Simulação e otimização termofluidodinâmica do circuito secundário de sistemas de aquecimento solar distrital - Lucas Paglioni Pataro Faria (2013)
The district heating systems (DHS) are widely used in northern Europe countries, however, in developing countries this technology is innovative, especially to meet the demand of hot water in social interest houses. The scope of this work is motivated by the increase in the number of solar heaters installed by the government in recent years in the joint housing of the low income population in Brazil, the technical challenges inherent in the deployment of these systems from the development of new models ofsustainability technology. Initially, a research was made of typical types of combination of houses adopted in the State of Minas Gerais/Brazil for homes with built area of around 40m², 4 residents and family income of 3 minimum wages. For the establishment of minimum criteria to be adopted in selecting the type of optimization, such as minimizing the required recycling, energy consumption in pumping, the diameter of the pipes at the secondary circuit and thermal losses in distribution net, itwas developed computational algorithms that allow the generation of an results matrix with the consolidation of gains and identifying the disadvantages of each configuration founded. The final version, already concluded is developed on the software EES (Engineering Equation Solver) and Borland C++ Builder® , is based on the equations ofenergy, momentum and mass conservation and has by objective: Optimize the sizing of the diameters for the supply and return pipes (sub branches and branches) ensuring the lowest possible cost of installation; Determine the pressure and thermal losses in eachsection of the network checking the need of installation of water pumps for recirculation. For the initial simulation it was modeled a DHS composed of 3 blocks. The 1st and 2nd blocks have four houses each one and the 3rd block has only three houses. The optimization method adopted is an adaptation of the Gradient Method working together with the Genetic Method. The results after optimization show a reduction in the global costs of the system around 19.6% compared to the nonoptimizedmodel.