Super-hydrophobic anti-icing coating for power transmission tower steel structure and coating test method

Abstract

The invention discloses a super-hydrophobic anti-icing coating for a power transmission tower steel structure and a coating test method, and belongs to the technical field of material engineering and surface protection. Comprising the steps of super-hydrophobic coating formula optimization, geometric self-adaptive spraying and curing, extreme environment simulation testing, multi-source data acquisition, failure mechanism analysis and prediction, performance optimization and prediction and the like, the thickness of the prepared coating is 0.15 + / -0.008 mm, the coating covers a Q235 steel base material with the surface roughness of 2.0 + / -0.15 microns, and the coating has the good corrosion resistance under the environment of-40 DEG C to 60 DEG C, the wind speed of 15 m / s and the humidity of 85% RH. The icing amount is less than or equal to 30g / m < 2 >, and the deicing efficiency is greater than or equal to 92 The technical problems that in the prior art, a coating is insufficient in hydrophobic performance and durability, a coating process is poor in adaptability on a complex geometric structure, test environment simulation is not real, and a systematic optimization and prediction model is lacked are solved.

Description

Technical Field

[0001] This invention relates to the fields of materials engineering and surface protection technology, and in particular discloses a superhydrophobic anti-icing coating for transmission tower steel structures and a coating test method, which is suitable for improving the anti-icing performance of transmission tower steel structures in low temperature and high humidity environments. Background Technology

[0002] Transmission towers, as critical infrastructure for power transmission, are exposed to the natural environment for extended periods. In cold, humid winters or frigid regions, their steel structures are highly susceptible to icing. Icing significantly increases the load on the tower, potentially leading to serious accidents such as line galloping, breakage, or even tower collapse, threatening power grid safety.

[0003] Currently, the main methods for dealing with icing include physical de-icing, heating de-icing, and anti-icing coatings. Physical de-icing (such as manual knocking) is inefficient and easily damages the structure; heating de-icing consumes a lot of energy and has high operation and maintenance costs; while traditional hydrophobic or hydrophobic coatings can reduce ice adhesion to some extent, their hydrophobic properties are limited and their durability is insufficient, especially under extreme weather conditions, their anti-icing effect drops sharply, making it difficult to meet the long-term maintenance-free requirements of transmission towers.

[0004] Furthermore, existing technologies for testing the performance of anti-icing coatings are largely limited to conventional laboratory environments. They lack testing methods that can realistically simulate the complex environments of actual transmission tower operation (such as alternating low temperatures, strong winds, and freezing rain), as well as performance prediction and optimization methods based on multivariate interactions and data-driven approaches. This results in long coating development cycles, high costs, and incomplete and inaccurate performance evaluations. Therefore, there is an urgent need in this field for a comprehensive solution integrating efficient material preparation, precision coating processes, realistic environment simulation, in-depth failure analysis, and intelligent optimization prediction to improve the long-term anti-icing capability and operational safety of transmission tower steel structures in harsh environments. Summary of the Invention

[0005] Technical problems to be solved: In view of the technical problems existing in the background technology, the present invention provides a superhydrophobic anti-icing coating for steel structures of transmission towers and a coating testing method. The method aims to solve the technical problems in the prior art, such as insufficient hydrophobic performance and durability of coatings, poor adaptability of coating process to complex geometric structures, unrealistic test environment simulation, and lack of systematic optimization and prediction models.

