posted on 2022-12-23, 12:04authored bySibo Qu, Wei Zhang, Changfu You
Particle
group combustion presents a strong temporal
and spatial
inhomogeneity owing to the complicated interphase interactions. Based
on the data set from the fictitious domain method, the recurrent fully
connected and convolutional parallel neural network (R-FC&CNN)
architecture and its two comparable simplified models, that is, the
recurrent fully connected neural network (R-FCNN) and the recurrent
convolutional neural network (R-CNN) architectures, were constructed
for predicting the gas–solid momentum exchange coefficient,
β (kg·s–1·m–3),
average combustion rate per unit surface area of particles, rc̅/A (kg·s–1·m–2), and comprehensive NaCl release parameter,
γ,
selectively. A time sequence of average particle temperature, T̅ (K), and particle volume fraction, ε, which
can be extended in the matrix form, were constructed as the features
selectively according to their correlation with the target physical
quantity. The average relative error, δ̅, and coefficient
of determination, R2, were used as the
evaluators. Through final testing, in the mild combustion domain,
the R-CNN and R-FCNN models with simple structures showed good performance
for β(δ̅ = 0.13, R2 =
0.8) and rc̅/A (δ̅ = 0.04, R2 = 0.84), respectively, while in the severe
combustion
domain, the R-FC&CNN model, with more complete features and functional
structure, performed the best (for β, δ̅ = 0.12, R2 = 0.85; for rc̅/A, δ̅ = 0.05, R2 = 0.92; and for γ, δ̅=0.05,R2=0.96). A fine-tuning and interpolated prediction
method was developed to investigate further the model’s expansibility.
Both ensured acceptable performance on a new similar problem. In summary,
the feasibility of physical meaning-oriented machine learning--based
model(s) for predicting the combustion characteristics of nonuniformly
distributed particles was confirmed.