Effective evaluation and prediction of aerosol transport deposition in the human respiratory tracts are critical to aerosol drug delivery and evaluation of inhalation products. Establishment of an in ...vitro-in vivo correlation (IVIVC) requires the understanding of flow and aerosol behaviour and underlying mechanisms at the microscopic scale. The achievement of the aim can be facilitated via computational fluid dynamics (CFD) based in silico modelling which treats the aerosol delivery as a two-phase flow. CFD modelling research, in particular coupling with discrete phase model (DPM) and discrete element method (DEM) approaches, has been rapidly developed in the past two decades. This paper reviews the recent development in this area. The paper covers the following aspects: geometric models of the respiratory tract, CFD turbulence models for gas phase and its coupling with DPM/DEM for aerosols, and CFD investigation of the effects of key factors associated with geometric variations, flow and powder characteristics. The review showed that in silico study based on CFD models can effectively evaluate and predict aerosol deposition pattern in human respiratory tracts. The review concludes with recommendations on future research to improve in silico prediction to achieve better IVIVC.
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Scale-up of mills is critical to the design and operation of industrial grinding circuits. This paper presented a scale-up model based on the discrete element method (DEM) simulation to predict the ...performance of tumbling ball mills. The mills of different sizes partially filled with steel balls and ground particles were operated at different loading and speeds. The breakage energy characterized by the damping energy on the ground particles were analysed. In particular, a breakage model was adopted to link the breakage energy with particle mechanical properties to predict particle breakage in the mills. The predicted grinding rates of the particles under different conditions were comparable to experimental measurements. Results indicated that while particle-particle contacts were dominant in the flow, particle-ball contacts were the main breakage mechanism of particles. Power draw and grinding rate were not always positive correlated. Excessive mill speeds caused more power consumption but resulted in reduced grinding rate. Based on the simulation data, two scale-up models were proposed to predict power draw and grinding rate. The models were tested with larger mills and show excellent prediction on power draw and reasonable accuracy on grinding rate.
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•Tumbling mills filled with grinding balls and particles were simulated using DEM.•The damping energy on particles is more suitable than power draw to characterise mill efficiency.•Grinding rate of mills can be calculated by linking the damping energy and powder properties.•Scale-up models to predict power draw and grinding rate of mills were developed and validated.
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•The effect of particle breakage during compaction on compact structure was investigated.•A watershed segmentation method was developed to characterise pore structure of ...compacts.•Pore size and throat diameter decreased exponentially with reducing particle sizes.•Spontaneous percolation dynamics of compacts was also affected by particle breakage.•Relations between pore structure and percolation behaviour were established.
This work investigated the effect of particle breakage on the pore structure of compacts formed with the discrete element method. The watershed segmentation method was developed to quantify pore properties of the compacts. Results showed the pore size, throat diameter and throat length followed the log-normal distributions for all the compacts except for compacts in which significant particle breakage occurred. With particle breakage, the mean pore size and mean throat diameter decreased exponentially with reducing particle size. The pore structure was also explored through spontaneous interparticle percolation. Results showed that the residence time of the percolating particles followed the log-normal distributions. The coefficient of the radial dispersion increased linearly with depth for all compacts except for a compact with particle breakage. The relations between pore structure and percolation behaviour were established. With decreasing pore size and throat diameter, the mean residence time increased exponentially but the dispersion coefficient had a linear decrease.
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•An SVR data model was proposed to predict particle flow in rotating drums.•The model was trained and tested by the data generated from DEM simulations.•Angle of repose and collision ...energy were adopted to characterise particle flow.•Effects of drum size and operation condition were predicted.•SVR prediction compared well with DEM results.
This work developed a data-driven model combined with the discrete element method (DEM) to predict the features of the particle flow inside a drum. The SVR (Support Vector Machine for Regression) method was adopted to predict two important properties of particle flow, angle of repose and collision energy. The model was trained and tested using 142 sets of data generated from the DEM simulations. The Kennard-Stone (K-S) method, due to its advantages over random selection method, was adopted to select training data. The optimal values of the parameters in the SVR model were determined by the grid-search method. Results showed the robust SVR model was able to predict angle of repose and collision energy under different conditions, such as changing drum size, rotation speed, particle-wall sliding friction and filling level, reasonably well with R2 values of 0.92 and 0.86, respectively. The relatively less accurate prediction on collision energy was discussed. The study showed that this approach can be implemented to link off-line DEM simulation with rapid prediction of particle behaviour in various industrial applications.
