International Journal of Turbomachinery, Propulsion and Power

The study of low-speed jets into crossflow is critical to the performance of gas turbines. Film cooling is a method to maintain manageable blade temperatures in turbine sections while increasing turbine inlet temperatures and turbine efficiencies. Initially, cooling holes were cylindrical. Film cooling jets from these discrete round holes were found to be very susceptible to jet liftoff, which reduces surface effectiveness. Shaped holes have become prominent for improved coolant coverage. Fan-shaped holes are the most common design and have shown good improvement over round holes. However, fan-shaped holes introduce additional parameters to the already complex task of modeling cooling effectiveness. Studies of these flows range in hole lengths from those found in actual turbine blades to very long holes with fully developed flow. The flow within the holes themselves is difficult to study as there is limited optical access. However, the flow within the holes has a strong effect on the resulting properties of the jet. This study presents velocity and vorticity fields measured using high-resolution magnetic resonance velocimetry (MRV) to study three different fan-shaped hole geometries at two blowing ratios. Because MRV does not require line of sight, it provides otherwise hard-to-obtain experimental data of the flow within the film cooling hole in addition to the mainflow measurements. By allowing measurement within the cooling hole, MRV shows how a poor choice of diffuser start point and angle can be detrimental to film cooling if overall hole length and cooling flow velocity are not properly accounted for in the design. The downstream effect of these choices on the jet height and counter-rotating vortex pair is also observed.

Emin Issakhanian

International Journal of Turbomachinery Propulsion and Power

In-Hole Measurements of Flow Inside Fan-Shaped Film Cooling Holes and Downstream Effects

扇形薄膜冷却孔内流动及下游效应的孔内测量

In this study, a thorough experimental investigation of the synchronous blade vibrations of a radial turbine is performed for different IGV configurations. First, the blade modes are measured experimentally and calculated numerically. Subsequently, the vibrations are recorded with two redundant measurement systems during real operation. Strain gauges were applied on certain blades, while a commercial blade-tip-timing system is used for the measurement of blade deflections. The experimentally determined vibration properties are compared with numerical estimations. Initially, the vibrations recorded with the “nominal” IGV were presented. This IGV primarily generates nodal diameter (ND) 0 vibrations. Subsequently, the impact of two different IGV configurations is examined. First, a mistuned IGV, which has the same number of vanes as the “nominal” IGV is examined. By intentionally varying the distance between the vanes, additional low engine order excitations are generated. Moreover, an IGV with a higher number of vanes is employed to induce excitations at higher frequency modes and ND6 vibrations. Certain vibrations are consistently measured across all IGV configurations, which cannot be attributed to the spiral turbine casing. In addition, a turbine–compressor interaction has been observed.

Marios Sasakaros

Markus Schafferus

Manfred Wirsum

Arthur Zobel

Damian Vogt

Alex Nakos

Bernd Beirow

Experimental Investigation of Synchronous-Flow-Induced Blade Vibrations on a Radial Turbine

径流式涡轮叶片同步流动诱发振动实验研究

Turbine blades for modern turbomachinery applications often exhibit complex twisted designs that aim to reduce aerodynamic losses, thereby improving the overall machine performance. This results in intricate internal cooling configurations that change their spanwise orientation with respect to the rotational axis. In the present study, the local heat transfer in a generic two-pass turbine cooling channel is investigated under engine-similar rotating conditions (Ro={0⋯0.50}) through the transient Thermochromic Liquid Crystal (TLC) measurement technique. Three different angles of attack (α={−18.5∘;+8∘;+46.5∘}) are investigated to emulate the heat transfer characteristics in an internal cooling channel of a real turbine blade application at different spanwise positions. A numerical approach based on steady-state Reynolds-averaged Navier–Stokes (RANS) simulations in ANSYS CFX is validated against the experimental method, showing generally good agreement and, thus, qualifying for future heat transfer predictions. Experimental and numerical data clearly demonstrate the substantial impact of the angle of attack on the local heat transfer structure, especially for the radially outward flow of the first passage, owing to the particular Coriolis force direction at each angle of attack. Furthermore, results underscore the strong influence of the rotational speed on the overall heat transfer level, with an enhancement effect for the radially outward flow (first passage) and a reduction effect for the radially inward flow (second passage).

David Gutiérrez de Arcos

Christian Waidmann

Rico Poser

Jens von Wolfersdorf

Michael Göhring

Rotationally Induced Local Heat Transfer Features in a Two-Pass Cooling Channel: Experimental–Numerical Investigation

两流程冷却通道中旋转诱导局部传热特性：实验-数值研究

Considerable progress has been achieved in recent decades in understanding the phenomena related to the onset of condensation in steam flows, both experimentally and especially numerically. Nevertheless, there is still a certain disagreement between the different numerical models used. Unfortunately, the available experimental validation data are not sufficiently detailed to allow for proper validation of computational fluid dynamics (CFD) simulations. Therefore, this paper presents new experimental data for condensing steam flows, acquired in a supersonic nozzle according to Barschdorff, at the Institute of Thermal Turbomachinery Laboratory (ITSM) at the University of Stuttgart. A steady inlet pressure of approximately 784 mbar was set for three inlet temperatures down to 100.2 ∘C. Condensation onset is accurately captured across the nozzle, using down to 1 mm spatial resolution for both pneumatic and light spectra measurements. CFD simulations were performed using the commercial solver ANSYS CFX. The droplet diameters are numerically overestimated by approximately a factor of 1.5. Disagreement has been found between original Barschdorff’s experiments and measurements at ITSM. However, there is a good agreement in terms of the pressure distribution along the nozzle axis between experimental and numerical results. The reproducibility of the results is excellent.

