Pollutants, pathogens and public transport – ventilation, dispersion and dose

Preamble

The ventilation of buses and trains has come to be of some significance to the travelling public in recent years for a number of reasons. On the one hand, such vehicles can travel through highly polluted environments, such as urban highways or railway tunnels, with high levels of the oxides of nitrogen, carbon monoxide, hydrocarbons and particulate matter that can be drawn into the passenger compartments with potentially both short- and long-term health effects on passengers. On the other, the covid-19 pandemic has raised very significant concerns about the aerosol spread of pathogens within the enclosed spaces of trains and buses. There is a basic dichotomy here – to minimise the intake of external pollutants into vehicles, the intake of external air needs to be kept low, whilst to keep pathogen risk low, then high levels of air exchange between the outside environment and the internal space are desirable. This post addresses this issue by developing a common analytical framework for pollutant and pathogen dispersion in public transport vehicles, and then utilises this framework to investigate specific scenarios, with a range of different ventilation strategies.

The full methodology is given in the pdf that can be accessed via the button opposite. This contains all the technical details and a full bibliography. Here we give an outline of the methodology and the results that have been obtained.

Analysis

The basic method of analysis is to use the principle conservation of mass of pollutant or pathogen into and out of the cabin space. In words this can be written as follows.

Rate of change of mass of species inside the vehicle = inlet mass flow rate of species + mass generation rate of species within the vehicle – outlet mass flow rate of species– mass flow rate of species removed through cleaning, deposition on surfaces or decay.

This results in the equation shown in Box 1 below, which relates the concentration in the cabin to the external concentrations, the characteristics of the ventilation system and the characteristics of the pollutant or pathogen. The basic assumption that is made is of full mixing of the pollutant or pathogen in the cabin. The pdf gives full details of the derivation of this equation, and of analytical solutions for certain simple cases. It is sufficient to note here however that this is a very simple first order differential equation that can be easily solved for any time variation of external concentrations of pollutant generation by simple time stepping methods. For gaseous pollutants, the rate of deposition and the decay rate are both zero which leads to a degree of simplification.

Box 1. The concentration equation

The pdf also goes on to consider the pollutant or pathogen dose that passengers would be subjected to – essentially the integration of concentration of time history – and then uses this in a simple model of pathogen infection. This results in the infection equation shown in Box 2. Essentially it can be seen that the infection risk is proportional to the average concentration in the cabin and to journey length.

Box 2. Infection equation

The main issue with this infection model is that it assumes complete mixing of the pathogen throughout the cabin space and does not take account of the elevated concentrations around an infected individual. A possible way to deal with this is set out in the pdf. Further work is required in this area.

Ventilation types

The concentration and infection equations in Boxes 1 and 2 do not differentiate between the nature of the ventilation system on public transport vehicles. Essentially there are five types of ventilation.

  • Mechanical ventilation by HVAC systems
  • Ventilation through open windows
  • Ventilation through open doors
  • Ventilation by a through flow from leakage at the front and back of the vehicle (for buses only)
  • Ventilation due to internal and external pressure difference across the envelope.

Simple formulae for the air exchange rates per hour have been derived and are shown in Box 3 below. By substituting typical parameter values the air exchange rates are of the order of 5 to 10 air changes per hour for the first four ventilation types, but only 0.1 for the last. Thus ventilation due to envelope leakage will not be considered further here, although it is of importance when considering pressure transients experienced by passengers in trains.

Box 3. Ventilation types

Scenario modelling

In what follows, we present the results of a simple scenario analysis that investigates the application of the above analysis for different types of vehicle with a range different ventilation systems, running through different transport environments. We consider the following vehicle and ventilation types.

  • An air-conditioned diesel train, with controllable HVAC systems.
  • A window and door ventilated diesel train.
  • A bus ventilated by windows, doors, and externally pressure generated leakage.

Two journey environments are considered.

  • For the trains, a one-hour commuter journey as shown in figure 1, beginning in an inner-city enclosed station, running through an urban area with two stations and two tunnels, and then through a rural area with three stations (figure 1).
  • For buses, a one-hour commuter journey, with regular stops, through city centre, suburban and rural environments (figure 2).

Results are presented for the following scenarios.

  • Scenario 1. Air-conditioned train on the rail route, with HVACs operating at full capacity throughout.
  • Scenario 2. As scenario 1, but with the HVACs turned to low flow rates in tunnels and enclosed stations, where there are high levels of pollutants.
  • Scenario 3. Window ventilated train on rail route with windows open throughout and doors opened at stations.
  • Scenario 4. As scenario 3, but with windows closed.
  • Scenario 5. Window, door and leakage ventilated bus on bus route with windows open throughout and doors opened at bus stops.
  • Scenario 6. As scenario 5, but with windows closed.

Details of the different environments and scenarios are given in tables 1 and 2.  Realistic, if somewhat arbitrary levels of environmental and exhaust pollutants are specified for the different environments – high concentrations in cities and enclosed railway and bus stations and lower concentrations in rural areas. The air exchange rates from different mechanisms are also specified, with the values calculated from the equations in Box 3. Note that, in any development of this methodology, more detailed models of the exhaust emissions could be used that relate concentrations at the HVAC systems and window openings to concentrations at the stack, which would allow more complex speed profiles to be investigated, with acceleration and deceleration phases.

Figure 1. The rail route

Figure 2. The bus route

Table 1. The rail scenarios

Table 2. The bus scenarios

The results of the analysis are shown in figures 3 and 4 below for the train and bus scenarios respectively. Both figures show time histories of concentrations for NO2, PM2.5, CO2 and Covid-19, together with the external concentrations of the pollutants.

For Scenario 1, with constant air conditioning, all species tend to an equilibrium value that is the external value in the case of NO2 and PM2.5, slightly higher than the external value for CO2 due to the internal generation and a value fixed by the emission rate for Covid 19.

For Scenario 2, with low levels of ventilation in the enclosed station and in the tunnels, NO2 and PM2.5 values are lower than scenario 1 at the start of the journey where the lower ventilation rates are used, but CO2 and Covd-19 concentrations are considerably elevated. When the ventilation rates are increased in the second half of the journey all concentrations approach those of Scenario 1.

The concentration values for scenario 3, with open windows, match those of Scenario 1 quite closely as the specified ventilation rates are similar. However, for Scenario 4, with windows shut and only door ventilation at stations, such as might be the case in inclement weather, the situation is very different, with steadily falling levels of NO2 and PM2.5, but significantly higher values of CO2 and Covid-19. The latter clearly show the effect of door openings at stations.

Figure 3. The train scenario results

Now consider the bus scenarios in figure 4. For both Scenario 5 with open windows and doors, and Scenario 6 with closed windows and open doors, the NO2 and PM2.5 values tend towards the ambient concentrations and thus fall throughout the journey as the air becomes cleaner in rural areas. The internally generated CO2 and Covid-19 concentrations for CO2 and Covid-19 are however very much higher for Scenario 6 than for Scenario 5.

Figure 5. The bus scenarios

The average values of concentration for all the scenarios is given in Table 3. The dose and, for Covid-19, the infection probability, are proportional to these concentrations. For NO2 and PM10 the average concentrations reflect the average external concentrations, and, with the exception of Scenario 4, where there is low air exchange with the external environment for part of the journey. The average concentrations for CO2 and Covid-19 for the less ventilated Scenarios 4 and 6 are significantly higher than the other. For Covid-19, the effect of closing windows on window ventilated trains and buses raises the concentrations, and thus the infection probabilities, by 60% and 76% respectively.

Table 3. Average concentrations

Closing comments

The major strength of the methodology described above is its ability, in a simple and straightforward way, to model pollutant and pathogen concentrations for complete journeys, and to investigate the efficacy of various operational and design changes on these concentrations. It could thus be used, for example, to develop HVAC operational strategies for a range of different journey types. That being said, there is much more that needs to be done – for example linking the methodology with calculations of exhaust dispersion around vehicles, with models of particulate resuspension or with models of wind speed and direction variability. It has also been pointed out above that the main limitation of the infection model is the assumption of complete mixing. The full paper sets out a possible way forward that might overcome this. Nonetheless the model has the potential to be of some utility to public transport operators in their consideration of pollutant and pathogen concentrations and dispersion within their vehicles.