This work presented a numerical study based on the discrete element method (DEM) to understand the effect of particle fragmentation on compaction dynamics and the properties of formed compacts. An ...improved fragmentation model based on the force criterion and the Apollonian fragments replacement was implemented to the model to mimic particle breakage. Through growth and relaxation of progeny particles, the fragmentation model was able to significantly reduce mass loss during particle fragmentation while maintain the mechanical response of the parent particles. The model was validated by comparing with literature data in terms of compaction curve and the evolution of particle size distribution (PSD). Three stages were identified in the Hecker plot highlighting the strong effect of particle fragmentation. The effect of compact height was investigated, showing particle fragmentation decreased with increasing compact height at the early stage of compaction due to the larger degree of particle rearrangement, but the final PSD was similar for all the compacts. Analysis indicated particle fragmentation energy accounted for 2% of the total input energy while more than 50% of input energy was to overcome the friction between particles.
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•The effect of particle fragmentation on compaction was numerically investigated.•A growth-relaxation algorithm was implemented to improve the accuracy of the fragmentation model.•The variations of force and pore structures with particle fragmentation were analysed.•The compaction mechanisms were characterised through energy analysis.
Comparisons of ANN prediction of collision energy and particle size with DEM simulations.
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•An ANN model was proposed to predict particle flow characteristics in rotating drums.•The ...model was based on the acoustic emission signals generated from DEM simulations.•The key features of the signals were obtained through principal component analysis.•Flow properties included filling level, particle size and energy distributions.•ANN predictions compared well with DEM simulations.
Rotating drums are widely used in industries for particle mixing, granulation and grinding. Linking internal particle flow condition with externally measured variables is crucial to online process monitoring and control. This work proposed a modelling framework to use an artificial neural network (ANN) model for quick prediction of particle flow based on the acoustic emission (AE) signals generated from the discrete element method (DEM) simulations. In total 131 DEM simulations were conducted under different conditions (i.e., different particle size distributions and filling levels). The AE signals on the drum surface were then obtained based on the simulated particle–wall collisions. Through FFT transformation and principal component analysis (PCA), 5 principal components (PCs) were obtained and, together with power draw, fed into the ANN model to predict to the unmeasurable internal flow conditions, including filling level and the distributions of particle size and internal collision energy. The back propagation neural network was adopted in the model. After being trained with 90 datasets, the ANN model was able to predict those internal variables with reasonable accuracy (R2 > 0.95). Finally, the potentials and limitations of the model to the optimal operation of drums were discussed.
Compaction behaviour and mechanical response of a compact show strong dependence on particle shape. In this study, a numerical model based on the discrete element method (DEM) was developed to study ...the compaction behaviour of spheroidal particles. In the model, particle shape was approximated by gluing multiple spheres together. A bonded particle model was adopted to describe interparticle bonding force. The DEM model was first validated by comparing the properties of packing of spheroids (packing density, coordination number) with literature data and then applied to both die compaction and unconfined compression. In die compaction, the effect of aspect ratio on the densification was mainly due to the difference in the initial packing. In unconfined compression, the increase in compressive strength with increasing aspect ratio was attributed to the increase in the number of interparticle bonding. The findings facilitate a better understanding of the relation of particle shape to the compaction behaviour and compact strength.
•Wear behaviour in a lab-scale HPGR is investigated.•Wear model and particle breakage are included in the DEM model.•Concave wear pattern was formed during the dynamic wear evolution.•Worn rolls ...reduce power draw of the HPGR mill, no effect on throughput.•Particle breakage and fracture energy with worn roll are analysed.
Wear due to compression and abrasion from particles is a critical issue for high pressure grinding rolls (HPGR) mills. Understanding the evolution of wear provides useful insight on its mechanisms and helps to mitigate the issue so mills are operated at their optimal states. This work presented numerical simulations based on the discrete element method (DEM) to analyse the formation and evolution of wear of a lab-scale HPGR mill and to investigate how wear affects mill performance and particle breakage. By coupling the DEM model with the Archard wear model and surface re-meshing, the simulated wear patterns were comparable with those observed in practice, showing a parabolic wear profile wear along the rolls with more severe wear in the centre. In addition, the rear part of the stud, compared with the middle and front parts, experienced more significant wear due to the combined effect of compression and abrasion. While increasing wear had no visible effect on throughput, it reduced mill power draw due to weaker compressive force from the rolls to particles. Furthermore, analysis on particle breakage showed that increasing wear reduced particle breakage and produced coarser products. Increasing wear also caused less damage to particles, suggesting more cycles are required to achieve targeted products.
