上悬窗条件下通风控制腔室火灾温度的实验研究

随着建筑结构的发展,上悬窗日益成为城市建筑窗户的选择,但这种通风条件下腔室火灾的发展特性仍缺乏研究.利用1:8相似比例的缩尺寸腔室火灾实验台,实验研究了上悬窗不同开口尺寸、不同开窗角度下,通风控制的腔室火灾温度发展特性.实验结果表明,通风控制条件下,腔室火灾温度随着上悬窗开口尺寸和开窗角度的增加而增加,并通过能量平衡分...     

Implications of network particle tracking (NPT) for ecological model interpretation

Network particle tracking (NPT), building on the foundation of network environ analysis (NEA), is a new development in the definition of coherence relations within and between connected systems. This paper evaluates three ecosystem models in a comparison of throughflow- and storage-based NEA and NPT. Compartments in models with high indirect effects and Finn cycling showed low correlation of NEA storage and throughflow with particle repeat visits and numbers of particles in compartments at steady state. Conversely, the correlation between NEA and NPT results was high with two models having lower indirect effects and Finn cycling. Analysis of ecological orientors associated with NEA showed NPT to fully support conventional NEA results when the common conditions of donor control and steady state are satisfied. Particle trajectories are recorded in the new concept of a particle “passport”. Ability to track and record particle in-system histories enables views of multiple scales and opens the possibility of making pathway-dependent modeling decisions. NPT may also enable modeling of time, allowing integration of Newtonian, organismal and stochastic modeling perspectives in a single comprehensive analysis.     

火源位置对腔室火流动特性影响的实验研究

腔室火流动特性是影响腔室火灾蔓延与通风状况的重要因素。通过一系列小尺度腔室火实验,研究了火源位置变化对腔室火流动特性的影响。实验结果表明,随着火源沿腔室底部从壁面向开口方向移动,在开口中性面以上,同一高度处压差与流速增大,中性面高度和烟气层高度均降低,并导致开口质量流率增大。与火源强度相比,火源位置变化对烟气层高度的影响更为显著。火源位置对中性面高度及烟气层高度的影响在壁面处及开口处更为显著,腔室中部位置变化的影响相对较小。火源由壁面向开口移动,会造成火焰高度降低和水平伸长量增加。基于实验数据,给出了考虑耦合火源位置的腔室内火焰水平伸长量的表达式。研究结果可为相关场景下的腔室火灾理论模型提供实验结果支撑。     

Consideration of translocation into a growth model of a plant organ

Taking account of the Bertalanffy's differential equation on animal growth, plant growth is also considered as the net result of anabolism and catabolism. When we, however, consider the growth of a plant organ, it is necessary to add a term of translocation because it plays an important role in the growth of plant organs, such as leaf and fruit. Considering translocation, therefore, a growth model of a plant organ was proposed on the basis of the compartment model for estimating the carbon balance in the organ by using the experimental data on translocation, photosynthesis and respiration of a tropical fruit of durian (Durio zibethinus Murray). The present growth model of a plant organ belongs to an extended Bertalanffy's growth equation, and was possible to be transformed into the simple Bertalanffy's growth equation on the basis of the proportionality between the growth and translocation rates. The Bertalanffy's growth equation of a plant organ was also possible to apply to that of the whole plant on the assumption of the allomeric relationship between a plant organ and the whole plant.     

Dynamic environ analysis of compartmental systems: A computational approach

Ecosystems are often modeled as stocks of matter or energy connected by flows. Network environ analysis (NEA) is a set of mathematical methods for using powers of matrices to trace energy and material flows through such models. NEA has revealed several interesting properties of flow–storage networks, including dominance of indirect effects and the tendency for networks to create mutually positive interactions between species. However, the applicability of NEA is greatly limited by the fact that it can only be applied to models at constant steady states. In this paper, we present a new, computationally oriented approach to environ analysis called dynamic environ approximation (DEA). As a test of DEA, we use it to compute compartment throughflow in two implementations of a model of energy flow through an oyster reef ecosystem. We use a newly derived equation to compute model throughflow and compare its output to that of DEA. We find that DEA approximates the exact results given by this equation quite closely – in this particular case, with a mean Euclidean error ranging between 0.0008 and 0.21 – which gives a sense of how closely it reproduces other NEA-related quantities that cannot be exactly computed and discuss how to reduce this error. An application to calculating indirect flows in ecosystems is also discussed and dominance of indirect effects in a nonlinear model is demonstrated.     

Dynamic compartment approach for modelling regimes of carbon cycle functioning in bog ecosystems

Svirezhev's method of dynamic model design by a given “storage-flow” diagram [Svirezhev Y.M., 1997. On some general properties of trophic networks. Ecol. Model. 99, 7–17] is developed and used for investigating dynamic regimes of carbon cycle functioning in a typical boreal transitional bog ecosystem. Ecosystems are often represented by static “storage-flow” diagrams reflecting their structure and matter or energy transfer between components at fixed time moments. Using the data of such diagrams aggregated in ecological field studies one can construct a dynamic model of the ecosystem to predict its future behaviour and to estimate a response to external perturbations—natural and human. Stability of both current equilibrium and possible alternative steady states and more complicated attractors are studied under two types of parameter perturbation: CO₂ atmospheric concentration increase initiated by greenhouse effect, and change in the rate of carbon output from dead organic matter and litter which depends on the water table level and possible peat excavation. Calculation of bifurcation curves gives areas in the parameter space where stable functioning of carbon cycle is provided. Steady states can be interpreted as raised bog, meadow, forest and fen. CO₂ concentration increase leads the current state of transitional bog to loose stability with appearance of oscillatory dynamics and further evolution to the chaotic attractor. The model is rich by chaotic solutions serving as transition regimes between regular steady and periodic attractors. Another chaotic regime is formed from forest equilibrium and exists in the same area of phase space where current equilibrium is stable.