[0006] Technical solution: The present invention provides a test method for superhydrophobic anti-icing coating on steel structures of transmission towers, comprising the following steps: Step S1, superhydrophobic coating formulation optimization: Using polydimethylsiloxane as the matrix material, heptadecafluorophenyltrimethoxysilane as the modifier, and hydroxyl-modified silica nanoparticles as the reinforcing filler, the formulation was optimized using response surface methodology based on Box-Behnken design to obtain the optimized formulation; wherein the silica mass ratio is 0.15, the nanoparticle mass ratio is 0.2, and the ultrasonic dispersion time is 30 min; the static contact angle θ of the optimized formulation coating is... c ≥152°, roll angle θ r <7°, hardness ≥3H; Step S2, Geometric Adaptive Spraying and Curing: The Q235 steel substrate is pretreated with mechanical sanding and plasma cleaning to achieve a surface roughness of 2.0±0.15μm, a cleanliness level of ASTM D4417 Method B 3a, and an adhesion level of ASTM D3359 5B. A geometric model of the substrate is established based on finite element analysis software, and the spraying path is optimized using particle swarm optimization algorithm. The spraying angle is controlled at 45°±3°, the speed at 0.25m / s, and the spray gun distance at 150mm, so that the target coating thickness is 0.15±0.008mm. Then, staged thermal curing is performed. Step S3, extreme environment simulation test: In the frozen environment test chamber, simulate the environmental conditions of temperature -40℃-60℃, wind speed 0-20m / s, and relative humidity 10%-95%, and use high-pressure nozzles to simulate freezing rain conditions, with water droplet diameter of 50±3μm and spray rate of 0.5 L / h, so as to form ice on the test sample. Step S4, Multi-source data acquisition: Use industrial cameras and laser rangefinders to acquire icy images and 3D point cloud data of the samples during the test process, use an improved iterative nearest point algorithm to perform data fusion, and store the spatiotemporal related data in the database; Step S5, Failure Mechanism Analysis and Prediction: For samples aged by freeze-thaw cycles and high-temperature exposure, the microstructure and elemental distribution are analyzed using scanning electron microscopy and energy dispersive spectroscopy to obtain data on crack width, porosity, and fluorine content; combined with a long short-term memory network model, the failure time of the coating is predicted. Step S6, Performance Optimization and Prediction: Multifactor variance analysis is used to quantify the interactive effects of temperature, wind speed, and roughness on icing amount; based on non-dominated sorting genetic algorithm, with durability, hydrophobicity, crack resistance and cost as multiple objectives, the coating thickness and roughness parameters are optimized to obtain the optimal solution of thickness 0.15mm and roughness 2.0μm; and a long short-term memory network model is used to predict the performance degradation of the coating in long-term environment.

[0007] Preferably, the ultrasonic dispersion in step S1 is performed at a frequency of 40 kHz and a power of 200 W, and the dispersion process is equipped with a water-cooled circulation system; the cluster particle size is verified to be ≤100 nm by SEM observation, and the surface fluorine content is verified to be 15%±0.5% by XPS analysis.

[0008] Preferably, in step S2, the power of the plasma cleaning is 200 W, the radio frequency is 13.56 MHz, the argon flow rate is 20 standard L / min, and the processing time is 5 min.

[0009] Preferably, the staged thermal curing in step S2 is carried out under nitrogen protection, with a curing environment humidity of 40%±5%RH and an oxygen content of <2%. The first stage is baked at 60℃ for 1 hour, and the second stage is baked at 80℃ for 2 hours.

[0010] Preferably, in step S2, the geometric modeling based on finite element analysis has a mesh density of 1000 nodes / m. 2 Encryption to 1500 nodes / m in the curvature region 2 The overlap rate of the spraying path optimized by the particle swarm optimization algorithm in the high curvature region is ≥95%.

[0011] Preferably, the freezing environment test chamber in step S3 is equipped with a semiconductor cooling / heating device, an ultrasonic humidifier, a variable frequency axial flow fan, and a high-pressure nozzle; the uniformity deviation of the ice coating amount is <1g / m³. 2 .

[0012] Preferably, in step S4, the industrial camera has a resolution of no less than 20 megapixels and a sampling frequency of 2 Hz; the laser rangefinder has a measurement accuracy of ±0.005 mm and a point cloud density of 150 points / m. 2 The registration error of the improved iterative nearest point algorithm is ≤0.3 mm.

[0013] Preferably, in step S5, the freeze-thaw cycle conditions are -20℃ to 10℃ for 12 cycles, each cycle lasting 4 hours, with a heating / cooling rate of 1℃ / min; the high-temperature exposure conditions are 50℃ and 85%±5% RH for 720 hours; the prediction period of the LSTM model is 1000 hours, and the root mean square error is ≤1°.

[0014] Preferably, in step S6, the population size of the non-dominated sorting genetic algorithm is 100, the number of iterations is 200, the crossover probability is 0.8, and the mutation probability is 0.1; the weight allocation of the performance indicators is: durability 35%, hydrophobicity 35%, crack resistance 20%, and cost 10%.

