MC_Demonstration_ver8_Student

MC_Demonstration_ver8_Student - Example spreadsheet for...

Info icon This preview shows pages 1–6. Sign up to view the full content.

View Full Document Right Arrow Icon
Example spreadsheet for Monte Carlo Uncertainty Analysis for air pollution modelling example. Spreadsheet considers uncertainty in four variables. Instructions: ENTER MEAN and STANDARD DEVIATION in yellow cells for each variable using the units of that variable Summary statistics appear in the green areas below. and change the security level to medium. Then, close and reopen the fil The Worksheet 'Analysis' has several answers to that you can check your work Four CDFs' provides examples of the graph of the cumulative dis INPUT FOR MONTE CARLO ANALYSIS    Variables: Emission Rate Windspeed Ambient temperature Q (g/s) degrees K/m N=0/LN=1 1 1 0 0 < Mean 400.0 4.0 15.0 0.020 Stand. Deviation 75.0 0.5 3.0 0.004 1.  N=normal distribution, LN=lognormal distribution (natural logartithm) 2.  All other variables for the Gaussian plume model set equal to their values from HW 3.  CDFs of these 4 random variables are plotted in sheet 'Four CDFs'. 4.  To set the variables to deterministic values, assign Standard  deviations  values of 0.0001, 0.0001, 0.0001 and 0.000001 to Q, Us, Ta and dq/dz, respectively,  for the variable of interest. For the lognormal distribution Computed Value of Log-Space Mean and Log-Space Standard Deviation log-mean 5.9742 1.3785 - - log-St. Deviation 0.1859 0.1245 - - Computed Skew for selected model Real-space skew 0.57 0.38 0.00 0.00 MONTE CARLO RESULTS Generated Values Emission Rate Windspeed Ambient temperature (500 samples) Q (g/s) degrees K/m Sample Average 407.54 3.99 14.89 0.02 Sample Stan. Dev. 80.23 0.50 3.01 0.004 Sample Skew 0.51 0.31 -0.16 0.07 AFTER inputs are entered:   Press   F9   to  RE-CALCULATE  all spreadsheet cells. WARNING : If you could not enable macros when the spreadsheet was opened, go to d θ/ dz U s  (m/s) T a  (degrees C) d θ/ dz U s  (m/s) T a  (degrees C)
Image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Summary of Monte Carlo Analysis Maximum Concentrations Min #MACRO? Lower Quartile #MACRO? Median #MACRO? Upper Quantile #MACRO? Maximum #MACRO? g/m 3
Image of page 2
Jery Stedinger and Bryan Tolson  Cornell University   April 20, 2001; revised 4-04; D. Cowen corrected plotting 4-07 le and enable macros. k. stribution of generated random variables. 0 fits a normal dist. with this mean & stand. dev. 1 fits a lognormal dist with this mean and stand. dev. W 8 in macro.   Maximum  Concentration #MACRO? #DIV/0! #MACRO? o 'Tools', select 'Macros', then select 'Security' (g/m 3 )
Image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 4
RANDOM VARIABLE VALUES GENERATED Using Visual functio Basic Macro Functio Emission Rate Windspeed Ambient temperature MAX conc Q (g/s) degrees K/m 494.900 4.382 10.857 0.016 #MACRO? 434.350 3.865 9.831 0.015 #MACRO? 641.915 4.893 11.494 0.023 #MACRO? 331.881 3.358 19.036 0.021 #MACRO? 320.031 4.710 18.225 0.013 #MACRO? 360.486 3.733 16.220 0.015 #MACRO? 393.103 3.089 13.844 0.019 #MACRO? 362.329 4.896 16.866 0.019 #MACRO? 526.312 4.176 15.841 0.021 #MACRO? 372.534 3.887 14.775 0.019 #MACRO? 392.769 3.479 10.977 0.024 #MACRO? 374.860 4.714 6.479 0.021 #MACRO?
Image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 6
This is the end of the preview. Sign up to access the rest of the document.
  • Fall '08
  • CARR
  • Enable Macros, and enable macros, file and enable, Generated DATA, Emission Rate Windspeed, Spreadsheet considers uncertainty

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern