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Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python

Xuying Ma, Ian Longley, Jennifer Salmond, Jay Gao

《环境科学与工程前沿（英文）》 2020年第14卷第3期 doi: 10.1007/s11783-020-1221-5

摘要： PyLUR comprises four modules for developing and applying a LUR model. It considers both conventional and novel potential predictor variables. GDAL/OGR libraries are used to do spatial analysis in the modeling and prediction. Developed on Python platform, PyLUR is rather efficient in data processing. Land use regression (LUR) models have been widely used in air pollution modeling. This regression-based approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment. Although conceptually quite simple, its successful implementation requires detailed knowledge of the area, expertise in GIS, statistics, and programming skills, which makes this modeling approach relatively inaccessible to novice users. In this contribution, we present a LUR modeling and pollution-mapping software named PyLUR. It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently. This self-developed software comprises four modules: a potential predictor variable generation module, a regression modeling module, a model validation module, and a prediction and mapping module. The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR (with similar functions implemented on R language platform) in terms of model accuracy, processing efficiency and software stability. The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined. Furthermore, PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.

关键词： LUR Air pollution modelling GIS spatial analysis GDAL/OGR Python Pollutant concentration mapping

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Application of python-based Abaqus preprocess and postprocess technique in analysis of gearbox vibration

Guilian YI, Yunkang SUI, Jiazheng DU

《机械工程前沿（英文）》 2011年第6卷第2期页码 229-234 doi: 10.1007/s11465-011-0128-z

摘要：

To reduce vibration and noise, a damping layer and constraint layer are usually pasted on the inner surface of a gearbox thin shell, and their thicknesses are the main parameters in the vibration and noise reduction design. The normal acceleration of the point on the gearbox surface is the main index that can reflect the vibration and noise of that point, and the normal accelerations of different points can reflect the degree of the vibration and noise of the whole structure. The K-S function is adopted to process many points’ normal accelerations as the comprehensive index of the vibration characteristics of the whole structure, and the vibration acceleration level is adopted to measure the degree of the vibration and noise. Secondary development of the Abaqus preprocess and postprocess on the basis of the Python scripting programming automatically modifies the model parameters, submits the job, and restarts the analysis totally, which avoids the tedious work of returning to the Abaqus/CAE for modifying and resubmitting and improves the speed of the preprocess and postprocess and the computational efficiency.

关键词： Abaqus secondary development Python language vibration and noise reduction K-S function vibration acceleration level