[0015] This invention also discloses a superhydrophobic anti-icing coating for steel structures of transmission towers, prepared by the above method; the coating has a thickness of 0.15±0.008 mm and covers a Q235 steel substrate with a surface roughness of 2.0±0.15 μm. Under conditions of -40℃ to 60℃, wind speed of 15 m / s, and RH humidity of 85%, the icing amount is ≤30 g / m³. 2 De-icing efficiency ≥92%.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention possesses superior anti-icing and durability properties. Through nanocomposite technology and surface energy regulation, a coating with ultra-high hydrophobicity (contact angle >152°) and low ice adhesion (roll-off angle <7°) is obtained. The ice load <22g / m² and the de-icing efficiency ≥92% are far superior to traditional coatings (traditional coatings have an ice load of 50-100 g / m² and a de-icing efficiency of 50%-70%). After high-temperature exposure (50°C, 720h), the hydrophobic angle retention rate is >88%, the adhesion is ≥4B, and after rigorous aging tests, the performance degradation rate is <5%, with a predicted failure time extension of more than 35%. 2. This invention features a highly efficient intelligent coating process. It innovatively adopts "geometric adaptive spraying" combined with FEA and PSO algorithms, which solves the problem of uniform coating of complex steel structures (such as angle steel, channel steel, and steel pipes). The thickness deviation is <4%, the coverage is increased by 12%, the spraying efficiency is increased by 15%, and the cost is reduced by 22%. 3. This invention realizes a real and reliable testing and evaluation system, constructs a testing system that can accurately simulate the actual operating environment of transmission towers (-40~60℃, 0-20m / s wind speed, 10-95% RH), and combines multi-source data fusion technology to achieve quantitative and high-precision measurement of the icing process and results. The test time is shortened by 22%, energy consumption is reduced by 15%, ICP registration error is ≤0.3mm, and ANOVA and LSTM models improve the performance prediction accuracy by 18%, truly simulating the operating environment of transmission towers; 4. This invention optimizes data-driven and predictive capabilities, and for the first time systematically applies models such as RSM, ANOVA, NSGA-II and LSTM to this field, realizing full-process optimization and prediction from formulation and process to long-term performance, shortening the optimization cycle by 30% and improving the prediction accuracy to over 90%, providing a scientific basis for coating design and engineering applications. 5. This invention is environmentally friendly and economically beneficial. The coating has a low VOC content (<40g / L), the surface treatment process is environmentally friendly, meets green manufacturing requirements (compliant with GB 8978), and reduces environmental pollution by 35%. It improves overall performance and reduces maintenance costs by 28%, significantly reducing the risk of icing accidents and maintenance costs of transmission towers. It is suitable for environments with high cold (-30℃ in Northeast China), high humidity (85% RH in coastal areas), and strong winds (15 m / s in Northwest China), and has great engineering application value. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall process flow provided for an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will be described in conjunction with the accompanying drawings. Figure 1 The technical solutions of the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0019] Example 1: This embodiment of the invention provides a superhydrophobic anti-icing coating test method for transmission tower steel structures, taking Q235 angle steel as an example for superhydrophobic anti-icing coating and testing, but is not limited to this, and is applicable to structures such as copper plates, aluminum plates, flat steel, round steel, steel pipes, and channel steel, combined with... Figure 1 As shown, the specific implementation steps of the method of the present invention are as follows: I. Optimization of superhydrophobic coating formulation: Using polydimethylsiloxane (PDMS, viscosity 500 cSt) as the matrix material, heptadecafluorophenyltrimethoxysilane (99% purity) as the modifier, and hydroxyl-modified silica nanoparticles (20-50 nm particle size) as the reinforcing filler, the formulation was optimized using response surface methodology (RSM) based on Box-Behnken design (BBD). An optimization function was constructed, with optimization variables including the silica mass ratio (0.1-0.2), the nanoparticle mass ratio (0.15-0.25), and the ultrasonic dispersion time (20-40 min). Through 15 sets of experiments, a silica mass ratio of 0.15 and a nanoparticle mass ratio of 0.2 were determined. Ultrasonic dispersion was performed for 30 min under constant temperature conditions using an ultrasonic device with a frequency of 40 kHz and a power of 200 W. A water-cooled circulation system was used during the dispersion process, with the temperature controlled at 25℃±2℃ and a stirring speed of 300 rpm, forming a micro-nano composite coating structure. The static contact angle θ of the coating surface was [value missing]. c and roll angle θ r Satisfy: θ c ≥152°, θr ≤7°; where the contact angle is modeled using the Young-Laplace equation: In the formula: γ SG γ SL γ LG These represent the interfacial tensions at solid-gas, solid-liquid, and liquid-gas interfaces, respectively. The gamma ray spectroscopy was reduced by optimizing the nanoparticle filling rate. SL Up to <18 mJ / m 2 To achieve ultra-low surface energy.

[0020] The formulation optimization function is as follows: ; In the formula: Y is the comprehensive performance index (weighted sum of contact angle and hardness); x1, x2, and x3 are the mass ratio of fluorinated silane, the mass ratio of nanoparticles, and the ultrasonic dispersion time, respectively; β i is the regression coefficient.

[0021] The optimal ratio was determined through 15 sets of experiments (x1=0.15, x2=0.2, x3=30 min). The resulting mixture was subjected to performance tests. SEM observation confirmed that the cluster particle size was ≤100 nm, and XPS analysis confirmed that the surface fluorine content was 15%±0.5%. The static contact angle was measured to be 156°, the roll-off angle to be 6.5°, and the hardness to be 3H (GB / T6739). SEM observation showed that the nanocluster particle size was approximately 85 nm, and the volatile organic compound (VOC) content was ≤40 g / L (GB / T 23986), meeting environmental protection requirements. The contact angle was improved by 18%, and the formulation stability was improved by 20%.

[0022] II. Geometric Adaptive Spraying and Curing: The Q235 steel substrate was mechanically sanded using a 120-grit abrasive belt at a pressure of 0.5 MPa for 3 minutes, resulting in a surface roughness Ra of 2.1 μm. Subsequent plasma cleaning further improved surface cleanliness and adhesion. The plasma power was 200 W, the radio frequency was 13.56 MHz, the argon flow rate was 20 standard L / min, and the treatment time was 5 minutes. This resulted in a surface roughness of 2.0 ± 0.15 μm, a cleanliness level meeting ASTM D4417 Method B 3a, and an adhesion test (grid method, 6×6 scratch) meeting ASTM D3359 5B. The surface contact angle after treatment was measured to be <10°.

[0023] Among them, the interface shear stress model is as follows: ; In the formula: τ is the shear stress; μ = 0.08 is the friction coefficient (better than 0.15 for traditional coatings); σn The normal stress indicates that the abrasive surface reduces the risk of failure by 35%.

[0024] Surface roughness is calculated using an integral formula: ; In the formula: z(x) is the surface profile height; L is the measurement length. The optimized Ra is 2.0±0.15μm, and the adhesion is improved by 22%. For locally corroded surfaces (corrosion area ≤30%), acid washing (5% hydrochloric acid, 30min) followed by neutralization (pH 7.0±0.2) is used, and the surface cleanliness meets the GB / T 8923 standard.

[0025] A geometric model of the substrate was established using finite element analysis (FEA) software (Abaqus 2020). Mesh generation was performed for different curvature features (such as angle steel edges and channel steel grooves). An FEA model of the angle steel was created using Abaqus software, and the mesh was refined in the corner region (curvature radius 5 mm) to a density of 1000 nodes / m. 2 Encryption to 1500 nodes / m in the curvature region 2 Geometric Adaptive Spraying Path Optimization Algorithm Based on Finite Element Analysis (FEA): ; In the formula: h(x,y) is the coating thickness; h0=0.15 mm is the target thickness; S is the substrate surface; v(t) is the spray arm speed; K(x,y) is the surface curvature; λ1=0.1, λ2=0.05 are the trade-off coefficients; T is the spraying time.

[0026] Particle swarm optimization (PSO) was employed to reduce thickness deviation and improve coverage. The spraying path parameters were optimized, and the overlap rate of the spraying path in areas with high curvature was ≥95%. The robot spraying arm was controlled to spray at a spraying angle of 45°±3°, a speed of 0.25m / s, and a spray gun distance of 150mm to achieve uniform coating with a target thickness of 0.15±0.008mm.

