JSSSJournal of Sensors and Sensor SystemsJSSSJ. Sens. Sens. Syst.2194-878XCopernicus PublicationsGöttingen, Germany10.5194/jsss-7-85-2018Temperature sensing in underground facilities by Raman optical frequency domain reflectometry using fiber-optic communication cablesTemperature sensing in underground facilitiesBrüneMarkusmarkus.bruene@rub.deFurianWilhelmHillWielandPflitschAndreasRuhr-Universität Bochum, Universitätsstraße 150, 44801 Bochum, GermanyHumboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, GermanyNKT Photonics GmbH, Schanzenstraße 39, Building D9–D13, 51063 Cologne, GermanyMarkus Brüne (markus.bruene@rub.de)20February201871859028September201714December20172January2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://jsss.copernicus.org/articles/7/85/2018/jsss-7-85-2018.htmlThe full text article is available as a PDF file from https://jsss.copernicus.org/articles/7/85/2018/jsss-7-85-2018.pdf
Gaining information on climatic conditions in subway tunnels is the key to
predicting the propagation of smoke or toxic gases in these infrastructures
in the case of a fire or a terrorist attack. As anemometer measurements are
not economically suitable, the employment of alternative monitoring methods
is necessary. High-resolution temperature sensing with Raman optical frequency domain reflectometry (OFDR) using
optical communication fiber cables shows great potential as it allows the
surveillance of several kilometers of underground transport facilities
without the need for installing sensing equipment in the tunnels. This paper
presents first results of a study using this approach for monitoring subway
tunnels. In the Berlin subway, temperature data gathered from newly installed
as well as pre-installed communication cables were evaluated and compared to
reference data from temperature loggers. Results are very promising as high
correlations between all data can be achieved showing the potential of this
approach.
Introduction and motivation
Correct climatic boundary conditions are fundamental for achieving relevant
results from computational fluid dynamic (CFD) simulations. This is of
particular importance for predicting the propagation of fire-induced smoke in
underground transport facilities. For instance, the velocity and direction of
airflow in tunnels of a subway system are quite complex. During operation,
running trains push air forward, described as the well-known piston effect
. In the case of a subway fire, train traffic is stopped
immediately, and the background airflow re-establishes within a few minutes
. Knowledge on the flow velocity in the adjacent tunnels is
required to set up valid CFD simulations for predicting the propagation of
smoke and for identifying safe evacuation routes. Gaining the airflow
information from anemometer readings is not suitable. Monitoring each tunnel
mouth of each station in a subway system would lead to a massive application
of anemometers which would incur tremendous costs due to the high number of
devices and especially the required wiring and installation work. Airflow,
however, can be derived from temperature information along the tunnels by
using the distributed temperature sensing (DTS) method, based on the Raman
scattering effect . Subway systems already have a wide
network of optical fiber cables installed, so it is a promising idea to use
those for temperature sensing. This study focuses on stepping forward to
gain airflow information of subway systems for reasonable costs.
Temperature sensing method
The techniques used in this study are based on the method of distributed
temperature sensing (DTS). DTS systems rely on the fact that an optical fiber
can function as a linear sensor as well as a transmission medium. Thus, DTS
devices are an interesting alternative to multiplexed measurement points as
one fiber-optic cable can potentially replace thousands of single sensors and
therefore reduce the costs for installation, maintenance and readout while
simplifying the whole process of data acquisition . Physical
measurement dimensions change the attenuation of light in the fiber in a way
that allows a high-accuracy determination of the location of an external
effect like air temperature . Optical fibers consist
of silica glass (SiO2) waveguides and, thus, are small, light and insensitive to
electromagnetic fields . DTS systems use the Raman
backscattering effect, which depends on temperature . The coherent optical frequency domain reflectometry (OFDR), which
was used in this study, is characterized by high sensitivity and a large
dynamic measurement range. Traditionally, OFDR systems were developed in
reference to Rayleigh scattering, whereas the techniques used in this
study rely on its application for Raman backscatter measurements. OFDR
devices employ lasers in a quasi-continuous wave mode and narrow-band
detection of backscattered photons. Raman scatter light and the complex
signal processing using the fast Fourier transform (FFT)
The OFDR controller
employed in this study uses a three-channel design with an additional
reference channel besides the two channels to measure the above-mentioned
Stokes and anti-Stokes bands . Its laser
continually emits a sinus-shaped frequency, which is connected to the fiber
cable through an optical module. This module also filters the backscattered
Raman light and converts it into electrical signals using photodetectors.
After some transformations, e.g., low-frequency mixing and inverse FFT
, the Raman signal is available as two backscatter curves
with amplitudes proportional to the intensity of the Raman backscattering at
the observed location. As proposed by , the temperature of
the optical fiber then is obtained from the ratio of the two measurement
channels, i.e., the intensities of anti-Stokes and Stokes band. The advantage
of using OFDR-DTS temperatures for obtaining airflow information lies in
replacing thousands of single temperature loggers or cost-intensive
anemometer which needs elaborately wiring for data acquisition and power
supply. Installation work inside operating tunnels might not even require
the use of pre-installed communication cables.
Schematic layout of the used sensor cables: (a) new sensor
cable and (b) pre-installed communication cable.
Setup of measurements, (a) recorded daily mean temperatures by new sensor cables, (b) and pre-installed
communication cables (c) between June 2016 and February 2017. The observed
temperatures of the new sensor cable and the pre-installed communication cable are very similar.
