Xiaoqing Tian
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Xiaoqing Tian has a total of 31 co-authors. Here are the top co-authors by number of shared publications:
- Zehao Feng
Institute for Energy Research, Jiangsu University, Zhenjiang, China
2 shared publications - Binbin Li
Binzhou Bohai Piston Co., Ltd., Binzhou 256602, China
1 shared publication - Chao Lu
School of Materials Science and Engineering, Guangdong Ocean University, Yangjiang 529500, China
1 shared publication - Di Tie
School of Materials Science and Engineering, Guangdong Ocean University, Yangjiang 529500, China
1 shared publication - J. C. Gu
Institute for Energy Research, Jiangsu University, Zhenjiang, China
1 shared publication - Jie Liu
School of Materials Science and Engineering, Guangdong Ocean University, Yangjiang 529500, China
1 shared publication
Latest Publications
- 21 September 2026
- Published by Pleiades Publishing Ltd in Fluid Dynamics
- Vol. 61 (5) , 83
Visualization Study of the Effects of Fuel Viscosity and Surface Tension on Cavitation Flow in Full-Scale Transparent Nozzles
The pursuit of cleaner combustion in engines has positioned alternative fuels as a critical enabler, yet their diverse range of viscosities and surface tensions poses a fundamental challenge to the regulation of nozzle internal flow behavior. In this study, full-scale transparent nozzles and high-speed microscopic imaging are employed to directly visualize the fuel internal flow. Additionally, glycerol–AES (sodium fatty alcohol ether sulfate)–aqueous solutions are formulated to decouple the effects of viscosity and surface tension, enabling a quantitative and independent investigation of their impact on cavitation flow. The results show that cavitation inside the nozzle undergoes clear temporal evolution during injection. Three distinct cavitation regimes were observed and defined, which are geometry-induced cavitation, needle-originated string cavitation, and hole-sac string cavitation. Their evolution is governed by the coupled effect of flow separation, vortex dynamics, and nozzle geometry. Surface tension is shown to have a negligible influence on string cavitation, in contrast, fuel viscosity plays a critical and non-monotonic role by modulating vortex evolution. Higher viscosity delays the inception of string cavitation while enhancing its stability and duration. Another key finding is that the relative position between the nozzle hole inlet and the conical surface of the needle valve tip significantly affect the initial position and reflux zone position of the fuel flow and separation in the sac, thereby determining whether increased viscosity enhances or suppresses the cavitation intensity. This study provides the experimental basis and the theoretical guidance for application of alternative fuels and design and optimization of nozzle structures.
- 21 September 2026
- Published by Pleiades Publishing Ltd in Fluid Dynamics
- Vol. 61 (5) , 83
Visualization Study of the Effects of Fuel Viscosity and Surface Tension on Cavitation Flow in Full-Scale Transparent Nozzles
The pursuit of cleaner combustion in engines has positioned alternative fuels as a critical enabler, yet their diverse range of viscosities and surface tensions poses a fundamental challenge to the regulation of nozzle internal flow behavior. In this study, full-scale transparent nozzles and high-speed microscopic imaging are employed to directly visualize the fuel internal flow. Additionally, glycerol–AES (sodium fatty alcohol ether sulfate)–aqueous solutions are formulated to decouple the effects of viscosity and surface tension, enabling a quantitative and independent investigation of their impact on cavitation flow. The results show that cavitation inside the nozzle undergoes clear temporal evolution during injection. Three distinct cavitation regimes were observed and defined, which are geometry-induced cavitation, needle-originated string cavitation, and hole-sac string cavitation. Their evolution is governed by the coupled effect of flow separation, vortex dynamics, and nozzle geometry. Surface tension is shown to have a negligible influence on string cavitation, in contrast, fuel viscosity plays a critical and non-monotonic role by modulating vortex evolution. Higher viscosity delays the inception of string cavitation while enhancing its stability and duration. Another key finding is that the relative position between the nozzle hole inlet and the conical surface of the needle valve tip significantly affect the initial position and reflux zone position of the fuel flow and separation in the sac, thereby determining whether increased viscosity enhances or suppresses the cavitation intensity. This study provides the experimental basis and the theoretical guidance for application of alternative fuels and design and optimization of nozzle structures.
