- author = "R. R. Sonolikar and M. P. Patil and R. B. Mankar and S. S. Tambe and B. D. Kulkarni",
- title = "Genetic Programming based Drag Model with Improved Prediction Accuracy for Fluidization Systems",
- journal = "International Journal of Chemical Reactor Engineering",
- year = "2017",
- volume = "15",
- number = "2",
- pages = "20160210",
- month = jan # " 11",
- keywords = "genetic algorithms, genetic programming, Fluidisation, drag force, modeling, computational fluid dynamics",
- ISSN = "1542-6580",
- publisher = "De Gruyte",
-
URL = "
https://research.mitwpu.edu.in/publication/genetic-programming-based-drag-model-with",
-
URL = "https://doi.org/10.1515/ijcre-2016-0210",
-
DOI = "
doi:10.1515/ijcre-2016-0210",
- size = "13 pages",
- abstract = "The drag coefficient plays a vital role in the modeling of gas-solid flows. Its knowledge is essential for understanding the momentum exchange between the gas and solid phases of a fluidization system, and correctly predicting the related hydrodynamics. There exists a number of models for predicting the magnitude of the drag coefficient. However, their major limitation is that they predict widely differing drag coefficient values over same parameter ranges. The parameter ranges over which models possess a good drag prediction accuracy are also not specified explicitly. Accordingly, the present investigation employs Geldart group B particles fluidization data from various studies covering wide ranges of Re and epsilons to propose a new unified drag coefficient model. A novel artificial intelligence based formalism namely genetic programming (GP) has been used to obtain this model. It is developed using the pressure drop approach, and its performance has been assessed rigorously for predicting the bed height, pressure drop, and solid volume fraction at different magnitudes of Reynolds number, by simulating a 3D bubbling fluidised bed. The new drag model has been found to possess better prediction accuracy and applicability over a much wider range of Re and epsilon than a number of existing models. Owing to the superior performance of the new drag model, it has a potential to gainfully replace the existing drag models in predicting the hydrodynamic behaviour of fluidized beds.",
-
notes = "Supplementary Materials:
https://www.degruyter.com/document/doi/10.1515/ijcre-2016-0210/downloadAsset/suppl/ijcre-2016-0210ad.zip
https://www.degruyter.com/journal/key/ijcre/html",
