuels. NOx emissions were measured for each fuel, and no significant difference was identified across all operating conditions. As a result, no changes to the water injection rate for NOx control were required when switching fuels, which confirmed the predictions of the preliminary flame temperature modelling. Measurable reductions in dust, CO, and SO2 emissions were observed during HVO operation. These emissions reductions are respectively attributed to the low ash and aromatic contents of HVO, the increased hydrogen content of HVO relative to gas oil, and that HVO is essentially sulfur-free. HVO also enables significant lifecycle CO2 emissions reductions of over 90% compared with fossil diesel. In this trial, ∼163 tCO2 emissions were avoided by using HVO. The success of this demonstration provides evidence for future site conversion and has led to successful HVO demonstrations on other liquid fuel and dual-fuel GTs in the Uniper fleet. Long-duration testing and monitoring is required to build the evidence base regarding the impact of HVO operation on fuel storage, fuel delivery, and hot gas path components. To the authors’ knowledge, this field trial is the first successful demonstration of HVO use in an industrial gas turbine in the world.
Multi-Disciplinary Optimization of Thermodynamic Cycles for Large-Scale Heat Pumps With Simultaneous Component Design
Authors: Gollasch Jens , Lockan Michael , Stathopoulos Panagiotis , Nicke Eberhard
Published: September 2023

The performance of high temperature heat pumps is highly dependent on the efficiency of its main components, which need to be optimally matched especially in closed cycles. The design process is therefore a challenging task as many disciplines and varying modeling depths need to be considered. Consequently, this is usually a sequential procedure beginning with cycle definition and raising the fidelity for component design. Fundamental design decisions are made based on assumptions for component performance. Mistakes in the phase of cycle definition are hard to be reversed in later design stages. Therefore, the present work introduces holistic approaches for multi-disciplinary design of closed Brayton cycles. Aerodynamic compressor design with 2D throughflow analysis and geometry based heat exchanger sizing are simultaneously optimized with thermodynamic cycle parameters. The presented methodologies are making use of highly sophisticated design tools drawing on many years of experience in gas turbine design. The results demonstrate that holistic heat pump optimization can be successfully performed with reasonable computational effort. The advantages compared to conventional sequential design are elaborated. Comparison of two optimization concepts indicates that splitting up the design vectors of cycle and components shows the tendency to improve robustness. Finally, the trade-off between system compactness and performance is demonstrated with a multi-objective optimization study.
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

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

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

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

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

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

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

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

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

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.