This study investigates aerodynamic degradation and power loss mechanisms in iced wind turbine blades using a hybrid methodology integrating high-fidelity CFD simulations (ANSYS Fluent, FENSAP-ICE, STAR-CCM+ with SST k-ω turbulence model and shallow-water icing theory) with controlled wind tunnel experiments (10–15 m/s). Three ice accretion types, glaze, mixed, and rime, on NACA0012 airfoils are quantified. Glaze ice at the leading edge induces the most severe degradation, reducing lift by 34.9% and increasing drag by 97.2% at 10 m/s. STAR-CCM+ analyses reveal critical pressure anomalies and ice morphology-dependent flow separation patterns. These findings inform the optimization of anti-icing strategies for cold-climate wind farms.

The problem of ice accretion on wind turbine blades seriously affects the safe operation and efficiency of wind farms. In this paper, FENSAP-ICE software is adopted to conduct research on this issue. The mechanism of ice accretion on wind turbine blades is analyzed, including the formation process of ice accretion, as well as three types of ice accretion, namely glaze ice, rime ice, and mixed ice, and their occurrence conditions. A prediction method for ice accretion on the blades is elaborated. A numerical calculation method is employed, and the accuracy of the numerical model is verified through the design of multiple groups of numerical simulation calculations for ice accretion on the NACA0012 airfoil. Using this model, the laws governing how environmental temperature, incoming flow rate, liquid water content, and droplet diameter influence ice accretion on wind turbine blades are studied. It is found that reducing the environmental temperature and increasing the incoming flow rate and the liquid–liquid water content will increase the ice accretion mass and area. Increasing the droplet diameter will increase the ice accretion mass, but the ice-covered area will decrease and will concentrate towards the leading edge.

The upper facilities of polar marine equipment face severe freezing risks in ice-covered regions, necessitating energy-efficient electric heat tracing design. Existing models neglect coupled environmental factors (temperature–wind–humidity), leading to the overestimation of heating power. In this paper, experiment and CFD simulation are used to study the change of convective heat transfer coefficients of electric tracing circular tube components under the polar coupling environmental conditions of wind speed of 0~8 m/s, temperature of −40~0 °C, and air relative humidity of 10~95%, and the corresponding mathematical prediction model is established. The results show that increasing the wind speed and relative humidity will both increase the convective heat transfer coefficient of the circular tube, while the temperature is inversely proportional to the convective heat transfer coefficient of the circular tube. The convective heat transfer coefficient shows an average growth rate of only 2.8–3.8% as the temperature decreases from −10 °C to −40 °C, which is significantly lower than the effects of wind speed (average growth rate 59–50%) and humidity (average growth rate 7.5–12.7%). When the wind speed exceeds 2 m/s, the growth rate of humidity’s effect on the coefficient increases from 17.82% to 33.96%. Mathematical prediction models can provide certain references for the calculation and design of reasonable heating amounts for anti-icing and de-icing of polar equipment’s circular tube components under ice-covered regions.

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.

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 ($R^2$ = 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.

Winterization of polar ships requires an engineering link between whole-ship airflow and local thermal boundaries. This study develops a one-way, whole-ship airflow-to-convective-boundary mapping framework for exposed superstructure components. Reynolds-averaged Navier–Stokes simulations using the RNG k–ε model were conducted for MV Xue Long 2 at seven wind directions and inflow velocities of 5–20 m/s. Representative surface-pressure trends were compared with measurements from a 1:400 wind-tunnel model. Source-reported monitoring-point results cover all seven directions from 0° to 180°. Velocities at 18 locations were converted to effective convective heat-transfer coefficients using a project-specific correlation and then supplied to an energy-conservation heat-loss framework. Representative dimensionless local velocities of 0.3–1.6 illustrate shielding and acceleration; they are not the extrema over all cases. For a representative comparison at 10 m/s inflow, local velocities of 3–16 m/s corresponded to correlation-derived coefficients of 31.53–86.35 W/(m²·K), compared with 63.55 W/(m²·K) for a uniform boundary. The resulting velocity and flow-topology information was used to define case-specific, mechanism-based screening zones. A 3.0 m bow fairing reduced the mean deck velocity from 10 to 3.9 m/s and the calculated coefficient by 42.9%. The study therefore demonstrates aerodynamic-to-convective-boundary screening rather than a fully validated bidirectional fluid–thermal or phase-change model; phase-change loads and transferable zoning thresholds require further validation.