[0027] After spraying, the samples underwent phased curing. The first phase involved baking at 60℃ for 1 hour in air. The second phase was conducted under nitrogen protection, with a curing environment humidity of 40%±5% RH and an oxygen content of <2%, at 80℃ for 2 hours. The average coating thickness after curing was measured to be 0.152 mm, with a deviation of ±0.007 mm, a porosity of 2.8%, and an adhesion rating of 5B. The curing time was optimized using the thermal diffusion equation. ; In the formula: α = 0.15 mm 2 / s represents the thermal diffusivity of the PDMS coating. After curing, the coating porosity is <3%, the wear resistance (GB / T 1768) is improved by 18%, the process cost is reduced by 22% compared with traditional multi-layer coating, and the spraying efficiency is improved by 15%, making it suitable for industrial production.

[0028] III. Extreme Environment Simulation Test: A testing system capable of simulating complex environments was deployed, such as a freezing environment test chamber (internal dimensions 0.5m × 0.4m × 0.3m, insulation layer thickness 50mm, material polyurethane). The freezing environment test chamber is equipped with a semiconductor cooling / heating device, an ultrasonic humidifier, a variable frequency axial flow fan, and high-pressure nozzles. Coated angle steel samples were placed in the freezing environment test chamber, and the test conditions were set as follows: simulated temperature -40℃ to 60℃, wind speed 0 to 20m / s, and relative humidity 10% to 95%. High-pressure nozzles were used to simulate freezing rain conditions, with water droplet diameter of 50±3μm, spray rate of 0.5 L / h, and spray angle of 60°±5°. The high-pressure spray system was activated to simulate freezing rain for 5.5 hours to form ice on the test samples. The amount of ice was measured using an electronic balance (range 1000g, accuracy ±0.005g), with an accuracy of ±0.008g / m³. 2 The measured ice accumulation was ≤30 g / m², and the uniformity deviation of the ice accumulation was <1 g / m². 2 Testing efficiency is improved by 22% and energy consumption is reduced by 15%.

[0029] (1) The ice accumulation Mi and thickness hi are calculated quantitatively using the following formulas: ; In the formula: Δm is the mass of ice covering; A is the surface area; h j The thickness is measured by the laser rangefinder; n=6 is the number of measurement points.

[0030] (2) The test conditions were optimized using an improved particle swarm optimization (PSO) algorithm: ; In the formula: η d The de-icing efficiency is represented by t; the test time by t; and the energy consumption by E. Weights are w1=0.25, w2=0.25, w3=0.3, w4=0.1, and w5=0.1. PSO introduces adaptive inertial weights (initially 0.9, linearly decaying to 0.4, iterating 150 times). Optimal test conditions are determined: temperature -20℃, wind speed 10 m / s, humidity 85% RH, and 5.5 hours of freezing rain. A dynamic de-icing test is performed with a 10 m / s wind load applied for 30 minutes, and the de-icing efficiency is calculated. In the formula: Mr is the residual ice volume, and η is the measured amount. d ≥92% (the residual ice content of traditional coatings is 50%-70%).

[0031] IV. Multi-source data acquisition: An industrial camera (at least 20 megapixels, acquisition frequency of 2 Hz) and a laser rangefinder (measurement accuracy of ±0.005 mm, point cloud density of 150 points / m) were used. 2 During the testing process, icing images and 3D point cloud data of the samples were collected. An improved Iterative Closest Point (ICP) algorithm was used to perform high-precision fusion and registration of the multi-source data, achieving a registration error of 0.25 mm. The spatiotemporally correlated 3D icing data was stored in a database constructed using PostgreSQL and PostGIS, and millisecond-level synchronization was achieved via Network Time Protocol (NTP), with an accuracy of less than 1 millisecond and a data missing rate of less than 0.02%, ensuring data integrity and reliability. After the test, the icing amount of the sample was measured using an electronic balance to be 20 g / m².

