scholarly journals Measurement and Prediction Method of Compressibility Factor at High Temperature and High Pressure

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Xiaoxun Zhu ◽  
Bochao Xu ◽  
Zhonghe Han
Keyword(s):  
Experimental Data ◽  
High Pressure ◽  
High Temperature ◽  
Prediction Model ◽  
Experimental Method ◽  
Experimental Measurement ◽  
Measurement Method ◽  
Compressibility Factor ◽  
Prediction Method ◽  
Support Vector

In order to get the compressibility factorZof working fluid under different conditions, experimental measurement method ofZunder high pressure and high temperature and data mining method were studied in this paper. Experimental measurement method based on real gas state equation and prediction method based on Least Squares Support Vector Machine were proposed. First, an experimental method for measuringZat high temperature and high pressure was designed; in this method the temperature, pressure, and density (mass and volume) of corresponding state were measured and substituted into the actual gas equation of state, and thenZcan be calculated. Meanwhile, in order to obtain continuous value inT-pplane, Squares Support Vector Machines are introduced to establish the prediction model ofZ. Take Hexamethyldisiloxane, for example; the experimental data ofZwas obtained using the experimental method. Meanwhile the prediction model ofZ, which can be used as calculation function ofZ, was established based on those experimental data, and theZ(T: 500 K~800 K,p: 1.3 MPa~2.25 MPa) was calculated by using this calculation function. By comparison with this published data, it was found that the average relative error was 2.14%.

HPHT synthesis and enhanced TE performance of Te and Sn/Se elements binary-doped CoSb3

2019 ◽  
Vol 12 (01) ◽  
pp. 1850105 ◽  
Author(s):  
Hairui Sun ◽  
Pin Lv ◽  
Chao Wang ◽  
Yunxian Liu ◽  
Xiaopeng Jia ◽  
...  
Keyword(s):  
Experimental Data ◽  
Thermal Conductivity ◽  
High Pressure ◽  
High Temperature ◽  
Seebeck Coefficient ◽  
Grain Boundaries ◽  
Preparation Methods ◽  
Synthesis Time ◽  
Lattice Disorder ◽  
Positive Effect

A series of binary-doped CoSb3 with Te and Se/Sn bulk compounds Co4Sb[Formula: see text]TexSny/Sey ([Formula: see text] and 0.6, [Formula: see text] and 0.3), have been successfully prepared via a simple high pressure and high-temperature (HPHT) method. And, the influence of the doping elements on the microstructure of the samples synthesized under diverse pressures and the corresponding TE performance were studied in detail. Comparing with other preparation methods, the synthesis time of HPHT was acutely shortened. The obtained samples contain more grain boundaries, lattice disorder, dislocations and the possible “nanodot”, which have positive effect on reducing thermal conductivity. The experimental data indicate that the absolute values of Seebeck coefficient increases with pressure. What’s more, the thermal conductivities show a monotone decreasing trend as the synthesis pressure rises. The minimum value obtained is 1.93[Formula: see text]Wm[Formula: see text]K[Formula: see text] at normal temperature for Co4Sb[Formula: see text]Te[Formula: see text]Se[Formula: see text] prepared under 3[Formula: see text]GPa.

Comparison of 3D Unsteady Transient Conjugate Heat Transfer Analysis on a High Pressure Cooled Turbine Stage With Experimental Data

2017 ◽  
Author(s):  
Jong-Shang Liu ◽  
Mark C. Morris ◽  
Malak F. Malak ◽  
Randall M. Mathison ◽  
Michael G. Dunn
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Heat Flux ◽  
High Pressure ◽  
High Temperature ◽  
Gas Turbine ◽  
Conjugate Heat Transfer ◽  
Turbine Rotor ◽  
Turbine Stage ◽  
Cooling Flow