Manuel Ernesto Maqueo Martínez

Stefan Schippling

Markus Schatz

Damian M. Vogt

New Supersonic Nozzle Test Rig Used to Generate Condensing Flow Test Data According to Barschdorff

根据巴尔沙夫特，新超音速喷管试验台用于生成凝结流动试验数据

The head and flow rate of a pump characterize the pump performance, which help determine whether maintenance is needed. In the proposed method, instead of a traditional flowmeter and manometer, the operating points are identified using data collected from accelerometers and microphones. The dataset is created from a test rig consisting of a standard centrifugal water pump and measurement system. After implementing preprocessing techniques and Convolutional Neural Networks (CNNs), the trained models are obtained and evaluated. The influence of the sensor location and the performance of different signals or signal combinations are investigated. The proposed method achieves a mean relative error of 7.23% for flow rate and 2.37% for head with the best model. By employing two data augmentation techniques, performance is further improved, resulting in a mean relative error of 3.55% for flow rate and 1.35% for head with the sliding window technique.

Hanbing Ma

Oliver Kirschner

Stefan Riedelbauch

Systematic Comparison of Sensor Signals for Pump Operating Points Estimation Using Convolutional Neural Network

使用卷积神经网络对泵运行点进行传感器信号系统比较

The increasing utilisation of demand-controlled ventilation strategies leads to the frequent operation of fans under part-load conditions. To accurately predict the energy demand of a ventilation system with a fan array in the early design stages, models that calculate reliable results across the whole operating range are required. We present the comparison of a polynomial and a machine learning approach through support vector regression (SVR) to predict the fan performance over a wide range of typical operating points. For fitting and validation, we use experimental data. We investigate the extrapolation performance of both approaches. The SVR model achieves a slightly better representation of the experimental data with a lower error, especially when only sparse data are available. Both approaches yield similar results when the evaluation is conducted within the experimentally captured domain but deviates outside the domain. At operating points that are far from the experimentally captured domain, the polynomial models yield fan efficiencies that are physically plausible, while the SVR models drastically overpredict the fan efficiency. To rate the influence of such deviations towards modelling the actual energy demand, both approaches are applied to an operation simulation of a simplified office building. Both approaches yield similar results despite differing extrapolation capabilities.

Philipp Ostmann

Martin Rätz

Martin Kremer

Dirk Müller

Prediction of Fan Array Performance with Polynomial and Support Vector Regression Models

多项式和支持向量回归模型在风扇阵列性能预测中的应用

This research paper explores the use of machine learning to relate images of flame structure and luminosity to measured NOx emissions. Images of reactions produced by 16 aero-engine derived injectors for a ground-based turbine operated on a range of fuel compositions, air pressure drops, preheat temperatures and adiabatic flame temperatures were captured and postprocessed. The experimental investigations were conducted under atmospheric conditions, capturing CO, NO and NOx emissions data and OH* chemiluminescence images from 27 test conditions. The injector geometry and test conditions were based on a statistically designed test plan. These results were first analyzed using the traditional analysis approach of analysis of variance (ANOVA). The statistically based test plan yielded 432 data points, leading to a correlation for NOx emissions as a function of injector geometry, test conditions and imaging responses, with 70.2% accuracy. As an alternative approach to predicting emissions using imaging diagnostics as well as injector geometry and test conditions, a random forest machine learning algorithm was also applied to the data and was able to achieve an accuracy of 82.6%. This study offers insights into the factors influencing emissions in ground-based turbines while emphasizing the potential of machine learning algorithms in constructing predictive models for complex systems.

Iker Gomez Escudero

Vincent McDonell

Predictive Modeling of NOx Emissions from Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blends Using Flame Imaging and Machine Learning

使用火焰成像和机器学习预测氢气和氢气/天然气混合物贫燃直喷的NOx排放的建模

Abbreviation	IJTPP
Journal Impact	1.47
Quartiles(Global)	ENGINEERING, MECHANICAL(Q3)

ISSN	2504-186X
h-index	14

Publication Information	Publisher: Multidisciplinary Digital Publishing Institute (MDPI)，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2016，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate: 99.08
Journal Fees	$Details
Average review cycle	6 weeks

International Journal of Turbomachinery, Propulsion and Power

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In-Hole Measurements of Flow Inside Fan-Shaped Film Cooling Holes and Downstream Effects

Experimental Investigation of Synchronous-Flow-Induced Blade Vibrations on a Radial Turbine

Rotationally Induced Local Heat Transfer Features in a Two-Pass Cooling Channel: Experimental–Numerical Investigation

New Supersonic Nozzle Test Rig Used to Generate Condensing Flow Test Data According to Barschdorff

Systematic Comparison of Sensor Signals for Pump Operating Points Estimation Using Convolutional Neural Network

In-Hole Measurements of Flow Inside Fan-Shaped Film Cooling Holes and Downstream Effects

Experimental Investigation of Synchronous-Flow-Induced Blade Vibrations on a Radial Turbine

Rotationally Induced Local Heat Transfer Features in a Two-Pass Cooling Channel: Experimental–Numerical Investigation

Prediction of Fan Array Performance with Polynomial and Support Vector Regression Models

Predictive Modeling of NOx Emissions from Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blends Using Flame Imaging and Machine Learning