Computational Fluid Dynamics (CFD) Simulations of Spray Drying: Linking Drying Parameters with Experimental Aerosolization Performance
Purpose The purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics within a laboratory-scale spray dryer, and relate CFD-predicted drying parameters to powder aerosolization metrics from a reference dry powder in...
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Published in | Pharmaceutical research Vol. 37; no. 6; p. 101 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.06.2020
Springer Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Abstract | Purpose
The purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics within a laboratory-scale spray dryer, and relate CFD-predicted drying parameters to powder aerosolization metrics from a reference dry powder inhaler (DPI).
Methods
A CFD model of the Buchi Nano Spray Dryer B-90 was developed that captured spray dryer conditions from a previous experimental study producing excipient enhanced growth powders with L-leucine as a dispersion enhancer. The CFD model accounted for two-way heat and mass transfer coupling between the phases and turbulent flow created by acoustic streaming from the mesh nebulizer. CFD-based drying parameters were averaged across all droplets in each spray dryer case and included droplet time-averaged drying rate (κ
avg
), maximum instantaneous drying rate (κ
max
) and precipitation window.
Results
CFD results highlighted a chaotic drying environment in which time-averaged droplet drying rates (κ
avg
) for each spray dryer case had high variability with coefficients of variation in the range of 60–70%. Maximum instantaneous droplet drying rates (κ
max
) were discovered that were two orders of magnitude above time-averaged drying rates. Comparing CFD-predicted drying parameters with experimentally determined mass median aerodynamic diameters (MMAD) and emitted doses (ED) from a reference DPI produced strong linear correlations with coefficients of determination as high as R
2
= 0.98.
Conclusions
For the spray dryer system and conditions considered, reducing the CFD-predicted maximum drying rate experienced by droplets improved the aerosolization performance (both MMAD and ED) when the powders were aerosolized with a reference DPI. |
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AbstractList | PurposeThe purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics within a laboratory-scale spray dryer, and relate CFD-predicted drying parameters to powder aerosolization metrics from a reference dry powder inhaler (DPI).MethodsA CFD model of the Buchi Nano Spray Dryer B-90 was developed that captured spray dryer conditions from a previous experimental study producing excipient enhanced growth powders with L-leucine as a dispersion enhancer. The CFD model accounted for two-way heat and mass transfer coupling between the phases and turbulent flow created by acoustic streaming from the mesh nebulizer. CFD-based drying parameters were averaged across all droplets in each spray dryer case and included droplet time-averaged drying rate (κavg), maximum instantaneous drying rate (κmax) and precipitation window.ResultsCFD results highlighted a chaotic drying environment in which time-averaged droplet drying rates (κavg) for each spray dryer case had high variability with coefficients of variation in the range of 60–70%. Maximum instantaneous droplet drying rates (κmax) were discovered that were two orders of magnitude above time-averaged drying rates. Comparing CFD-predicted drying parameters with experimentally determined mass median aerodynamic diameters (MMAD) and emitted doses (ED) from a reference DPI produced strong linear correlations with coefficients of determination as high as R2 = 0.98.ConclusionsFor the spray dryer system and conditions considered, reducing the CFD-predicted maximum drying rate experienced by droplets improved the aerosolization performance (both MMAD and ED) when the powders were aerosolized with a reference DPI. The purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics within a laboratory-scale spray dryer, and relate CFD-predicted drying parameters to powder aerosolization metrics from a reference dry powder inhaler (DPI). A CFD model of the Buchi Nano Spray Dryer B-90 was developed that captured spray dryer conditions from a previous experimental study producing excipient enhanced growth powders with L-leucine as a dispersion enhancer. The CFD model accounted for two-way heat and mass transfer coupling between the phases and turbulent flow created by acoustic streaming from the mesh nebulizer. CFD-based drying parameters were averaged across all droplets in each spray dryer case and included droplet time-averaged drying rate (κ ), maximum instantaneous drying rate (κ ) and precipitation window. CFD results highlighted a chaotic drying environment in which time-averaged droplet drying rates (κ ) for each spray dryer case had high variability with coefficients of variation in the range of 60-70%. Maximum instantaneous droplet drying rates (κ ) were discovered that were two orders of magnitude above time-averaged drying rates. Comparing CFD-predicted drying parameters with experimentally determined mass median aerodynamic diameters (MMAD) and emitted doses (ED) from a reference DPI produced strong linear correlations with coefficients of determination as high as R = 0.98. For the spray dryer system and conditions considered, reducing the CFD-predicted maximum drying rate experienced by droplets improved the aerosolization performance (both MMAD and ED) when the powders were aerosolized with a reference DPI. Purpose The purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics within a laboratory-scale spray dryer, and relate CFD-predicted drying parameters to powder aerosolization metrics from a reference dry powder inhaler (DPI). Methods A CFD model of the Buchi Nano Spray Dryer B-90 was developed that captured spray dryer conditions from a previous experimental study producing excipient enhanced growth powders with L-leucine as a dispersion enhancer. The CFD model accounted for two-way heat and mass transfer coupling between the phases and turbulent flow created by acoustic streaming from the mesh nebulizer. CFD-based drying parameters were averaged across all droplets in each spray dryer case and included droplet time-averaged drying rate (κ avg ), maximum instantaneous drying rate (κ max ) and precipitation window. Results CFD results highlighted a chaotic drying environment in which time-averaged droplet drying rates (κ avg ) for each spray dryer case had high variability with coefficients of variation in the range of 60–70%. Maximum instantaneous droplet drying rates (κ max ) were discovered that were two orders of magnitude above time-averaged drying rates. Comparing CFD-predicted drying parameters with experimentally determined mass median aerodynamic diameters (MMAD) and emitted doses (ED) from a reference DPI produced strong linear correlations with coefficients of determination as high as R 2 = 0.98. Conclusions For the spray dryer system and conditions considered, reducing the CFD-predicted maximum drying rate experienced by droplets improved the aerosolization performance (both MMAD and ED) when the powders were aerosolized with a reference DPI. Purpose The purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics within a laboratory-scale spray dryer, and relate CFD-predicted drying parameters to powder aerosolization metrics from a reference dry powder inhaler (DPI). Methods A CFD model of the Buchi Nano Spray Dryer B-90 was developed that captured spray dryer conditions from a previous experimental study producing excipient enhanced growth powders with L-leucine as a dispersion enhancer. The CFD model accounted for two-way heat and mass transfer coupling between the phases and turbulent flow created by acoustic streaming from the mesh nebulizer. CFD-based drying parameters were averaged across all droplets in each spray dryer case and included droplet time-averaged drying rate ([kappa].sub.avg), maximum instantaneous drying rate ([kappa].sub.max) and precipitation window. Results CFD results highlighted a chaotic drying environment in which time-averaged droplet drying rates ([kappa].sub.avg) for each spray dryer case had high variability with coefficients of variation in the range of 60-70%. Maximum instantaneous droplet drying rates ([kappa].sub.max) were discovered that were two orders of magnitude above time-averaged drying rates. Comparing CFD-predicted drying parameters with experimentally determined mass median aerodynamic diameters (MMAD) and emitted doses (ED) from a reference DPI produced strong linear correlations with coefficients of determination as high as R.sup.2 = 0.98. Conclusions For the spray dryer system and conditions considered, reducing the CFD-predicted maximum drying rate experienced by droplets improved the aerosolization performance (both MMAD and ED) when the powders were aerosolized with a reference DPI. |
ArticleNumber | 101 |
Audience | Academic |
Author | Farkas, Dale Longest, P. Worth Hassan, Amr Hindle, Michael |
AuthorAffiliation | 1 Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA 2 Department of Pharmaceutics, Virginia Commonwealth University, Richmond, VA |
AuthorAffiliation_xml | – name: 2 Department of Pharmaceutics, Virginia Commonwealth University, Richmond, VA – name: 1 Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA |
Author_xml | – sequence: 1 givenname: P. Worth surname: Longest fullname: Longest, P. Worth email: pwlongest@vcu.edu organization: Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Department of Pharmaceutics, Virginia Commonwealth University – sequence: 2 givenname: Dale surname: Farkas fullname: Farkas, Dale organization: Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University – sequence: 3 givenname: Amr surname: Hassan fullname: Hassan, Amr organization: Department of Pharmaceutics, Virginia Commonwealth University – sequence: 4 givenname: Michael surname: Hindle fullname: Hindle, Michael organization: Department of Pharmaceutics, Virginia Commonwealth University |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32440940$$D View this record in MEDLINE/PubMed |
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References | Vehring, Foss, Lechuga-Ballesteros (CR7) 2007; 38 Longest, Tian, Li, Son, Hindle (CR32) 2012; 40 Bird, Steward, Lightfoot (CR4) 2002 Li, Leung, Gengenbach, Yu, Gao, Tang, Zhou, Chan (CR60) 2017; 530 Bass, Longest (CR53) 2018; 119 Poozesh, Lu, Marsac (CR30) 2018; 122 Masters (CR5) 1972 Finlay (CR6) 2001 CR37 Clift, Grace, Weber (CR57) 1978 Leong (CR58) 1987; 18 Longest, Tian, Walenga, Hindle (CR44) 2012; 29 Farkas, Hindle, Longest (CR18) 2018; 35 Nyborg (CR39) 1953; 25 Ferziger, Peric (CR26) 1999 Hoe, Ivey, Boraey, Shamsaddini-Shahrbabak, Javaheri, Matinkhoo, Finlay, Vehring (CR8) 2014; 31 Longest, Tian, Delvadia, Hindle (CR43) 2012; 46 CR9 CR49 Ghazanfari, Elhissi, Ding, Taylor (CR38) 2007; 339 Behara, Longest, Farkas, Hindle (CR16) 2014; 103 Baldelli, Boraey, Nobes, Vehring (CR25) 2015; 12 Bird, Steward, Lightfoot (CR46) 1960 Belotti, Rossi, Colombo, Bettini, Rekkas, Politis, Colombo, Balducci, Buttini (CR61) 2015; 93 CR41 Vehring (CR3) 2008; 25 Longest, Hindle, Das Choudhuri, Byron (CR42) 2007; 41 Newman, Busse (CR13) 2002; 96 Baldelli, Power, Miles, Reid, Vehring (CR24) 2016; 50 Vinchurkar, Longest (CR51) 2008; 37 Lee, Heng, Ng, Chan, Tan (CR59) 2011; 403 Ordoubadi, Gregson, Melhem, Barona, Miles, D’Sa, Gracin, Lechuga-Ballesteros, Reid, Finlay (CR55) 2019; 36 Longest, Hindle (CR28) 2012; 29 Chan, Kwok (CR2) 2011; 63 Geller, Weers, Heuerding (CR20) 2011; 24 Vargaftik (CR48) 1975 CR19 Feng, Boraey, Gwin, Finlay, Kuehl, Vehring (CR21) 2011; 409 Trujillo, Knoerzer (CR40) 2011; 18 Hindle, Longest (CR33) 2012; 64 Gosman, Ioannides (CR52) 1981; 7 Baldelli, Vehring (CR22) 2016; 50 Longest, Xi (CR50) 2007; 41 Vicente, Pinto, Menezes, Gaspar (CR10) 2013; 247 Son, Longest, Hindle (CR14) 2013; 443 Zhang, David, Wiedmann (CR36) 2007; 20 Fuchs, Sutugin (CR56) 1970 Tian, Longest, Li, Hindle (CR34) 2013; 26 Son, Longest, Tian, Hindle (CR15) 2013; 49 Weers, Miller (CR1) 2015; 104 Farkas, Hindle, Longest (CR17) 2018; 546 Longest, Spence, Holbrook, Mossi, Son, Hindle (CR27) 2012; 51 Ramachandran, Akbarzadeh, Paliwal, Cenkowski (CR31) 2018; 11 Longest, Kleinstreuer (CR54) 2005; 39 CR23 Green (CR47) 1997 Tian, Longest, Su, Walenga, Hindle (CR45) 2011; 42 Longest, Walenga, Son, Hindle (CR29) 2013; 26 Weers, Son, Glusker, Haynes, Huang, Kadrichu, Le, Li, Malcolmson, Miller, Tarara, Ung, Clark (CR11) 2019; 32 Schmid, Arpagaus, Friess (CR35) 2011; 16 Delvadia, Hindle, Longest, Byron (CR12) 2013; 26 S Vinchurkar (2806_CR51) 2008; 37 Y-J Son (2806_CR15) 2013; 49 G Zhang (2806_CR36) 2007; 20 R Clift (2806_CR57) 1978 PW Longest (2806_CR32) 2012; 40 2806_CR49 RB Bird (2806_CR4) 2002 A Feng (2806_CR21) 2011; 409 R Delvadia (2806_CR12) 2013; 26 WH Finlay (2806_CR6) 2001 RP Ramachandran (2806_CR31) 2018; 11 K Schmid (2806_CR35) 2011; 16 PW Longest (2806_CR54) 2005; 39 M Hindle (2806_CR33) 2012; 64 2806_CR19 G Tian (2806_CR34) 2013; 26 NB Vargaftik (2806_CR48) 1975 PW Longest (2806_CR28) 2012; 29 SP Newman (2806_CR13) 2002; 96 RB Bird (2806_CR46) 1960 S Hoe (2806_CR8) 2014; 31 J Vicente (2806_CR10) 2013; 247 PW Longest (2806_CR42) 2007; 41 D Farkas (2806_CR18) 2018; 35 K Masters (2806_CR5) 1972 T Ghazanfari (2806_CR38) 2007; 339 L Li (2806_CR60) 2017; 530 R Vehring (2806_CR7) 2007; 38 K Leong (2806_CR58) 1987; 18 AD Gosman (2806_CR52) 1981; 7 G Tian (2806_CR45) 2011; 42 FJ Trujillo (2806_CR40) 2011; 18 PW Longest (2806_CR44) 2012; 29 2806_CR23 DE Geller (2806_CR20) 2011; 24 NA Fuchs (2806_CR56) 1970 2806_CR9 A Baldelli (2806_CR24) 2016; 50 JH Ferziger (2806_CR26) 1999 S Belotti (2806_CR61) 2015; 93 SH Lee (2806_CR59) 2011; 403 K Bass (2806_CR53) 2018; 119 SRB Behara (2806_CR16) 2014; 103 PW Longest (2806_CR29) 2013; 26 JG Weers (2806_CR11) 2019; 32 Y-J Son (2806_CR14) 2013; 443 R Vehring (2806_CR3) 2008; 25 A Baldelli (2806_CR22) 2016; 50 M Ordoubadi (2806_CR55) 2019; 36 JG Weers (2806_CR1) 2015; 104 2806_CR37 WL Nyborg (2806_CR39) 1953; 25 PW Longest (2806_CR43) 2012; 46 S Poozesh (2806_CR30) 2018; 122 D Farkas (2806_CR17) 2018; 546 2806_CR41 HK Chan (2806_CR2) 2011; 63 A Baldelli (2806_CR25) 2015; 12 PW Longest (2806_CR50) 2007; 41 PW Longest (2806_CR27) 2012; 51 DW Green (2806_CR47) 1997 |
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SSID | ssj0008194 |
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Snippet | Purpose
The purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics... The purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics within a... Purpose The purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics... PurposeThe purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics... PURPOSEThe purpose of this study was to develop a new computational fluid dynamics (CFD)-based model of the complex transport and droplet drying kinetics... |
SourceID | pubmedcentral proquest gale crossref pubmed springer |
SourceType | Open Access Repository Aggregation Database Index Database Publisher |
StartPage | 101 |
SubjectTerms | Administration, Inhalation Aerosols Analysis Biochemistry Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Chemistry, Pharmaceutical Computer applications Computer Simulation Drug Compounding - methods Dry Powder Inhalers Drying Excipients - chemistry Fluid dynamics Hydrodynamics Inhalers L-leucine Leucine Mass transfer Medical Law Models, Chemical Particle Size Pharmacology/Toxicology Pharmacy Research Paper Simulation methods Spray Drying |
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Title | Computational Fluid Dynamics (CFD) Simulations of Spray Drying: Linking Drying Parameters with Experimental Aerosolization Performance |
URI | https://link.springer.com/article/10.1007/s11095-020-02806-y https://www.ncbi.nlm.nih.gov/pubmed/32440940 https://www.proquest.com/docview/2405451359 https://search.proquest.com/docview/2406306089 https://pubmed.ncbi.nlm.nih.gov/PMC8672377 |
Volume | 37 |
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