- Open Access
Robust Combustion Prediction for Alternative-Fuel Engines Using an Equilibrium Optimizer-Based Echo State Network with ϵ-Insensitive SVR Readout
Alternative-fuel engines, such as diesel–compressed natural gas (CNG) dual fuel systems, exhibit increased cycle-to-cycle combustion variability, placing demanding requirements on the accuracy and robustness of prediction models for key combustion parameters. Echo state networks (ESNs), owing to their reservoir computing architecture, can capture nonlinear temporal dynamics, yet their performance is sensitive to reservoir hyperparameters, and the conventional linear readout trained by minimizing mean squared error is susceptible to outliers in noisy observations. This paper proposes a robust ESN framework based on the equilibrium optimizer (EO), termed EO-Robust-ESN, that automatically searches for key model hyperparameters and replaces the conventional squared loss with the -insensitive loss by adopting linear support vector regression (SVR) to train the readout weights, thereby enhancing prediction robustness under noisy conditions. Results on the Mackey–Glass chaotic time series benchmark and a peak in-cylinder pressure series from a diesel–CNG dual fuel engine demonstrate that EO significantly outperforms the genetic algorithm and manual tuning on the Mackey–Glass benchmark, reducing the mean RMSE by approximately 5.6% relative to GA, and achieves comparable accuracy with higher search stability on the series. The -insensitive SVR readout further reduces prediction errors, with the MSE on the noisy series reduced by approximately 42% compared with the ridge regression readout, suggesting that the proposed framework provides an effective data-driven tool for robust combustion prediction in alternative-fuel engines.Open AccessRobust Combustion Prediction for Alternative-Fuel Engines Using an Equilibrium Optimizer-Based Echo State Network with ϵ-Insensitive SVR Readout
Alternative-fuel engines, such as diesel–compressed natural gas (CNG) dual fuel systems, exhibit increased cycle-to-cycle combustion variability, placing demanding requirements on the accuracy and robustness of prediction models for key combustion parameters. Echo state networks (ESNs), owing to their reservoir computing architecture, can capture nonlinear temporal dynamics, yet their performance is sensitive to reservoir hyperparameters, and the conventional linear readout trained by minimizing mean squared error is susceptible to outliers in noisy observations. This paper proposes a robust ESN framework based on the equilibrium optimizer (EO), termed EO-Robust-ESN, that automatically searches for key model hyperparameters and replaces the conventional squared loss with the -insensitive loss by adopting linear support vector regression (SVR) to train the readout weights, thereby enhancing prediction robustness under noisy conditions. Results on the Mackey–Glass chaotic time series benchmark and a peak in-cylinder pressure series from a diesel–CNG dual fuel engine demonstrate that EO significantly outperforms the genetic algorithm and manual tuning on the Mackey–Glass benchmark, reducing the mean RMSE by approximately 5.6% relative to GA, and achieves comparable accuracy with higher search stability on the series. The -insensitive SVR readout further reduces prediction errors, with the MSE on the noisy series reduced by approximately 42% compared with the ridge regression readout, suggesting that the proposed framework provides an effective data-driven tool for robust combustion prediction in alternative-fuel engines. - Open Access
Effect of Heat Treatment on Microstructure and Corrosion Resistance of Al-Si-Mg-Zr-Cu-Sc Alloy
Aluminum–silicon (Al-Si) alloys are widely used in aerospace, automotive manufacturing, power electronics, marine engineering and other fields due to their excellent physical properties. However, their corrosion resistance is insufficient in harsh service environments. In this study, a variety of characterization methods were adopted, including scanning electron microscopy (SEM), X-ray diffraction (XRD), electrochemical measurements (electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization), immersion corrosion tests, and scanning vibrating electrode technique (SVET). The results show that the appropriate heat treatment regime can significantly enhance the corrosion resistance of the alloy, while improper aging parameters will aggravate the corrosion tendency. The optimal heat treatment regime is solution treatment at 500 °C for 4 h followed by aging at 200 °C for 48 h. Under this condition, the corrosion current density (icorr) is as low as 79.30 μA/cm2, and the low-frequency impedance modulus and phase angle in EIS tests are optimal. The as-extruded alloy exhibits severe localized corrosion, while the heat-treated alloy transforms into mild and uniform corrosion. The underlying mechanism is that heat treatment induces the formation of uniformly distributed nanoscale Mg2Si and Al3(Sc,Zr) precipitates, which synergistically improve the corrosion resistance of the alloy by weakening micro-galvanic coupling and facilitating the formation of a stable passive film.
Open AccessEffect of Heat Treatment on Microstructure and Corrosion Resistance of Al-Si-Mg-Zr-Cu-Sc Alloy
Aluminum–silicon (Al-Si) alloys are widely used in aerospace, automotive manufacturing, power electronics, marine engineering and other fields due to their excellent physical properties. However, their corrosion resistance is insufficient in harsh service environments. In this study, a variety of characterization methods were adopted, including scanning electron microscopy (SEM), X-ray diffraction (XRD), electrochemical measurements (electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization), immersion corrosion tests, and scanning vibrating electrode technique (SVET). The results show that the appropriate heat treatment regime can significantly enhance the corrosion resistance of the alloy, while improper aging parameters will aggravate the corrosion tendency. The optimal heat treatment regime is solution treatment at 500 °C for 4 h followed by aging at 200 °C for 48 h. Under this condition, the corrosion current density (icorr) is as low as 79.30 μA/cm2, and the low-frequency impedance modulus and phase angle in EIS tests are optimal. The as-extruded alloy exhibits severe localized corrosion, while the heat-treated alloy transforms into mild and uniform corrosion. The underlying mechanism is that heat treatment induces the formation of uniformly distributed nanoscale Mg2Si and Al3(Sc,Zr) precipitates, which synergistically improve the corrosion resistance of the alloy by weakening micro-galvanic coupling and facilitating the formation of a stable passive film.
- Open Access
Spatial Distribution Characteristics of Droplet Size and Velocity in a Methanol Spray
The atomization performance of methanol fuel plays a crucial role in enhancing methanol engine efficiency, contributing to the decarbonization of the shipping industry. The droplet microscopic characteristics of methanol spray were experimentally investigated using a single-hole direct injection injector in a constant volume chamber. The particle image analysis (PIA) system equipped with a slicer was employed for droplet detecting at a series of measurement positions in both the dense spray region and dilute spray region, then the spatial distributions of droplet size and velocity were examined. Key findings reveal distinct atomization behaviors between dense and dilute spray regions. Along the centerline, the methanol spray exhibited poor atomization, characterized by a high concentration of aggregated droplets, interconnected liquid structures, and large liquid masses. In contrast, the spray periphery demonstrated effective atomization, with only well-dispersed individual droplets observed. Droplet size distribution analysis showed a sharp decrease from the dense region to the dilute region near the nozzle. In the spray midbody, droplet diameter initially decreased significantly within the dense spray zone, stabilized in the transition zone, and then exhibited a slight increase in the dilute region—though remaining smaller than values observed at the central axis. Velocity measurements indicated a consistent decline in the axial velocity component due to air drag. In contrast, the radial velocity component displayed irregular variations, attributed to vortex-induced flow interactions. These experimentally observed droplet behaviors provide critical insights for refining spray models and enhancing computational simulations of methanol injection processes.
Open AccessSpatial Distribution Characteristics of Droplet Size and Velocity in a Methanol Spray
The atomization performance of methanol fuel plays a crucial role in enhancing methanol engine efficiency, contributing to the decarbonization of the shipping industry. The droplet microscopic characteristics of methanol spray were experimentally investigated using a single-hole direct injection injector in a constant volume chamber. The particle image analysis (PIA) system equipped with a slicer was employed for droplet detecting at a series of measurement positions in both the dense spray region and dilute spray region, then the spatial distributions of droplet size and velocity were examined. Key findings reveal distinct atomization behaviors between dense and dilute spray regions. Along the centerline, the methanol spray exhibited poor atomization, characterized by a high concentration of aggregated droplets, interconnected liquid structures, and large liquid masses. In contrast, the spray periphery demonstrated effective atomization, with only well-dispersed individual droplets observed. Droplet size distribution analysis showed a sharp decrease from the dense region to the dilute region near the nozzle. In the spray midbody, droplet diameter initially decreased significantly within the dense spray zone, stabilized in the transition zone, and then exhibited a slight increase in the dilute region—though remaining smaller than values observed at the central axis. Velocity measurements indicated a consistent decline in the axial velocity component due to air drag. In contrast, the radial velocity component displayed irregular variations, attributed to vortex-induced flow interactions. These experimentally observed droplet behaviors provide critical insights for refining spray models and enhancing computational simulations of methanol injection processes.
