the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Jun 2024
A quality control method based on physical constraints and data-driven collaborative for wind observations along high-speed railway lines
Abstract. This study proposed a new quality control method via physical constraints and data-driven collaborative artificial intelligence (PD-BX) to reduce wind speed measurement errors caused by the complex environment along high-speed railway lines, achieving enhanced accuracy and reliability. On the one hand, based on the special structure in railway assembly, the physical constraint model of the railway electrical catenary supports and anemometers were experimentally established. The performance of the physical model in the wind field was simulated based on FLUENT software and the environmental change characteristics of the anemometer in the railway area were analyzed. On the other hand, to solve the constrained error mapping expression under different wind conditions, a data-driven model of hyperparameter optimization (BO-XGBoost) is introduced to perform error compensation on physical relationships. Through the PD-BX method, the RMSE of the railway anemometer was reduced by 2.497 from 2.790 to 0.293, achieving quality control of wind observations along the high-speed railway lines and providing reliable results for improving the accuracy of the high-speed railway early warning system.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(1978 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1978 KB) - Metadata XML
- BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
This study introduces a novel quality control method, physical constraints and data-driven collaborative artificial intelligence (PD-BX), aimed at reducing wind speed measurement errors caused by the complex environments surrounding high-speed railway lines, thereby enhancing the accuracy and reliability of measurements.
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1006', Anonymous Referee #1, 24 Jun 2024
The manuscript presents a PD-BX method to address the shadow effects of catenary pillar and improve the wind speed measurements along the high-speed railway lines. The RMSE of railway anemometer was reduced, providing enhanced accuracy and reliability of wind measurement. The manuscript is well written, and all figures are clear. Please see my comments and suggestions for minor edits below.
General comments:
The CFD model was performed under a standard state at 25 C, and I assume the dry condition. However, under extreme wind conditions, e.g. strong thunderstorms, relative humidity would be high, and heavy rain is also expected. How does authors’ model perform under such extreme conditions? Also, in real world, it requires quick response of wind speed under such extreme conditions. How long does it take for authors’ model from data processing to wind speed results?
Based on the PD-BX method, authors reduced the uncertainty of wind velocities caused by catenary pillar. Beside velocities, anemometer can also detect wind directions. Do catenary pillars lead to uncertainties of wind directions, e.g. shadow effects. If so, can we also use this PD-BX method to correct the wind directions?
Citation: https://doi.org/10.5194/egusphere-2024-1006-RC1 - AC1: 'Reply on RC1', jiajun Chen, 01 Sep 2024
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RC2: 'Comment on egusphere-2024-1006', Anonymous Referee #3, 13 Oct 2024
The manuscript presents a PD-BX method aimed at mitigating the shadow effects of catenary pillars and improving wind speed measurements along high-speed railway lines. Overall, the quality of this paper is good. However, certain sections of the English require improvement. Additionally, the following issues need to be addressed and clarified:
- Strong winds are a significant factor impacting high-speed rail operations. This paper does not discuss the measures that various countries have implemented to respond to mitigate the effects of strong winds; could the authors provide more specific information on this? Additionally, there are few mentions of rail safety incidents caused by strong winds. It is recommended that the authors include more detailed information on the hazards associated with these incidents.
- In the computational fluid dynamics (CFD) analysis presented by the authors, only dry air and standard atmospheric pressure conditions are specified. Comment 1 supplemented the simulation experiments with temperature, humidity and pressure. Please request that the authors to provide additional fluid dynamic parameters for equations (5) and (6).
- In the experiment, the authors employed machine learning algorithms to reduce errors. Why not consider simple and convenient physical methods to reduce errors? For example, adjusting the relative position between the anemometer and the catenary support structure could be effective.
- There is a point of confusion in Section 4 regarding the results and analysis. Generally, the critical limit for wind speed is set at 15 m/s. However, in Figures 10 and 11, it appears that this article utilities 13 m/s as the threshold for wind speed alerts. Could the authors clarify the reasoning behind this discrepancy?
Citation: https://doi.org/10.5194/egusphere-2024-1006-RC2 - AC2: 'Reply on RC2', jiajun Chen, 16 Oct 2024
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1006', Anonymous Referee #1, 24 Jun 2024
The manuscript presents a PD-BX method to address the shadow effects of catenary pillar and improve the wind speed measurements along the high-speed railway lines. The RMSE of railway anemometer was reduced, providing enhanced accuracy and reliability of wind measurement. The manuscript is well written, and all figures are clear. Please see my comments and suggestions for minor edits below.
General comments:
The CFD model was performed under a standard state at 25 C, and I assume the dry condition. However, under extreme wind conditions, e.g. strong thunderstorms, relative humidity would be high, and heavy rain is also expected. How does authors’ model perform under such extreme conditions? Also, in real world, it requires quick response of wind speed under such extreme conditions. How long does it take for authors’ model from data processing to wind speed results?
Based on the PD-BX method, authors reduced the uncertainty of wind velocities caused by catenary pillar. Beside velocities, anemometer can also detect wind directions. Do catenary pillars lead to uncertainties of wind directions, e.g. shadow effects. If so, can we also use this PD-BX method to correct the wind directions?
Citation: https://doi.org/10.5194/egusphere-2024-1006-RC1 - AC1: 'Reply on RC1', jiajun Chen, 01 Sep 2024
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RC2: 'Comment on egusphere-2024-1006', Anonymous Referee #3, 13 Oct 2024
The manuscript presents a PD-BX method aimed at mitigating the shadow effects of catenary pillars and improving wind speed measurements along high-speed railway lines. Overall, the quality of this paper is good. However, certain sections of the English require improvement. Additionally, the following issues need to be addressed and clarified:
- Strong winds are a significant factor impacting high-speed rail operations. This paper does not discuss the measures that various countries have implemented to respond to mitigate the effects of strong winds; could the authors provide more specific information on this? Additionally, there are few mentions of rail safety incidents caused by strong winds. It is recommended that the authors include more detailed information on the hazards associated with these incidents.
- In the computational fluid dynamics (CFD) analysis presented by the authors, only dry air and standard atmospheric pressure conditions are specified. Comment 1 supplemented the simulation experiments with temperature, humidity and pressure. Please request that the authors to provide additional fluid dynamic parameters for equations (5) and (6).
- In the experiment, the authors employed machine learning algorithms to reduce errors. Why not consider simple and convenient physical methods to reduce errors? For example, adjusting the relative position between the anemometer and the catenary support structure could be effective.
- There is a point of confusion in Section 4 regarding the results and analysis. Generally, the critical limit for wind speed is set at 15 m/s. However, in Figures 10 and 11, it appears that this article utilities 13 m/s as the threshold for wind speed alerts. Could the authors clarify the reasoning behind this discrepancy?
Citation: https://doi.org/10.5194/egusphere-2024-1006-RC2 - AC2: 'Reply on RC2', jiajun Chen, 16 Oct 2024
Peer review completion
Journal article(s) based on this preprint
This study introduces a novel quality control method, physical constraints and data-driven collaborative artificial intelligence (PD-BX), aimed at reducing wind speed measurement errors caused by the complex environments surrounding high-speed railway lines, thereby enhancing the accuracy and reliability of measurements.
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Xiong Xiong
Yanchao Zhang
Xin Chen
Yingchao Zhang
Xiaoling Ye
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1978 KB) - Metadata XML