scholarly journals Particle agglomeration in flows: Fast data-driven spatial decomposition algorithm for CFD simulations

2022 ◽  
pp. 103962
Author(s):  
Kerlyns Martínez Rodríguez ◽  
Mireille Bossy ◽  
Christophe Henry
Keyword(s):  
Decomposition Algorithm ◽  
Data Driven ◽  
Particle Agglomeration ◽  
Cfd Simulations ◽  
Spatial Decomposition

CFD simulations of droplet and particle agglomeration in an industrial counter-current spray dryer

2018 ◽  
Vol 29 (7) ◽  
pp. 1724-1733 ◽  
Author(s):  
M. Jaskulski ◽  
P. Wawrzyniak ◽  
I. Zbiciński
Keyword(s):  
Spray Dryer ◽  
Particle Agglomeration ◽  
Cfd Simulations ◽  
Counter Current

scholarly journals An out-of-distribution-aware autoencoder model for reduced chemical kinetics

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Pei Zhang ◽  
Siyan Liu ◽  
Dan Lu ◽  
Ramanan Sankaran ◽  
Guannan Zhang
Keyword(s):  
Chemical Kinetics ◽  
Kinetic Models ◽  
Chemical Kinetic ◽  
Reacting Flows ◽  
Training Data ◽  
Data Driven ◽  
Data Sets ◽  
Cfd Simulations ◽  
Reduced Chemistry ◽  
Continuum Scale

<p style='text-indent:20px;'>While detailed chemical kinetic models have been successful in representing rates of chemical reactions in continuum scale computational fluid dynamics (CFD) simulations, applying the models in simulations for engineering device conditions is computationally prohibitive. To reduce the cost, data-driven methods, e.g., autoencoders, have been used to construct reduced chemical kinetic models for CFD simulations. Despite their success, data-driven methods rely heavily on training data sets and can be unreliable when used in out-of-distribution (OOD) regions (i.e., when extrapolating outside of the training set). In this paper, we present an enhanced autoencoder model for combustion chemical kinetics with uncertainty quantification to enable the detection of model usage in OOD regions, and thereby creating an OOD-aware autoencoder model that contributes to more robust CFD simulations of reacting flows. We first demonstrate the effectiveness of the method in OOD detection in two well-known datasets, MNIST and Fashion-MNIST, in comparison with the deep ensemble method, and then present the OOD-aware autoencoder for reduced chemistry model in syngas combustion.</p>

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