[0032] Among them, an improved Iterative Closest Point (ICP) algorithm is used for registration: ; In the formula: p i q i The coordinates are the point cloud and the model coordinates, respectively; R and T are the rotation and translation matrices; the registration error RMSE ≤ 0.3 mm.

[0033] V. Failure Mechanism Analysis and Prediction: Another group of samples prepared using the same process were subjected to freeze-thaw cycles and high-temperature exposure aging treatment. The freeze-thaw cycle conditions were -20℃ to 10℃ for 12 cycles, with each cycle lasting 4 hours and a heating / cooling rate of 1℃ / min. The high-temperature exposure conditions were 50℃ and 85%±5% RH for 720 hours.

[0034] Cyclic freeze-thaw tests were used to evaluate the stability of the coating, and freeze-thaw stress was quantified and calculated using a thermal expansion model. In the formula: E = 1.2 GPa is the coating modulus; α = 300 × 10⁻⁶ -6 / ℃ is the coefficient of thermal expansion; ΔT=30℃.

[0035] The microstructure and elemental distribution were analyzed using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). Key parameters such as crack width (≤8 μm), porosity (≤4%), and fluorine content (15.2% ± 0.5%) were quantified. Pseudo-color coding (red indicates cracked areas, and blue indicates stable areas) was used to visually reflect the failure characteristics. The maximum crack width was observed to be 7 μm, the average porosity was 3.5%, and the surface fluorine content was 15.1%.

[0036] Combining a Long Short-Term Memory (LSTM) network model, with a prediction period of 1000 hours, root mean square error ≤1°, and 5000 training data points, this model, using input microstructure and environmental parameters, predicts the trend of contact angle decay and adhesion reduction in coatings, thus accurately predicting their failure time with an accuracy exceeding 90%. The failure time is calculated using the Weibull distribution. ; In the formula: η=1050 h is the characteristic lifetime; β=2.7 is the shape parameter, which extends the failure time by 35%.

[0037] Among them, the output hydrophobicity angle descent rate and adhesion level of the LSTM model are: ; In the formula: x t h is the input feature of t; t c t The hidden state and memory units are FC; FC is a fully connected layer. The LSTM model uses the Adam optimizer (learning rate 0.001), is trained for 200 epochs, and has a prediction accuracy RMSE ≤ 1° (hydrophobic angle), meeting the requirements for long-term failure prediction.

[0038] VI. Performance Optimization and Prediction: Multivariate analysis of variance (ANOVA) was used to quantify the interactive effects of multiple environmental and process variables, such as temperature, wind speed, and roughness, on icing performance (significance level p<0.01). ; In the formula: Y is the amount of ice accumulation or hydrophobic angle; α i β j γ k The effects of temperature, wind speed, and surface treatment are respectively, and δ represents the error.

[0039] Based on the test data of 30 samples with different thicknesses and roughnesses accumulated in the early stage, ANOVA analysis was conducted to confirm that temperature and wind speed have a highly significant effect on the amount of icing (p<0.001).

[0040] Based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II), multi-objective optimization of coating thickness (0.12-0.18 mm) and surface roughness (1.8-2.2 μm) is performed, considering durability, hydrophobicity, crack resistance, and cost. NSGA-II optimizes coating thickness and surface roughness: ; In the formula: P represents the overall performance; η d For de-icing efficiency; θ c σ is the contact angle; fFor failure stress, C is the process cost; weights w1=0.35, w2=0.35, w3=0.2, w4=0.1.

[0041] The NSGA-II algorithm (population 100, iterations 200, crossover probability 0.8, mutation probability 0.1) was run to optimize the coating with weights of 35% durability, 35% hydrophobicity, 20% crack resistance, and 10% cost. The Pareto optimal solution was obtained with a thickness of 0.15 mm and a roughness of 2.0 μm. Finally, a performance prediction system was constructed using a Long Short-Term Memory (LSTM) network model. By inputting multi-dimensional environmental variables, the system accurately predicted the coating's performance retention over a period of 1200 hours, predicting a contact angle retention rate >90%, thus verifying the long-term effectiveness of the proposed solution.

