In this article,a new characteristic finite difference method is developed for solving miscible displacement problem in porous media.The new method combines the characteristic technique with mass-preserving interpolat...In this article,a new characteristic finite difference method is developed for solving miscible displacement problem in porous media.The new method combines the characteristic technique with mass-preserving interpolation,not only keeps mass balance but also is of second-order accuracy both in time and space.Numerical results are presented to confirm the convergence and the accuracy in time and space.展开更多
National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies a...National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches can be found in the literature. This study reviews and categorizes these. The review is followed by a discussion of the relevant methods for the disaggregation of climate action plans. It is seen that methods employing proxy data, machine learning models, and geostatistical ones are the most relevant methods for the spatial disaggregation of national energy and climate plans. The analysis offers guidance for selecting appropriate methods based on factors such as data availability at the municipal level and the presence of spatial autocorrelation in the data.As the urgency of addressing climate change escalates, understanding the spatial aspects of national energy and climate strategies becomes increasingly important. This review will serve as a valuable guide for researchers and practitioners applying spatial disaggregation in this crucial field.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.22CX03020A).
文摘In this article,a new characteristic finite difference method is developed for solving miscible displacement problem in porous media.The new method combines the characteristic technique with mass-preserving interpolation,not only keeps mass balance but also is of second-order accuracy both in time and space.Numerical results are presented to confirm the convergence and the accuracy in time and space.
基金funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No.101036458.
文摘National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches can be found in the literature. This study reviews and categorizes these. The review is followed by a discussion of the relevant methods for the disaggregation of climate action plans. It is seen that methods employing proxy data, machine learning models, and geostatistical ones are the most relevant methods for the spatial disaggregation of national energy and climate plans. The analysis offers guidance for selecting appropriate methods based on factors such as data availability at the municipal level and the presence of spatial autocorrelation in the data.As the urgency of addressing climate change escalates, understanding the spatial aspects of national energy and climate strategies becomes increasingly important. This review will serve as a valuable guide for researchers and practitioners applying spatial disaggregation in this crucial field.
