WITHDRAWN: A novel approach to MP-PIC: Continuum particle model for dense particle flows in fluidized beds

Author(s):  
Vikrant Verma ◽  
Johan T. Padding
Author(s):  
Yu Feng ◽  
Clement Kleinstreuer

Dense particle-suspension flows in which particle-particle interactions are a dominant feature encompass a diverse range of industrial and geophysical contexts, e.g., slurry pipeline, fluidized beds, debris flows, sediment transport, etc. The one-way dispersed phase model (DPM), i.e., the conventional one-way coupling Euler-Lagrange method is not suitable for dense fluid-particle flows [1]. The reason is that such commercial CFD-software does not consider the contact between the fluid, particles and wall surfaces with respect to particle inertia and material properties. Hence, two-way coupling of the Dense Dispersed Phase Model (DDPM) combined with the Discrete Element Method (DEM) has been introduced into the commercial CFD software via in-house codes. As a result, more comprehensive and robust computational models based on the DDPM-DEM method have been developed, which can accurately predict the dynamics of dense particle suspensions. Focusing on the interaction forces between particles and the combination of discrete and continuum phases, inhaled aerosol transport and deposition in the idealized tracheobronchial airways [2] was simulated and analyzed, generating more physical insight. In addition, it allows for comparisons between different numerical methods, i.e., the classical one-way Euler-Lagrange method, two-way Euler-Lagrange method, EL-ER method [3], and the present DDPM-DEM method, considering micron- and nano-particle transport and deposition in human lungs.


Particuology ◽  
2014 ◽  
Vol 13 ◽  
pp. 134-144 ◽  
Author(s):  
Nageswara Rao Narni ◽  
Mirko Peglow ◽  
Gerald Warnecke ◽  
Jitendra Kumar ◽  
Stefan Heinrich ◽  
...  

Author(s):  
Chitra Dangwal ◽  
Marcello Canova

Abstract Predicting the chemical and physical processes occurring in Lithium-ion cells with high-fidelity electrochemical models is today a critical requirement to accelerate the design and optimization of battery packs for automotive and aerospace applications. One of the common issues associated with electrochemical models is the complexity of parameter identification, particularly when relying only on experimental data obtained via non-invasive techniques. This paper presents a novel approach to improve the common methods of parameter calibration that consists of matching the predicted terminal voltage to test data via optimization methods. The study is conducted for an NMC-graphite cell, modeled using a reduced order Extended Single Particle Model (ESPM). The proposed approach relies on using a large-scale Particle Swarm Optimization (PSO), modified by including a term that accounts for the parameter sensitivity information, such that the rate of convergence and robustness of the algorithm to obtain a consistent solution in the presence of uncertainties in the initial conditions are significantly improved.


AIChE Journal ◽  
2013 ◽  
Vol 59 (11) ◽  
pp. 4077-4099 ◽  
Author(s):  
Simon Schneiderbauer ◽  
Stefan Puttinger ◽  
Stefan Pirker

2008 ◽  
Vol 187 (1) ◽  
pp. 68-78 ◽  
Author(s):  
Francesco Di Natale ◽  
Amedeo Lancia ◽  
Roberto Nigro

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