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Calculate wear v.s. curve radius and estimate total wear   

1. Press Help->Examples Html and under OPTI select file optif/wear_quasi.optif
   
   
2. In genfile, copy file runf/Master.tsimf to runf/wear_quasi.quasif
   
   
3. Execute file optif/wear_quasi.optif
   
   
4. Present the results in MPLOT:
   1. Press Help->Examples Html and under MPLOT select file mplotf/wear_cur_opti_scal.mplotf
   2. Run file mplotf/wear_cur_opti_scal.mplotf
   3. Do not select any ident use the default mplot_id
   
   
5. Present the results in MTABLE:
   1. Press Help->Examples Html and under MTABLE select file wear.mtablef
   2. Set IDENT_FILES= id/wear_quasi*.id in order to only select wear calculations
   3. Run file mtablef/wear.mtablef
   4. Open resultfile mtabler/wear.mtabler
   
   
6. Multiply the wear with a curve distribution for the track, in order to estimate the total average wear. As an example use the following curve distribution:

    Curve           Ratio of       
    Radiuses        tot.length     
    ---------------------------    
     300- 400       .012           
     400- 500       .010           
     500- 600       .036           
     600- 700       .031           
     700- 800       .002           
     800- 900       .022           
     900-1000       .010           
    1000-1200       .027           
    1200-1400       .028           
    1400-1600       .019           
    1600-1800       .013           
    1800-2000       .013           
    2000-2500       .029           
    2500-           .748           
   

   For the average wear calculations script genoctave can be used.
   
   
7. In order to improve the average wear rate of the vehicle, open file runf/wear_quasi.quasif and under "Change of vehicle data parameters" set:

     coupl p_lin36 kmba_=    0.    0. -(mc+2*mb)*9.81/8        
                                               0.    0.    0.  
                            4e6    0.    0.    0.    0.    0.  
                             0.   .5e6   0.    0.    0.    0.  
                             0.    0. 1200e3   0.    0.    0.  
                             0.    0.    0.    0.    0.    0.  
                             0.    0.    0.    0.    0.    0.  
                             0.    0.    0.    0.    0.    0.  
   

   Rerun the above example 3-6) again.