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A numerical implementation of the Dynamic Thermal Network method for long time series simulation of conduction in multi-dimensional non-homogeneous solids

机译:动态热网络方法用于多维非均匀固体中传导的长时间序列模拟的数值实现

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摘要

The Dynamic Thermal Network (DTN) approach to the modelling of transient conduction was conceived by Claesson (1999,2002,2003) as an extension of the network representation of steady-state conduction processes. The method is well suited to the simulation of building fabric components such as framed walls and thermally massive structures such as basements but can also be applied to the long timescale simulation of other conduction problems. The theoretical basis of the method and its discretized form is outlined in this paper and a new numerical procedure for the calculation of the necessary weighting factor data is presented. Such data has previously been generated for three-dimensional bodies by a heuristic process of blending analytical solutions and numerical data. The numerical approach reported here has the advantage of accommodating parametric representations of multi-dimensional geometries and allows the data to be produced in an automated fashion and so more easily incorporated into simulation tools. Enhancements to the data reduction procedure and a generalised approach to representing complex boundary conditions are also presented. The numerical procedure has been validated by a series of comparisons with analytical conduction heat transfer solutions and discretization errors were found to be acceptably small. Compared to numerical methods, calculations using the DTN method were found to be up to four orders of magnitude quicker but with comparable accuracy.
机译:Claesson(1999,2002,2003)将动态热网络(DTN)方法用于瞬态传导建模,将其作为稳态传导过程的网络表示的扩展。该方法非常适合于模拟建筑结构部件(如框架墙)和热大体积结构(如地下室),但也可以应用于其他传导问题的长时间仿真。本文概述了该方法的理论基础及其离散形式,并提出了一种新的数值方法,用于计算必要的加权因子数据。先前已经通过混合分析解决方案和数值数据的启发式方法为三维物体生成了此类数据。此处报告的数值方法的优点是可以容纳多维几何的参数表示形式,并允许以自动方式生成数据,因此更容易将其合并到仿真工具中。还介绍了数据缩减程序的增强功能和代表复杂边界条件的通用方法。数值程序已通过与分析传导热传递解决方案的一系列比较得到验证,并且离散误差被发现很小。与数字方法相比,使用DTN方法进行的计算被发现快了四个数量级,但准确性相当。

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