The Journal October 2018 Volume 136 Part 4

Transcription

1 ARTICLE AS PUBLISHED IN The Journal October 2018 Volume 136 Part 4 If you would like to reproduce this article, please contact: Alison Stansfield MARKETING DIRECTOR Permanent Way Institution alison.stansfield@thepwi.org PLEASE NOTE THE OPINIONS EXPRESSED IN THIS JOURNAL ARE NOT NECESSARILY THOSE OF THE EDITOR OR OF THE INSTITUTION AS A BODY.

2 Use of inherent standard deviations as track design parameters AUTHOR: Constantin Ciobanu CEng, MCIHT, FPWI Principal Engineer Atkins - SNC Lavalin Part 2 This article follows on from the paper The Track Geometry Standard Deviation Calculator by David Marriott, published in the previous issue of the July Journal - Volume 136 Part 3. INTRODUCTION In the previous article, David Marriott presented how the track recording car measured data for top and lateral alignments are filtered and described the output the Butterworth filter produces for design alignments. This design output contains artefacts a non-real set of data generated by the filter at every point where the design elements change. The standard deviation of the filtered design alignment, calculated over 1/8 of mile section of track, is often called inherent standard deviation or design standard deviation the alternative concept, from design perspective, of the track quality standard deviation (or measured standard deviation) which is calculated for each 1/8 mile of the track recording traces and collected in the Colour-Coded Quality (CCQ) charts. The design standard deviations are sometimes seen as an objective or indicative measure of the quality of the design and even used as design parameters. This article aims to prove that the design standard deviations cannot be used to evaluate the quality of a design alignment because no consistent relationship can be found between them and various design parameters. The article presents a few relevant cases to demonstrate this and it is not intended to be an exhaustive debate about design standard deviations nor to challenge the track quality assessment principles. For some readers, very familiar with the track measurement system and with the track quality assessment process, most of the things presented here might seem obvious or even trivial. However, this article is addressed to a wider track engineering audience, involved in many ways in the track design process but not very familiar with the theory behind the track measurement using track recording cars. Note: All the design (inherent) standard deviation calculations presented in this article are generated using the TGSD Calculator, developed in 2007 by David Marriott. DESIGN GEOMETRY AND TRACK QUALITY STANDARD DEVIATION Modern track recoding cars use an inertial system which measures the shape of the track rails in 3D space. A Butterworth filter is used to electronically process this raw data, outputting the relevant track irregularity traces AL35, AL70, Top Left, Top Right, MT70 (BS EN , GC/EH , R. Lewis ). The Butterworth filter is not a railway track specific filter. It has a very wide range of radio-electronic applications from radar signal processing to high fidelity audio amplification, but it is also used to process other type of data, for example in filtering macroeconomic data for business analysis (Pollock 2014). When processing the track recording car measurements, the Butterworth filter is very efficient in removing a large amount of data which is not significant for the track quality assessment process and this includes the track design shape (Marriott -2018). What remains after filtering as track recording traces are the vertical or lateral deviations from the ideal, design shape, filtered to two cutoff wavelengths, 35m for all speeds and 70m for speeds of 80mph or above. The track quality standard deviations of the top or lateral trace, are a measure of the variation of the track from its ideal un-deformed shape (NR/L2/TRK/2102, NR/L2/TRK/001, BS EN , UIC 518). They are closely related to the quality of riding and passenger comfort, as the irregularities they synthetically represent can cause resonance effects in the vehicle suspension systems. By extension, it seems logical to assume that the standard deviation (SD) of the filtered design geometry can be, similarly, a measure of the riding quality over the perfectly installed track alignment. However, that would be the case if the design alignment would contain design irregularities of short wavelengths. The track irregularities, which are an input for the track quality standard deviation, are deviations from the ideal track shape represented by the design alignment, assumed to be installed perfectly without any kind of irregularity. Hence, we would not expect to find any irregularity caused by the design in the track recording car trace. All the design elements used for track alignment design, are characterised by constant functions (i.e. constant curvature, constant gradient, constant rate of change of various geometrical parameters). Filtering individually any type of design element produces a null result there are in fact no irregularities specific to any of the elements used in track design (Marriott 2018). However, when analysing a real design alignment, comprising of a succession of distinct design elements, the filter is producing an artefact. An artefact is a wave-like shape in the filtered design output at every point where a design element changes to another, the rest of the design shape being entirely removed. This artefact is caused by the way the Butterworth filter processes the sudden change in the variation of design data and is not an objective design irregularity, present on track. Figure 1 shows the artefacts produced by filtering a design horizontal alignment to 35m cut off wavelength. 46

3 The change from one design element to another is a well-defined design point, subjected to various sets of standard design rules which define its compliance from safety and comfort perspectives. As exemplified in Figure 1, the filter is turning any point of design change into a wave-like shape, of low amplitude values, developed over a significant length of design. If the geometrical element is too short, the wave generated by the previous element change interferes with the following one, generating the complex shape of the filtered design. For the filtered top, the vertical alignment and the cant designs are processed together and their filtered output has a similar dampened wave-like shape. Seeing this wave-like shape of the filtered data, we would be inclined to conclude that it is the result of a dynamic simulation of a vehicle passing over some transitions or sudden changes in geometry a view the author of this article has had for quite a while. Due to the fact that the horizontal and cant transitions and the vertical curves are also generally shorter than the other design elements, the standard deviation of the filtered design is generally higher when such elements are present. Presumably supporting the idea of this being the result of a kind of dynamic modelling, highlighting the influence various transitions have on the quality of riding. But that is not the case the Butterworth filter output is not equivalent to a vehicle dynamic modelling output. In the previous article (Marriott ), this process of turning the design change point into the wave-like artefact is compared to the Mercator projection used to produce the map of the World, where the Earth s poles are converted to lines, identical in length to Earth s Equator. Similar to this well-known distortion, the Butterworth filter used to process the track measured data, turns the points where the design elements change into wave-like lines, this being a by-product, an artefact, an imperfection of the filtering system designed to filter periodical functions and not sudden changes from one design shape to another. The filter removes the entire 3D shape of the track and has just this minor imperfection of generating a set of values of maximum a few millimeters in amplitude at every change in design geometry. From this point of view the filter is well-chosen and very efficient; nothing presented in this article is an argument against using the Butterworth filter in the track measurement process. The shape and characteristics of the filtering artefact that appears at any change from one design alignment element to the next are related to how significant that change is. From this point of view, it can be debated that the inherent standard deviations can be used to compare different versions of a design to optimize and improve the design proposal. The following sections will clarify this view. CANT TRANSITIONS The best way to prove that there is no correlation between the filtered design shape and the dynamic response of a vehicle, when passing over the design alignment, is to look at the influence the cant design has on the lateral and vertical filtered design traces. Figure 2 shows the filtered top trace for a design which consists of a cant alignment with two changes of cant element, each producing a distinct artefact in the filtered trace. The vertical alignment is presumed of constant gradient. Figure 3 shows the filtered vertical design trace for the same design cant alignment but for adverse cant - the inner rail is lifted. In the case shown in figure 2, on the circular curve the cant deficiency is 95 mm and for adverse cant (figure 3) the cant deficiency is 395 mm. Even though, from a vehicle dynamics perspective, the adverse cant case is significantly worse than the normal cant, the filtered output for the two cases is almost the same, shown on the raised rail profile for each case. The standard deviation is calculated for the worst top profile and is the same for the two cases, WT35 SD (normal or adverse cant) = mm. For real designs, the filtering artefacts of the changes in the vertical profile can interfere and overlap with the artefacts of the filtered cant alignment. Moreover, independently of the horizontal curvature, if no design cant is applied the top, standard deviations will almost always be lower than the ones for the design which includes cant. That is not in anyway an indication that the absence of cant is improving the track quality. Whilst the absence of cant increases the cant deficiency and its rate of change, it reduces the design SD values, which is contrary to what would be expected if the design SDs reflected the quality of the design. Figures 2 and 3 quote a radius but that is relevant only from the point of view of calculating the cant deficiency as from a design SD perspective there is no correlation between the horizontal alignment and the designed cant. The cant is a vertical adjustment of one of the rails. From a track measurement perspective, its design values and deviations are visible in the vertical traces and have no influence on the horizontal traces we would expect if that trace would be related to a dynamic response or a representation of lateral or vertical accelerations. Figure 1. A design horizontal alignment and its filtered counterpart 47

4 Due to this output, the design cant has no influence on the horizontal design standard deviations but only on the top (vertical) ones. However, according to the track design principles, the cant design is very much related to the horizontal alignment. Any assessment of the proposed cant is done in relation with the design horizontal alignment. The design SDs are not making this correlation. These are not anomalies of the measurement process they prove that no connection exists between the cant artefact generated by the filtering process and the dynamics of the vehicle. Figure 2. Normal cant alignment and its filtered counterpart These two facts, that the cant has no influence on the horizontal inherent standard deviation, and that for the vertical design SD is irrelevant if the design cant is normal or adverse are major arguments against using the design standard deviations as track design parameters or for reviewing a design output. OVERLAPPING DESIGN ELEMENTS Figure 4 shows a cant design which includes a 75 m cant transition from 0 to 100 mm. This design overlaps with a gradient change from 0% to 0.1%. The position of the cant transition is fixed and the gradient change point (PVI) is sliding along the selected 1/8 of mile to see how the MT70 standard deviation vary as a result of this combination of vertical and cant changes. Figure 3. Adverse cant and its filtered counterpart The figure shows the design MT70 SD in relation to the chainage of the point of vertical intersection (PVI). The maximum MT 70 SD value is mm reached when the PVI coincides with the start of cant transition (50 m). The minimum MT70 SD value is mm, reached when the PVI coincides with the end of the cant transition (125 m). As figure 4 shows, the design MT70 SD varies significantly as the PVI slides along the cant transition and on the adjacent constant cant sections. The dash-dotted line in the design MT70 graph is the case when a 30m long vertical curve is used to smoothen the gradient change. The mid-point of this curve is placed at the PVI. Again, the minimum design SD is for the case when the PVI is at the end of the cant transition, the vertical curve located between chainages 110 m and 140 m, half on the cant transition and half on the constant 100 mm cant section. The design SD in this case is mm. For an un-experienced design engineer the case shown in Figure 4 might suggest a new and surprising best practice rule: The best option is to place a gradient change at the end of cant transition; in that case the design quality is twice as good compared to the case when the gradient change is placed at the beginning of the transition. Figure 4. Design MT70 SD for overlapping cant transition and gradient change 48

5 Figure 5. A super-red design SD Table 1. Standard deviation subtraction (simulated data) Table 2. Minimum element length based on the two seconds rule If a vertical curve is used, the ideal location is when the mid-point of the curve is at the end of the cant transition and the curve spreads half on the cant transition and half on the following constant cant section. But these conclusions are wrong as the design standard deviations shown here are just the result of the combination of two filtering artefacts - two sets of non-real data - one generated by the point where the cant elements change and the other by the point of gradient change or by the two points where the vertical alignment changes from gradient to parabolic curve and back to linear gradient. Looking to logically explain the design SD values in figure 4, we might argue that cant transition is, in fact, a short gradient applied on one rail only and it might seem logical to keep that elevation increase trend by placing the gradient change point where the cant transition ends. However there is no dynamic analysis or any other technical justification which indicate that the quality of riding for this combination of design elements will match or be close to the shape shown in the design MT70 SD graph. Certainly, we cannot say the design riding quality is twice as good for the PVI placed at the end of