Giovanni Solari 1953-2020

See the source image

On April 19th 2021 an online memorial event was held to celebrate the life of Prof Giovanni Solari of the University of Genoa who died five months previously. His career is well described in a memorial article in the Journal of Wind Engineering that can be found here. I was one of over 20 friends and colleagues who spoke at the event. My short contribution is given below.

Giovanni Solari has left us a very considerable legacy, and I would like to briefly consider three aspects of this. The first is his legacy to the wind engineering community. He was the first President of the International Association of Wind Engineering and held that role from 2003 to 2007. But that role involved much more than a ceremonial aspect. He was instrumental in turning the IAWE from a very loose association that met for an extended supper every four years at the major conferences to a legally organised society with a properly formulated constitution, member organisations and a functioning secretariat. This involved much work with lawyers (an unenviable task) and much travelling and discussion. In a real way the existence of an international wind engineering community is one of Giovanni’s major legacies.

The second aspect I want to mention is his intellectual legacy. Giovanni had the gift of being able to take a complex physical or engineering problem, often in the field of structural dynamics, and to express this problem mathematically in such a way that he could obtain closed form solutions for the engineering parameters of interest. These were often complex but allowed a proper appreciation of the role of different material and loading properties to be understood and generalised. Giovanni was the master of the closed form solution. In these days, when it is so easy simply to throw computer power at a difficult problem through complex CFD of FE analysis, the need for such closed form solution becomes all the greater to inform calculations and to actually understand the issues in depth. Giovanni’s intellectual legacy, of doing the hard thinking and analysis before resorting to numerical calculation, is a very important one to keep hold of.

The third of the legacies I want to mention is a personal one. I believe I first met Giovanni at the first European Conference on Wind Engineering in the early 1990s. Certainly we began to correspond after that (and remember those were the days before the instant gratification of emails) and I paid a memorable visit to Genoa around that time where the highlight for me was the ability to spend some hours in the library, which was much better resourced in wind engineering terms than that of my own institution. I was received with courtesy and kindness and Giovanni spent time showing me around the city that he clearly loved. Over the years that same courtesy and kindness has been shown by Giovanni to numerous people – from research students at the very start of their careers to the more senior of us. And that is how many of us, myself included, who remember him – for his personal legacy as much as for his undoubted scholarship, organisational and intellectual legacies, as the kindest and most courteous of friends and colleagues. He will be very much missed by many in the community.

Giovanni – Requiescat in pace

The Midland Tornado of 1545

Damage caused by the Birmingham Tornado

Preamble

In the recently published book “Come wind, come weather” (Lichfield Press, 2021), Trevor James draws attention to the Midland Tornado of 1545 which caused very considerable damage along a very long storm track in Derbyshire. A description of the damage from that time is given in the Derbyshire edition of the Magna Britannia in 1817 and is reproduced below. In this short post, the nature of the storm will first be discussed and then we will make some estimates of the windspeeds that occurred, using modern damage scales. I will then address the question as to whether the event was actually a tornado, or some other type of wind storm.

The event

From the Magna Britannia.

” At Darbie the 25th daye of June 1545.

“Welbeloved sonne I recomend me unto you, gevyng you Godds blessyng & myne. Son this is to sertifie you of soche straunge newes, as that.hath of late chauns,ed in thes p’ties; that is to wytt, apon Satterday last past, being the 20th daye of this moneth, on Say’te Albons day, we had in thes p’tyes great tempest. … wether, about xi of the clok before none: & in the same tempest, The dev[ill] as we do suppose beganne in Nedewood, wch is ix myles from Da[rbie]; & there he caste downe a great substance of wood; & pulled up by the rotts: & from thens he came to Enwalle [Etwall] wher at one Mre Powret [Porte] dothe dwell, & he pulled downe ij great elmes, that there was a dossyn or xvj loode apon a piese of them; & went to the churche & pullyd up the leade, & flonge it apon a great elme that stondyth a payer of butt lenghthes from the churche, &. … it hangyd apon the bowys lyke stremars; & afte. …….. tourned. …… & the grounsells upwards & some layd bye apon. ….. heape &. ……. that was apon viij bayes long he set it a…….. gge & the. …… ro[ots] sett upwards; & he hathein the same towne lefte not past iiij or v housses hole. And from thence he came a myle a this syde, & there grewe opon Ix or iiijxx wyllowes, & apon xij or xvi he hathe brokyn in the mydds, & they were as great as a mans body: & so he lefte them lyke a yard and a half hye: And from thence he went to Langley, wch is lyke iiij myles from Darby, & there he hath pullyd downe a great p’te of the churche, & rowled up the leade & lefte it lyeing, & so went to Syr Wyllam Bassetts place in the same [towne] & all so rente it, & so pullyd a great parte of it downe wth his. …..& the wood that growethe abowte his place, & in his parke he pulled downe his pale & dryve out his deare, & pulled downe his woods, & so[me] broken in the mydds that was xvj or xx loode of wood of some one tre. And after that he went into the towne to Awstens housse of Potts & hath slayne his sonne & his ayer, & perused all the hole towne, that he hath left not past ij hole howsses in the same towne. And from thence he went to Wy’dley lane, & there a nourse satt wt ij chylderen uppon her lappe before the fyre, & there he flonge downe the sayde howse, & the woman fell forwards ap[on the] yongechyl[dren] afore the fyre, & a piese of ty’ber fell apon her. …… & so killed [her] but the chylderen were savyd, & no more hurte, [and none] of the house left standyng but the chymney, & there as the house stode, he flange a great tre, that there is viij or x lood of wood apon it. And from thence he went to Belyer [Belper] & there he hath pullyd & rent apon xl housses; & from thence he wente to Belyer [Belper] wood & he hathe pullyd downe a wonderous thyng of wood & kylled many bease; & from thens to Brege [Heage] & there hath he pulled downe the chappyl & the moste parte of the towne; & from thens to WynfeldmaJ that is the Erie of Shrowseberys [Wingfield Park], & in the parke he pulled him downe a lytell…… & from thens to Manfyld [Mansfield] in Shrewood & there I am sure he hath done [no] good, & as it is sayd he hathe donne moche hurte in Chesshire &….. shire. And as the noyse goeth of the people ther felle in some places hayle stons as great as a mans fyste, & some of them had prynts apon them lyke faces. This is trewe & no fables, there is moche more hurte done besyds, that were to moche to wryte, by the reporte of them that have sene it; and thus fare you well.”

James is persuaded that the account is genuine, not least by the mention of the damage to the chapel at Heage. The church at Heage was indeed officially a chapel (dependent upon another church) at the time and there are records elsewhere that indicate it was rebuilt after the storm. James quotes a further source (Warkworth’s Chronicle) which again suggests strong winds in Cheshire and Lancashire on that day.

The personification of the event as the “devil” is of interest and may reflect both the belief that such events were demonically rather than divinely inspired but might also refer to the name of such events – indeed even today small whirlwinds are referred to as dust-devils or something similar.

The description allows the track of the storm to be determined quite accurately, and this is shown on the map of figure below. In all the track where precise damage details are given seems to have been about 40 km long at least.

The track of the event. Purple lines indicate the boundary of Derbyshire and the purple circle is Derby itself. Red circles indicate the places where damage occurred, and green arrows indicate the storm track

Wind speeds

On the assumption that we are dealing with an tornado here, rather than another type of windstorm (see below), is it possible to obtain estimates of what the windspeeds actually were? Tornado windspeeds are usually estimated by inspecting the damage that they cause, and then using a damage classification method to determine the broad range of wind speeds that would cause that damage. Two methods are commonly used – the Enhanced Fujita (EF) scale developed in the US, and the T scale developed by the Tornado Research Association TORRO. Extracts from the damage descriptors are given below.

Enhanced Fujita EF Scale

EF2         49–60m/s      Roofs torn off from well-constructed houses; foundations of frame homes shifted; mobile homes completely destroyed; large trees snapped or uprooted; light-object missiles generated; cars lifted off ground.

EF3         60-74m/s      Entire stories of well-constructed houses destroyed; severe damage to large buildings such as shopping malls; trains overturned; trees debarked; heavy cars lifted off the ground and thrown; structures with weak foundations are badly damaged.

EF4 74-89m/s Well-constructed and whole frame houses completely leveled; some frame homes may be swept away; cars and other large objects thrown and small missiles generated.

TORRO T scale

T4           52 – 61m/s           Motor cars levitated. Mobile homes airborne / destroyed; sheds airborne for considerable distances; entire roofs removed from some houses; roof timbers of stronger brick or stone houses completely exposed; gable ends torn away. Numerous trees uprooted or snapped.

T5          62- 72m/s            Heavy motor vehicles levitated; more serious building damage than for T4, yet house walls usually remaining; the oldest, weakest buildings may collapse completely.

T6           73 – 83m/s          Strongly built houses lose entire roofs and perhaps also a wall; windows broken on skyscrapers, more of the less-strong buildings collapse.

To give some context, the Birmingham Tornado of 2005 (pictured above), one of the strongest in recent years, was classified as a T5 event.

It is immediately clear that these descriptions are very subjective and the classification of damage into a particular class is not straightforward. It is perhaps less clear that each of these scales is to some extent culturally dependent. The EF scale reflects North American building and vehicle types, and the T scale reflects building and vehicle types from the UK in the 1970s when the scale was first produced. Neither reflects building practice in the 1540s and neither were there any cars to be lifted up in that period!  Nonetheless, the description of 1545 can be used within these classifications at least in an approximate way. I would thus classify the 1545 event as borderline EF2 / EF3 or borderline T4/T5. These classifications give a wind speed range around 135 mph or 60 m/s. However,  Neaden, in a study of tornado risk for the HSE, based on the above description, assigns a category of T6 to the event, giving a wind speed of at least 73m/s. This again illustrates the subjectivity of the classification.

The T scale also gives a classification based on path length

T4 2.2 to 4.6km

T5 4.7 to 9.9km

T6 10 to 21km

T7 22 to 46km

On the basis of path length Neaden again gives a T6 classification, although the length as shown in figure 1 suggests a T7 classification. My view would be that the event should properly be categorised as EF2/EF3 or T4/T5 with an unusually long path length but the subjectivity of this assessment must again be emphasised.

Was it a tornado?

The question that then needs to be addressed is whether or not the 1545 event was a tornado or some other storm type – presumably one of the usual extra-tropical cyclones that pass across the UK quite frequently. Even in such storms there are known to be smaller tracks of major damage. In the 1987 storm for example there was a swathe of extreme damage to trees in a arc a few miles wide across the south of England. This was attributed to a high-level jet of wind sweeping down to ground level – a phenomenon know as a “sting jet” because of the scorpion tail-like cloud formation with which such events are often associated. 

The points that suggest the event was a tornado are firstly the existence of a coherent storm track, albeit significantly longer than would normally be the case, and secondly the fact that the event occurred in June, when extra-tropical cyclones are uncommon but tornadoes are. On the other hand, the point that suggest the damage was due to an extra-tropical cyclone is the reference in more than one source to concurrent strong winds in Cheshire and Lancashire. Indeed, wind speeds of 60 m/s have been measured in extra-tropical cyclones in the past – for the 1987 Burns Night storm for example the peak wind speed was somewhere around this value.

Referring again to the work of Neaden, his data indicates that between 1800 and 1985 there were around 10 tornadoes with a classification of T5 or higher in Derbyshire. This indicates that one would occur on average every 20 years or so. One would expect that most of these would have path lengths of a few kilometres and thus the effects would be localised – and in a rural county like Derbyshire not much damage might be recorded.

Now, one might expect that an extra-tropical cyclone with wind speeds of the order of 60m/s would only occur in lowland Britain once every 200 to 300 years – indeed the 1987 storm was assessed as having this return period. Thus they are very rare events indeed.

A comparison of the likelihoods of a T5 tornado and an extra-tropical storm with the same windspeeds thus suggests to me that it is most likely that the 1545 event was indeed a tornado with an EF2 / EF3 or T4/T5 classification, albeit with an unusually long storm track, but also that it was quite possibly embedded in a larger extra-tropical cyclone of some strength. However, as with any other historical phenomenon of this type, absolute certainty as to its cause is of course not possible.

International Wind Engineering seminars 2020/21 – some reflections

A Japanese version of this post can be found here

Between October 2020 and March 2021, I organised a series of six International Wind Engineering Seminars, through the University of Birmingham, my employer before I retired. These were sponsored by the International Association of Wind Engineering (IAWE) and delivered via Zoom. On the web page for this seminar series, I give the justification for organising it as follows.

“Because of the Covid19 pandemic, opportunities for the international wind engineering community to meet physically have been very much restricted and are likely to remain so for at least the next year. To enable the community to continue to interact with each other, at least in a virtual way, the University of Birmingham is organizing a series of six seminars via Zoom from October 2020 to March 2021.”

In this post, I want to reflect on how these seminars were delivered and received, what lessons might be learnt, and ask some questions concerning the future.

Each seminar consisted of a main speaker, followed by either a panel discussion or between two and four shorter presentations. The dates and topics are given in table 1. As these seminars were set up in some haste in August / September 2020, I mainly called upon my circle of contacts to be the main speakers at the events, and they suggested other speakers or panel members. I am indebted to all the speakers for taking part and spending considerable time in preparation. The nature of the delivery and follow up evolved over the course of the series. After the first seminar it became clear that I could not both chair the sessions and organise the questions in Chat to put to the speakers. Thus, from seminars 2 to 6, I was assisted by Grace Yan from Missouri who collated all the questions that were put on Chat and forwarded them to me to put to the speakers. Her help was hugely appreciated. For seminars 3, 4 and 6 the presenters and panelists were also asked to provide written answers to questions, and these were posted on the web pages that were for each of the seminars. All the presentations (and for seminars 5 and 6 the questions and answers) were recorded using the Zoom Record function and these recordings were place on my YouTube site and linked to the appropriate page. These pages also included talk abstracts and speaker biographies. After the third seminar I realised that YouTube could not be accessed from all parts of the world, so a link to the Zoom cloud versions was also given. From seminar 4 onwards, these could also be downloaded as required. The time chosen for the seminars (after the first) was 12.00 UK time, this being the best compromise for most time zones, with the exception of the west coast of the America and Australasia. I tried to institute a separate Q and A session for these time zones a day or so after the seminar, but there was insufficient take up to make it worthwhile. Thus the whole process was a considerable learning experience for me.

Table 1 Seminar dates, titles and speakers

It must be mentioned at this point that the third seminar occurred shortly after the death of Prof Giovanni Solari, who was instrumental in the setting up of the IAWE, and the speaker, Prof Kareem, paid tribute to him in his talk.

UniGe Giovanni Solari
Prof. Giovanni Solari

Table 2 shows the bare statistics for the seminars. The size of the distribution list for publicity grew through the series from the original 688 of the mailing list for the abortive BBAA conference to 1525 for seminar 6.  By seminar 3 the size of the list became so large that my e mail account was temporarily stopped as it was thought it had been hacked and was sending out spam. Thereafter I sent the information around in smaller batches. The number of registrants varied between 279 and 616, although only around 50 to 70% of these actually connected. The number of video views was also encouraging although again one must interpret these numbers cautiously as only around 20 to 30% of the views were for more than a few minutes. Note that these statistics are up to March 14th 2021 only, and as the views continued for several months after each seminar, the number of video views for the 2021 seminars will not be the final values.

Table 2 Seminar statistics (up to March 14th 2021)

Table 3 shows a breakdown of the views of the seminar web pages by month (which includes links to the videos). As expected these peak just before and just after the seminar, but all the seminars attract a significant number of views for a number of months after the event, which suggest that the subject matter is of ongoing interest. Again, note that this date only extends to the middle of March 2021,and a significant number of views could be expected for the later seminars after this date.

Table 3 Views of seminar web pages (up to March 15th 2021)

Table 4 shows the location of those who registered, as far as could be judged from email addresses. The generic .com address contains registrants from a wide variety of countries, and this rather skews the results. Nonetheless, it can be seen that whilst those countries where wind engineering is well established are well represented, a very wide range of countries was represented overall.

