格子玻尔兹曼方法及其在大气湍流研究中的应用

文章的目的是对格子玻尔兹曼方法进行系统的介绍，格子玻尔兹曼方法(Lattice Boltzmann Method)的出现直接来源于20世纪60年代的元胞自动机(Cellular Automata)思想，而这一方法用于解决流动现象时，又可以追溯到19世纪的分子运动论，求解的是Boltzmann提出的玻尔兹曼输运方程，因此将这一方法称为格子玻尔兹曼方法，之前也被称为格子气自动机(Lattice Gas Automaton)。该方法多用于研究复杂现象，如材料晶体凝聚时的生长过程、城市土地利用的演化等方面。在20世纪70年代由Hardy、Pomeau和Pazzis建立了第一个用于研究流体运动的格子气自动机，此后，这一方法被广泛用来模拟各种流动问题，诸如二相流、孔隙介质中的渗流等，并根据这一方法开发了相应的商业软件PowerFlow。同时，格子玻尔兹曼方法由于其在微观水平描述运动的特点，成为研究湍流的一个很好的数值计算工具，特别是用其进行直接数值模拟(DNS)计算，成为继传统的差分法、有限体积法和谱方法之后的又一有力的手段。而作为大气运动的一个主要现象的大气湍流，比普通湍流更加复杂，在这里着重介绍了大气湍流的特点和应用格子玻尔兹曼方法模拟湍流的发展过程。     

Complexity theory, spatial simulation models, and land use dynamics in the Northern Ecuadorian Amazon

Our research questions and analytical approaches are used to examine coupled human-natural systems in the Northern Ecuadorian Amazon. They are based on complexity theory and extend from our earlier work in Cellular Automata (CA) in which land use/land cover (LULC) change patterns were spatially simulated to examine deforestation and agricultural extensification on household farms. The basic intent is to understand linkages between people and the environment by explicitly considering pattern-process relationships and the nature of feedback mechanisms among social, biophysical, and geographical factors that influence LULC dynamics within the study area. In this research, we describe how our CA modeling approach emphasizes the human dimensions of LULC change by including socio-economic and demographic characteristics at the household-level along with biophysical data that describe the resource endowments of farms, geographic accessibility of farms to roads and communities, and the evolving nature of human-environment interactions over time and space in response to exogenous and endogenous factors.A LULC change scenario is examined by comparing model outcomes generated for a base CA model and an alternative CA model to explore the effects of increases in household income on land use change patterns at the farm level, achieved as a consequence of improved geographic accessibility to roads and communities and increased off-farm employment as a household livelihood strategy. Growth or transitions rules in our CA model, as well as neighborhood associations are sensitive to socio-economic and demographic factors of households, resource endowments of farms, geographic accessibility, and the uncertainty associated with peasant farming in a frontier setting. Model outcomes indicate that increases in household income are associated with more land in pasture and more land being cultivated for crops as a result of greater access to agricultural markets. In addition, more land in secondary forest succession occurs as a consequence of greater access to roads and communities, thereby, affording a better opportunity for off-farm employment and greater levels of household income.     

A hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model

Cellular automata (CA) models are commonly used to model vegetation dynamics, with the genetic algorithm (GA) being one method of calibration. This article investigates different GA settings, as well as the combination of a GA with a local optimiser to improve the calibration effort. The case study is a pattern-calibrated CA to model vegetation regrowth in central Victoria, Australia. We tested 16 GA models, varying population size, mutation rate, and level of allowable mutation. We also investigated the effect of applying a local optimiser, the Nelder?Mead Downhill Simplex (NMDS) at GA convergence. We found that using a decreasing mutation rate can reduce computational cost while avoiding premature GA convergence, while increasing population size does not make the GA more efficient. The hybrid GA-NMDS can also reduce computational cost compared to a GA alone, while also improving the calibration metric. We conclude that careful consideration of GA settings, including population size and mutation rate, and in particular the addition of a local optimiser, can positively impact the efficiency and success of the GA algorithm, which can in turn lead to improved simulations using a well-calibrated CA model.     

Calibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning

The reliability of raster cellular automaton (CA) models for fine-scale land change simulations has been increasingly questioned, because regular pixels/grids cannot precisely represent irregular geographical entities and their interactions. Vector CA models can address these deficiencies due to the ability of the vector data structure to represent realistic urban entities. This study presents a new land parcel cellular automaton (LP-CA) model for simulating urban land changes. The innovation of this model is the use of ensemble learning method for automatic calibration. The proposed model is applied in Shenzhen, China. The experimental results indicate that bagging-Naïve Bayes yields the highest calibration accuracy among a set of selected classifiers. The assessment of neighborhood sensitivity suggests that the LP-CA model achieves the highest simulation accuracy with neighbor radius r = 2. The calibrated LP-CA is used to project future urban land use changes in Shenzhen, and the results are found to be consistent with those specified in the official city plan.     

