Based on Python language, this system implements the Wuzhou Meteorological Statistical Yearbook system, which automatically calculates and generates Excel table. This system can accurately and quickly collect annual m...Based on Python language, this system implements the Wuzhou Meteorological Statistical Yearbook system, which automatically calculates and generates Excel table. This system can accurately and quickly collect annual meteorological data, form meteorological yearbook reports for government departments, and is of great significance for the development of meteorological data business by the Wuzhou Meteorological Bureau.展开更多
With the development of the economic and low⁃carbon society,high⁃performance building(HPB)design plays an increasingly important role in the architectural area.The performance of buildings usually includes the buildin...With the development of the economic and low⁃carbon society,high⁃performance building(HPB)design plays an increasingly important role in the architectural area.The performance of buildings usually includes the building energy consumption,building interior natural daylighting,building surface solar radiation,and so on.Building performance simulation(BPS)and multiple objective optimizations(MOO)are becoming the main methods for obtaining a high performance building in the design process.Correspondingly,the BPS and MOO are based on the parametric tools,like Grasshopper and Dynamo.However,these tools are lacking the data analysis module for designers to select the high⁃performance building more conveniently.This paper proposes a toolkit“GPPre”developed based on the Grasshopper platform and Python language.At the end of this paper,a case study was conducted to verify the function of GPPre,which shows that the combination of the sensitivity analysis(SA)and MOO module in the GPPre could aid architects to design the buildings with better performance.展开更多
Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the methods could be implemented...Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the methods could be implemented more efficiently in a local manner and that the local approaches could match or even surpass the accuracy of the global implementations. In this work, three localization approaches are compared: a local RBF method, a partition of unity method, and a recently introduced modified partition of unity method. A simple shape parameter selection method is introduced and the application of artificial viscosity to stabilize each of the local methods when approximating time-dependent PDEs is reviewed. Additionally, a new type of quasi-random center is introduced which may be better choices than other quasi-random points that are commonly used with RBF methods. All the results within the manuscript are reproducible as they are included as examples in the freely available Python Radial Basis Function Toolbox.展开更多
基金Supported by Research Project of Guangxi Meteorological Bureau(GUIQIKE 2021M07)Scientific Research and Technology Development Project of Wuzhou Meteorological Bureau(WUQIKE Z2021009)Innovation Team Project of Wuzhou Meteorological Bureau.
文摘Based on Python language, this system implements the Wuzhou Meteorological Statistical Yearbook system, which automatically calculates and generates Excel table. This system can accurately and quickly collect annual meteorological data, form meteorological yearbook reports for government departments, and is of great significance for the development of meteorological data business by the Wuzhou Meteorological Bureau.
文摘With the development of the economic and low⁃carbon society,high⁃performance building(HPB)design plays an increasingly important role in the architectural area.The performance of buildings usually includes the building energy consumption,building interior natural daylighting,building surface solar radiation,and so on.Building performance simulation(BPS)and multiple objective optimizations(MOO)are becoming the main methods for obtaining a high performance building in the design process.Correspondingly,the BPS and MOO are based on the parametric tools,like Grasshopper and Dynamo.However,these tools are lacking the data analysis module for designers to select the high⁃performance building more conveniently.This paper proposes a toolkit“GPPre”developed based on the Grasshopper platform and Python language.At the end of this paper,a case study was conducted to verify the function of GPPre,which shows that the combination of the sensitivity analysis(SA)and MOO module in the GPPre could aid architects to design the buildings with better performance.
文摘Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the methods could be implemented more efficiently in a local manner and that the local approaches could match or even surpass the accuracy of the global implementations. In this work, three localization approaches are compared: a local RBF method, a partition of unity method, and a recently introduced modified partition of unity method. A simple shape parameter selection method is introduced and the application of artificial viscosity to stabilize each of the local methods when approximating time-dependent PDEs is reviewed. Additionally, a new type of quasi-random center is introduced which may be better choices than other quasi-random points that are commonly used with RBF methods. All the results within the manuscript are reproducible as they are included as examples in the freely available Python Radial Basis Function Toolbox.
