Modeling of coupled transfer of water, heat and solute in saline loess considering sodium sulfate crystallization

Xu, Jian; Lan, Wei; Ren, Cheng; Zhou, Xiangang; Wang, Songhe; Yuan, Jian

doi:10.1016/j.coldregions.2021.103335

Cited by 93 publications

(30 citation statements)

References 33 publications

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“…mongolica, and larch forests may be because of the soil organic matter content, a determinant of soil quality, which can affect the physical, chemical, and biological characteristics of soil [20]. The balance between the mineralization rate and the organic matter input of different types of carbon determines the accumulation degree of SOC, and the organic matter input is mainly up to the amount of organic matter and the humization coefficient of organic residues [21]. The variation in the SOC content is mainly affected by the vegetation type under the same climate and soil parent material [22].…”

Section: Discussionmentioning

confidence: 99%

Effects of Forest Types on SOC and DOC in the Permafrost Region of the Daxing’anling Mountains

Du¹,

Wang²,

Wu³

et al. 2022

Processes

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There is a “symbiotic relationship” between permafrost and the forest ecosystem; the melted permafrost provides sufficient water for forest growth, and the forest ecosystem plays an important role in protecting the permafrost. Aiming to study the effects of different forest types on soil organic carbon (SOC) and dissolved organic carbon (DOC) in the permafrost region of the Daxing’anling Mountains, this research focuses on the soil of the three forest types of pinus sylvestris var. mongolica forest, larch forest, and birch forest in Beiji Village, Mohe County, Daxing’anling Region, and collected vertical profile soil samples from the three soil layers of 0–10, 10–20, and 20–30 cm at three different sites types (upslope, mesoslope, and downslope) in August 2017. The results show that the forest type is the main influencing factor for the content of SOC and DOC. The site type has a significant effect on the content of SOC and DOC in the three forest types, but the difference varies slightly (p > 0.05). The content of SOC and DOC is negatively correlated with the depth of the soil layer of the vertical profile. The geodetector data analysis shows that there are significant differences (p < 0.05) among the contents of SOC and DOC in the three forest types. In conclusion, this study contributes to an in-depth understanding of carbon storage, the carbon dynamics of SOC, and the effects of different forest types on carbon balance in permafrost regions, and it provides a scientific basis for the study of the carbon cycle mechanism in permafrost regions.

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Section: Discussionmentioning

confidence: 99%

Effects of Forest Types on SOC and DOC in the Permafrost Region of the Daxing’anling Mountains

Du¹,

Wang²,

Wu³

et al. 2022

Processes

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“…According to the result, the UCS is highly correlated with cement. For input variables, most of the correlations are relatively low (less than 0.5), suggesting that these variables will not produce multicollinearity problems [72][73][74].…”

Section: Datasetmentioning

confidence: 99%

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

et al. 2022

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High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.

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“…From the establishment of mathematical models for theoretical studies [17][18][19][20][21][22][23][24][25] to quantitative analysis by high-precision experiments [26][27][28][29], the methods researchers use to solve scientific problems are constantly advanced. At the same time, the advancement of science and technology and the increasing sophistication of CFD technology [30] have brought great changes to the research methods of impinging jets.…”

Section: Introductionmentioning

confidence: 99%

A Numerical Study of a Submerged Water Jet Impinging on a Stationary Wall

Wang

Liu³

et al. 2022

JMSE

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The impinging jet is a classical flow model with relatively simple geometric boundary conditions, and it is widely used in marine engineering. In recent years, scholars have conducted more and more fundamental studies on impact jets, but most of the classical turbulence models are used in numerical simulations, and the accuracy of their calculation results is still a problem in regions with large changes in velocity gradients such as the impact zone. In order to study the complex flow characteristics of the water flow under the condition of a submerged jet impacting a stationary wall, the Wray–Agarwal turbulence model was chosen for the Computational Fluid Dynamics (CFD) numerical simulation study of the impacting jet. Continuous jets with different Reynolds numbers and different impact heights H/D were used to impact the stationary wall, and the results show that the jet flow structure depends on the impact height and is relatively independent of the Reynolds number. With the increase in the impact height, the diffusion of the jet reaching the impact area gradually increases, and its velocity gradually decreases. As the impact height increases, the maximum pressure coefficient decreases and the rate of decrease increases gradually, and the dimensionless pressure distribution is almost constant. In this paper, the flow field structure and pressure characteristics of a continuous submerged jet impacting a stationary wall are explored in depth, which is of great guidance to engineering practice.

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Modeling of coupled transfer of water, heat and solute in saline loess considering sodium sulfate crystallization

Cited by 93 publications

References 33 publications

Effects of Forest Types on SOC and DOC in the Permafrost Region of the Daxing’anling Mountains

Effects of Forest Types on SOC and DOC in the Permafrost Region of the Daxing’anling Mountains

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

A Numerical Study of a Submerged Water Jet Impinging on a Stationary Wall

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