Welcome to Francis Academic Press

Academic Journal of Materials & Chemistry, 2026, 7(1); doi: 10.25236/AJMC.2026.070113.

CFD–PBM Simulation of Multiphase Flow Component Combustion Synthesized Nanoparticles Using QMOM and TEMOM

Author(s)

Kai Wang

Corresponding Author:
Kai Wang
Affiliation(s)

College of Science, China Jiliang University, Hangzhou, 310018, China

Abstract

Flame synthesis is a major method for controllable nanoparticle preparation, which involves coupled turbulence, heat-mass transfer and particle dynamic behaviors. Numerical simulation is an effective means to reveal its internal mechanism. This work establishes a coupled CFD-PBM framework to investigate TiO₂ nanoparticle formation via thermal decomposition of titanium tetraisopropoxide (TTIP) in a reacting flow reactor. The Reynolds-averaged Navier–Stokes (RANS) equations combined with standard k–ε model are adopted to solve gas flow, heat transfer and species transport in ANSYS Fluent. For particle population dynamics, two moment methods are applied: the built-in Quadrature Method of Moments (QMOM) is used for moment calculation and diagnostic analysis; the Taylor-expansion Method of Moments (TEMOM) is realized via four User-Defined Scalars (UDS) to achieve flexible moment closure. The model considers TTIP decomposition, TiO₂ nucleation, surface growth and Brownian coagulation. Simulation results identify key regions of particle formation and evolution, and compare the performance of QMOM and TEMOM. QMOM presents sharper moment peaks with high sensitivity to spatial variation, while TEMOM obtains smoother distribution, lower numerical dissipation and computational cost. The combined framework integrates the advantages of two methods, providing a reliable tool for mechanism analysis and parametric research of flame-synthesized nanoparticles.

Keywords

Turbulent reacting flow; QMOM; TEMOM; Population balance model; Nanoparticle coagulation

Cite This Paper

Kai Wang. CFD–PBM Simulation of Multiphase Flow Component Combustion Synthesized Nanoparticles Using QMOM and TEMOM. Academic Journal of Materials & Chemistry (2026), Vol. 7, Issue 1: 89-101. https://doi.org/10.25236/AJMC.2026.070113.

References

[1] Li S, Ren Y, Biswas P, et al. Flame aerosol synthesis of nanostructured materials and functional devices: Processing, modeling, and diagnostics[J]. Progress in Energy and Combustion Science, 2016, 5:51-59. 

[2] Wang Y, Xie Y, Guo X, et al. Morphological convergence in solid-state synthesis: Unveiling the critical role of TiO2 precursor size for high-performance H2TiO3 lithium ion-sieves[J]. Particuology, 2026, 11:01-13.

[3] Ju J, Wu Y, Meng Y, et al. Multiscale numerical simulation of nano-TiO2 particle formation and evolution in an industrial flame reactor[J]. Chemical Engineering Science, 2026, 323:123244.

[4] Yu M, Lin J, Chan T. Numerical simulation of nanoparticle synthesis in diffusion flame reactor[J]. Powder Technology, 2007, 181(1): 9-20.

[5] He S, Shang C, Lu H, et al. Experimental and numerical study of TiO2 nanoparticle evolution in a diffusion flame reactor[J]. Combustion and Flame, 2025, 273: 113965.

[6] Bagheri H, Hashemipour H, Ghader S. Population balance modeling: application in nanoparticle formation through rapid expansion of supercritical solution[J]. Computational Particle Mechanics, 2019, 6(4): 721-737.

[7] Chan L T, Liu S, Yue Y. Nanoparticle formation and growth in turbulent flows using the bimodal TEMOM[J]. Powder Technology, 2018, 323: 507-517.

[8] Mingliang X, Qing H. Solution of Smoluchowski coagulation equation for Brownian motion with TEMOM[J]. Particuology, 2022, 70: 64-71.

[9] Can, T., et al., Simulation of Aerosol Evolution within Background Pollution for Nucleated Vehicle Exhaust via TEMOM[J]. Applied Sciences, 2021. 11(10): 4552–4552.

[10] Chen J, Li W, Li D, et al. Characterization of nanoparticle agglomerate motion based on the TEMOM method[J]. Powder Technology, 2025, 455: 120711.

[11] Yang X, Qian Z, Xie X, et al. Interpretable machine learning coupled with CFD-PBM analysis for heat transfer enhancement of ice slurry in SRTT[J]. International Communications in Heat and Mass Transfer, 2026, 172(4): 110461.

[12] Liang J, Xiong H, Hu Z, et al. Three-dimensional CFD-PBM insights into bubble dynamics and interfacial momentum transfer in bubble columns[J]. Separation and Purification Technology, 2026, 388: 136527.

[13] Fakharnezhad A, Kelesidis G, Berry J, et al. Nucleation, surface growth and coagulation of soot by hierarchical modeling[J]. Powder Technology, 2026, 469(1): 121747.

[14] Manis K L, Ge J, Kim A C, et al. Population Balance Models for Catalytic Depolymerization: From Elementary Steps to Multiphase Reactors. [J]. Accounts of chemical research, 2025, 58(12): 1847-1855.

[15] Bier R, Briesen H. Model reduction for 2D population balance models integrating particle shape dynamics: Theoretical derivation and application to crystallization systems[J]. Chemical Engineering Science, 2025, 318: 122087-122087.

[16] Sarigiannis D, Peck J, Mountziaris T, et al. Vapor Phase Synthesis of II-IV Semiconductor Nanoparticles in a Counterflow Jet Reactor[J]. MRS Proceedings, 2000, 616(1): 41.

[17] Shettigar A N, Bi Q, Toorman E. Assimilating Size Diversity: Population Balance Equations Applied to the Modeling of Microplastic Transport. [J]. Environmental science & technology, 2024, 58(36):16112-16120.