- Open Access
Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing
Superhydrophobic surfaces have garnered significant attention due to their pivotal roles in various fields. Femtosecond laser technology provides a feasible means for inducing superhydrophobic microstructures on material surfaces. However, due to the unclear influence mechanisms of process parameters, as well as the high cost and time-consuming nature of experiments, identifying the optimal femtosecond laser processing parameters within the process space remains a significant challenge. To address this issue, a process optimization framework that couples machine learning and genetic algorithms was proposed and successfully applied to the optimization of femtosecond laser-induced groove structures on TC4 alloy surfaces. Firstly, based on 64 sets of experimental data, the effects of the power, scanning speed, and scanning interval on the micro-groove structures and their wetting properties were discussed in detail. Furthermore, by utilizing this small sample dataset, various machine learning algorithms were employed to establish a prediction model for the contact angle, among which support vector regression demonstrated the optimal predictive accuracy. Three additional dimensional variables, i.e., the number of effective pulses, energy deposition rate, and roughness, were also added to the original dataset vectors as extra dimensions to participate in and guide the model training process. The prediction model was further coupled into a genetic algorithm to achieve the quantitative design of femtosecond laser processing. Compared to the best hydrophobicity in the original dataset, the contact angle of the designed process was improved by 5.5%. The proposed method provides an ideal solution for accurately predicting wetting properties and identifying optimal processes, thereby accelerating the development and application of femtosecond laser-induced superhydrophobic microstructures.
Open AccessMachine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing
Superhydrophobic surfaces have garnered significant attention due to their pivotal roles in various fields. Femtosecond laser technology provides a feasible means for inducing superhydrophobic microstructures on material surfaces. However, due to the unclear influence mechanisms of process parameters, as well as the high cost and time-consuming nature of experiments, identifying the optimal femtosecond laser processing parameters within the process space remains a significant challenge. To address this issue, a process optimization framework that couples machine learning and genetic algorithms was proposed and successfully applied to the optimization of femtosecond laser-induced groove structures on TC4 alloy surfaces. Firstly, based on 64 sets of experimental data, the effects of the power, scanning speed, and scanning interval on the micro-groove structures and their wetting properties were discussed in detail. Furthermore, by utilizing this small sample dataset, various machine learning algorithms were employed to establish a prediction model for the contact angle, among which support vector regression demonstrated the optimal predictive accuracy. Three additional dimensional variables, i.e., the number of effective pulses, energy deposition rate, and roughness, were also added to the original dataset vectors as extra dimensions to participate in and guide the model training process. The prediction model was further coupled into a genetic algorithm to achieve the quantitative design of femtosecond laser processing. Compared to the best hydrophobicity in the original dataset, the contact angle of the designed process was improved by 5.5%. The proposed method provides an ideal solution for accurately predicting wetting properties and identifying optimal processes, thereby accelerating the development and application of femtosecond laser-induced superhydrophobic microstructures.
- Open Access
- 6 September 2024
- Published by Open Access Publishing Group in European Journal of Education Studies
- Vol. 11 (11)
THE INFLUENCE OF FAMILY DYSFUNCTION ON ADOLESCENT PSYCHOLOGICAL HEALTH: A LITERATURE REVIEW-BASED STUDY
The increasing prevalence of adolescent psychological health issues worldwide has become a significant public health concern affecting the future. Families, as the primary environment providing material and emotional support for adolescent development, play a crucial role in fostering growth and psychological well-being. This study employed a literature review methodology, using keywords such as family dysfunction, adolescents, and psychological health, to search for definitions of family dysfunction and relevant literature about its effects on adolescents' psychological health. The review study summarized the definitions of family dysfunction, related research, and its impact on adolescent psychological health. The findings suggest that addressing the impact of family dysfunction on adolescent psychological health effectively requires collaborative efforts from families, schools, and society. Establishing collaborative prevention and intervention mechanisms among family, schools and society, providing educational programs to improve family function, and adopting family-centered intervention strategies are essential steps toward solving this critical issue. Article visualizations:
- 6 September 2024
- Published by Open Access Publishing Group in European Journal of Education Studies
- Vol. 11 (11)
Open AccessTHE INFLUENCE OF FAMILY DYSFUNCTION ON ADOLESCENT PSYCHOLOGICAL HEALTH: A LITERATURE REVIEW-BASED STUDY
The increasing prevalence of adolescent psychological health issues worldwide has become a significant public health concern affecting the future. Families, as the primary environment providing material and emotional support for adolescent development, play a crucial role in fostering growth and psychological well-being. This study employed a literature review methodology, using keywords such as family dysfunction, adolescents, and psychological health, to search for definitions of family dysfunction and relevant literature about its effects on adolescents' psychological health. The review study summarized the definitions of family dysfunction, related research, and its impact on adolescent psychological health. The findings suggest that addressing the impact of family dysfunction on adolescent psychological health effectively requires collaborative efforts from families, schools, and society. Establishing collaborative prevention and intervention mechanisms among family, schools and society, providing educational programs to improve family function, and adopting family-centered intervention strategies are essential steps toward solving this critical issue. Article visualizations: