Yan Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yan Chen has a total of 26 co-authors. Here are the top co-authors by number of shared publications:
Chenyang Liu
3 shared publications- Chenyang Liu
School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 116024, China
3 shared publications - Dayong Zhang
School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, China
3 shared publications - Dayong Zhang3 shared publications
Haiming Wen
3 shared publicationsMeng Cui
3 shared publications
Latest Publications
- 30 July 2026
- Published by Elsevier in Ocean Engineering
- Vol. 365
Digital twin-driven performance prognosis and health management for deck machinery of polar vessels
- 30 July 2026
- Published by Elsevier in Ocean Engineering
- Vol. 365
Digital twin-driven performance prognosis and health management for deck machinery of polar vessels
- Open Access
- 20 May 2026
- Published by MDPI AG in Atmosphere
- Vol. 17 (5) , 525
Interpretable Predictive Model and Multi-Factor Coupling Mechanism of Convective Heat Transfer on Heated Cylinders in Polar Marine Environments
In response to the problems of high energy consumption and difficulty in precise regulation of electric tracing anti-icing systems for polar marine engineering equipment in low-temperature, strong-wind, and high-humidity environments, this paper conducts experimental measurement and predictive modeling research on the convective heat transfer characteristics of electric heat-traced circular cylinders in cross-flow. First, a controllable environmental experimental system was set up to obtain 144 sets of steady-state convective heat transfer data under different combinations of wind speed, temperature, humidity, and heat flux density. Based on this, a Nusselt number (Nu) prediction model using a fully connected Deep Neural Network (DNN) was constructed, and its performance was evaluated through five-fold cross-validation. The results show that the DNN model can effectively capture nonlinear mapping relationships among multiple factors, and its prediction accuracy ( = 0.9828) is superior to that of traditional machine learning models. Furthermore, the Shapley Additive Explanations (SHAP) method was introduced to analyze the multi-factor coupling mechanisms, quantify the contribution of each input variable to the Nu prediction, and provide a data-driven reference for the optimization of engineering parameters under extreme polar conditions.- 20 May 2026
- Published by MDPI AG in Atmosphere
- Vol. 17 (5) , 525
Open AccessInterpretable Predictive Model and Multi-Factor Coupling Mechanism of Convective Heat Transfer on Heated Cylinders in Polar Marine Environments
In response to the problems of high energy consumption and difficulty in precise regulation of electric tracing anti-icing systems for polar marine engineering equipment in low-temperature, strong-wind, and high-humidity environments, this paper conducts experimental measurement and predictive modeling research on the convective heat transfer characteristics of electric heat-traced circular cylinders in cross-flow. First, a controllable environmental experimental system was set up to obtain 144 sets of steady-state convective heat transfer data under different combinations of wind speed, temperature, humidity, and heat flux density. Based on this, a Nusselt number (Nu) prediction model using a fully connected Deep Neural Network (DNN) was constructed, and its performance was evaluated through five-fold cross-validation. The results show that the DNN model can effectively capture nonlinear mapping relationships among multiple factors, and its prediction accuracy ( = 0.9828) is superior to that of traditional machine learning models. Furthermore, the Shapley Additive Explanations (SHAP) method was introduced to analyze the multi-factor coupling mechanisms, quantify the contribution of each input variable to the Nu prediction, and provide a data-driven reference for the optimization of engineering parameters under extreme polar conditions. - Open Access
- 7 April 2026
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 14 (7) , 685
Physics-Informed Neural Networks and Deep Reinforcement Learning for Optimal Anti-Icing Strategies of Circular Tube Components in Polar Vessels
In polar environments, icing on ship deck surfaces severely compromises navigation safety. Conventional electric trace heating systems operate in continuous heating mode, resulting in high energy consumption. This study proposes an intelligent periodic heating control method that integrates physics-informed neural networks (PINNs) and deep reinforcement learning (DRL) for energy-efficient anti-icing of circular pipe components on polar vessels. Using a polar coupled environment simulation platform, experiments were conducted on electric heating anti-icing for circular pipe components. Temperature data under various heating modes were collected, and a physically constrained PINN temperature prediction model was constructed, achieving high prediction accuracy with limited samples (test set R2 = 0.9091; 5-fold cross-validation R2 = 0.8877 ± 0.0312). The DRL agent trained in this virtual environment autonomously optimized the heating strategy, yielding optimal cycle parameters: heating ratio D = 0.722 and cycle duration τ = 88 s. While maintaining surface temperatures above 0 °C against a −10 °C ambient baseline, this strategy achieved a unit energy consumption of 0.27 kJ/°C, representing a 63% reduction compared to conventional continuous heating. This study provides a data-physics fusion control approach for polar vessel anti-icing systems, demonstrating strong potential for engineering applications.
