Abstract
This paper compares approaches for accurate numerical modeling of transients in the pipe element of district heating systems. The distribution grid itself affects the heat flow dynamics of a district heating network, which subsequently governs the heat delays and entire efficiency of the distribution. For an efficient control of the network, a control system must be able to predict how “temperature waves” move through the network. This prediction must be sufficiently accurate for real-time computations of operational parameters. Future control systems may also benefit from the accumulation capabilities of pipes. In this article, the key physical phenomena affecting the transients in pipes were identified, and an efficient numerical model of aboveground district heating pipe with heat accumulation was developed. The model used analytical methods for the evaluation of source terms. Physics of heat transfer in the pipe shells was captured by one-dimensional finite element method that is based on the steady-state solution. Simple advection scheme was used for discretization of the fluid region. Method of lines and time integration was used for marching. The complexity of simulated physical phenomena was highly flexible and allowed to trade accuracy for computational time. In comparison with the very finely discretized model, highly comparable transients were obtained even for the thick accumulation wall.
Highlights
In the few decades, the district heating (DH) will undergo substantial changes
This paper deals with transients in circular pipes, where the accumulation of heat into surrounding material has been considered in detail
Model is a combination of the analytic description of a heat transfer between the fluid and the solid wall, simple advection scheme, turbulent diffusion, and a finite element scheme dealing with the circular wall in which the shape functions are based on the steady-state solution
Summary
In the few decades, the district heating (DH) will undergo substantial changes. Utilization of renewable sources and waste heat (both often fluctuate) aim at the reduction of carbon dioxide emissions and fossil fuel consumption [3]. Suppliers, and network providers will become members of the multilevel heat market. The production and consumption of heat or cold can be shifted between seasons using large long-term thermal storage, which has a lower relative price than smaller ones [4]. In such a complicated arrangement, the network must be sufficiently flexible, predictive, and intelligent
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