[0042] This invention presents a highly efficient and intelligent coating process. It innovatively employs a "geometric adaptive spraying" method combined with FEA and PSO algorithms, solving the challenge of uniform coating on complex steel structures (such as angle steel, channel steel, and steel pipes). The thickness deviation is <4%, coverage is increased by 12%, spraying efficiency is improved by 15%, and costs are reduced by 22%. Furthermore, a reliable testing and evaluation system is employed to accurately simulate the actual operating environment of transmission towers (-40~60℃, 0-20m / s wind speed, 10-95%). The testing system for RH (Resistant Hydroelectric Power) combined with multi-source data fusion technology has achieved quantitative and high-precision measurement of the icing process and results, reducing test time by 22%, energy consumption by 15%, ICP registration error ≤0.3mm, and improving performance prediction accuracy by 18% using ANOVA and LSTM models, realistically simulating the operating environment of transmission towers. It optimizes data-driven and predictive capabilities, and for the first time systematically applies models such as RSM, ANOVA, NSGA-II, and LSTM to this field, achieving full-process optimization and prediction from formulation and process to long-term performance, shortening the optimization cycle by 30%, and improving prediction accuracy to over 90%, providing a scientific basis for coating design and engineering applications.

[0043] Example 2: This invention also discloses a superhydrophobic anti-icing coating for transmission tower steel structures, prepared by the above method; the coating thickness is 0.15±0.008 mm, covering a Q235 steel substrate with a surface roughness of 2.0±0.15 μm. Under conditions of -40℃ to 60℃, wind speed of 15 m / s, and RH humidity of 85%, the icing amount is ≤30 g / m³. 2 De-icing efficiency ≥92%.

[0044] The anti-icing coating of this invention exhibits superior anti-icing and durability properties. Through nanocomposite and surface energy regulation, an ultra-high hydrophobicity (contact angle >152°) and low ice adhesion (roll-off angle <7°) coating is obtained. The ice load <22g / m² and the de-icing efficiency ≥92% are far superior to traditional coatings (traditional coatings have an ice load of 50-100g / m² and a de-icing efficiency of 50%-70%). After high-temperature exposure (50°C, 720h), the hydrophobic angle retention rate is >88%, the adhesion is ≥4B, and after rigorous aging tests, the performance degradation rate is <5%, and the predicted failure time is extended by more than 35%.

[0045] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A test method for superhydrophobic anti-icing coating on steel structures of transmission towers, characterized in that, Includes the following steps: Step S1, superhydrophobic coating formulation optimization: using polydimethylsiloxane as the matrix material, heptadecafluorophenyltrimethoxysilane as the modifier, and hydroxyl-modified silica nanoparticles as the reinforcing filler, the formulation was optimized using response surface methodology based on Box-Behnken design to obtain the optimized formulation; The silica mass ratio was 0.15, the nanoparticle mass ratio was 0.2, and the ultrasonic dispersion time was 30 min; the static contact angle θ of the optimized formulation coating was... c ≥152°, roll angle θ r <7°, hardness ≥3H; Step S2, Geometric Adaptive Spraying and Curing: The Q235 steel substrate is pretreated with mechanical sanding and plasma cleaning to achieve a surface roughness of 2.0±0.15μm, a cleanliness level of ASTM D4417 Method B 3a, and an adhesion level of ASTM D33595B. A geometric model of the substrate is established based on finite element analysis software, and the spraying path is optimized using particle swarm optimization algorithm. The spraying angle is controlled at 45°±3°, the speed at 0.25m / s, and the spray gun distance at 150mm, so that the target coating thickness is 0.15±0.008mm. Then, staged thermal curing is performed. Step S3, extreme environment simulation test: In the frozen environment test chamber, simulate the environmental conditions of temperature -40℃-60℃, wind speed 0-20m / s, and relative humidity 10%-95%, and use high-pressure nozzles to simulate freezing rain conditions, with water droplet diameter of 50±3μm and spray rate of 0.5 L / h, so as to form ice on the test sample. Step S4, Multi-source data acquisition: Use industrial cameras and laser rangefinders to acquire icy images and 3D point cloud data of the samples during the test process, use an improved iterative nearest point algorithm to perform data fusion, and store the spatiotemporal related data in the database; Step S5, Failure Mechanism Analysis and Prediction: For samples aged by freeze-thaw cycles and high-temperature exposure, the microstructure and elemental distribution are analyzed using scanning electron microscopy and energy dispersive spectroscopy to obtain data on crack width, porosity, and fluorine content; combined with a long short-term memory network model, the failure time of the coating is predicted. Step S6, Performance Optimization and Prediction: Multifactor variance analysis is used to quantify the interactive effects of temperature, wind speed, and roughness on icing amount; based on non-dominated sorting genetic algorithm, with durability, hydrophobicity, crack resistance and cost as multiple objectives, the coating thickness and roughness parameters are optimized to obtain the optimal solution of thickness 0.15mm and roughness 2.0μm; and a long short-term memory network model is used to predict the performance degradation of the coating in long-term environment.

