ES.333 | A Look Into Mean Squared Displacement @MITES333 | Uploaded May 2014 | Updated October 2024, 4 hours ago.
Produced by Edgar Aranda-Michel; created May 5, 2014
The purpose of this video is to provide an intuitive understanding and working sense of the Mean Squared Displacement algorithm. The audience is MIT students who have taken the Biological Engineering flavored intro thermodynamics class 20.110/20.111 In course 20, Biological Engineering, at MIT, the subject of Mean Squared Displacement comes up in several of the core classes. However, there is an assumed mastery of this subject that is never covered. This video tries to fill this gap to better prepare students
I first cover the basis behind Brownian motion and how it leads to diffusion. Contextual examples are provided as fluids with greatly differing viscosities are examined.
Once this base has been established we introduce the Mean Squared Displacement (MSD) algorithm. A data set is walked through for two times values (Tau=1 and Tau=2) to give a sense of how to apply this algorithm.
The different conclusions that can be drawn from MSD are illustrated and explained.
Links:
Modeling Brownian Motion Java Applet- http://www.phy.ntnu.edu.tw/ntnujava/htmltag.php?code=users.ntnu.fkh.browianmotion_pkg.browianmotionApplet.class&name=browianmotion&muid=2
Acknowledgements:
Graham Ramsay and Dave Cluster -- Helped out with everything
Maxine Jones -- Expert Script Reviewer
Professor Paco and Professor Hwang -- Brownian Simulation
Matlab- Generated all the Gaussian Images
Google and Youtube- The rest of the pictures and the Videos.
Produced by Edgar Aranda-Michel; created May 5, 2014
The purpose of this video is to provide an intuitive understanding and working sense of the Mean Squared Displacement algorithm. The audience is MIT students who have taken the Biological Engineering flavored intro thermodynamics class 20.110/20.111 In course 20, Biological Engineering, at MIT, the subject of Mean Squared Displacement comes up in several of the core classes. However, there is an assumed mastery of this subject that is never covered. This video tries to fill this gap to better prepare students
I first cover the basis behind Brownian motion and how it leads to diffusion. Contextual examples are provided as fluids with greatly differing viscosities are examined.
Once this base has been established we introduce the Mean Squared Displacement (MSD) algorithm. A data set is walked through for two times values (Tau=1 and Tau=2) to give a sense of how to apply this algorithm.
The different conclusions that can be drawn from MSD are illustrated and explained.
Links:
Modeling Brownian Motion Java Applet- http://www.phy.ntnu.edu.tw/ntnujava/htmltag.php?code=users.ntnu.fkh.browianmotion_pkg.browianmotionApplet.class&name=browianmotion&muid=2
Acknowledgements:
Graham Ramsay and Dave Cluster -- Helped out with everything
Maxine Jones -- Expert Script Reviewer
Professor Paco and Professor Hwang -- Brownian Simulation
Matlab- Generated all the Gaussian Images
Google and Youtube- The rest of the pictures and the Videos.