In order to have higher power to weight ratio and higher efficiency gas turbine engines, turbine inlet temperatures continue to rise. State-of-the-art turbine inlet temperatures now exceed the turbine rotor material capability. Accordingly, one of the best methods to protect turbine airfoil surfaces is to use film cooling on the airfoil external surfaces. In general, sizable amounts of expensive cooling flow delivered from the core compressor are used to cool the high temperature surfaces. That sizable cooling flow, on the order of 20% of the compressor core flow, adversely impacts the overall engine performance and hence the engine power density. With better understanding of the cooling flow and accurate prediction of the heat transfer distribution on airfoil surfaces, heat transfer designers can have a more efficient design to reduce the cooling flow needed for high temperature components and improve turbine efficiency. This in turn lowers the overall specific fuel consumption (SFC) for the engine. Accurate prediction of rotor metal temperature is also critical for calculations of cyclic thermal stress, oxidation, and component life. The utilization of three-dimensional computational fluid dynamics (3D CFD) codes for turbomachinery aerodynamic design and analysis is now a routine practice in the gas turbine industry. The accurate heat-transfer and metal-temperature prediction capability of any CFD code, however, remains challenging. This difficulty is primarily due to the complex flow environment of the high-pressure turbine, which features high speed rotating flow, coupling of internal and external unsteady flows, and film-cooled, heat transfer enhancement schemes. In this study, conjugate heat transfer (CHT) simulations are performed on a high-pressure cooled turbine stage, and the heat flux results at mid span are compared to experimental data obtained at The Ohio State University Gas Turbine Laboratory (OSUGTL). Due to the large difference in time scales between fluid and solid, the fluid domain is simulated as steady state while the solid domain is simulated as transient in CHT simulation. This paper compares the unsteady and transient results of the heat flux on a high-pressure cooled turbine rotor with measurements obtained at OSUGTL.

Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

scholarly journals Experimental method for in situ determination of material textures at simultaneous high pressure and high temperature by means of radial diffraction in the diamond anvil cell

2009 ◽  
Vol 80 (10) ◽  
pp. 104501 ◽  
Author(s):  
Hanns-Peter Liermann ◽  
Sébastien Merkel ◽  
Lowell Miyagi ◽  
Hans-Rudolf Wenk ◽  
Guoyin Shen ◽  
...  
Keyword(s):  
High Pressure ◽  
High Temperature ◽  
Experimental Method ◽  
Diamond Anvil Cell ◽  
Diamond Anvil

scholarly journals Life prediction of slewing bearing based on isometric mapping and fuzzy support vector regression

2019 ◽  
Vol 42 (1) ◽  
pp. 94-103 ◽  
Author(s):  
Weigang Bao ◽  
Hua Wang ◽  
Jie Chen ◽  
Bo Zhang ◽  
Peng Ding ◽  
...  
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Life Prediction ◽  
Feature Fusion ◽  
Prediction Method ◽  
Support Vector ◽  
Isometric Mapping ◽  
Slewing Bearing ◽  
Time Frequency ◽  
Life Prediction Model

The monitoring data of slewing bearing is massive. In order to establish accurate life prediction model from complex vibration signal of slewing bearing, a life prediction method based on manifold learning and fuzzy support vector regression (SVR) is proposed. Firstly, the multiple features are extracted from time domain and time-frequency domain. Then isometric mapping (ISOMAP) is used to reduce high-dimensional features to low-dimensional features that can reflect degeneration of slewing bearing well. Finally, the fuzzy SVR is used to predict the life degradation trend of slewing bearing. The results show that: (1) Multi-feature fusion after ISOMAP can obtain more comprehensive degradation indicator. (2) The complexity of the life prediction model is simplified and the real-time life degradation trend of slewing bearing can be well predicted by fuzzy SVR, so it is very suitable to predict life degradation trend of slewing bearing based on massive data well. The time of prediction on average is reduced by 72.7%. The mean absolute error (MAE) and root mean square error (RMSE) of prediction are reduced by 73% and 59% respectively compared with traditional methods. The accuracy of prediction is greatly improved.

scholarly journals Design Point Simulation of Multispool Industrial Compressor Trains

1994 ◽  
Author(s):  
Lars J. Blendulf ◽  
Graham D. Smith
Keyword(s):  
Experimental Data ◽  
High Pressure ◽  
Steam Turbine ◽  
Gas Turbine ◽  
Function Theory ◽  
Ammonia Synthesis ◽  
Compressibility Factor ◽  
Real Gas ◽  
Design Point ◽  
Endothermic Reactions

Software commonly used to predict Aeroengine performance has been adapted for the design point simulation of two large industrial compressor sets. A NOx gas compressor and an air compressor on a common shaft driven by a steam turbine and a tail gas turbine are modelled accurately after the inclusion of both exothermic and endothermic reactions in the NOx gas compressor. Experiment and theory agree to within 1.5%. Results for the simulation of a three spool Ammonia Synthesis compressor with a deliver pressure of 207 bar agree to within 1% of experimental data. Since complex gas mixtures were used, Mollier diagrams or Compressibility factor techniques were not used and, instead, Departure Function theory was adopted to accommodate the real gas behaviour found at the high pressure.

Sign in / Sign up

Export Citation Format

Share Document