scholarly journals Real-Time Prediction of Operating Parameter of TBM during Tunneling

2021 ◽  
Vol 11 (7) ◽  
pp. 2967
Author(s):  
Hang-Lo Lee ◽  
Ki-Il Song ◽  
Chongchong Qi ◽  
Jin-Seop Kim ◽  
Kyoung-Su Kim
Keyword(s):  
Real Time ◽  
Operating Parameter ◽  
Tunnel Boring Machine ◽  
Ordinary Least Squares ◽  
Operating Conditions ◽  
Operating Parameters ◽  
Time Varying ◽  
Practical Applications ◽  
Time Prediction ◽  
Geological Conditions

With the increasing use of the tunnel boring machine (TBM), attempts have been made to predict TBM operating parameters. Prediction of operating parameters is still an important step in the adaptability of the TBM for the future. In this study, we employ a walk forward (WF) prediction method based on ARIMAX, which can consider time-varying features and geological conditions. This method is applied to two different TBM projects to evaluate its performance, and is then compared with WF based on ordinary least squares (OLS). The simulation results show that the ARIMAX predictor outperforms the OLS predictor in both projects. For practical applications, an additional analysis is carried out according to the real-time prediction distance. The results show that time series-based ARIMAX provides meaningful results in 8 rings (11 m) or less of real-time prediction distance. The WF based on ARIMAX can provide reasonable TBM operating conditions with time-varying data and can be utilized in decision-making to improve excavation performance.

Real-Time Prediction of Incipient Lean Blowout in a Partially Premixed, Liquid-Fueled Gas Turbine Combustor

2007 ◽  
Author(s):  
Tongxun Yi ◽  
Ephraim J. Gutmark
Keyword(s):  
Real Time ◽  
Gas Turbine ◽  
Flame Structure ◽  
Rayleigh Distribution ◽  
Operating Conditions ◽  
Statistical Characteristics ◽  
Lean Blowout ◽  
Time Prediction ◽  
Two Indices ◽  
Partially Premixed

The present paper addresses real-time prediction of incipient lean blowout (LBO) in partially premixed, liquid-fueled gas turbine combustors. Near-LBO combustion is characterized by the “intensified” low-frequency combustion oscillations, typically below 30 Hz. Two indices, namely the normalized chemiluminescence RMS and the normalized cumulative duration of LBO precursor events, are recommended for LBO prediction. Both indices are associated with the statistical characteristics of the flame structure, which changes from the normal distribution to the Rayleigh distribution at the approach of LBO. Both indices change little within a large range of equivalence ratios and start to shoot up only when LBO is approached. To use the two indices for LBO prediction, one needs to perform a detailed a priori LBO mapping under simulated engine operating conditions. However, the mapping can be done without running the engines very close to LBO.

scholarly journals Investigation of Operating Parameters and Degradation of Photovoltaic Panels in a Photovoltaic Power Plant

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3631 ◽  
Author(s):  
Dušan Kudelas ◽  
Marcela Taušová ◽  
Peter Tauš ◽  
Ľubomíra Gabániová ◽  
Ján Koščo
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Operating Time ◽  
Operating Parameter ◽  
Detailed Examination ◽  
Operating Conditions ◽  
Operating Parameters ◽  
Photovoltaic Panels ◽  
Photovoltaic Power ◽  
Lower Temperature

Recently, the use of photovoltaic (PV) cells and the increase in the number of photovoltaic power plants has led to a detailed examination of their operating parameters. In this article, we discuss material and operating parameter influences on the performance and efficiency of photovoltaic panels in a photovoltaic power plant. The plant consisted of 3600 pieces of polycrystalline PV panels from Renewable Energy Corporation (REC) Solar (type REC 230AE) with a maximum power of 230 Wp. Parameter measurements were made three years after the power plant was started. The measured and computed data were statistically processed using multidimensional statistical methods where the relationships between input and output variables were examined, which was subsequently quantified by regression analysis. Using the ANOVA, the variability of the measured efficiency of the panels and the performance for individual years was examined. Efficiency has been found to increase significantly over the years. The reason for this is the statistically proven prevailing operating time of the PV power plant in conditions with lower temperature than standard operating conditions (25 °C). Ageing was not confirmed in optimal conditions and calculated efficiency was constant.

scholarly journals Short Data-Based Output-Only Identification for Time-Varying Systems with Fast Dynamic Evolution

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Zhi-Sai Ma ◽  
Qian Ding
Keyword(s):  
Ordinary Least Squares ◽  
Operating Conditions ◽  
Dynamic Evolution ◽  
Model Parameters ◽  
Time Interval ◽  
Short Time Interval ◽  
Time Varying ◽  
Tracking Accuracy ◽  
Output Only ◽  
Fast Dynamic

