Investigation of Similarity Criteria for an Internally-Cooled Turbine Vane Based on Artificial Neural Networks: Part I — Comparison of Similarity Criteria

2020 ◽  
Author(s):  
Weicheng Zhao ◽  
Zhongran Chi ◽  
Shusheng Zang
Keyword(s):  
Biot Number ◽  
Similarity Criteria ◽  
Coolant Temperature ◽  
Ann Model ◽  
Modeling Error ◽  
Temperature Ratio ◽  
Cooling Effectiveness ◽  
Internally Cooled ◽  
Temperature And Pressure ◽  
Artificial Neural

Abstract This study explores the suitable criterion for the temperature and pressure modeling to measure the overall cooling effectiveness and the reason leading to modeling deviation at relatively low temperature and pressure test condition (Part II), taking the internally-cooled Mark II vane as an example. The method used in this study includes Artificial Neural Network (ANN), Conjugate Heat Transfer (CHT) Computational Fluid Dynamics (CFD) and experiments (Part II). The average overall cooling effectiveness of the vane was selected as the target modeling parameter. After comparing the modeling error, the results show that matched-Biot number criterion has the best performance at both constant coolant temperature and temperature ratio conditions. The maximum modeling error is 6.4% and 1.2% for those two conditions, respectively. Furthermore, the performance of these similarity criteria based on empirical correlations without the ANN model were also investigated, which is more feasible in engineering use. The accuracy of matched-Biot number criterion has an obvious decrease in this condition, but it is still the best selection at constant coolant temperature conditions. While the mass flow ratio criterion becomes the most accurate one at constant temperature ratio conditions.

Numerical Study on Analogy Principle of Overall Cooling Effectiveness in Engine and Laboratory Condition

2018 ◽  
Author(s):  
Gang Xie ◽  
Cun-liang Liu ◽  
Lin Ye ◽  
Rui Wang
Keyword(s):  
Heat Transfer ◽  
Laboratory Condition ◽  
Biot Number ◽  
Gas Turbines ◽  
Momentum Flux ◽  
Flux Ratio ◽  
Temperature Ratio ◽  
Cooling Effectiveness ◽  
Momentum Flux Ratio ◽  
Engine Condition

The overall cooling effectiveness, which represents the distribution of dimensionless temperature on gas turbines surface, is an important parameter for conjugate heat transfer analysis of gas turbines. Generally, it is difficult to measure the overall cooling effectiveness in engine condition. However, the overall cooling effectiveness can be measured in the laboratory by matching the appropriate parameters to those of the actual turbine blade. Thus, it is important to evaluate the key parameters of matching methods. In this paper, the effects of adiabatic film effectiveness and Biot number on the overall cooling effectiveness were investigated with an impingement/effusion model by numerical simulation, in which 3-D steady RANS approach with the k–ω SST turbulence model were used. The tested plate had 8 cylinder hole rows of 30 degree inclined angle, and the internal cooling employed staggered array jet impingements. The matching performance was evaluated by comparing the results in both typical engine condition and laboratory condition. The analogy principles were discussed in detail, the results showed that the overall cooling effectiveness can be matched by using different matching principles in different lab condition. The theoretical analysis was verified by numerical results. The distribution and values of overall cooling effectiveness can be matched well between engine condition and lab condition by matching both temperature ratio, mainstream side Biot number and blowing ratio. If the temperature ratio is mismatched, the momentum flux ratio will be an important parameter for overall cooling effectiveness. Matching momentum flux ratio will reduce the difference of the adiabatic cooling effectiveness and heat transfer ratio between engine condition and laboratory condition.

scholarly journals Artificial Neural Network Model Prediction of Bitumen/Light Oil Mixture Viscosity under Reservoir Temperature and Pressure Conditions as a Superior Alternative to Empirical Models