Table 4 Locations of registrants

Thus the numbers suggest that there was a significant number of wind engineers around the world who appreciated the seminar series and found them useful, and indeed that is what has been suggested by the informal feedback I have received. Again, caution is required to avoid over interpretation – the level of engagement with online seminars is likely to be much less than with in person presentations – I for one tend to do things such as checking my e mail / cricket scores when attending such virtual events – but not when I am chairing of course! But broadly the seminar series seems to have met a need. But there are needs it hasn’t addressed, for example the inclusion of a social aspect for informal discussion and the inclusion of young researchers in a meaningful way etc. To address this sort of issue, other formats can be envisaged – for example I can think of the following.

  • Specific discussion topics could be set, and potential attendees asked to submit short abstracts of a two minute, two slide talk, from which a balanced group of young and established researchers could be selected for a series of short presentations and a more relaxed discussion. These could be recorded and put on-line for all to see.
  • Interviews (by me or others) of a range of wind engineers, talking about their careers, their successes and failures etc., which could again be recorded and put on-line.
  • The use of a platform such as Gather Town, which seems to allow for multiple individual conversations within a group structure and could be used for, say, virtual poster sessions (but note I have never used this, although on the face of things it seems potentially useful.)

And there are no doubt other possibilities. The question then arises as to what should happen next. I don’t intend to organise any more such seminars till September at least – amongst other things I wish to watch a number of cricket matches rather than just checking the scores, and to re-acquaint myself with a number of heritage railways in Wales. So, I put the following questions to the wind engineering community.

  • Should something similar be organised for next winter as I suspect international travel won’t resume in any real sense until Summer 2022 at best? Note that I am not necessarily implying that should something felt to be necessary, then I would be the one to organise it!
  • If so, what should the format be – just one speaker, or more than one speaker, or something completely different?
  • Are there any suggestions for topics and speakers?
  • Are there any other suggestions for possible related activities, such as I mention above.

There is also a larger question of course about the future of the four year cycle of Wind Engineering conferences and whether such a cycle is still sustainable – see for example the initiative of Glasgow University which is urging academics to reduce overseas travel as part of the greening of its activities. But that is a discussion for others to have within the IAWE committee.

Please make any comments in the comment box attached to this post, or, if you wish, email me directly on c.j.baker@bham.ac.uk. Thanks in advance.

Some thoughts on ventilation and pathogen concentration build up

Modeling airflow scenarios in classrooms
Covid spread from CFD studies

Introduction

Up till recently most attention had been focused on the spread of Covid-19 by near field transmission – being in close proximity to an infected person for a certain amount of time, and rather ad hoc social distancing rules have been imposed to attempt to reduce transmission. However, there is another aspect of transmission – the gradual build up of pathogen concentrations in the far field in enclosed spaces due to inadequate ventilation. The importance of this mode of transmission is beginning to be recognised – see for example a recent seminar hosted by the University of Birmingham. The main tool that seems to have been used for both near and far field dispersion is Computational Fluid Dynamics (CFD) – see the graphic above from the University of Minnesota for example. Now whilst such methods are powerful and can produce detailed information, they are very much situation specific and not always easy to generalise. This post therefore develops a simple (one could even say simplistic) method for looking at the far field build up of pathogens in an enclosed space, in a very general way, to try to obtain a basic understanding of the issues involved and arrive at very general conclusions.

The model

We begin with equation (1) below. This is a simple differential equation that relates the rate of change of concentration of pathogen in an enclosed volume to the pathogen emitted from one or more individuals via respiration and the pathogen removed by a ventilation system. This assumes that the pathogen is well mixed in the volume and is a simple statement of conservation of volume.

From the point of view of an individual, the important parameter is the pathogen dose. This is given by equation (2) and is the volume of pathogen ingested over time through respiration. The respiration rate here is assumed to be the same as that of the infected individual.

Equations (1) and (2) can be expressed in the normalised form of equations (3) and (4) and simply solved to give equations (5) and (6).

Equations (5) and (6) are plotted in figures 1 and 2. Note that an increment of 1.0 in the normalised time in this figure corresponds to one complete air change in the enclosed volume. It can be seen that after around three complete air changes the concentration of pathogen reaches an equilibrium value and the dose increases linearly, whatever the starting concentration. To the level of approximation that we are considering here we can write the relationship between normalised dose and time in the form of equation (7), which results in the non-normalised form of equation (8).

Assuming that there is a critical dose, the critical time after which this occurs is then given by equation (9).

Equation (9), although almost trivial, is of some interest. It indicates that the time required for an individual to receive acritical dose of pathogen is proportional to the volume of the enclosure and the ventilation rate. This is very reasonable – the bigger the enclosure and the higher the ventilation, the longer the time required. The critical time is inversely proportional to the concentration of the emission, which is again reasonable, but inversely proportional to the square of the respiration rate. This is quite significant and a twofold increase in respiration rate (say when taking exercise or dancing) results in the time for a critical dose being reduced by a factor of 4, or alternatively the need for ventilation rate to increase by a factor of 4 to keep the critical time constant. Similarly if there are two rather than one infected individuals in the space, then the respiration rate will double, with a reduction in the critical time by a factor of four.

Discussion

Now consider the implications of this equation for two specific circumstances that are of concern to me – travelling on public transport (and particularly trains) and attending church services. With regard to the former, perhaps the first thing to observe is that there is little evidence of Covid-19 transmission on trains, and calculated risks are low. In terms of the far field exposure considered here, respiration rates are likely to be low as passengers will in general be relaxed and sitting. This will increase the time to for a critical dose. On modern trains there will be an adequate ventilation system, and the time to reach a critical dose will be proportional to its performance. Nonetheless the likelihood of reaching the critical level increases with journey time – thus there is a prima facie need for better ventilation systems on trains that undergo longer journeys than those that are used for short journeys only. For trains without ventilation systems (such as for example the elderly Class 323 stock I use regularly on the Cross City line) has window ventilation only, and in the winter these are often shut. Thus ventilation rates will be low and the time to achieve a critical dose will be small.

See the source image
Class 323 at Birmingham New Street

Now consider the case of churches. Many church buildings are large and thus from equation (9) the critical times will be high. However most church buildings do not possess a ventilation system of any kind, and ventilation is via general leakage. Whilst for many churches this leakage this can be considerable (….the church was draughty to day vicar….), some are reasonable well sealed – this will thus, from equation (9) tend to reduce the critical time. In this case too the respiration rate is important. As noted above the critical time is proportional to the respiration rate squared. As the rate increases significantly when singing, this gives a justification for the singing bans that have been imposed.

File:Thornbury.church.interior.arp.750pix.jpg - Wikimedia Commons
Church interior – Wikipedia Commons

The above analysis is a broad brush approach indeed, and in some ways merely states the obvious. However it does give something of a handle on how pathogen dose is dependent on a number of factors, that may help in the making of relevant decisions. To become really useful a critical dose and initial pathogen concentration need to be specified together with site specific values of enclosed volume, ventilation rate and expected respiration rates. This would give at least approximate values of the time taken to reach a critical dose in any specific circumstance.

Tornadoes and debris

Get the facts about tornadoes - Chronicle Media

The debris trajectory animations of Figures 6 to 11 were provided by Professor Mark Sterling, whose ability to use advanced EXCEL functions seems to be significantly greater than mine. His contribution is much appreciated.

Previous work

In 2017 Mark Sterling and I published the paper “Modelling wind field and debris flight in tornadoes”, which described the integration of a tornado wind field model and the debris flight equations to look at the pattern of compact debris movement in tornadoes of different types. Typical results for falling and flying debris are shown in figure 1 below and give an indication of the complexity of the debris trajectories that were predicted.

Figure 1. Debris Trajectories from 2017 model

Now whilst the tornado wind model that was used in the analysis was a considerable improvement over those that existed at the time, in that it gave a consistent three dimensional velocity formulation, it did however have one major drawback. This was the fact that the vertical velocity component was unbound and increased with height, albeit quite slowly. In a more recent paper in 2020 “The lodging of crops by tornadoes”, we developed an improved model, in which the vertical velocity peaked at a certain height and then decreased at greater heights. In this blog post I will briefly explore  the use of this wind model to predict compact debris flight paths using the same methodology as in the first paper, and in doing so will illustrate the importance of the tornado model on debris trajectory prediction.