集成GIS和细胞自动机模型进行地理时空过程模拟与预测的新方法

目前商用地理信息系统（GIS）不能完整地表达地理实体的时态信息和时空关系，缺乏时空分析和时空动态模拟的能力，这已成为GIS界的一个共识，然而，未来GIS在各应用领域的深入发展以及在实现“数字地球”战略过程中，都要求发展新的时空分析和模拟方法，细胞自动机（Cellular Automaton)是一种“自下而上”的动态模拟建模框架，具有模拟地理复要系统时空演化过程的能力，首先将标准CA模型的4元组进行扩展以满足GIS环境下时空动态模型的要求，然后以城市土地利用演化这一动态过程为例，建立了土地利用演化动态模拟与预测模型（LESP），最后运用此模型对包头市城市扩展和土地可持续利用演化进行了比较成功的模拟和预测。     

Integrating the system dynamic and cellular automata models to predict land use and land cover change

Land use and land cover change (LULCC) is a widely researched topic in related studies. A number of models have been established to simulate LULCC patterns. However, the integration of the system dynamic (SD) and the cellular automata (CA) model have been rarely employed in LULCC simulations, although it allows for combining the advantages of each approach and therefore improving the simulation accuracy. In this study, we integrated an SD model and a CA model to predict LULCC under three future development scenarios in Northern Shanxi province of China, a typical agro-pastoral transitional zone. The results indicated that our integrated approach represented the impacts of natural and socioeconomic factors on LULCC well, and could accurately simulate the magnitude and spatial pattern of LULCC. The modeling scenarios illustrated that different development pathways would lead to various LULCC patterns. This study demonstrated the advantages of the integration approach for simulating LULCC and suggests that LULCC is affected to a large degree by natural and socioeconomic factors.     

A computational model of the digestive gland epithelial cell of marine mussels and its simulated responses to oil-derived aromatic hydrocarbons

This paper describes a computational model of digestive gland epithelial cells (digestive cells) of marine mussels. These cells are the major environmental interface for uptake of contaminants, particularly those associated with natural particulates that are filtered from seawater by mussels. Digestive cells show well characterised reactions to exposure to lipophilic xenobiotics, such as oil-derived aromatic hydrocarbons (AHs), which accumulate in these cells with minimal biotransformation. The simulation model is based on processes associated with the flux of carbon through the cell. Physiological parameters such as fluctuating food concentration, cell volume, respiration, secretion/excretion, storage of glycogen and lipid, protein/organelle turnover (autophagy/resynthesis) and export of carbon to other tissues of the mussel are all included in the model. The major response to AHs is induction of increased autophagy in these cells. Simulations indicate that the reactions to AHs and food deprivation correspond well with responses measured in vivo.     

Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images

Cellular automata (CA) have proven to be very effective for simulating and predicting the spatio-temporal evolution of complex geographical phenomena. Traditional methods generally pose problems in determining the structure and parameters of CA for a large, complex region or a long-term simulation. This study presents a self-adaptive CA model integrated with an artificial immune system to discover dynamic transition rules automatically. The model’s parameters are allowed to be self-modified with the application of multi-temporal remote sensing images: that is, the CA can adapt itself to the changed and complex environment. Therefore, urban dynamic evolution rules over time can be efficiently retrieved by using this integrated model. The proposed AIS-based CA model was then used to simulate the rural-urban land conversion of Guangzhou city, located in the core of China’s Pearl River Delta. The initial urban land was directly classified from TM satellite image in the year 1990. Urban land in the years 1995, 2000, 2005, 2009 and 2012 was correspondingly used as the observed data to calibrate the model’s parameters. With the quantitative index figure of merit (FoM) and pattern similarity, the comparison was further performed between the AIS-based model and a Logistic CA model. The results indicate that the AIS-based CA model can perform better and with higher precision in simulating urban evolution, and the simulated spatial pattern is closer to the actual development situation.     

细胞自动机在地震波传播研究中的应用

本文基于细胞自动机在地震波传播研究中的最近成果，阐述了其基本的研究思路与理论框架，给出了一个计算实例．在此基础上就细胞自动机方法与传统波动方程的衔接关系及复杂介质中地震波正反问题作了讨论，并对研究前景进行展望．