温度变化对饱和带土壤传热随机性影响的实验研究

Science Technology and Engineering（2019）

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Abstract

土壤热传递过程中存在着一定的随机现象,在众多影响因素中温度显得尤为重要.为了研究温度变化对土壤传递随机性的影响,选取土壤类型为砾砂,U型埋管为热源,在水位差为30 cm的渗流条件下采集了不同热源温度、不同土壤环境温度U型管下游沿渗流方向饱和带中6个测点24 h的逐时温度数据.采用GM(1,1)灰色模型计算确定性数据,以达到从实测温度中分离出随机温度的目的;进而对随机温度进行统计学分析,研究热源温度、土壤环境温度变化对随机温度的影响.最后采用相对灵敏度法,判断两种温度变化对随机温度的影响.结果表明,热源温度增加随机温度分布更分散,土壤环境温度增加随机温度分布更集中,随机温度对于热源温度变化更为敏感.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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