2. The test method for superhydrophobic anti-icing coating for transmission tower steel structures according to claim 1, characterized in that, In step S1, the ultrasonic dispersion frequency was 40 kHz and the power was 200 W. The dispersion process was equipped with a water-cooled circulation system. The cluster particle size was verified to be ≤100 nm by SEM observation and the surface fluorine content was verified to be 15%±0.5% by XPS analysis.

3. The test method for superhydrophobic anti-icing coating for transmission tower steel structures according to claim 1, characterized in that, In step S2, the plasma cleaning power is 200 W, the radio frequency is 13.56 MHz, the argon flow rate is 20 standard L / min, and the processing time is 5 min.

4. The test method for superhydrophobic anti-icing coating for transmission tower steel structures according to claim 1, characterized in that, In step S2, the phased thermal curing is carried out under nitrogen protection. The curing environment has a humidity of 40%±5% RH and an oxygen content of <2%. The first stage is baked at 60℃ for 1 hour, and the second stage is baked at 80℃ for 2 hours.

5. The test method for superhydrophobic anti-icing coating for transmission tower steel structures according to claim 1, characterized in that, In step S2, geometric modeling based on finite element analysis is performed with a mesh density of 1000 nodes / m. 2 Encryption to 1500 nodes / m in the curvature region 2 ; The overlap rate of the spraying path optimized by the particle swarm optimization algorithm in the high curvature region is ≥95%.

6. The test method for superhydrophobic anti-icing coating for transmission tower steel structures according to claim 1, characterized in that, In step S3, the freezing environment test chamber is equipped with a semiconductor cooling / heating device, an ultrasonic humidifier, a variable frequency axial flow fan, and a high-pressure nozzle; the uniformity deviation of the ice coating amount is <1g / m³. 2 .

7. The test method for superhydrophobic anti-icing coating for transmission tower steel structures according to claim 1, characterized in that, In step S4, the industrial camera has a resolution of no less than 20 megapixels and a sampling frequency of 2 Hz; the laser rangefinder has a measurement accuracy of ±0.005 mm and a point cloud density of 150 points / m. 2 The registration error of the improved iterative nearest point algorithm is ≤0.3 mm.

8. The test method for superhydrophobic anti-icing coating for transmission tower steel structures according to claim 1, characterized in that, In step S5, the freeze-thaw cycle conditions are -20℃ to 10℃ for 12 cycles, each cycle lasting 4 hours, with a heating / cooling rate of 1℃ / min; the high-temperature exposure conditions are 50℃ and 85%±5% RH for 720 hours; the prediction period of the LSTM model is 1000 hours, and the root mean square error is ≤1°.

9. The test method for superhydrophobic anti-icing coating for transmission tower steel structures according to claim 1, characterized in that, In step S6, the population size of the non-dominated sorting genetic algorithm is 100, the number of iterations is 200, the crossover probability is 0.8, and the mutation probability is 0.

1. The weight allocation of the performance indicators is: durability 35%, hydrophobicity 35%, crack resistance 20%, and cost 10%.

10. A superhydrophobic anti-icing coating for steel structures of transmission towers, characterized in that, The coating is prepared by the method described in any one of claims 1-9; the coating thickness is 0.15±0.008 mm, covering a Q235 steel substrate with a surface roughness of 2.0±0.15 μm, and its icing amount is ≤30 g / m² under an environment of -40℃ to 60℃, wind speed of 15 m / s, and humidity of 85% RH. 2 De-icing efficiency ≥92%.

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