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8520
Author(s):  
Ronald Ssebadduka ◽  
Nam Nguyen Hai Le ◽  
Ronald Nguele ◽  
Olalekan Alade ◽  
Yuichi Sugai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Empirical Models ◽  
Feedforward Neural Network ◽  
Ann Model ◽  
Reservoir Temperature ◽  
Light Oil ◽  
Temperature And Pressure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Herein, we show the prediction of the viscosity of a binary mixture of bitumen and light oil using a feedforward neural network with backpropagation model, as compared to empirical models such as the reworked van der Wijk model (RVDM), modified van der Wijk model (MVDM), and Al-Besharah. The accuracy of the ANN was based on all of the samples, while that of the empirical models was analyzed based on experimental results obtained from rheological studies of three binary mixtures of light oil (API 32°) and bitumen (API 7.39°). The classical Mehrotra–Svrcek model to predict the viscosity of bitumen under temperature and pressure, which estimated bitumen results with an %AAD of 3.86, was used along with either the RVDM or the MVDM to estimate the viscosity of the bitumen and light oil under reservoir temperature and pressure conditions. When both the experimental and literature data were used for comparison to an artificial neural network (ANN) model, the MVDM, RVDM and Al-Besharah had higher R2 values.

Investigation of Similarity Criteria for an Internally-Cooled Turbine Vane Based on Artificial Neural Networks: Part II — Experimental Validation

2020 ◽  
Author(s):  
Weicheng Zhao ◽  
Jingying Wei ◽  
Zhongran Chi ◽  
Shusheng Zang
Keyword(s):  
Suction Side ◽  
Similarity Criteria ◽  
Inlet Temperature ◽  
Temperature Deviation ◽  
Temperature Boundary ◽  
Internally Cooled ◽  
Aerodynamic Parameters ◽  
Temperature And Pressure ◽  
Cfd Method ◽  
Dynamics Simulations

Abstract In the second part of this two-part paper, several CHT (Conjugate Heat Transfer) CFD (Computational Fluid Dynamics) simulations were conducted at low temperature and pressure conditions which a 100KW or a 400KW blower can provided, in order to investigate the reason caused the modeling deviation when the matched-Biot number criterion is used for modeling. The CFD method used in this paper is the same as the Part I. Afterwards, several experiments were carried out at a mainstream condition which the 100 KW blower can provide to validate whether the deviations predicted by CFD would appear at that target modeling conditions. The mainstream inlet temperature and pressure are 307 K and 113 kPa, respectively. The model used in this paper is the Mark II turbine as well. The CFD and experiment results show that the main source of the modeling deviation comes from the suction side, the minimum deviation for the four modeling tests has over 0.05 (minimum wall temperature deviation has over 0.75 K). Apart from that, the reason leading to the suction side deviation is the variation of temperature boundary layer, which is caused by the unmatched aerodynamic parameters (such as mainstream Mach number and Reynolds number).

Full-Coverage Discrete Hole Film Cooling: Investigation of the Effect of Variable Density Ratio

1994 ◽  
Vol 116 (3) ◽  
pp. 587-596 ◽  
Author(s):  
F. Bazdidi-Tehrani ◽  
G. E. Andrews
Keyword(s):  
Surface Area ◽  
Film Cooling ◽  
Density Ratio ◽  
Variable Density ◽  
Coolant Temperature ◽  
Wall Surface ◽  
Temperature Ratio ◽  
Cooling Effectiveness ◽  
Cooling Performance ◽  
Full Coverage

Experimental results of the overall and adiabatic cooling effectiveness for full-coverage discrete hole film cooling are presented for a range of practical geometries. The results are reported for various hot gas mainstream-to-coolant temperature (density) ratios, in the realistic range of 1.0–3.2. The variation of this ratio was achieved by increasing the crossflow mainstream temperature, over the range 300–930 K. For combustor wall film cooling applications, the overall cooling effectiveness increased significantly with the number of holes per unit wall surface area, over the range of 4306–26910 m−2 and with the hole size, in the range of 1.0–2.2 mm, due to the improvement in film cooling. The effect of varying the mainstream-to-coolant temperature ratio, in the present range of 1.0–3.2, on the film cooling performance was shown to be small and no consistent trends were established for various configurations, for the coolant mass flow rates per unit wall surface area, less than 0.4 kg/sm2. At a higher value of 0.89 kg/sm2, an increase in the temperature ratio improved the film cooling performance slightly.