The tornado wind model

Figure 2. Velocities from 2020 model

The expressions for the radial, circumferential and vertical velocities in the 2020 model are given in figure 2. Here the velocities are normalized by the maximum circumferential velocity and the radial and vertical distances by the radius at which the maximum velocity occurs. Note that this is different from the 2017 paper where the maximum radial velocity was used for normalization. The parameter K is related to what will be termed the swirl ratio S (the ratio of the maximum circumferential to maximum radial velocity) by a function of the parameter gamma, which is a shape parameter that affects the shape of the radial and vertical profiles. (Unfortunately this web template doesn’t support Greek letters, so I have to spell them out). Figure 3 shows typical velocity profiles for different values of this parameter.  It can be seen that for gamma = 2, the peak of the vertical velocity is at the vortex centre, as in a typical single cell vortex, whilst for higher values it moves away from the centre becoming more like a two cell vortex (but note there is no downflow at the vortex centre in this case.

Debris flight equations

The equations for compact debris flight are given in figure 4. These are the same as in the 2017 paper, although expressed a little differently. The debris velocities (lowercase) in the three directions are again normalized by the maximum tangential tornado velocity. Two dimensionless parameter are identified – the Tachikawa number Ta that relates the flow force on the debris particle to its weight, and a tornado Froude number Fr. Different dimensionless parameters were used in the 2017 paper, because of the different reference velocity that was used

Figure 4. Debris flight equations

Solutions

Figure 5. Base case parameters

Putting together the velocity equations in figure 2 and the particle flight equations in figure 4, it can be seen that there are four parameter that define debris trajectories – the tornado parameters S, gamma and Fr, and the debris Tachikawa number Ta. In addition any one flight trajectory will be defined, at least in its early stages by the dimensionless values of the radius and height at its release point. If these six parameters are specified then the equations of debris flight can be solved in a straightforward manner.  In what follows we define a base case situation as in figure 5, and then vary each of the parameters around this base case value. We present the results in the animations of figures 6 to 11.Each animation shows four plots – the trajectories projected onto a vertical plane through the tornado centre; the trajectories projected onto a horizontal plane; the trajectories in a rotating plane in the radial and vertical directions, and a plot of the variation of particle kinetic energy with time. The latter acts as a damage indicator of debris flight, but also clearly shows whether or not the solution converges or diverges with time. Note that the dimensionless time shown in the kinetic energy plots is proportional to the time of revolution of the vortex – a time of 2 pi corresponds to one vortex revolution. 

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Figure 6. Effect of variations in Tachikawa number

First consider the effect of changing Tachikawa number, Ta – see Figure 6. This represents changes in the nature of the debris. A low value of Ta represents heavy debris and vice versa. It can be seen that at low values of Ta, the debris tracks can reach significant heights and the debris undergoes a diverging motion when viewed in the radius / height plane, with a diverging kinetic energy oscillation. At some point in the trajectory the debris hits the ground and the energy falls to zero. The base case situation at Ta = 100 is still mildly diverging but the trajectory does not intersect the ground plane for the length of the calculation. As Ta increases further, the debris takes up a stable path in the radius / height plane travels around a small circular trajectory, with the kinetic energy converging to a stable value. This suggest that light debris can reach an equilibrium where it is held aloft by the tornado. The position around which the circular motion takes place is around a normalized radius of 1.3 and a normalized height of 0.9. The value of height is much less than calculated in the 2017 paper, reflecting the fact that the vertical velocity does not decrease indefinitely with height for the new model as it did in the old.

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Figure 7. Effect of variations in Froude number

The effect of variations in Froude number is shown in Figure 7. The primary effect that increase in Fr has is to increase the centrifugal force on the debris. At low values, the trajectories are stable and similar to that of the base case. As the values increase above 1.0 the oscillations become larger due to the increased centrifugal forces and eventually become unstable, with the trajectories meeting the ground at high values.

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Figure 8. Effect of variation in Swirl Ratio

The effects of variations in the Swirl ratio shown in Figure 8 are complex, with diverging trajectories (and ground impact) at both low and high values, and a region of stable trajectories between values of around 1.0 to 1.9. At low values the trajectories are destabilized by the high values of radial velocity, and at high values are destabilized by high values of the circumferential velocity.

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Figure 9 Effect of variations in gamma

The change in values of gamma from the one cell form of gamma = 2 to the quasi-two cell form of gamma = 4 shown in Figure 9 results in little change to the debris trajectories from the base case, although the oscillations in the kinetic energy fall as gamma increases.

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Figure 10. Effect of variations in radial starting position

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Figure 11. Effect of variations in vertical starting position

The debris trajectories remain stable as the normalized radius varies between 1 and 1.9 but outside those limits the trajectories diverge and intersect with the ground (Figure 10). Similarly the trajectories are only stable for normalized values for height between 0.8 and 1.2 (Figure 11). Thus the starting point window for the trajectories to ultimately attain a stable form is quite small.

Concluding remarks

A number of points arise from the results presented above.

  • Even for the simple wind and debris flight formulation adopted, debris trajectories can be quite complex.
  • A comparison of the results obtained with the old and the new wind field model show very considerable differences, due to the different vertical velocity formulation. analysis reveals that the debris trajectories can be specified by a small number of debris and tornado parameters, with the Tachikawa number and the Swirl Ratio being the most significant.
  • There are regions within parameter space for which the debris trajectories become stable – i.e. the debris flies indefinitely.

Modelling of extreme wind gusts

Nomenclature

This post addresses the issue of the use of what has become known as the “Chinese Hat” gust model. The use of this title has become increasingly problematic over recent years for obvious reasons, and I will no longer use it, but will instead refer to the “CEN extreme gust model” in what follows.

The CEN extreme gust model

In a number of situations in wind engineering, some sort of deterministic (as opposed to stochastic) gust model is required in order to determine structural response. One such case is in the determination of the risk of overturning of road or rail vehicles in high winds. A methodology of this type is set out in CEN (2018), where an extreme gust model is described.  This model was originally developed in wind loading studies for wind turbines as a time dependent gust to be applied to calculate wind turbine loading at one fixed location (Bierbooms and Cheng, 2002). As such, it is perfectly adequate and a good representation of an average extreme gust in high wind conditions.  In the methodology of CEN however, it is re-interpreted as a stationary spatially varying gust. This must be regarded as a very significant assumption for which, in my view, there is little justification. Nonetheless the formulation has proved useful practically and we begin by considering it in a little more detail.

For a wind normal to the track, the extreme gust formulation is given by equation (1) on Box 1. Note that the “characteristic frequency” of the gust is calculated from standard wind engineering methods for temporally, rather than spatially, varying gusts. Equation (1) is a generalised form of that given in CEN (2018) to remove some of the constants that tie the expression to a particular location and topography through specific values of peak factor and the turbulence intensity (the ratio of the standard deviation to the mean velocity). The time dependence is recovered through the passage of the train passing through this gust at a speed v = xt to give equation (2). It can be seen that the gust thus has a maximum value of (1+ peak factor x turbulence intensity) when t = 0 and decreases to unity for small and large times. It is symmetrical about t = 0. The velocity relative to the train is then found by the vector addition of this gust velocity with the vehicle velocity to give a time varying value.

To enable the gust profile to be specified, the characteristic frequency f is required. This is specified in equations (3) to (5). These equations are again in a more generalized form than given in CEN (2018), where a value of the upper limit of integration is fixed at 1 Hz, together with an implicit value of the turbulence length scale of around 75m. The genesis of the 4.18 factor is however not clear to me.  Equation (3) shows that the calculation of the characteristic frequency is thus based on the calculation of the zero-crossing rate of temporal fluctuations through the use of the velocity spectrum. Again, note that these parameters describe a time varying rather than a spatially varying velocity, and their use is not formally consistent with a spatially varying gust. From equations (3) to (5), it can be seen that the normalized characteristic frequency is a function of the normalized upper limit of integration. A numerical solution of these equations was carried out and the following empirical line fitted to the results for a value of the latter greater than 1.5 (which is the realistic range) – equation (6). From equations (2) and (6) we thus obtain equation (7). Although the overall methodology cannot be regarded as wholly sound, equation (7) does (in principal) significantly simplify its use and also allows the implicit wind parameters in the method to be explicitly defined.

Box 1 Equations 1 to 7

Is there a better methodology?

It can be seen from the above that the CEN  methodology thus does not fully describe a typical gust as seen by a moving train, which would vary both spatially and temporally, and can at best be regarded as an approximation, although its practical utility must be acknowledged. Ideally, if such an approach is to be used, a gust that varies both in space and time is really required.  Such a gust was used in the SNCF route assessment method of Cleon and Jourdain (2001), where the shape of the gust is appropriately described as a rugby ball. This method was however for very specific wind characteristics and does not seem to have found widespread use. Thus in this post, we investigate the possibility of developing a spatially and temporally varying gust, that can be expressed in a simple form (ideally similar to equation (2)) for practical use.

Towards a new model

In this section we will draw on experimental results for extreme gust characteristics in both temporal and spatial terms to construct a simple, if empirical model, that fulfills the function of the CEN (2018) model without the theoretical drawbacks.

We consider first the full-scale experimental data analysed by Sterling et al (2006) which used conditional sampling to determine the average 99.5th percentile gust profile for four anemometers on a vertical mast with heights between 1m and 10m. These results thus give the time variation in gust speed as the gust passes the anemometers. They showed that the gust profiles could be well approximated by the formula shown in equation (8) (Box 2). The parameter G in this equation is the equivalent of the peak factor multiplied by the turbulence intensity in equation (2) and for these measurements was 0.786.  n was -0.096, and the value of m depended upon whether t was greater or less than zero. For t < 0, i.e. on the rising limb, m was 0.1, whilst for t > 0, on the falling limb, m was 0.2. The gust shape was thus asymmetric with a maximum at t = 0.  This curve was a good fit to all the gust profiles throughout the height range. In what follows we will use a rather different curve fit expression to the same data, more consistent with that used in CEN (2018) – equation (9). It was found that the best fit value of b  was equal to 0.5 for all t, whilst the best fit values of a were 0.49 for the rising gust and 0.37 for the falling gust. This expression thus describes the temporal variation of wind speed as a gust passes through the measuring point

To describe the lateral spatial variation of the gust profile, we use the data of Baker (2001) who presents conditionally sampled peak events for pressure coefficients along a 2m high horizontal wall. This data allows the lateral extent of the gusts to be determined, from the variation of the time varying pressure coefficient divided by the mean value of the coefficient and then assuming that the gust velocity variation can be found from equation (10). The spatial variations of velocity were then fitted by a curve of the form of equation (11). g was found to be 6.16 and d was found to be 0.7.

On the basis of the above expressions one can thus write the expression of equation (12), which describes the variation of the gust velocity in both space and time. The movement of the train through the gust can again be allowed for by letting x = vt (equation (13)).

Box 2 Equations 8 to 13

Model comparison

Box 3 sets out the formulations of the CEN extreme gust model and the model derived here. In some ways they are similar in form, with an exponential formula that is primarily a function of normalized time. Whilst the CEN model is symmetric around t = 0, the new model has a degree of asymmetry because of the different values of the curve fit parameters for t < 0 and t > 0. However an examination of the new model suggest that the asymmetric term may be small, and thus Box 3 also shows an approximate version of the new model where this term is neglected.

Box 3 Model Summary

Figure 1 shows a comparison of these three models for the following parameter values – peak factor = 3.0; turbulence intensity = 0.25; train speed = 75m/s; mean wind speed = 25m/s; turbulence length scale = 75m, upper frequency of integration = 1.0Hz. It can be all three models are similar in form, showing a sharp peak at t = 0. The full and approximate forms of the new model are almost indistinguishable, showing that the approximation suggested above is valid. The main difference is that the CEN model has a much greater spread in time than the new model. This difference persists whatever input parameters are chosen.

Figure 1 Model Comparison

At this point it is necessary to consider again the genesis of the models – the CEN model resulted from an application of a time varying gust model as a spatially varying gust model, whilst the new model was developed based on measured temporal and spatial gust values. As such, I would expect the latter to be more accurate. The broad spread of the CEN gust may result from an application of the time varying along wind statistics to a cross wind spatial gust. Since it is known that that longitudinal integral scale is several times larger than the lateral integral scale, this would result in a wider spread of the gust than would be realistic. This is to some extent confirmed by the period of the two gusts – around 2s for the CEN gust and around 0.8s for the new model. For a train speed of 75m/s, this corresponds to gust widths of 150m and 60m – roughly approximating to the expected the longitudinal and lateral turbulence integral scales.

Concluding remarks

In this post I have looked again at the CEN extreme gust method and raised concerns about its fundamental assumptions. I have also developed an equivalent, but perhaps more rigorous, methodology based on experimental data for wind conditions at ground level. This strongly suggests that the CEN gusts are spatially larger than they should be, which suggests its long term use should be reviewed. However, when used to compare the crosswind behaviour of individual trains, rather than in an absolute sense, it is probably quite adequate.  

References

Baker C J, 2001, Unsteady wind loading on a wall, Wind and Structures 4, 5, 413-440. http://dx.doi.org/10.12989/was.2001.4.5.413

Bierbooms, W., Cheng, P.-W., 2002. Stochastic gust model for design calculations of wind turbines. Journal of Wind Engineering and Industrial Aerodynamics 90 (11), 1237e1251. https://doi.org/10.1016/S0167-6105(02)00255-6.

CEN, 2018. Railway Applications d Aerodynamics d Part 6: Requirements and Test Procedures for Cross Wind Assessment. EN 14067-6:2018.

Cleon, L., Jourdain, A., 2001. Protection of line LN5 against cross winds. In: World Congress on Rail Research, Köln, Germany.

Sterling M, Baker C, Quinn A, Hoxey R, Richards P, 2006, An investigation of the wind statistics and extreme gust events at a rural site, Wind and Structures 9, 3, 193-216, http://dx.doi.org/10.12989/was.2006.9.3.193

Some musings on tornado vortex models

From Wikipedia

Recently I have been considering the fundamental nature of a range of analytical models of tornado like vortices, and have written up my musings as an extended essay that can be downloaded at “Some musings on tornado vortex models”. In the essay I look at the class of tornado models that are solutions of the Navier-Stokes or Euler equations. It is clear that they all share a common analytical basis based on the assumption, either implicit or explicit, that the three velocity components (radial, vertical and circumferential) can each be specified by the multiple of two functions – one a function of radius only, and one a function of height only. Assumptions are made concerning the nature of one particular velocity component, and this assumption then allows the other components to be calculated from the continuity and momentum equations via the method of separating the variables. The recognition of this commonality allows a common analytical formulation to be developed that underlies all the models.

Those models that are solutions of the full Navier-Stokes equations (the Burgers-Rott, Sullivan and Vasistas et al models) derive velocity component formulae that are functions of Reynolds number. In the context of a full-scale tornado, this is a Reynolds number based on turbulence eddy viscosity rather than molecular viscosity. The assumptions required to obtain analytical solutions result in vertical velocities that are unbound with height and in some cases radial velocities that are unbound with distance from the vortex centre.  

Those models that are solutions of the Euler equations (two by Baker and Sterling  and two new models A and B) have, on the whole, rather more realistic formulations of the velocity components and, with one exception, all components for these models are bound in the vertical and radial directions. Instead of the Reynolds number, the velocity components are functions of constants of integration that relate to the Swirl ratio – the ratio of the maximum circumferential to radial velocities. As the circumferential velocity profiles in these models fall to zero at ground level in a reasonably realistic way, the boundary layer at the bottom of the tornado is modeled to some extent. The common analytical framework of these models allows, in principle, the derivation of a large number of different models, provided that they are of a form that allows the solutions to be obtained through simple integrations.  However the drawback of such models is that the pressure is zero at the ground for all distances from the vortex core and thus the dip in pressure at the centre of tornadoes is not modeled. This is broadly a consequence of viscous effects not being properly modeled near the ground. 

Whilst most of the models represent single cell tornado vortices, two of them – those of Sullivan and new model B – give solutions for two cell vortices. The essay shows that the Sullivan model, based on the Navier-Stokes equations, has a more general form than that given in the original paper and can model one-cell and two-cell vortices and the transition between them. New model B, based on the Euler equations is also able to model both sorts of vortex.  

The essay concludes that further work is required in two areas. Firstly there is a need to develop methods that do not rely on the assumption that the velocity components are multiples of two functions – one of radius and one of height – as recent experimental data suggests that the vortex radius can vary significantly with height. Secondly, the tornado boundary layer needs to be modeled in a more satisfactory way than at present, and the essay suggest that this might be done through matching a viscous solution of the Navier-Stokes equation near the ground, with an inviscid solution from the Euler solution away from the ground.  I may have more to say on this in the future.

Measuring the behaviour of pedestrians in high winds

This post outlines some of the results from the project “The safety of pedestrians, cyclists and motor vehicles in highly turbulent urban wind flows” funded by the UK Engineering and Physical Sciences Research Council. The work that is described below involved a number of colleagues, whose contribution to the project was significantly greater than mine, particularly Dr Zhenru Shu, Dr Mike Jesson, Dr Andrew Quinn, and Prof Mark Sterling. Their contribution is gratefully acknowledged.

1. Introduction

The assessment of wind conditions around new buildings has become standard practice over recent years, either by wind tunnel testing or through the use of CFD calculations. The assessments usually concentrate on two aspects – the effect of wind conditions on human comfort and thus the usability of the area around the building; and the effect of high wind conditions on human safety and stability. It is with the latter that this paper is concerned. In general the criterion for assessing a site for pedestrian safety is based on a gust wind speed of a specified magnitude with a specified probability of occurring, that is deemed to be at the safety limit. Current UK practice is illustrated in Figure 1 below. There is a great deal of variability in the specification of this windspeed and the specification is usually based on largely subjective data from questionnaires etc. Following a fatality caused by high winds around a new building in the city of Leeds, a major research project was funded by the UK Engineering and Science Research Council to enable the University of Birmingham to investigate the safety of vehicles and pedestrians around high-rise buildings. This included full-scale wind measurements and the assessment of the ability of different wind tunnel and CFD techniques to replicate these measurements. In addition tests were carried out to make quantitative measurements of human response in gusty winds, using instrumentation mounted on volunteers. As will be appreciated by any reader who has tried to make full scale wind measurements of any type, the setting up of the experimental apparatus usually guarantees that strong winds will not occur, and the same phenomenon was observed for these tests. The two winter seasons that were available for these measurements had relatively few storms, and only two trials could be carried out. As a result, although some very interesting results were obtained and will be presented in what follows, they must be regarded as provisional and tentative. More work is required to obtain a fuller dataset of human response measurements of the type that are presented here.

Figure 1 Current UK practice for specifying wind comfort and safety (Values given are for mean wind speed and percentage of time exceeded)

2. The trials

Figure 2. The test site showing the walking route along the Biosciences building and the reference anemometer site on the Moorhead Tower

The trials on the response of pedestrians to high winds were carried out on the campus of the University of Birmingham (figure 2). A walking route of length 63m was set up in the centre of the campus. Eight sonic anemometers were placed 2m above the ground at 9m intervals along the route.  A reference anemometer was installed at the top of the nearby high rise Muirhead Tower. A reference anemometer was mounted at the top of the Moorhead Tower. All the anemometers sampled at 10 samples / sec, and data was recorded on an AntiLog data logger. Human response was measured using GaitUp Physilog (combined accelerometer and gyroscope) sensors. Sensors were attached to both feet of the subjects, and provided details of walking speed and stride parameters every second through GaitUp’s proprietary software. A third sensor was placed on the back of a safety jacket worn by the subjects and thus gave details of upper body acceleration.  

Two trials were carried out – October 2017 during Storm Ophelia, and in February 2019 (figure 3. In total there were 15 subjects, with weights ranging from 54 to 110kg, and ages between 28 and 75. Each subject was asked to walk along the test route 10 times in each direction during which the gait and acceleration information was measured. 

Figure 3 Wind conditions during the trials

3. Analysis

The overall wind conditions at the reference site on the Muirhead Tower are shown in figure 3 for the two test periods. It can be seen that in each case the wind is from the South-West (shown in longer term analysis to strongly be the prevailing wind direction), with gust speeds up to 18m/s

Before the data could be analysed, some data preparation was required. Firstly the gait data and accelerometer time series had to be synchronized with the anemometer time series of velocities and the raw accelerometer data was transformed into horizontal and vertical co-ordinates. The time series of velocity and direction relative to the subjects were then derived form the stationary anemometer data as the subject walked along the route. A histogram of gust speed distribution, as experienced by the volunteers, for the two trials is shown in figure 4.

Figure 4 Histogram of gust velocities

Initial inspection of the data showed that there was very significant variability between each recorded walk along the track. This was in part due to the normal variation in wind conditions with higher gust speeds on some walks than on other, but it also seemed that the reaction of subjects varied both with time and between subjects. A typical set of results is shown in figure 5.  The direction of travel of the subject is from 0 to 63m. The wind speed relative to the subject can be seen to have a maximum of around 12 m/s in this case (associated with the corner flow from an adjacent building). The horizontal and vertical accelerometer data show slight oscillations around the gust position gust with the former having an average value of zero, and the latter an average value of 1.0. Most of the gait measurements (cycle time, stride length, stride speed) revealed little change in behaviour as the subjects walked along the route, all remaining approximately constant along the walk in most conditions.  The one parameter that did show variation was the swing width – the lateral variation of the foot during a stride cycle. In particular rapid changes in swing width were sometimes (but not always) observed as the subjects encountered gusts – see the graph for swing width gradient.

Figure 5 Wind, acceleration and gait parameters for typical gust (green symbols indictate left foot, blue symbols indicate right foot)

At the highest gust speeds that were recorded, there were three events where the subject became unstable to a variable extent. Figure 6 shows the experimental data for one such case. Here it can be seen that at the gust position there are significant vertical and horizontal acceleration responses, and all the gait parameters show a response at the event. The swing width response is again the most noticeable.

Figure 6 Wind, acceleration and gait parameters for strong gust

A somewhat more quantitative approach to the data is possible by looking at the various responses statistically. In what follows we consider the results from both trials, for all subjects, as one dataset. Figure 7 shows the percentage of such gusts in which the subjects showed a swing width response (with either the left or right swing width changing by more than 0.06m in one second) and acceleration response (where an acceleration response greater than 0.05g could be detected) or an instability response (with an acceleration response greater than 0.4g). In considering these results the low number of gust events in the upper velocity bands need to be considered, as does the subjectivity of the response limits used. These points being made, it can be seen that for even low speed gusts of magnitude less than 10m/s, around 50% of the gusts result in a swing width response (which are mostly unconscious responses not registered by the subject). The frequency of such responses rise rapidly for gust speeds above 10 m/s, and all gusts over 14 m /s show such a response. Acceleration responses become significant at gust speeds of about 10m/s, and are observed for all gusts above 16m/s. Instability responses begin to occur at gust speeds over 14m/s, although it should be noted here that only a very small number of such events (3) were observed.

Figure 7 Frequency of different types of response

4. Concluding remarks

The results for human response in gusts presented here suggest that three levels of response can be identified – swing width response , upper body acceleration response and instability response, with the frequency of each such response increasing with wind speed. However it must be emphasised once more that the number of bot high speed gust events and the number of subjects was too small for a valid statistical analysis to be carried out, and more data is required before firmer conclusions can be drawn.

Train crosswind performance – is there a “best” shape?

ICE 3 Velaro

This post arises out of a discussion with a number of colleagues on the issue of train overturning, in particular Mr Terry Johnson and Dr Dave Soper. Their (perhaps inadvertent) contribution to the development of the ideas set out below is gratefully acknowledged, although the responsibility for any inadequacies and errors must remain mine.

