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基于PC老化行為的西藏大氣環境嚴酷度預測

Prediction of atmospheric environmental severity in Tibet based on polycarbonate (PC) aging behavior

  • 摘要: 通過研究聚碳酸酯(PC)在西藏自治區(西藏)10個典型大氣站點自然暴露試驗1年的老化行為,以色差作為指標評估西藏地區PC服役大氣環境嚴酷度時空分布規律. 采集了西藏10個典型大氣站點8類環境因素數據年均值(2021年4月—2022年3月),分析了西藏地區氣候環境特點及氣候分布區域,為環境嚴酷度評價提供準確輸入. 通過在10個站點開展自然環境試驗,研究發現PC老化過程中表觀失光率、色差逐漸上升,力學性能如拉伸強度、拉伸斷裂應變等波動性下降,最終選擇以規律性能較好的色差作為PC老化評估指標. 通過Pearson 相關性研究分析,認為各類環境因素與地理信息坐標等信息之間有信息冗余,環境參量可以進一步優選. 通過灰色關聯度、回歸分析,篩選出影響PC老化的4個敏感環境因子分別為:日照時間、海拔高度、平均相對濕度、降水時間;通過構建反向傳播人工神經網絡(BP-ANN)模型并優化模型參數,建立具有良好訓練精度及泛化能力的“環境?材料”映射模型. 不同學習精度訓練結果表明,過低的學習精度將導致訓練程度不夠,預測精度較低;過高的學習精度將導致過擬合,使預測陷入局部最小值. 輸入西藏全域28個城市氣象數據,輸入已訓練好的人工神經網絡模型,預測得到西藏28個城市PC老化一年色差值. 基于Griddate插值計算,得到西藏地區嚴酷度空間分布地圖,結果顯示低海拔的藏東嚴酷度較低,而高海拔藏西無人區及藏北高原等地區嚴酷度較高.

     

    Abstract: The spatiotemporal distribution of atmospheric environmental severity in Tibet was evaluated and predicted based on polycarbonate (PC) chromatic aberrations. This study collected the annual average data (April 2021 to March 2022) of eight types of environmental factors from 10 typical atmospheric sites in Tibet. The climatic characteristics and climate distribution areas were analyzed to obtain accurate input to evaluate environmental severity. Natural environmental tests were conducted at 10 sites to analyze the regulation of PC degradation. The results showed that the gloss decreased and chromatic aberration gradually increased during PC aging, and mechanical properties, such as tensile strength and tensile strain at break, decreased with fluctuations. Thus, chromatic aberration was selected as a PC aging evaluation index owing to its excellent performance. Pearson’s correlation analysis was used to determine the information redundancy hidden in various environmental factors and geographic information coordinates. The environmental parameters were further optimized, and the factors highly related to PC aging were sunshine time, altitude, average relative humidity, and precipitation time. The “environmental material” mapping model with excellent training precision and generalizability was established using the Back Propagation Artificial Neural Network. By inputting the environmental data of 28 cities in Tibet into the well-built models, the severity was predicted and visualized to form spatial distribution maps using the Griddate interpolation method. The results showed that the low-altitude areas in eastern Tibet presented low severity. By training with different learning accuracies, the results revealed that low learning precision caused insufficient training and led to low prediction accuracy, whereas high learning precision led to overfitting and a prediction of the local minimum. The meteorological data of the 28 cities in Tibet were loaded into a well-trained artificial neural network model to predict the chromatic aberration value of PC aging in 28 cities in Tibet. A spatial distribution map of severity in Tibet was obtained based on the Griddate interpolation calculation. The results indicated that severity was much higher in summer than in winter, and the severity of the northwest area was the highest even in winter. The exact quantitative evaluations of severity played a significant role in the safety service for the equipment and facilities in Tibet.

     

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