the cant transition compared to the design where the PVI is at the beginning of the transition. Another significant argument to consider is that the results shown in figure 4 are the same, whether the design cant is normal or adverse although there is a major difference from comfort and safety of riding perspective between the two. Various such design combinations can be analysed and some might actually seem to make sense, to match what we, based on our engineering understanding and experience, would expect to see as good design. However, we will very often find cases, perhaps not as extreme as the one shown in figure 4, where we will discover the lack of connection between the inherent SDs and what we know to be a good track design proposal. INFLUENCE OF THE ARTEFACTS The filtering artefacts spread along a significant length and sometimes their effect in the design SD is high, even at a significant distance from the design element change that caused the artefact. Figure 5 shows such an example, with a simple curve followed by a straight section. The curve and its transitions are designed for 60mph. Within the following eighth after the curve (100/2) the maximum allowable speed changes to 80 mph for which the 70m cutoff wavelength band needs to be considered. The combined artefact caused by of the start and end of the transition curve designed compliantly for 60mph spreads extensively on the 100/2 eighth and the design AL70 on this eighth is mm, a value that falls within the very poor (super-red) quality band as defined in NR/L2/TRK/01/mod11. On the 100/2 eighth the track is straight and there are no design changes however, if the track recording car running from the low speed section to high speed section would measure this track, installed perfectly, without any alignment irregularities, it will report that very poor SD. However, if the track recording car will measure the same ideal track, running in the opposite direction, the continuous straight will not generate any artefacts and the same eighth (100/2) will have a zero AL70 SD value. This anomaly is known by maintenance engineers, which sometimes receive very poor quality CCQ reports for sections of track that include or are adjacent to a speed change and are arranged similarly to the case presented in figure 5. This particular case does not explain all the high SDs found in the CCQ charts and should be considered only in the context of this article. Although the artefacts of the filtered design are not real track irregularities and do not represent an accurate image of the riding quality over the perfectly installed track, these artefacts currently are embedded in the measured traces and have an influence on the track quality standard deviations calculated from these traces. MANIPULATING SD VALUES A simple solution to remove the effect of the design artefacts and get the actual track quality SD might seem to be the subtraction of the design SDs from the track quality SDs computed from the track recording car traces. However, the standard deviation is a non-linear function (1) and subtraction or addition cannot be directly applied in this case the standard deviation of the actual track irregularities cannot be calculated by subtracting the design SD from the measured SD (2). 49

6 Table 1 shows three cases of simulated track measurements over a section of track that includes design artefacts for which the design AL70 SD is As we can see from the table, the SDs of the actual track irregularities are not equal to the difference between the SDs of the measured track (simulated data) and the design SD. In addition, the trend of the SD difference does not match the trend of the actual track irregularities SD; the design artefacts have the potential to hide significant track irregularities, as we can see in the second case for which the actual track irregularities SD is Taking out the filtering artefact of the design from the measured data is technically possible, however, it requires a more complex procedure. As part of Network Rail s CP6 Intelligent Infrastructure Programme, Atkins and SNC Lavalin, working together with national and international experts, are aiming to define a methodology for the removal of the design filtering artefacts and provide a more accurate way of assessing the track quality. CONCLUSIONS The track design standards define a complex set of rules the designer must consider for a compliant design. The designer is looking for more than this; he is looking for a reliable design quality assessment tool, able to give an objective image of how good a design is. For example, in our design proposals we do our best to avoid overlapping vertical curves with horizontal transitions and keep them as far apart as possible within the specific constraints of the site. But when far apart means good, or good enough, is up to our engineering judgement and sometimes up to subjective preferences or even myths saying: keeping this element 6.2 m (or m?!) away from another is good enough but if that distance is 6.05 m (12.15 m) then that is a no-no even though the standard design parameters are practically the same for the good-enough and no-no cases. In such circumstances it would be very useful to have an objective way to measure how good a design is. To have an objective measure of the design quality that will challenge the subjective rules of preferential engineering, which sometime define arbitrary and not very well justified design constraints. And we, the design engineers, saw the inherent standard deviations as being that objective measure of design quality and the Track Geometry SD Calculator developed in 2007 by David Marriott as the tool to assess the design. Unfortunately, to the personal disappointment of the author also, the inherent standard deviations calculated using the TGSD Calculator are not the holy grail of design quality checking. The Calculator was not developed for this purpose (Marriott 2018). There is extensive British and international research on the impact track geometry has on the riding comfort (VTI notat 56A-2003, TR- DOS , UIC Code 518) which can be used as a base to develop design review tools based on dynamic vehicle response. However, we already have a design rule based on that the two seconds rule (NR/L2/TRK/ ). This best practice rule recommends a minimum design element length dependant on speed (table 2) which would allow the vehicle oscillations caused by the passage over a design element change to attenuate before passing over the next point of design element change. Quite often when designing a track alignment within the footprint of the existing railway, various site constraints force the designer to use design element lengths significantly shorter than those required by the two seconds rule, still complying with the other design standard rules and limits. This is a justified compromise. Changing these short lengths by some metres or shifting these elements slightly to marginally avoid overlapping would not generate miraculous improvements in riding quality. The improved design will still remain a compromise, no matter how much better the design SDs might be in that case. The examples and arguments discussed in this article are not challenging the track measurement principles and are not in any way implying that the track quality assessment based on standard deviations is wrong. Following on from the previous article written by David Marriott and from the examples presented in this article, the main arguments against using design (inherent) standard deviations as design parameters are as follows: a) The design SDs are calculated from the artefacts caused by the Butterworth filter when processing the position and characteristics of the points where a track design element change to another. The design SDs are independent of the design speed and do not reflect the dynamic behaviour of a railway vehicle. b) The vertical (Top) SD is a combination (summation) of the effect of cant and gradient changes. From this perspective, cant (level adjustment of one rail) and gradient (level adjustment of both rails) are equivalent. Whereas, the dynamic effect of a gradient change is completely different to the dynamic effect of a cant change and this is well reflected in the track design rules. c) The vertical (Top) and horizontal (Alignment) SDs are calculated independently of each other hence they cannot measure the extent to which the designer has applied cant to compensate for the effect of curvature in his design. ACKNOWLEDGEMENTS The author thanks David Marriott for his clarifying explanations and comments about design standard deviations, which were essential for writing this article. The author also thanks Geoff Kennedy for his very useful comments and suggestions. REFERENCES BS EN (2014) Railway applications - Track - Track geometry quality - Part 6: Characterisation of track geometry quality. British Standards Institution. GC/EH0038 (1993) Track Recording Handbook. British Railway Board. NR/L2/TRK/2102 (2017) Design and construction of track. Issue 8. Network Rail. NR/L2/TRK/001/Mod 11 (2013) Inspection and maintenance of Permanent Way. Track geometry Inspections and minimum actions. Issue 6. Network Rail. TR-DOS-017 (1986) Passenger comfort during high speed curving. Analysis and conclusions. British Rail Research. UIC Code 518 (2005) Testing and approval of railway vehicles from the point of view of their dynamic behaviour Safety Track fatigue Ride quality. International Union of Railways UIC. VTI notat 56A-2003 (2003) UIC comfort tests. Investigations of ride comfort and comfort disturbance and circular curves. UIC & Swedish National Road and Transport Research Institute. R. Lewis (2011) Track Geometry Recording and Usage. Notes for a lecture to Network Rail. D. Marriott (2007) Track Geometry SD Calculator v 1.1. User s Guide. TGCS David C. Marriott Ltd. D. Marriott (2018) The Track Geometry Standard Deviation Calculator. PWI Journal, Volume 136. Part 3. S. Pollock (2014) Econometric Filters. Working Paper No. 14/07. Department of Economics. University of Leicester. 50

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