Many engineering systems change appreciably over a relatively short time interval due to their fast evolution in the dynamics. Time-varying (TV) system’s ambient excitation is usually difficult to measure under operating conditions, and its dynamics have to be determined without measuring the excitation. Therefore, short data-based output-only identification for TV systems with fast dynamic evolution is considered in this paper. Deterministic parameter evolution methods are known to track fast dynamic evolution by postulating TV model parameters as deterministic functions of time and selecting proper functional subspaces. However, these methods require a significant number of parameters to represent complicated time-dependencies and dynamics characterized by larger numbers of degrees-of-freedom. In such cases, the ordinary least squares estimation may lead to less accurate or even unreliable estimates. A ridge regression-based deterministic parameter evolution method is proposed to overcome ill-posed problems via regularization and subsequently assessed through numerical and experimental validation. Comparative results confirm the advantages of the proposed method in terms of achievable natural frequency and power spectral density tracking, accuracy, and resolution of TV systems with fast dynamic evolution, when the response data length is relatively short.

Recurrent neural networks for real-time prediction of TBM operating parameters

2019 ◽  
Vol 98 ◽  
pp. 225-235 ◽  
Author(s):  
Xianjie Gao ◽  
Maolin Shi ◽  
Xueguan Song ◽  
Chao Zhang ◽  
Hongwei Zhang
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Networks ◽  
Operating Parameters ◽  
Time Prediction

Real-Time Prediction of Tropical Cyclone Intensity Using COAMPS-TC

2012 ◽  
Author(s):  
J. D. Doyle ◽  
R. M. Hodur ◽  
S. Chen ◽  
H. Jin ◽  
Y. Jin ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Real Time ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
Time Prediction

Relationships between Population Dynamics and Operating Parameters in an Activated Sludge-Plant by Statistical Analysis

1992 ◽  
Vol 25 (4-5) ◽  
pp. 399-400 ◽  
Author(s):  
L. Cingolani ◽  
M. Cossignani ◽  
R. Miliani
Keyword(s):  
Population Dynamics ◽  
Statistical Analysis ◽  
Activated Sludge ◽  
Treatment Plant ◽  
Operating Conditions ◽  
Statistical Analyses ◽  
Operating Parameters ◽  
Aerobic Treatment

Statistical analyses were applied to data from a series of 38 samples collected in an aerobic treatment plant from November 1989 to December 1990. Relationships between microfauna structure and plant operating conditions were found. Amount and quality of microfauna groups and species found in activated sludge proved useful to suggest the possible causes of disfunctions.

Power Test System Development and Dynamic Performance State Estimation Based on Hub Motor Vehicle

2020 ◽  
Vol 13 (2) ◽  
pp. 126-140
Author(s):  
Jing Gan ◽  
Xiaobin Fan ◽  
Zeng Song ◽  
Mingyue Zhang ◽  
Bin Zhao
Keyword(s):  
Real Time ◽  
Dynamic Performance ◽  
Test System ◽  
Operating Conditions ◽  
Motor Vehicles ◽  
Maximum Speed ◽  
Performance Parameters ◽  
Power Performance ◽  
Power Test ◽  
The Real

Background: The power performance of an electric vehicle is the basic parameter. Traditional test equipment, such as the expensive chassis dynamometer, not only increases the cost of testing but also makes it impossible to measure all the performance parameters of an electric vehicle. Objective: A set of convenient, efficient and sensitive power measurement system for electric vehicles is developed to obtain the real-time power changes of hub-motor vehicles under various operating conditions, and the dynamic performance parameters of hub-motor vehicles are obtained through the system. Methods: Firstly, a set of on-board power test system is developed by using virtual instrument (Lab- VIEW). This test system can obtain the power changes of hub-motor vehicles under various operating conditions in real-time and save data in real-time. Then, the driving resistance of hub-motor vehicles is analyzed, and the power performance of hub-motor vehicles is studied in depth. The power testing system is proposed to test the input power of both ends of the driving motor, and the chassis dynamometer is combined to test so that the output efficiency of the driving motor can be easily obtained without disassembly. Finally, this method is used to carry out the road test and obtain the vehicle dynamic performance parameters. Results: The real-time current, voltage and power, maximum power, acceleration time and maximum speed of the vehicle can be obtained accurately by using the power test system in the real road experiment. Conclusion: The maximum power required by the two motors reaches about 9KW, and it takes about 20 seconds to reach the maximum speed. The total power required to maintain the maximum speed is about 7.8kw, and the maximum speed is 62km/h. In this article, various patents have been discussed.

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