Metastatic osteosarcoma usually has an unsatisfactory response to the current standard chemotherapy and causes poor prognosis. Currently, epithelial-mesenchymal transition (EMT) is reported as a ...critical event in osteosarcoma metastasis. Glaucocalyxin A, a bioactive ent-kauranoid diterpenoid, exerts anti-cancer effect on osteosarcoma by inducing apoptosis in previous study. However, the effect of Glaucocalyxin A on EMT and metastasis of osteosarcoma is unclear. In this study, we investigated the potential mechanisms of Glaucocalyxin A on EMT and metastasis of osteosarcoma. We found that Glaucocalyxin A inhibited migration and invasion of MG-63 and 143B cells. Moreover, Glaucocalyxin A increased the protein and mRNA levels of E-cadherin and decreased the protein and transcription expression of N-cadherin, Vimentin. Glaucocalyxin A also inhibited the protein and mRNA levels of EMT-associated transcription factor including Snail and Slug. Furthermore, Glaucocalyxin A inhibited transforming growth factor-β1 (TGF-β1)-induced migration, invasion and EMT of low-metastatic osteosarcoma U2OS cells. Glaucocalyxin A inhibited TGF-β-induced phosphorylation of Smad 2/3 in osteosarcoma U2OS cells. Finally, we established transplanted metastatic models of highly metastatic osteosarcoma 143B cells. Glaucocalyxin A inhibited lung metastasis in vivo. Interestingly, Glaucocalyxin A increased the protein expression of E-cadherin and reduced the protein expression of N-cadherin and Vimentin. Glaucocalyxin A inhibited the protein expression of Snail and Slug in vivo. In summary, this study demonstrated that Glaucocalyxin A inhibited EMT and TGF-β1-induced EMT by inhibiting TGF-β1/Smad2/3 signaling pathway in osteosarcoma. Therefore, Glaucocalyxin A might be a promising candidate against the metastasis of human osteosarcoma.
•Glaucocalyxin A reverses epithelial-mesenchymal transition (EMT) in osteosarcoma.•Glaucocalyxin A prevents TGF-β1-induced EMT by inhibiting Smad2/3 pathway in osteosarcoma.•Glaucocalyxin A inhibits osteosarcoma metastasis to lung in the metastatic model.
Feed particles experience significant reduction in size and particle strength in high pressure grinding rolls (HPGR) due to strong compression. This paper presents a combined experimental and ...numerical study of the mechanics of cement particle breakage in a lab-scale HPGR under different conditions. A model based on the discrete element method (DEM) was developed and coupled with a multi-physics model and a particle breakage model to mimic the dynamics of particles in the HPGR. Through careful calibration of the model parameters, the model was able to generate results in good agreements with the experiments in terms of throughput, power consumption, product size distribution and mill productivity. The strength of the product particles, which was characterised by particle fracture energy, was analysed in the simulations and results showed much weaker and broader fracture energy distributions compared with the feed particles. Increasing roll speed increased throughput and power consumption but had little effect on working gap, product size and particle fracture energy. Higher roll speeds also significantly increased the pressure on the floating roll (but not on the fixed roll), which may result in more severe wear on the roll. Increasing pressure in general reduced throughput due to smaller working gaps and finer and weaker product particles. Furthermore, the over-sized (larger than 0.045 mm) product particles were fed into the mill to investigate the effect of multiple grinding. The particles passing through the 2nd grinding were much smaller and weaker, and the simulation results were comparable to the experiments. The study demonstrated that both particle size and particle strength need to be considered properly in DEM models to provide accurate prediction on the performance of HPGR under various condition.
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•Particle breakage in a lab-scale HPGR was investigated.•Dynamic response of the HPGR and particle breakage were included in the DEM model•The simulations agreed well with the experiments•Roll speed and roll pressure affected the size and strength of the products•Both the size and strength of feeds need to be considered to predict the performance of HPGR
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