- 7 April 2026
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 14 (7) , 685
Open AccessPhysics-Informed Neural Networks and Deep Reinforcement Learning for Optimal Anti-Icing Strategies of Circular Tube Components in Polar Vessels
In polar environments, icing on ship deck surfaces severely compromises navigation safety. Conventional electric trace heating systems operate in continuous heating mode, resulting in high energy consumption. This study proposes an intelligent periodic heating control method that integrates physics-informed neural networks (PINNs) and deep reinforcement learning (DRL) for energy-efficient anti-icing of circular pipe components on polar vessels. Using a polar coupled environment simulation platform, experiments were conducted on electric heating anti-icing for circular pipe components. Temperature data under various heating modes were collected, and a physically constrained PINN temperature prediction model was constructed, achieving high prediction accuracy with limited samples (test set R2 = 0.9091; 5-fold cross-validation R2 = 0.8877 ± 0.0312). The DRL agent trained in this virtual environment autonomously optimized the heating strategy, yielding optimal cycle parameters: heating ratio D = 0.722 and cycle duration τ = 88 s. While maintaining surface temperatures above 0 °C against a −10 °C ambient baseline, this strategy achieved a unit energy consumption of 0.27 kJ/°C, representing a 63% reduction compared to conventional continuous heating. This study provides a data-physics fusion control approach for polar vessel anti-icing systems, demonstrating strong potential for engineering applications.
- Open Access
- 20 March 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (6) , 3016
Multi-Scale Attention Transformer for Oil-Temperature Prediction in Hydraulic Systems of Polar Ship Decks
In polar environments, the thermoviscous behavior and heat dissipation characteristics of deck hydraulic systems are severely affected, resulting in response delays and increased failure risk during high-load operations such as anchor retrieval. To address the limited availability of polar field test samples and the multi-scale nature of oil-temperature responses—featuring short-term abrupt variations and slow-varying hysteresis—this study proposes a Multi-Scale Attention Transformer (MSA-Transformer). Through parallel multi-scale attention branches, the model collaboratively captures both transient and gradual dynamics, thereby improving prediction robustness under polar extreme cold conditions. Based on anchor-retrieval test data collected in Genhe, China’s Cold Pole, at −30 °C, −35 °C, and −40 °C, a dataset containing 18 load cycles was constructed. Experimental results based on 5-fold stratified cross-validation results show that the MSA-Transformer achieves the best performance across evaluation metrics, attaining an average coefficient of determination (R2) of 0.9119 along with the lowest error rates (MAE, RMSE, MSE) on the test set, thereby outperforming LSTM, CNN-LSTM, and the standard Transformer. This work provides an effective tool for state prediction, maintenance optimization, and anomaly early warning in polar deck hydraulic systems, supporting the intelligent health management of hydraulic equipment.
- 20 March 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (6) , 3016
Open AccessMulti-Scale Attention Transformer for Oil-Temperature Prediction in Hydraulic Systems of Polar Ship Decks
In polar environments, the thermoviscous behavior and heat dissipation characteristics of deck hydraulic systems are severely affected, resulting in response delays and increased failure risk during high-load operations such as anchor retrieval. To address the limited availability of polar field test samples and the multi-scale nature of oil-temperature responses—featuring short-term abrupt variations and slow-varying hysteresis—this study proposes a Multi-Scale Attention Transformer (MSA-Transformer). Through parallel multi-scale attention branches, the model collaboratively captures both transient and gradual dynamics, thereby improving prediction robustness under polar extreme cold conditions. Based on anchor-retrieval test data collected in Genhe, China’s Cold Pole, at −30 °C, −35 °C, and −40 °C, a dataset containing 18 load cycles was constructed. Experimental results based on 5-fold stratified cross-validation results show that the MSA-Transformer achieves the best performance across evaluation metrics, attaining an average coefficient of determination (R2) of 0.9119 along with the lowest error rates (MAE, RMSE, MSE) on the test set, thereby outperforming LSTM, CNN-LSTM, and the standard Transformer. This work provides an effective tool for state prediction, maintenance optimization, and anomaly early warning in polar deck hydraulic systems, supporting the intelligent health management of hydraulic equipment.