Identification of Heat Supply Network Pipeline Roughness Based on AIOX-GA
| Autor: | Shaoen Wu, Guanghuang Chen, Xingqi Zhou, Qingwu Fan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Optimization problem General Computer Science Scale (ratio) Computer science Pipeline (computing) Crossover General Engineering Heat supply Surface finish genetic algorithms Identification (information) Heating system heating pipeline network Auxiliary individuals Genetic algorithm pipeline roughness General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 34552-34563 (2020) |
| ISSN: | 2169-3536 |
| Popis: | The scale of heating system is expanding day by day and the structure of pipeline network is becoming more and more complex. Therefore, it is urgent for heating enterprises to establish accurate hydraulic models of pipeline network to assist their operation and management. The pipe roughness of heating pipeline is critical to hydraulic models, but unfortunately it is however uncertain. Thus, it is necessary to obtain the resistance coefficient values of pipe roughness of heating pipeline. At present, the pipeline roughness is commonly estimated with optimization calculation based on collected measurement data of heating systems. The optimization problem is however multi-dimensional and complex to solve. In this work, an auxiliary individual oriented crossover genetic algorithm (AIOX-GA) is proposed to optimize the problem of estimating the resistance coefficient values of pipe roughness. AIOX-GA adopts a crossover framework and is an auxiliary individual-oriented scheme, which is helpful to solve multi-dimensional problems. The performance of the improved algorithm is evaluated with simulation experiments. The results show that the proposed algorithm can accurately estimate the pipeline roughness and effectively improve the identification accuracy. |
| Databáze: | OpenAIRE |
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