Effects of Coolant Density, Specific Heat Capacity, and Biot Number on Turbine Vane Cooling Effectiveness

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
S. Luque ◽  
T. V. Jones ◽  
T. Povey
Keyword(s):  
Heat Capacity ◽  
Specific Heat ◽  
Specific Heat Capacity ◽  
Biot Number ◽  
Density Ratio ◽  
Flux Ratio ◽  
Coolant Temperature ◽  
Gas Mixture ◽  
Principle Of Superposition ◽  
Cooling Effectiveness

This paper describes the effects of coolant-to-mainstream density ratio and specific heat capacity flux ratio (the product of blowing ratio and specific heat capacity ratio) on the overall cooling effectiveness of high pressure (HP) turbine vanes. Experimental measurements have been conducted at correct engine-matched conditions of Mach number, Reynolds number, turbulence intensity, and coolant-to-mainstream momentum flux ratio. Vanes tested were fully cooled production parts from an engine currently in service. A foreign gas mixture of SF6 and Ar was selected for injection as coolant in the facility so that density and blowing ratios were also matched to the engine situation. The isentropic exponent of the foreign gas mixture coincides with that of air. Full-coverage surface maps of overall cooling effectiveness were acquired by an infrared (IR) thermography technique at a range of mainstream-to-coolant temperature ratios. Measurements were subsequently scaled to engine conditions by employing a new theory based on the principle of superposition and a recovery and redistribution temperature demonstrated in previous papers. It is shown that the two aerodynamically matched situations of air- and foreign-gas-cooled experiments give virtually the same effectiveness trends and patterns. Actual levels differ, however, on account of specific heat capacity flux ratio differences. The effect is described and quantified by a one-dimensional analytical model of the vane wall. Differences in Biot number with respect to engine conditions are discussed as they also influence the scaling of turbine metal temperatures.

Cooling Effectiveness Measurements With Thermal Radiometry in a Turbine Cascade

2000 ◽  
Author(s):  
H. K. Moon ◽  
R. Jaiswal
Keyword(s):  
Heat Transfer ◽  
Temperature Distribution ◽  
Real Time ◽  
Background Radiation ◽  
Temperature Measurements ◽  
Coolant Temperature ◽  
Turbine Cascade ◽  
Temperature Ratio ◽  
Cooling Effectiveness ◽  
Engine Condition

Airfoil temperature measurements in a hot cascade were traditionally conducted with thermocouples in spite of their limitations. In the present work, a real-time full imaging of the airfoil temperature distribution is demonstrated in a turbine cascade using a thermal radiometry system. Two synthetic sapphire windows provided infrared (IR)-viewing access from the outside. The apparent emissivity of the test airfoil was calibrated with thermocouples buried flush into the wall. The turbine cascade, fabricated with actual engine hardware, provided heat transfer similarity by matching Re, Ma, and Tu. The effect of gas to coolant temperature ratio (Tg/Tc) on the cooling effectiveness was investigated. Heating (“reverse” cooling) of the test airfoil in a relatively cold mainstream air resulted in a much more detailed temperature image than the normal (forward) cooling case, as it significantly reduced the background radiation. A methodology to correct the cooling effectiveness obtained at different gas to coolant temperature ratios than the engine condition was developed and has been experimentally validated.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

scholarly journals Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

2021 ◽  
Vol 18 (10) ◽  
pp. 5110
Author(s):  
Shu-Farn Tey ◽  
Chung-Feng Liu ◽  
Tsair-Wei Chien ◽  
Chin-Wei Hsu ◽  
Kun-Chen Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Characteristic Curve ◽  
Patient Discharge ◽  
Ann Model ◽  
Healthcare Settings ◽  
High Area ◽  
Icd 10 ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

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