UIC comfort tests - VTI

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Figure 11-1shows the votes (averaged for each position) plotted against thevalues of the derived parameters N’ VA and P CT in the form of scatter plots. Data foreach tilt compensation level are differentiated by colour. The best-fit line from asingle-parameter regression is also shown.5544Scaled Votes32Scaled Votes321100 1 2 3 4 5N'va80% compensation 60% compensation 40% compensationNon-tiltingBest fit00 5 10 15 20 25P CT80% compensation 60% compensation 40% compensationNon-tiltingBest fitFigure 11-1 Scatter plots - votes against N’ VA and P CT.Both the derived measurements are intended to represent ride quality, so thatsome correlation with the recorded votes could be expected. In fact the correlationcoefficients are 0.536 for N’ VA and 0.517 for P CT .If the current measurements corresponded with the results of the earlier ERRIB153 tests, the scaled votes would have a 1:1 correspondence with the N’ VAparameter, and the best-fit line would pass through the origin. The non-zerointercept is an indication that other factors are also affecting the comfort in thecurve transitions.Single parameter regressions were also made for the individual parameters,measured with all three averaging windows. The correlation coefficients of thevotes against these parameters are shown in Table 11-2.Table 11-2 Correlation coefficients - scaled votes against individual parameters.& y& max& y&& max θ &maxθ max1.0-second base 0.522 0.440 0.268 0.2920.4-second base 0.541 0.458 0.238 0.2470.1-second base 0.562 0.492 0.241 0.280Both lateral parameters have significantly greater correlation with the votes thanthe roll parameters. Indeed, the maximum lateral acceleration with the shorteraveraging windows is better correlated with the votes than are either N’ VA or P CT .The lateral parameters show the best correlation to the votes with the 0.1-second window, while the roll parameters show the best correlation with the 1.0-second window. The parameters with a 0.4-second window appear between thetwo.The scaled votes are plotted against the four individual parameters (with a 0.1-second window) in Figure 11-2.VTI notat 56A-2003 63
Note that very high values of lateral acceleration have been recorded (up to2.08 m/s 2 ), even with the train operating with 80% tilt compensation, when the tiltsystem should be compensating for most of the lateral acceleration. Closerexamination shows that the highest values of acceleration are all recorded in theleading vehicle, where the tilt system response might be expected to be worse.5544Scaled Votes32Scaled Votes321100.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0Lateral jerk (0.1s base)80% compensation 60% compensation 40% compensationNon-tiltingBest fit00.0 0.5 1.0 1.5 2.0 2.5Lateral acceleration (0.1s base)80% compensation 60% compensation 40% compensationNon-tiltingBest fit5544Scaled Votes32Scaled Votes321100 5 10 15 20Roll acceleration (0.1s base)80% compensation 60% compensation 40% compensationNon-tiltingBest fit00 1 2 3 4 5 6 7 8Roll velocity (0.1s base)80% compensation 60% compensation 40% compensationNon-tiltingBest fitFigure 11-2 Scatter plots – votes against individual parameters (from left upperto right below: Lateral jerk (0.1s) Lateral acceleration (0.1s), roll acceleration(0.1s) and roll velocity (0.1s)).The spread of data clearly justifies the decision to test with 40% and 60% tilt aswell as 80%.11.1.7 Relationship between parameters – simple transitionsAs well as understanding the relationships of the measured parameters to therecorded votes, it is also important to understand the relationships of the parameterswith each other. If two parameters are strongly correlated with each other,then including both in the same multiple regression will give invalid results.The P CT parameter is derived directly from three of the individual parametersused in this study, so it can only be used in a regression with N’ VA , and not withany of the individual parameters.64 VTI notat 56A-2003
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VTI notat 56A • 2003VTI notat 56A
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ContentsPageAbbrivations and Notati
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10.5.1 Transitions 5311 Evaluation
Page 7 and 8:
Abbrivations and NotationsAEAT AEA
Page 9 and 10:
chosen test track with a minimum of
Page 11 and 12: 1.8.2 ModelsThree different models
Page 13 and 14: 3 What does comfort mean in the con
Page 15 and 16: Representation of passengers’ opi
Page 17 and 18: 4 How comfort evolves with speed4.1
Page 19 and 20: Each element may provoke a potentia
Page 21 and 22: 5.2.2 Theoretical behaviour of thos
Page 23 and 24: Nominal acceleration in the horizon
Page 25 and 26: 6 Actual evaluation rulesAll the fo
Page 27 and 28: Important variancesS 0 = var(y)Sr =
Page 29 and 30: 7 Choice of the test zone7.1 Choice
Page 31 and 32: 9.008.007.006.00roll velocity5.004.
Page 33 and 34: 8 Description of the test8.1 The tr
Page 35 and 36: 8.3.3 Synchronisation between votes
Page 37 and 38: 8.5 Registration of data, calculati
Page 39 and 40: DRIVER CAB8.6.2 Occupied places2224
Page 41 and 42: 9.3 Study of the car body angle9.3.
Page 43 and 44: 9.4.2 Influence of length of the co
Page 45 and 46: 9.4.4 Additional discomfort due to
Page 47 and 48: 10.1.1 ConclusionsThe actual distri
Page 49 and 50: 10.3 Seat positionThe influence of
Page 51 and 52: Average votes for the different tra
Page 53 and 54: Figures 10-5 to 10-10 show the infl
Page 55 and 56: Influence by roll velocity on voteA
Page 57 and 58: 5.0Influence by Pct on voteAll cate
Page 59 and 60: 11 Evaluation of local comfort11.1
Page 61: 11.1.4 Scaling of votesThe N’ VA
Page 65 and 66: 554433N'vaN'va221100 1 2 3 4Lateral
Page 68 and 69: Multiple regressions have therefore
Page 70 and 71: With the 1.0-second window, the jer
Page 72 and 73: The regression has been repeated wi
Page 74 and 75: (Table 11-12)Object: Simple and adj
Page 76 and 77: simple transitions, and with the re
Page 78 and 79: Table 11-18 Regression for track en
Page 80 and 81: Some characteristic data points of
Page 82 and 83: The probability that all coefficien
Page 84 and 85: Second conclusionThis also proves t
Page 86 and 87: number of real votes3025201510500,2
Page 88 and 89: 11.2.2 ParametersFour parameters we
Page 90 and 91: There are some observations from th
Page 92 and 93: Table 11-30 Two-parameter regressio
Page 94 and 95: Three-parameter regression - N'VA,
Page 96 and 97: The three-parameter regression was
Page 98 and 99: 12 Evaluation of average comfort12.
Page 100 and 101: Table 12-2 shows that the correlati
Page 102 and 103: 3.53.5332.52.5NVA21.5NVA21.5110.50.
Page 104 and 105: Table 12-5 Multiple regression for
Page 106 and 107: This is the preferred result to be
Page 108 and 109: 12.2.5 Density of importance of eac
Page 110 and 111: 13.3 Conclusions in relationship wi
Page 112 and 113:
An identical observation can be mad
Page 114 and 115:
13.7.3 Evaluation of local comfort
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Appendix 1Page 1 (3)1 Preparing the
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Appendix 1Page 3 (3)2.5 Publish res
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Appendix 2Page 2 (10)For this progr
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Appendix 2Page 4 (10)Vertical accel
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Appendix 2Page 6 (10)Influence of l
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Appendix 2Page 8 (10)Red line : 0.1
Page 128 and 129:
Appendix 2Page 10 (10)Study of aver
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Appendix 3Page 2 (2)Evaluation of h
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