The growth environment of corps requires necessary improvements by Chinese solar greenhouses with Pad–Fan Cooling (PFC) systems for reducing their high temperatures in summer. Although computational fluid dynamics (CFD) could dynamically display the changes in humidity, temperature, and wind speed in solar greenhouses, its computational efficiency and accuracy are relatively low. In addition, the use of PFC systems can cool down solar greenhouses in summer, but they will also cause excessive humidity inside the greenhouses, thereby reducing the production efficiency of crops. Most existing studies only verify the effectiveness of a single machine learning (such as ARMA or ARIMA) or deep learning model (such as LSTM or TCN), lacking systematic comparison of different models. In the current study, two machine learning algorithms and three deep learning algorithms were used for their ability to predict a PFC system’s cooling effect, including on humidity, temperature, and wind speed, which were examined using Auto Regression Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Time Convolutional Network (TCN), and Glavnoe Razvedivatelnoe Upravlenie (GRU), respectively. These results show that deep learning algorithms are significantly more effective than traditional machine learning algorithms in capturing the complex nonlinear relationships and spatiotemporal changes inside solar greenhouses. The LSTM model achieves R² values of 0.918 for temperature, 0.896 for humidity, and 0.849 for wind speed on the test set. TCN showed strong performance in identifying high-frequency fluctuations and extreme nonlinear features, particularly in wind speed prediction (test set R² = 0.861). However, it exhibited limitations in modeling certain temperature dynamics (e.g., T6 test set R² = 0.242) and humidity evaporation processes (e.g., T7 training set R² = −0.856). GRU delivered excellent performance, achieving a favorable balance between accuracy and efficiency. It attained the highest prediction accuracy for temperature (test set R² = 0.925) and humidity (test set R² = 0.901), and performed only slightly worse than TCN in wind speed prediction. In summary, deep learning models, particularly GRU, offer more reliable methodological support for greenhouse microclimate prediction, thereby facilitating the precise regulation of cooling systems and scientifically informed crop management.

In the design of gel phase change composite wall materials for solar greenhouses, the alteration of material composition could directly affect the thermal performance of gel phase change composite wall materials. In order to obtain better suitable gel phasechange composite wall material for solar greenhouses, Na₂HPO₄·12H₂O-based gel phasechange materials with different content of ingredient (Na₂SiO₃·9H₂O, C₃₅H₄₉O₂₉, KCl, and nano-α-Fe₂O₃) were obtained via the Taguchi method and machine learning algorithms, such as Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Trees (GBDT). The result shows that the GBDT is more suitable for the thermal performance optimization prediction of gel phase change composite wall materials, including time cooling (TC), latent heat of phase change (ΔH_m), supercooling degree (ΔT), and phase change temperature (T_m). The determination coefficient (R²) of time cooling (TC), latent heat of phase change (ΔH_m), supercooling degree (ΔT), and phase change temperature (T_m) by GBDT are 0.9987, 0.99965, 1, and 0.9995, respectively. The mean absolute error (MAE) coefficient percentage of supercooling degree (ΔT), phase change temperature (T_m), latent heat of phase change (ΔH_m), and time of cooling (TC) by GBDT are 0.32%, 0.25%, 0.17%, and 0.26%, respectively. The root mean square error (RMSE) of supercooling degree (ΔT), phase change temperature (T_m), latent heat of phase change (ΔH_m), and time of cooling (TC) by GBDT are 0.41%, 0.32%, 0.19%, and 0.35%, respectively. The optimal result predicted by GBDT is Na₂HPO₄·12H₂O + 5% Na₂SiO₃·9H₂O + 12% KCl + 0.2% Nano-α-Fe₂O₃ + 3% C₃₅H₄₉O₂₉, which was verified by experiments.

The content of modified materials in multicomponent gel phase change materials directly affects their performance characteristics. To investigate the influence of different contents of modified materials on the performance features of Na₂HPO₄·12H₂O-based multicomponent Gel Phase Change Materials, four single factors (Na₂SiO₃·9H₂O, C₃₅H₄₉O₂₉, KCl, and nano-α-Fe₂O₃) and their interactions were selected as influencing factors. Using the Taguchi method with an L₂₇(3¹³) orthogonal array, multi-step melt–blending experiments were conducted to prepare a novel multi-component phase change material. The characteristics of the new multi-component phase change material, including supercooling degree (ΔT), phase change temperature (T_m), latent heat of phase change (ΔH_m), and cooling time (CT), were obtained. In addition, characterization techniques such as DSC, SEM, FT-IR, and XRD were employed to analyze its thermal properties, microscopic morphology, chemical stability, and crystal structure. Based on the experimental results, the signal-to-noise ratio (S/N) was used to rank the influence of each factor on the quality characteristics, and the p-value from analysis of variance (ANOVA) was employed to evaluate the significance of each factor on the performance characteristics. Then, the effects of each significant factor on the characteristics of the multiple gel phase change materials were analyzed in detail, and the optimal mixing ratio of the new multiple gel phase change materials was selected. The results showed that Na₂SiO₃·9H₂O, KCl, and α-Fe₂O₃ were the most critical process parameters. This research work enriches the selection of composite gel phase change materials for solar greenhouses and provides guidance for the selection of different modified material contents using Na₂HPO₄·12H₂O as the starting material.

In order to achieve fast and accurate life damage online monitoring of the steam turbine rotor, it was significant to propose an empirical-statistical model using a machine learning algorithm instead of finite element simulation to improve the effect of operation. The finite element method was used to calculate the maximum stress during the start-up schedule. The linear CDM (Continuum Damage Mechanics) and nonlinear CDM were applied to assess the creep-fatigue damage of the steam turbine rotor. A empirical-statistical model of a 600 MW steam turbine rotor was established by using temperature change rate and maximum stress according to the finite element result samples, which is proposed by compared R2 of SVR (Support Vector Regression), LSTM (Long Short-Term Memory) and RRM (Ridge Regression Method), which was also verified by finite element simulation under a random start-up parameters. The results showed that the creep-fatigue damage could be calculated by nonlinear CDM for more safety rather than linear CDM. The R2 of SVR (Support Vector Regression), LSTM (Long Short-Term Memory) and RRM were 0.9377, 0.9647 and 0.999, respectively. RRM was more suitable for the empirical-statistical model establishment of the steam turbine rotor. By comparing the empirical-statistical model result and finite element result under random parameters of the start-up schedule, the error is 0.51%.

This exploratory study compared early-winter thermal conditions in east–west (E-W) and north–south (N-S) tomato ridge blocks within one Chinese solar greenhouse from 22 November to 3 December 2025. A thin-film partition separated the blocks. We measured canopy-air, soil-surface, plastic-film, north-wall, and rear-slope temperatures and used transient computational fluid dynamics (CFD) to interpret spatial patterns. Mean canopy-air temperatures were 15.34 °C (E-W) and 15.22 °C (N-S), whereas mean soil-surface temperatures were 14.43 and 13.31 °C, respectively. On a representative cloudy day, daytime soil-surface temperature was 2.48 °C lower in the N-S block. The CFD model reproduced broad surface-temperature cycles on both representative days; canopy-air comparison was restricted to the sunny day from 12:30 to 21:00. Under the tested operating conditions, layout-related contrasts were more evident at the soil surface than in mean canopy air. Because the comparison involved two unreplicated blocks within one greenhouse and did not include crop-response measurements, these findings should be interpreted as exploratory thermal associations rather than evidence of a general causal or agronomic advantage of either ridge orientation.