1. Introduction

In recent decades a great deal of research has been carried out to investigate the safety of trains in high cross winds, primarily to determine the wind speeds at which overturning will occur, and the risk of a wind induced accident (Baker et al, 2019). This usually takes the form of the determination of the aerodynamic forces and moment coefficient for a particular train, the use of these coefficients to determine the cross wind characteristic (CWC) – effectively a plot of accident wind speed against vehicle speed – and then some sort of risk analysis on the route over which the train will run. The first two steps are usually the concern of train manufacturers and are undertaken when the design of the train, at least in terms of overall shape and size, is fairly well advanced. The third step is usually the concern of the infrastructure operator. One question that is not often asked however is whether there is a “best” design for a train to minimise the risk of a wind induced accident, and thus to maximise safety. This has been addressed to some extent by a number of recent investigations that used a combination of CFD methods to calculate the forces and moments on a train, and optimisation methods to consider the effect of changes to train geometry. It is not however clear as to what should be the objective function of such optimisation – for example a number of different force or moment coefficients for a range of different yaw angles could be chosen. This post addresses this issue though an analysis of accident risk and investigates the aerodynamic parameters required to minimise this risk 

2. Aerodynamic force and moment coefficients

In a recent book (Baker et al, 2019) the author suggests a way of parameterising train aerodynamic force and moment data that seems to have a wide validity. This is set out in Box 1 below, in which the formulation for lee rail rolling moment coefficient is given, and is illustrated for a specific case. It applies equally well to side and lift force coefficient data. It can be seen that the form of the rolling moment / yaw angle curve is specified by four parameters – the coefficients at yaw angles of 30 and 90 degrees and exponent shape factors that describe the shape of curve, n1 and n2. Figure 1 shows a comparison of this methodology with side force coefficient data from the CEN codes (CEN, 2018) and the AeroTRAIN project (Paradot et al, 2015) as given in Baker et al (2019). All this data was obtained in a consistent way, with an STBR ground simulation in low turbulence wind tunnels. The agreement can be seen to be in general good and gives some confidence in the use of the parameterisation in what follows. The biggest discrepancy is in the transition region between the high and low yaw angle regimes, but it will be seen that this is not particularly critical to the argument that follows.

Box 1. Force and moment coefficient parameterisation
Figure 1 Parametrisation curve fit (from Baker et al, 2019)

3. Crosswind characteristics

The method used to specify the crosswind characteristic is also taken from Baker et al (2019) and is set out in Box 2. Using this methodology, the CWC are functions of n1 and n2, the ratio of the moment coefficients at yaw angles of 90 and 30 degrees, and what is defined as a characteristic wind speed, which is itself a function of train and track parameters. Box 2 gives the formulation for flat straight track, with a wind angle normal to the track – a fuller form can be found in Baker et al (2019). A comparison of this method with the results from CEN (2018) and Paradot et al (2015) is given in figure 2, again from Baker et al (2019). Agreement can be seen to be good, and this gives further confidence in the use of the methodology in what follows. 

Box 2 Calculation of cross wind characteristics
Figure 2 Cross wind characteristic curve fit (from Baker eta al, 2019)

Box 2 also indicates how the accident risk can be calculated for a specific reference site using the Weibull distribution to specify wind speed probabilities. We assume a section of railway of a specified length, with specified values of the Weibull parameters and a typical service pattern, and we then express the CWCs as a plot of train speed against the probability that a wind induced accident will occur in the section, rather than accident wind speed. This enables us to better address the question as to what is a “good” vehicle in cross wind terms, as it will highlight the relative importance in risk terms of different vehicle speed ranges. 

4. Analysis

Figure 3 shows the calculated CWC, expressed as both an accident wind speed plot and as a risk plot, for what we will take as our base case. The parameters for this case are shown in the figure. The plot of accident wind speed against vehicle speed shows a reduction in the former as the latter increases, as would be expected. There is a break in gradient, at the point of transition between the low yaw angle (at high speed) and high yaw angle (at low speed) formulations of Box 2. Figure 2 shows that this is quite typical of the calculated CWCs from Paradot et al (2015). The plot of site risk against vehicle speed shows an increase in risk with the vehicle speed. At the vehicle speed of 350 km/hr the logarithmic risk is around -8 (but remember that this absolute value is completely arbitrary). The risk falls by an order of magnitude as the speed decreases through the low yaw angle range to around 100 km/h, with an increased rate of fall for low speeds, where the high yaw angle formulation becomes relevant.

Figure 3 CWC for base case

Figures 4 to 7 show the effect on the CWCs of changing the parameters for the moment characteristics. As the low yaw angle exponent n1 is varied between 1.3 and 1.7, there are variations of about half a magnitude in risk for the higher train velocities, although this varies through the speed range. This parameter is typically around 0.9 to 1.1 for lorries, 1.2 to 1.4 for blunt nosed trains, 1.4 to 1.6 for streamlined trains, and 1.7 to 2.0 for trailing vehicles. As the high yaw angle exponent n2 is varied, the variations in accident velocity and risk are confined to the low speed range as would be expected, although here the variations in risk can be several orders of magnitude. As the lee rail rolling moment coefficient at 30 degrees is varied between 3 and 5, there can be seen to be very significant variations in both accident wind speeds and risk throughout the speed range. For variations in the lee rail rolling moment coefficient at 90 degrees only the low speed accident wind speeds and risk levels are affected as would be expected. From these graphs it can be concluded that the risk of an overturning accident will be reduced for high vehicle speeds if n1 increases and the lee rail rolling moment coefficient at 30 degrees decreases; and for low vehicle speeds if n2 increases (becomes less negative) and the lee rail rolling moment at 90 degrees decreases. Of the parameters the 30 degree coefficient produces most change in accident wind speeds and risk levels across the speed range, and is perhaps where most design effort should be concentrated.

Now let us consider specific trains. Table 1 shows, for the CEN (2018) and AeroTRAIN (Paradot et al, 2015) trains, the maximum train speed, the values of the four parameters that define the rolling moment characteristic, the characteristic velocity, and the risk at the maximum operating speed. Those shaded red indicate values that would increase risk significantly above the average, and those shown in green indicate values that would decrease risk significantly the average. It can be seen that of these trains the ICE3, IR and Silbering has the “best” values of rolling moment coefficient. For the ICE3 this is presumably due to the nose shape, resulting in low levels of lift and side force, and thus rolling moment. For the IR and Silberling however, these low values are due to the lack of underbody blockage, at least as modelled in the wind tunnel tests. The ICE3 values of n1 and n2 are around the average, whilst those for the IR and Silberling are low, and would thus tend to increase risk. The worst train in terms of rolling moment coefficients is the double deck M6BX. The IC4, RevCo and ZTER also have high values of the coefficient at 90 degrees.

Table 1 Performance of a range of trains

The risk at the maximum speed for the all trains, with one exception, is between -7.3 and -8.4 i.e. it varies by one order of magnitude. The ICE3, TGV, ZTER and IR have the lowest risk and the M6BX the highest for the standard site. This risk variation is perhaps less than would be expected, and is partly caused by the reduction in risk with the reduction in maximum operating speed. The outlier from the range of -7.3 to -8.4 is the ADR, which has a low value of -9.1, which is due to its high mass and high resulting characteristic velocity. 

Concluding remarks

From the above, it can be seen that for high speed trains, the aerodynamic parameter that most affects the overturning risk is the lee rail rolling moment coefficient in the low yaw angle range, characterised by the value at 30 degrees. In these terms the ICE3 shape is “best”. However this does not necessarily apply for lower speeds, when the higher yaw angle range becomes of importance. These points being made there are some important caveats.

  • The overturning wind speed and thus accident risk depends upon a range of parameters as well as the aerodynamic characteristics. Train mass is particularly important.
  • Similarly the infrastructure characteristics are important, and accident wind speed and risk will be affected by can’t and topography.
  • Perhaps most importantly, the level of risk is determined by the nature of the train operation itself – if speed limits are imposed in high winds, it is quite possible that the most important aerodynamic characteristics will move from those in the low yaw angle range to those in the high yaw angle range.

One further point is of interest. In Baker at al (2019) the head pressure pulse magnitudes and wake slipstream gust velocities are tabulated for orange of trains. Of those trains included, the Velaro (i.e. the ICE3) has both the lowest pressure pulse magnitude and the lowest slipstream gust velocities, suggesting that the nose / tail shape of this train has considerable aerodynamic advantages.

References

Baker, C., Johnson, T., Flynn, D., Hemida, H., Quinn, A., Soper, D., Sterling, M. (2019) Train Aerodynamics – Fundamental and Applications, Elsevier.

CEN, 2018. Railway applications — Aerodynamics — Part 6: Requirements and test procedures for cross wind assessment. EN 14067-6:2018. 

Paradot, N., Gregoire, R., Stiepel, M., Blanco, A., Sima, M. et al., 2015. Crosswind sensitivity assessment of a representative Europe-wide range of conventional vehicles. Proceedings of the Institution of Mechanical Engineers. Part F Journal of Rail and Rapid Transit 229 (6), 594-624.