Publicação
Prediction models for rail temperatures validated with experimental measurements
| Resumo: | Rail temperatures play an important role when understanding and predicting rail track’s instabilities. An energy balance model called CNU was used to simulate rail temperatures, validated with FEA analysis, and compared with field-collected data. The model uses weather data and accounts for the solar position to improve temperature prediction. In addition, a python package is developed to solve the thermal lumped model including specific modifications on the model. Both simplified and Finite Element Analysis (FEA) models are in good agreement. Compared with the collected data, the model reaches an R² of 0.914. |
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| Autores principais: | Frigeri, Ary Vinicius Nervis |
| Outros Autores: | Minhoto, Manuel; Piloto, Paulo A.G.; Silva, Dyorgge Alves |
| Assunto: | Rail temperature Finite element analysis Prediction models |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | Rail temperatures play an important role when understanding and predicting rail track’s instabilities. An energy balance model called CNU was used to simulate rail temperatures, validated with FEA analysis, and compared with field-collected data. The model uses weather data and accounts for the solar position to improve temperature prediction. In addition, a python package is developed to solve the thermal lumped model including specific modifications on the model. Both simplified and Finite Element Analysis (FEA) models are in good agreement. Compared with the collected data, the model reaches an R² of 0.914. |
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