Discrete Phase Model (DPM).
Fine Particle Model (FPM).
The DPM is primarily used to simulate the transport of large, individual particles, whereas the FPM is primarily used to simulate the transport of populations of a large number of small particles.
Large, supermicron, particles, are usually formed by mechanical processes.
Submicron particles are usually formed by chemical processes.
Particles larger than 1 micron are typicall generated by atomization and grinding processes. [Mechanical, The DPM in FLUENT is primarily useful for simulating the dynamics of these supermicron particles], particles less than one micron are typically generated by chemical routes [Chemical, the FPM is primarily useful for simulating the dynamics of these submicron particles.].
Although the DPM can simulate the transport of a distribution of particles, it is really a spatial distribution of similarly sized particles, whereas the FPM simulates the transport of a distribution of differently sized particles.
Also, although the DPM simulates diffusion by using a stochastic walk procedure in Lagrangian simulations, the FPM solves particle diffusion using the coupled convection-diffusion solution scheme inherent in Fluent’s Eulerian CFD solvers.
Feature: FPM -- DPM
Typical size range : <10>=1 micron
Effect of processes on: Particle size distribution -- Single particles
Characteristic size distribution: Lognormal -- Rosin-Rammler
Solution technique: Eulerian -- Lagrangian
Cluster-particle, particle-particle interactions: Nucleation, Coagulation -- None
Brownian motion treated as: Diffusion -- Additional force
FPM: There must be a continuous size distribution of particles in the simulation domain.
DPM: The motion of single particles can be simulated.
So, based on http://university.fluent.com/forum and www.aerosolmodeling.com and the facts from Kalasee et, al. (2007), FPM is the best Model to Simulate Aerosol Particles Transport in this Thesis.
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Tuesday, May 15, 2007
aerosol-properties-Euler or DPM?
If my research (temporary) focus is how to show the Particles Velocity Magnitude (m/s) in any time (and or space ) AND the Particles trajectory in time by time (and or space), what Method should I use?...Eulerian or DPM ?
Because NH [emailing: Injection Modeling, 2007] not sure which method works better, but do know that DPM can track the particles and velocity of the particles very easily, Lasman already write verification in page or folder: Aerosols-Properties-DPM or FPM? Have a nice look!.
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Because NH [emailing: Injection Modeling, 2007] not sure which method works better, but do know that DPM can track the particles and velocity of the particles very easily, Lasman already write verification in page or folder: Aerosols-Properties-DPM or FPM? Have a nice look!.
http://continuousimprovement.blogsome.com
http://Life4aaK.blogspot.com
aerosol-research-journals
Aerosol and Air Quality Research
Aerosol Science and Technology (AS&T)
Atmospheric Environment
Journal of Aerosol Medicine: Deposition, Clearance, and Effects in Lung
Journal of Aerosol Science
Journal of Environmental Monitoring (JEM)
any sources: www.aerosol.com
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Aerosol Science and Technology (AS&T)
Atmospheric Environment
Journal of Aerosol Medicine: Deposition, Clearance, and Effects in Lung
Journal of Aerosol Science
Journal of Environmental Monitoring (JEM)
any sources: www.aerosol.com
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List Of Contents: Newest
[1]. THESIS
aerosol-properties
geometry-GAMBIT
CFD-FLUENT
aerosol-particles-transport-modeling
aerosol-energy-engineering-PSU
aerosol-Indonesia-Info
aerosol-control-solution
aerosol-outdoor-environment
aerosol-indoor-environment
aerosol-effects
aerosol-example-products-commercial
aerosol-research (seminar, conference, grant, ..., scholarship)
[2]. StudyInHatYaiPSU
dormitory-insurance
banking-ATM-send-receive-money
shopping
immigration
student-Visa
Police-Station
Consulate-General-RI-Songkhla
Free-Time
Registration-Pumpkin-Building
Children-School
International-School-Children
Thanks-a-Lot .:::::. Kob Khun Krab Kab
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aerosol-properties
geometry-GAMBIT
CFD-FLUENT
aerosol-particles-transport-modeling
aerosol-energy-engineering-PSU
aerosol-Indonesia-Info
aerosol-control-solution
aerosol-outdoor-environment
aerosol-indoor-environment
aerosol-effects
aerosol-example-products-commercial
aerosol-research (seminar, conference, grant, ..., scholarship)
[2]. StudyInHatYaiPSU
dormitory-insurance
banking-ATM-send-receive-money
shopping
immigration
student-Visa
Police-Station
Consulate-General-RI-Songkhla
Free-Time
Registration-Pumpkin-Building
Children-School
International-School-Children
Thanks-a-Lot .:::::. Kob Khun Krab Kab
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StudyInPSU-PocketGuidance
1. Menghubungi calon Advisor
2. Mendaftar On Line
3. Mendaftar Langsung di Pumkin Building
4. Menunggu KTM ATM selama 1 bulan
Police Station
Konjend RI Songkhla
Hiburan
Pumpkin Building
Registrasi
Sekolah Anak
International Anak
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2. Mendaftar On Line
3. Mendaftar Langsung di Pumkin Building
4. Menunggu KTM ATM selama 1 bulan
Police Station
Konjend RI Songkhla
Hiburan
Pumpkin Building
Registrasi
Sekolah Anak
International Anak
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Monday, May 14, 2007
Airflow Modeling
Airflow Modeling: Efforts to Find the Better Models for Building Air Quality Simulation.
1Lasman Parulian Purba, 2Elieser Tarigan
1Master of Engineering Student in Mechanical Engineering Department
Prince of Songkhla University, HatYai Campus, Thailand, 90110
2Kampus UBAYA Tenggilis
Jalan Raya Kalirungkut, Surabaya, Indonesia
Contact Person:
Elieser Tarigan, Ph. D
Kampus UBAYA Tenggilis
Jalan Raya Kalirungkut, Surabaya, Indonesia
Phone: +62-31-298-1000, jumapurba@yahoo.com, las.et.nic@gmail.com
Abstract. All combustion sources, such as motor vehicle, industrial combustion processes, burning, cooking, heating, and tobacco smoking, generate large quantities of fine (aerodynamic diameter smaller than 2.5 micro-meter) and ultra-fine (smaller than 0.1 micro-meter) particles. Smaller particles can penetrate deeper into the respiratory tract and therefore have a higher potential to induce health effects than larger particles.
Suspended particulate matter can serve as nuclei and carriers for airborne viruses and bacteria, resulting in the spread of diseases. In addition, fine particles themselves can deposit in the lungs and cause respiratory diseases. As people spend about 90% of their lifetime indoors, indoor particulate matter can have great impact on human health. Thus, a good understanding of particle transport (included in dynamics flow) is crucial for creating healthy indoor environments.
In this paper, more than four model simulation of indoor airflow for building is reviewed. Hope fully, this help decisions maker have many consideration to improve or manage airflow for building. This is an effort to improve working environment in factory that seems to be severely affected by particulate matter.
Keywords: particulate matter, airflow, building, simulation.
1 Introduction
Health problems related to the environment have become a major source of concern all over the world. The health of the population depends upon good quality air, water, soil, food and many other factors, can be analyzed in terms of the costs, as a part of consequences of failure in Risk Analysis. The environmental consequences of an event can also have other serious consequences, which affect company’s reputation. It’s needed to establish measures that can eliminate or considerably reduce hazardous factors from the human environment to minimize the associated health risks. The ability to achieve these objectives is in great part dependent on the development of suitable experimental, modeling and interpretive techniques, which will allow a balanced assessment of the risk involved as well as suggesting ways in which the situation can be improved. The interaction between environmental risk and health is often complex and can involve a variety of social, occupational and lifestyle factors. This emphasizes the importance of considering an interdisciplinary approach in related with environmental indoor health.
To raise efforts for promoting healthy working-environment, sustainable management of building and or factory that must have indoor room, this paper will present deep supporting information that needed to consider mainly about indoor air quality’s factors. Primary goal of this paper is to present existing information in ways compatible with other engineering disciplines, then to contribute to the body of knowledge itself in order to advance our understanding of controlling Particulate Matter that is a part of contaminants in the indoor air environment.
...
To help summarized the information provided in this paper, inclusion of four model simulation of indoor airflow for building is reviewed to help gathering prediction the velocity and pollutant concentrations at arbitrary points inside a building or room. This paper is organized into eight sections beginning with fundamental concept of risk, the main factors affecting contaminant dispersion, characteristic of Indoor air environment, aerosol as one kind of particulate matter and contaminants, impact of aerosol, type of indoor air flow and standard that related to indoor air quality, and the respiratory system: ending with the elements of future computational methods that possible to used to predict contaminant particles transport in the vicinity of process equipped with industrial ventilation systems, that currently more then four Simulation in the world-internetworking-advanced-search-based.
...
...
References
1. Michael D. Sohn, Michael G. Apte, Richard G. Sextro, Alvin C. K. Lai, Predicting size-resolved particle behaviour in multizone buildings, Atmospheric Environment (2006),DOI:10.1016/ j.atmosenv.2006.10.010.
2. Nazaroff and Cass, 1989. Mathematical modeling of indoor aerosol dynamics. Environmental Science and Technology 23 (2), 157-166
3. Feustel, H. E., 1999. COMIS-An international multizone air-flow and contaminant transport model. Energy and buildings 30, 3-18
4. Dols, W.S., Walton, G.N, 2002. CONTAMW 2.0 User Manual, National Institute of Standards and Technology, NISTIR 6921
5. Tareq Hussein, Hannele Korhonen, Erik Herrmann, Kaarle H¨ameri, Kari E. J. Lehtinen, and Markku Kulmala. 2005. "Emission Rates Due to Indoor Activities: Indoor Aerosol Model Development, Evaluation, and Applications," Aerosol Science and Technology, 39:1111–1127, Copyright: American Association for Aerosol Research, ISSN: 0278-6826 print / 1521-7388 online; DOI: 10.1080/02786820500421513
6. Zhang, Z. and Chen, Q. 2006. “Experimental measurements and numerical simulations of particle transport and distribution in ventilated rooms,” Atmospheric Environment, 40(18), 3396-3408.
7. Klepeis, N.E., Nelson, W.C., Ott, W.R., Robinson, J.P., Tsang, A.M., Switzer, P., Behar, J.V., Hern, S.C., and Engelmann, W.H., 2001, “The national human activity pattern survey (NHAPS): A resource for assessing exposure to environmental pollutants,” Journal of Exposure Analysis and Environmental Epidemiology, Vol. 11 (3), pp 231-252.
8. Aliage, C. and Winqvist, K., 2003, “Commnet les femmes et les homes utilisent leurs temps-Résultats de 13 pays européens,” Eurostat, KS-NK-03-012-FR-N.
9. The Practitioner’s Guide to Health Effects, Contaminant and Environmental Factors from MANAGING IAQ, http://books.google.com/books?vid=ISBN0881734403&id=xuRIkdtnvSEC&pg=RA1-PA127&lpg=RA1-PA127&ots=b0iaukSIaJ&dq=software+of+indoor+air+quality+of+building&sig=l0pHm2DdRLrLgXxDK9q_S_M7HCs#PRA1-PA65,M1 accesed 19th February 2007
10. http://www.cecer.army.mil/earupdate/nlfiles/2000/SIRTSPRTv5.xls Engineer Installation Management accessed 19th February 2007
11. www.p2pays.org/ref/22/21472.pdf Green Building Rating System Version 2, 2000, Leadership in Energy and Environmental Design
29. James H.,V. Aerosol Science for Industrial Hygienist. Pergamon. Great Brittain by Bookcraft, Bath. 1995;1]
30. "http://en.wikipedia.org/wiki/Particulate" as in 04.12.2006
34. http://www.m-w.com/dictionary/aerosol as in 15December2006
35. Hinds,W.C.,AerosolTechnologyProperties, Behavior, and Measurement of Airborne Particles, ISBN-10: 0-471-19410-7 ISBN-13: 978-0-471-19410-1 - John Wiley & Sons (1999)
36. Kameel, R., Khalil, E. E., The Prediction of Airflow Regimes in Surgical Operating Theatres: A Comparison of Different Turbulence Models, 41st Aerospace Sciences Meeting and Exhibit., American Institute of Aeronautics and Astronautics, Reno-Nevada (2003).
37. Hussein, T., Indoor and Outdoor Aerosol Particle Size Characterization in Helsinki, Report Series in Aerosol Science N:o 74 (2005)
38. Nazaroff, W. W., Indoor Particles Dynamics, INDOOR AIR, Blackwell Munksgaard (2004).
39. Sørensen, D.N. and P.V. Nielsen, Quality control of computational fluid dynamics in indoor environments. Indoor Air, 2003. 13: p. 2-17.
more information?... contact: http://risk-tech2007.info, las.et.nic@gmail.com
http://continuousimprovement.blogsome.com
http://Life4aaK.blogspot.com
http://life4family.blogspot.com
1Lasman Parulian Purba, 2Elieser Tarigan
1Master of Engineering Student in Mechanical Engineering Department
Prince of Songkhla University, HatYai Campus, Thailand, 90110
2Kampus UBAYA Tenggilis
Jalan Raya Kalirungkut, Surabaya, Indonesia
Contact Person:
Elieser Tarigan, Ph. D
Kampus UBAYA Tenggilis
Jalan Raya Kalirungkut, Surabaya, Indonesia
Phone: +62-31-298-1000, jumapurba@yahoo.com, las.et.nic@gmail.com
Abstract. All combustion sources, such as motor vehicle, industrial combustion processes, burning, cooking, heating, and tobacco smoking, generate large quantities of fine (aerodynamic diameter smaller than 2.5 micro-meter) and ultra-fine (smaller than 0.1 micro-meter) particles. Smaller particles can penetrate deeper into the respiratory tract and therefore have a higher potential to induce health effects than larger particles.
Suspended particulate matter can serve as nuclei and carriers for airborne viruses and bacteria, resulting in the spread of diseases. In addition, fine particles themselves can deposit in the lungs and cause respiratory diseases. As people spend about 90% of their lifetime indoors, indoor particulate matter can have great impact on human health. Thus, a good understanding of particle transport (included in dynamics flow) is crucial for creating healthy indoor environments.
In this paper, more than four model simulation of indoor airflow for building is reviewed. Hope fully, this help decisions maker have many consideration to improve or manage airflow for building. This is an effort to improve working environment in factory that seems to be severely affected by particulate matter.
Keywords: particulate matter, airflow, building, simulation.
1 Introduction
Health problems related to the environment have become a major source of concern all over the world. The health of the population depends upon good quality air, water, soil, food and many other factors, can be analyzed in terms of the costs, as a part of consequences of failure in Risk Analysis. The environmental consequences of an event can also have other serious consequences, which affect company’s reputation. It’s needed to establish measures that can eliminate or considerably reduce hazardous factors from the human environment to minimize the associated health risks. The ability to achieve these objectives is in great part dependent on the development of suitable experimental, modeling and interpretive techniques, which will allow a balanced assessment of the risk involved as well as suggesting ways in which the situation can be improved. The interaction between environmental risk and health is often complex and can involve a variety of social, occupational and lifestyle factors. This emphasizes the importance of considering an interdisciplinary approach in related with environmental indoor health.
To raise efforts for promoting healthy working-environment, sustainable management of building and or factory that must have indoor room, this paper will present deep supporting information that needed to consider mainly about indoor air quality’s factors. Primary goal of this paper is to present existing information in ways compatible with other engineering disciplines, then to contribute to the body of knowledge itself in order to advance our understanding of controlling Particulate Matter that is a part of contaminants in the indoor air environment.
...
To help summarized the information provided in this paper, inclusion of four model simulation of indoor airflow for building is reviewed to help gathering prediction the velocity and pollutant concentrations at arbitrary points inside a building or room. This paper is organized into eight sections beginning with fundamental concept of risk, the main factors affecting contaminant dispersion, characteristic of Indoor air environment, aerosol as one kind of particulate matter and contaminants, impact of aerosol, type of indoor air flow and standard that related to indoor air quality, and the respiratory system: ending with the elements of future computational methods that possible to used to predict contaminant particles transport in the vicinity of process equipped with industrial ventilation systems, that currently more then four Simulation in the world-internetworking-advanced-search-based.
...
...
References
1. Michael D. Sohn, Michael G. Apte, Richard G. Sextro, Alvin C. K. Lai, Predicting size-resolved particle behaviour in multizone buildings, Atmospheric Environment (2006),DOI:10.1016/ j.atmosenv.2006.10.010.
2. Nazaroff and Cass, 1989. Mathematical modeling of indoor aerosol dynamics. Environmental Science and Technology 23 (2), 157-166
3. Feustel, H. E., 1999. COMIS-An international multizone air-flow and contaminant transport model. Energy and buildings 30, 3-18
4. Dols, W.S., Walton, G.N, 2002. CONTAMW 2.0 User Manual, National Institute of Standards and Technology, NISTIR 6921
5. Tareq Hussein, Hannele Korhonen, Erik Herrmann, Kaarle H¨ameri, Kari E. J. Lehtinen, and Markku Kulmala. 2005. "Emission Rates Due to Indoor Activities: Indoor Aerosol Model Development, Evaluation, and Applications," Aerosol Science and Technology, 39:1111–1127, Copyright: American Association for Aerosol Research, ISSN: 0278-6826 print / 1521-7388 online; DOI: 10.1080/02786820500421513
6. Zhang, Z. and Chen, Q. 2006. “Experimental measurements and numerical simulations of particle transport and distribution in ventilated rooms,” Atmospheric Environment, 40(18), 3396-3408.
7. Klepeis, N.E., Nelson, W.C., Ott, W.R., Robinson, J.P., Tsang, A.M., Switzer, P., Behar, J.V., Hern, S.C., and Engelmann, W.H., 2001, “The national human activity pattern survey (NHAPS): A resource for assessing exposure to environmental pollutants,” Journal of Exposure Analysis and Environmental Epidemiology, Vol. 11 (3), pp 231-252.
8. Aliage, C. and Winqvist, K., 2003, “Commnet les femmes et les homes utilisent leurs temps-Résultats de 13 pays européens,” Eurostat, KS-NK-03-012-FR-N.
9. The Practitioner’s Guide to Health Effects, Contaminant and Environmental Factors from MANAGING IAQ, http://books.google.com/books?vid=ISBN0881734403&id=xuRIkdtnvSEC&pg=RA1-PA127&lpg=RA1-PA127&ots=b0iaukSIaJ&dq=software+of+indoor+air+quality+of+building&sig=l0pHm2DdRLrLgXxDK9q_S_M7HCs#PRA1-PA65,M1 accesed 19th February 2007
10. http://www.cecer.army.mil/earupdate/nlfiles/2000/SIRTSPRTv5.xls Engineer Installation Management accessed 19th February 2007
11. www.p2pays.org/ref/22/21472.pdf Green Building Rating System Version 2, 2000, Leadership in Energy and Environmental Design
29. James H.,V. Aerosol Science for Industrial Hygienist. Pergamon. Great Brittain by Bookcraft, Bath. 1995;1]
30. "http://en.wikipedia.org/wiki/Particulate" as in 04.12.2006
34. http://www.m-w.com/dictionary/aerosol as in 15December2006
35. Hinds,W.C.,AerosolTechnologyProperties, Behavior, and Measurement of Airborne Particles, ISBN-10: 0-471-19410-7 ISBN-13: 978-0-471-19410-1 - John Wiley & Sons (1999)
36. Kameel, R., Khalil, E. E., The Prediction of Airflow Regimes in Surgical Operating Theatres: A Comparison of Different Turbulence Models, 41st Aerospace Sciences Meeting and Exhibit., American Institute of Aeronautics and Astronautics, Reno-Nevada (2003).
37. Hussein, T., Indoor and Outdoor Aerosol Particle Size Characterization in Helsinki, Report Series in Aerosol Science N:o 74 (2005)
38. Nazaroff, W. W., Indoor Particles Dynamics, INDOOR AIR, Blackwell Munksgaard (2004).
39. Sørensen, D.N. and P.V. Nielsen, Quality control of computational fluid dynamics in indoor environments. Indoor Air, 2003. 13: p. 2-17.
more information?... contact: http://risk-tech2007.info, las.et.nic@gmail.com
http://continuousimprovement.blogsome.com
http://Life4aaK.blogspot.com
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Aerosol Modeling in FLUENT
aerosol modeling in FLUENT
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NEWCHALENGER: To be expertice...
I. THESIS-AEROSOLs-Thailand
Aerosol energy engineering & Environmental effect of aerosol
Aerosol & Control..
GAMBIT Expert
FLUENT Expert
International Journals of Aerosol...
II. LIFE4FAMILY-AAK-Indonesia
2.1. Papers--National
2.2. Papers--International
2.3. Buku--Sistem Pengaturan dengan Komputer--GrahaIlmu.Com
2.4. Calon Buku--ETIKA PERKOMPUTERAN
2.5. Penelitian--National--BBI (LitMud & KW)
2.6. Award--DIKTI--Kepangkatan--Lektor(2006)
III. StudyInPSU-HatYai-inEnglish&/Indonesia-Panduan untuk orang Indonesia..
3.1. Dormitory
3.2. FoodCenter
3.3. SevenEleven and TescoLotus
3.4. PSU HatYai Gate
3.5. PumpkinBuilding 1st Registration Place
3.6. Banking
3.7. ThaiTradition
IV. OTHERS-Prospect-Int.Business&Engineering-HighLight for ThailandIndonesia
4.1. Rubber Indonesia -- Energy from Biomass -- Tinjauan Teknis & Ekonomi
4.2. Energy audit -- Boiler of Factories -- Potensi & Peluang
4.3. Palm oil (Crude Palm Oil) Indonesia -- Potensi & Tantangan
4.4. Mechatronics -- Mechanical -- Electronics ?
V. GENERAL-Knowlwedge-Self-Improvement
5.1. Converter [engineeringtoolbox.com]
5.2. Makalah-Populer-Siswa-Indonesia
5.3. Etika profesi-komputer-business-engineering
5.4. ...
VI. SWEET-Memories
6.1. Simalungun.net
6.2. ITS Surabaya
6.3. STIKOM Surabaya
6.4. UBAYA Surabaya
6.5. Ciputra Surabaya
6.6. ...
HatYai, Thailand on 18thMay2007
Team Blog's Developer
http://life4aak.blogspot.com/
http://continuousimprovement.blogsome.com
Lasman Parulian Purba, ST
M.Eng Students of Prince of Songkla University
Aerosol energy engineering & Environmental effect of aerosol
Aerosol & Control..
GAMBIT Expert
FLUENT Expert
International Journals of Aerosol...
II. LIFE4FAMILY-AAK-Indonesia
2.1. Papers--National
2.2. Papers--International
2.3. Buku--Sistem Pengaturan dengan Komputer--GrahaIlmu.Com
2.4. Calon Buku--ETIKA PERKOMPUTERAN
2.5. Penelitian--National--BBI (LitMud & KW)
2.6. Award--DIKTI--Kepangkatan--Lektor(2006)
III. StudyInPSU-HatYai-inEnglish&/Indonesia-Panduan untuk orang Indonesia..
3.1. Dormitory
3.2. FoodCenter
3.3. SevenEleven and TescoLotus
3.4. PSU HatYai Gate
3.5. PumpkinBuilding 1st Registration Place
3.6. Banking
3.7. ThaiTradition
IV. OTHERS-Prospect-Int.Business&Engineering-HighLight for ThailandIndonesia
4.1. Rubber Indonesia -- Energy from Biomass -- Tinjauan Teknis & Ekonomi
4.2. Energy audit -- Boiler of Factories -- Potensi & Peluang
4.3. Palm oil (Crude Palm Oil) Indonesia -- Potensi & Tantangan
4.4. Mechatronics -- Mechanical -- Electronics ?
V. GENERAL-Knowlwedge-Self-Improvement
5.1. Converter [engineeringtoolbox.com]
5.2. Makalah-Populer-Siswa-Indonesia
5.3. Etika profesi-komputer-business-engineering
5.4. ...
VI. SWEET-Memories
6.1. Simalungun.net
6.2. ITS Surabaya
6.3. STIKOM Surabaya
6.4. UBAYA Surabaya
6.5. Ciputra Surabaya
6.6. ...
HatYai, Thailand on 18thMay2007
Team Blog's Developer
http://life4aak.blogspot.com/
http://continuousimprovement.blogsome.com
Lasman Parulian Purba, ST
M.Eng Students of Prince of Songkla University
Sunday, May 13, 2007
concentration
http://en.wikipedia.org/wiki/Concentration
Table of concentration measures
Frequently used standards of concentration Measurement Notation Generic formula Typical units
atomic percentage (A) at.% %
atomic percentage (B) at.% %
Mass percentage - %
Mass-volume percentage - % though strictly %g/mL
Volume-volume percentage - %
Molarity M mol/L (or M or mol/dm3)
Molinity - mol/kg
Molality m mol/kg (or m)
Molar fraction Χ (chi) (fraction)
Formal F mol/L (or F)
Normality N N
Parts per hundred % (or pph) da.g/kg
Parts per thousand ‰ (or ppt*) g/kg
Parts per million ppm mg/kg
Parts per billion ppb µg/kg
Parts per trillion ppt* ng/kg
Parts per quadrillion ppq pg/kg
* Although 'ppt' is usually used to denote 'parts per trillion', it is on occasion used for 'parts per thousand'. Sometimes 'ppt' is also used as an abbreviation for precipitate.
Note (1) : The table above is described in terms of solvents and solutes; however the units given often also apply to other types of mixture.
Note (2) : The use of billion, trillion, quadrillion above follows the short scale usage of these words
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Table of concentration measures
Frequently used standards of concentration Measurement Notation Generic formula Typical units
atomic percentage (A) at.% %
atomic percentage (B) at.% %
Mass percentage - %
Mass-volume percentage - % though strictly %g/mL
Volume-volume percentage - %
Molarity M mol/L (or M or mol/dm3)
Molinity - mol/kg
Molality m mol/kg (or m)
Molar fraction Χ (chi) (fraction)
Formal F mol/L (or F)
Normality N N
Parts per hundred % (or pph) da.g/kg
Parts per thousand ‰ (or ppt*) g/kg
Parts per million ppm mg/kg
Parts per billion ppb µg/kg
Parts per trillion ppt* ng/kg
Parts per quadrillion ppq pg/kg
* Although 'ppt' is usually used to denote 'parts per trillion', it is on occasion used for 'parts per thousand'. Sometimes 'ppt' is also used as an abbreviation for precipitate.
Note (1) : The table above is described in terms of solvents and solutes; however the units given often also apply to other types of mixture.
Note (2) : The use of billion, trillion, quadrillion above follows the short scale usage of these words
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[ahlikaret]
KARET Indonesia diteliti orang Francis
http://www.cirad.fr/en/le_cirad/cirad_monde/pays.php?id=237
Indonesia
In Indonesia, agriculture and forestry represent almost 15% of GDP and employ 42% of the workforce. Food security, poverty alleviation and biodiversity management, particularly studies of the joint changes in societies and in island ecosystems as a result of climate change, are the main research priorities for CIRAD in the country. General agreements signed in 2004 with the Ministries of Agriculture and Forestry have helped to strengthen scientific partnerships and cooperation projects.
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http://www.cirad.fr/en/le_cirad/cirad_monde/pays.php?id=237
Indonesia
In Indonesia, agriculture and forestry represent almost 15% of GDP and employ 42% of the workforce. Food security, poverty alleviation and biodiversity management, particularly studies of the joint changes in societies and in island ecosystems as a result of climate change, are the main research priorities for CIRAD in the country. General agreements signed in 2004 with the Ministries of Agriculture and Forestry have helped to strengthen scientific partnerships and cooperation projects.
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http://Life4aaK.blogspot.com
http://godoproduction.blogspot.com
Wednesday, May 02, 2007
Diffusion Fluxes
Diffusion Fluxes
We call a flux purely diffusive if the only driving force is a concentration gradient and neither other driving forces nor convection terms exist.
...
taken from http://www.iue.tuwien.ac.at/phd/wimmer/node65.html
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We call a flux purely diffusive if the only driving force is a concentration gradient and neither other driving forces nor convection terms exist.
...
taken from http://www.iue.tuwien.ac.at/phd/wimmer/node65.html
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convection fluxes
Convection Fluxes
We call a flux purely convective if the diffusive part, driven by the concentration gradient, vanishes, i.e. a=0.
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We call a flux purely convective if the diffusive part, driven by the concentration gradient, vanishes, i.e. a=0.
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definition of flux
Fluxes...
The most crucial and computationally expensive task is to find an appropriate discrete representation for the flux components. To avoid unnecessary complications we look closely at just one term of the sum in (3.1-2), at the partial flux of quantity which is related to (or driven by) quantity . The transformation of the equations from physical space to computational space is linear and, therefore, we can merely consider the term
For numerical reasons we distinguish between the cases where we have
- only a diffusive flux,
- only a convective flux,
- diffusive and convective flux.
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The most crucial and computationally expensive task is to find an appropriate discrete representation for the flux components. To avoid unnecessary complications we look closely at just one term of the sum in (3.1-2), at the partial flux of quantity which is related to (or driven by) quantity . The transformation of the equations from physical space to computational space is linear and, therefore, we can merely consider the term
For numerical reasons we distinguish between the cases where we have
- only a diffusive flux,
- only a convective flux,
- diffusive and convective flux.
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Monday, April 23, 2007
what is adiabatic?
2nd Law of Thermodynamics
The second law is concerned with entropy, which is a measure of disorder. The second law says that the entropy of the universe increases
There are two classical statements of the second law of thermodynamics:
Kelvin & Planc"No (heat) engine whose working fluid undergoes a cycle can absorb heat from a single reservoir, deliver an equivalent amount of work, and deliver no other effect"
Clausius"No machine whose working fluid undergoes a cycle can absorb heat from one system, reject heat to another system and produce no other effect"
Both statements of the second law place constraints on the first law by identifying that energy goes downhill.
The second law is concerned with entropy (S), which is a measure of disorder. The second law says that the entropy of the universe increases. An increase in overall disorder is therefore spontaneous. If the volume and energy of a system are constant, then every change to the system increases the entropy. If volume or energy change, then the entropy of the system actually decrease. However, the entropy of the universe does not decrease.
For energy to be available there must be a region with high energy level and a region with low energy level. Useful work must be derived from the energy that would flows from the high level to the low level.
100% of the energy can not be transformed to work
Entropy can be produced but never destroyed
Efficiency of a heat machine
The efficiency of a heat machine working between two energy levels is defined in terms of absolute temperature:
η = ( Th - Tc ) / Th = 1 - Tc / Th(1)
where
η = efficiency
Th = temperature high level (K)
Tc = temperature low level (K)
As a consequence, to attain maximum efficiency the Tc would have to be as cold as possible. For 100% efficiency the Tc would have to equal 0 K. This is practically impossible, so the efficiency is always less than 1 (less than 100%).
Change in entropy > 0, irreversible process
Change in entropy = 0, reversible process
Change in entropy < 0, impossible process
Entropy is used to define the unavailable energy in a system. Entropy defines the relative ability of one system to act to an other. As things moves toward a lower energy level, where one is less able to act upon the surroundings, the entropy is said to increase.
For the universe as a whole the entropy is increasing!
Entropy definition
Entropy is defined as :
S = H / T (2)
where
S = entrophy (kJ/kg K)
H = enthalpy (kJ/kg)
T = absolute temperature (K)
A change in the entropy of a system is caused by a change in its heat content, where the change of entropy is equal to the heat change divided by the average absolute temperature (Ta):
dS = dH / Ta (3)
The sum of (H / T) values for each step in the Carnot cycle equals 0. This only happens because for every positive H there is a countering negative H, overall.
Carnot Heat CycleIn a heat engine, a gas is reversibly heated and then cooled. A model of the cycle is as follows: State 1 --(isothermal expansion) --> State 2 --(adiabatic expansion) --> State 3 --(isothermal compression) --> State 4 --(adiabatic compression) --> State 1State 1 to State 2: Isothermal ExpansionIsothermal expansion occurs at a high temperature Th, dT = 0 and dE1 = 0. Since dE = H + w, w1 = - H1. For ideal gases, dE is dependent on temperature only.State 2 to State 3: Adiabatic ExpansionThe gas is cooled from the high temperature, Th, to the low temperature, Tc. dE2 = w2 and H2 = 0 (adiabatic).State 3 to State 4: Isothermal CompressionThis is the reverse of the process between states 1 and 2. The gas is compressed at Tc. dT = 0 and dE3 = 0. w3 = - H3State 4 to State 1: Adiabatic CompressionThis is the reverse of the process between states 2 and 3. dE4 = w4 and H4 = 0 (adiabatic).The processes in the Carnot cycle can be graphed as the pressure vs. the volume. The area enclosed in the curve is then the work for the Carnot cycle because w = - integral (P dV). Since this is a cycle, dE overall equals 0. Therefore,-w = H = H1 + H2 + H3 + H4If you decrease Tc, then the quantity -w gets larger in magnitude.if -w > 0 then H > 0 and the system, the heat engine, does work on the surroundings.
The laws of thermodynamics were determined empirically (by experiment). They are generalizations of repeated scientific experiments. The second law is a generalization of experiments dealing with entropy--it is that the dS of the system plus the dS of the surroundings is equal to or greater then 0.
Entropy is not conserved like energy!
Example - Entropy Heating Water
A process raises 1 kg of water from 0 to 100oC (273 to 373 K) under atmospheric conditions.Specific enthalpy at 0oC (hf) = 0 kJ/kg (from steam tables) (Specific - per unit mass)Specific enthalpy of water at 100oC (hf) = 419 kJ/kg (from steam tables)
Change in specific entropy:
dS = dH / Ta
= (419 - 0) / ((273 + 373)/2)
= 1.297 kJ/kgK
Example - Entropy Evaporation Water to Steam
A process changes 1 kg of water at 100oC (373 K) to saturated steam at 100oC (373 K) under atmospheric conditions.Specific enthalpy of steam at 100oC (373 K) before evaporating = 0 kJ/kg (from steam tables)
Specific enthalpy of steam at 100oC (373 K) after evaporating = 2 258 kJ/kg (from steam tables)Change in specific entropy:
dS = dH / Ta
= (2 258 - 0) / ((373 + 373)/2)
= 6.054 kJ/kgK
The total change in specific entropy from water at 0oC to saturated steam at 100oC is the sum of the change in specific entropy for the water, plus the change of specific entropy for the steam.
Example - Entropy Superheated Steam
A process superheats 1 kg of saturated steam at atmospheric pressure to 150oC (423 K).
Specific total enthalpy of steam at 100oC (373 K) = 2 675 kJ/kg (from steam tables)Specific total enthalpy of superheated steam at 150oC (373 K) = 2 777 kJ/kg (from steam tables)
Change in specific entropy:
dS = dH / Ta
= (2 777 - 2 675) / ((423 + 373)/2)
= 0.256 kJ/kgK
Entropy table for superheated steam [http://www.engineeringtoolbox.com/superheated-steam-entropy-d_100.html]
If saturated steam is exposed to a surface with a higher temperature, its temperature will increase above the evaporating temperature. The steam is then described as superheated by the temperature degrees above saturation temperature.
Note! Steam cannot be superheated whilst it is still in the contact with water, because additional heat will evaporate more water, cooling down the superheated steam.
Superheated steam is produced by passing saturated steam through an additional heat exchanger.
Superheated steam is also called
surcharged steam
anhydrous steam
steam gas
taken from: http://www.engineeringtoolbox.com/law-thermodynamics-d_94.html as 23April2007
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The second law is concerned with entropy, which is a measure of disorder. The second law says that the entropy of the universe increases
There are two classical statements of the second law of thermodynamics:
Kelvin & Planc"No (heat) engine whose working fluid undergoes a cycle can absorb heat from a single reservoir, deliver an equivalent amount of work, and deliver no other effect"
Clausius"No machine whose working fluid undergoes a cycle can absorb heat from one system, reject heat to another system and produce no other effect"
Both statements of the second law place constraints on the first law by identifying that energy goes downhill.
The second law is concerned with entropy (S), which is a measure of disorder. The second law says that the entropy of the universe increases. An increase in overall disorder is therefore spontaneous. If the volume and energy of a system are constant, then every change to the system increases the entropy. If volume or energy change, then the entropy of the system actually decrease. However, the entropy of the universe does not decrease.
For energy to be available there must be a region with high energy level and a region with low energy level. Useful work must be derived from the energy that would flows from the high level to the low level.
100% of the energy can not be transformed to work
Entropy can be produced but never destroyed
Efficiency of a heat machine
The efficiency of a heat machine working between two energy levels is defined in terms of absolute temperature:
η = ( Th - Tc ) / Th = 1 - Tc / Th(1)
where
η = efficiency
Th = temperature high level (K)
Tc = temperature low level (K)
As a consequence, to attain maximum efficiency the Tc would have to be as cold as possible. For 100% efficiency the Tc would have to equal 0 K. This is practically impossible, so the efficiency is always less than 1 (less than 100%).
Change in entropy > 0, irreversible process
Change in entropy = 0, reversible process
Change in entropy < 0, impossible process
Entropy is used to define the unavailable energy in a system. Entropy defines the relative ability of one system to act to an other. As things moves toward a lower energy level, where one is less able to act upon the surroundings, the entropy is said to increase.
For the universe as a whole the entropy is increasing!
Entropy definition
Entropy is defined as :
S = H / T (2)
where
S = entrophy (kJ/kg K)
H = enthalpy (kJ/kg)
T = absolute temperature (K)
A change in the entropy of a system is caused by a change in its heat content, where the change of entropy is equal to the heat change divided by the average absolute temperature (Ta):
dS = dH / Ta (3)
The sum of (H / T) values for each step in the Carnot cycle equals 0. This only happens because for every positive H there is a countering negative H, overall.
Carnot Heat CycleIn a heat engine, a gas is reversibly heated and then cooled. A model of the cycle is as follows: State 1 --(isothermal expansion) --> State 2 --(adiabatic expansion) --> State 3 --(isothermal compression) --> State 4 --(adiabatic compression) --> State 1State 1 to State 2: Isothermal ExpansionIsothermal expansion occurs at a high temperature Th, dT = 0 and dE1 = 0. Since dE = H + w, w1 = - H1. For ideal gases, dE is dependent on temperature only.State 2 to State 3: Adiabatic ExpansionThe gas is cooled from the high temperature, Th, to the low temperature, Tc. dE2 = w2 and H2 = 0 (adiabatic).State 3 to State 4: Isothermal CompressionThis is the reverse of the process between states 1 and 2. The gas is compressed at Tc. dT = 0 and dE3 = 0. w3 = - H3State 4 to State 1: Adiabatic CompressionThis is the reverse of the process between states 2 and 3. dE4 = w4 and H4 = 0 (adiabatic).The processes in the Carnot cycle can be graphed as the pressure vs. the volume. The area enclosed in the curve is then the work for the Carnot cycle because w = - integral (P dV). Since this is a cycle, dE overall equals 0. Therefore,-w = H = H1 + H2 + H3 + H4If you decrease Tc, then the quantity -w gets larger in magnitude.if -w > 0 then H > 0 and the system, the heat engine, does work on the surroundings.
The laws of thermodynamics were determined empirically (by experiment). They are generalizations of repeated scientific experiments. The second law is a generalization of experiments dealing with entropy--it is that the dS of the system plus the dS of the surroundings is equal to or greater then 0.
Entropy is not conserved like energy!
Example - Entropy Heating Water
A process raises 1 kg of water from 0 to 100oC (273 to 373 K) under atmospheric conditions.Specific enthalpy at 0oC (hf) = 0 kJ/kg (from steam tables) (Specific - per unit mass)Specific enthalpy of water at 100oC (hf) = 419 kJ/kg (from steam tables)
Change in specific entropy:
dS = dH / Ta
= (419 - 0) / ((273 + 373)/2)
= 1.297 kJ/kgK
Example - Entropy Evaporation Water to Steam
A process changes 1 kg of water at 100oC (373 K) to saturated steam at 100oC (373 K) under atmospheric conditions.Specific enthalpy of steam at 100oC (373 K) before evaporating = 0 kJ/kg (from steam tables)
Specific enthalpy of steam at 100oC (373 K) after evaporating = 2 258 kJ/kg (from steam tables)Change in specific entropy:
dS = dH / Ta
= (2 258 - 0) / ((373 + 373)/2)
= 6.054 kJ/kgK
The total change in specific entropy from water at 0oC to saturated steam at 100oC is the sum of the change in specific entropy for the water, plus the change of specific entropy for the steam.
Example - Entropy Superheated Steam
A process superheats 1 kg of saturated steam at atmospheric pressure to 150oC (423 K).
Specific total enthalpy of steam at 100oC (373 K) = 2 675 kJ/kg (from steam tables)Specific total enthalpy of superheated steam at 150oC (373 K) = 2 777 kJ/kg (from steam tables)
Change in specific entropy:
dS = dH / Ta
= (2 777 - 2 675) / ((423 + 373)/2)
= 0.256 kJ/kgK
Entropy table for superheated steam [http://www.engineeringtoolbox.com/superheated-steam-entropy-d_100.html]
If saturated steam is exposed to a surface with a higher temperature, its temperature will increase above the evaporating temperature. The steam is then described as superheated by the temperature degrees above saturation temperature.
Note! Steam cannot be superheated whilst it is still in the contact with water, because additional heat will evaporate more water, cooling down the superheated steam.
Superheated steam is produced by passing saturated steam through an additional heat exchanger.
Superheated steam is also called
surcharged steam
anhydrous steam
steam gas
taken from: http://www.engineeringtoolbox.com/law-thermodynamics-d_94.html as 23April2007
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resources for FLUENT: *.MSH
RESOURCES file *.MSH for Tutorial FLUENT 6.X
http://www.liv.ac.uk/~em22/CFD/data/
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resources in standard deviation
how to calculate standard deviation:
http://faculty.tamu-commerce.edu/crrobinson/517/sdcalc.htm
calculator for statistics in internet:
http://www.easycalculation.com/statistics/learn-geometric-mean.php
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http://faculty.tamu-commerce.edu/crrobinson/517/sdcalc.htm
calculator for statistics in internet:
http://www.easycalculation.com/statistics/learn-geometric-mean.php
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example: calculating a standard deviation
x x*x
data1 79 6241
data2 85 7225
data3 92 8464
data4 87 7569
data5 93 8649
data6 99 9801
sum 535 47949
ave 89.16666667 7991.5
ave^2 7950.694444
std_dev^2 40.80555556 =7991.5-7950.694
std_dev 6.39 =square root of 40.81
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data1 79 6241
data2 85 7225
data3 92 8464
data4 87 7569
data5 93 8649
data6 99 9801
sum 535 47949
ave 89.16666667 7991.5
ave^2 7950.694444
std_dev^2 40.80555556 =7991.5-7950.694
std_dev 6.39 =square root of 40.81
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calculating std dev
Calculating a Standard Deviation
According to Drummond & Jones (2006), a standard deviation "is the numerical value that describes the spread of scores away from the mean and is expressed in the same units as the original scores. The wider the spread of scores, the larger the standard deviation."
A standard deviation is calculated by subtracting the mean of a distribution from the value of each individual variable in the distribution, squaring each resulting difference, summing these squared differences, then dividing this sum by the number of variables, and finally taking the square root of this quotient. The formula for this process is often represented as follows:
Alternately, Drummond & Jones (2006) use s in place of s . They also suggest the following formula as a more convenient means of calculating a standard deviation:
Now, apply this formula to calculate a standard deviation for the following distribution:
X1 = 79
X2 = 85
X3 = 92
X4 = 87
X5 = 93
X6 = 99
There are several intermediate calculations which must be performed before proceeding to the standard deviation calculation. Let's calculate the mean first. Remember that the mean is calculated by summing the variables then dividing by the number (count) of variables included in the distribution. In this instance the sum of the variables (79+85+92+87+93+99) equals 535. The count equals 6. When we divide 535 by 6, we get a quotient of 89.17. This is the mean for this distribution. Let's go ahead and square the mean, getting a value of 7951.29. Next, let's square each variable, then sum them [(79 x 79)+(85 x 85)+(92 x 92)+(87 x 87)+(93 x 93)+(99 x 99)]. Performing this calculation yields a value of 47,949. Then, we divide this value by 6, giving us 7991.5. Now, we can subtract the squared mean (7951.29) from 7991.5. This gives us a value of 40.21. Finally, we must take the square root of this value, arriving at our standard deviation value of 6.34.
Following the formula mathematically looks like this:
When we have a small sample (typically 20 or fewer variables) it is generally recommended that we substitute n-1 for n so that the standard deviation is not underestimated. Let's calculate a standard deviation using the original formula shown above, but substituting n-1 in place of n. We'll use the same variables as before. Remember, the mean equals 89.17. First, let's subtract the mean from each variable value: 79 - 89.17 = -10.17, 85 - 89.17 = -4.17, 92 - 89.17 = 2.83, 87 - 89.17 = -2.17, 93 - 89.17 = 3.83, and 99 - 89.17 = 9.83. Of course, 6-1 (n-1) equals 5. Next, we'll square these differences with the following respective results: 103.43, 17.39, 8.01, 4.71, 14.67, and 96.63. Then, let's sum those squares obtaining 244.84, divide by 5 (n-1 which is 6-1), obtaining 48.97 and take the square root of that quotient, giving us a standard deviation of 6.998, or 7.
Here it is presented mathematically:
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According to Drummond & Jones (2006), a standard deviation "is the numerical value that describes the spread of scores away from the mean and is expressed in the same units as the original scores. The wider the spread of scores, the larger the standard deviation."
A standard deviation is calculated by subtracting the mean of a distribution from the value of each individual variable in the distribution, squaring each resulting difference, summing these squared differences, then dividing this sum by the number of variables, and finally taking the square root of this quotient. The formula for this process is often represented as follows:
Alternately, Drummond & Jones (2006) use s in place of s . They also suggest the following formula as a more convenient means of calculating a standard deviation:
Now, apply this formula to calculate a standard deviation for the following distribution:
X1 = 79
X2 = 85
X3 = 92
X4 = 87
X5 = 93
X6 = 99
There are several intermediate calculations which must be performed before proceeding to the standard deviation calculation. Let's calculate the mean first. Remember that the mean is calculated by summing the variables then dividing by the number (count) of variables included in the distribution. In this instance the sum of the variables (79+85+92+87+93+99) equals 535. The count equals 6. When we divide 535 by 6, we get a quotient of 89.17. This is the mean for this distribution. Let's go ahead and square the mean, getting a value of 7951.29. Next, let's square each variable, then sum them [(79 x 79)+(85 x 85)+(92 x 92)+(87 x 87)+(93 x 93)+(99 x 99)]. Performing this calculation yields a value of 47,949. Then, we divide this value by 6, giving us 7991.5. Now, we can subtract the squared mean (7951.29) from 7991.5. This gives us a value of 40.21. Finally, we must take the square root of this value, arriving at our standard deviation value of 6.34.
Following the formula mathematically looks like this:
When we have a small sample (typically 20 or fewer variables) it is generally recommended that we substitute n-1 for n so that the standard deviation is not underestimated. Let's calculate a standard deviation using the original formula shown above, but substituting n-1 in place of n. We'll use the same variables as before. Remember, the mean equals 89.17. First, let's subtract the mean from each variable value: 79 - 89.17 = -10.17, 85 - 89.17 = -4.17, 92 - 89.17 = 2.83, 87 - 89.17 = -2.17, 93 - 89.17 = 3.83, and 99 - 89.17 = 9.83. Of course, 6-1 (n-1) equals 5. Next, we'll square these differences with the following respective results: 103.43, 17.39, 8.01, 4.71, 14.67, and 96.63. Then, let's sum those squares obtaining 244.84, divide by 5 (n-1 which is 6-1), obtaining 48.97 and take the square root of that quotient, giving us a standard deviation of 6.998, or 7.
Here it is presented mathematically:
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Monday, April 02, 2007
Rubber Indonesia: Potential Export
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Friday, March 30, 2007
preparations
[1]. Th Full Papers that I've been already read and make summary
[2].
[3].
[4].
[5].
[6].
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[2].
[3].
[4].
[5].
[6].
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