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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.