Cold spots are clearly associated with the emergency exits and gratings. Channels 1 and 6 show a
relatively cool tunnel section at about 650 and 700 m, which is related to the above flowing river Panke.
Between 100 and 200 m a warm section was observed in Channels 2 and 3, which is exactly the
location of siding tracks, where trains park during off-peak and cause additional heat transfer.
Inside the station, the cable tray is located underneath the platforms (cooler areas) and inside
utility rooms (warmer areas).
Localization of exact positions (±0.5 m) by using freezing spray (a) and the response
of recorded temperatures of the fiber-optic cable using a measurement configuration with a spatial
sample rate of 1 m (b).
Short time response of temperature changes due to the parking of a train close to the tunnel
walls with different sample rates. The blue lines represent the observed values on the same date
(6 September 2016). The spatial resolution was set to 5 m.
Setup of measurements
Inside the Berlin subway system, a Raman-OFDR DTS measured temperature
profiles along a 1 and a 2 km tunnel route including two
subway stations using a dedicated fiber-optic sensor cable with two optical
fibers with a diameter of 4.1 mm connected to the OFDR controller
device. In addition four fibers of a pre-existing communication cable with up
to 192 fibers with a diameter of 20.1 mm were also used for
temperature sensing (see Fig. ). Channel 2 (new sensor cable) and
Channel 3 (pre-installed communication cable) connect the subway stations
Osloer Straße (Olu) and Residenzstraße (RE), crossing the station
Franz-Neumann-Platz (FN), while Channel 1 (new sensor cable) and Channel 5
(pre-installed communication cable) head south to the station Pankstraße
(Pk).
The sensor cable has installed with a 10 cm gap from the tunnel walls.
As the tunnel walls are mostly cooler than the tunnel air, the heat flux will
have a lowering effect on the measured temperature. The influence will
increase by a smaller distance to the tunnel wall, high temperature
difference between tunnel walls and air, and lower airflow velocity inside
the tunnels. Relative temperature changes are of interest and the changes are
very slow, so this effect can neglected.
The OFDR controller device can only measure one channel at the same time, so
this leads to a temporal repetition rate of 4.5 min. The used
measurement configuration results in a temperature uncertainty of
0.1 ∘C (see Table ). Similar accuracies can achieved by
using PT 100-based sensors. Several stand-alone temperature loggers have also
been installed along the cable to validate the temperature data (see Fig. ). The fiber-optic links have patched at every station leading to
extra losses. This introduced higher demands for the temperature calibration
on each segment. Temperature data were logged at the beginning, termination
and patch link of the cables.
Specifications OFDR measurements during standard measurements.
Controller modelDTS 200 SM ManufacturerLIOS technology Fiber typesingle mode Fiber-optic channels6 Spatial sample rate5 m Repetition rate4.5 min Channels010203040506Cable length (m)950204019601950940930Data points190408392390188186Measurement time (Ch.) (s)306060603030Measurement time per point (ms)158147153154159161Temperature uncertainty (∘C)0.10.10.10.10.10.1Recorded temperatures in the tunnels
Figure also shows the temperatures from June 2016 to February
2017. At a glance, great similarities between the new sensor cable and the
pre-installed communication cable become apparent. Although the data range
does not cover a full year, shifted annual variations could be observed. The
temperature maxima were recorded in October while the minimum occurs in
March. This shifting effect is due to the depths of the tunnels. At closer
look cold spots were observed at several distances from the OFDR controller,
which correspond quite well to the locations of emergency exits and other
openings to the above ground. The sidings north of Osloer Straße are used
to park non-operating trains during the night break, causing the hot area
observable around 100–200 m in Channel 2 and 3.
Sensor calibration and localization of control loggers
In addition to pre-calibration performed by LIOS software, data were
calibrated by the authors using data loggers at positions near the ending
points of the fiber-optic cables. With these reference temperatures, we could
eliminate the effect of differential attenuation between Stokes and
anti-Stokes signals increasing with increasing distance from the controller
module. To assess the measurement quality of the fiber-optic cables, the
exact position in terms of cable meter (±0.5 m) at the location of the
stand-alone loggers must be known. This was achieved by cooling the
fiber-optic cables using freezing spray and using a measurement configuration
with a spatial sample rate of 1 m (see Fig. ).
Response time
As the temperature differences are very low inside the tunnels, a fast
response time in temperature changes is not very important. Figure shows a location where subway trains were parked during off-peak
times, causing a sudden air temperature change of 3 K. The recorded
temperature reflects this within 5 min for quick repetition rates
(23 s) measuring each data point in 56 ms and using a spatial
sample rate of 1 m and up to 20 min for the standard configuration (see
Table ).
Correlations among stand-alone data loggers.
New sensor cable Pre-installed communication cable LoggerChannelCable meterR2ChannelCable meterR2N2Ch018450.981Ch058350.982118 0194Ch015110.984Ch055010.983118 0256Ch013480.979Ch053380.978118 0279Ch021030.989Ch03730.978116 83511Ch024830.98Ch034530.974116 83313Ch027930.985Ch037630.985116 83316Ch0212950.989Ch0312350.984116 82718Ch0216200.985Ch0315600.984116 821
Comparison of measured temperatures by new sensor cable (a) and pre-installed communication
cable (b) with stand-alone control temperature loggers at the same position (top), and their correlations (bottom).
Temperature profile around emergency exit D190. (a) Randomly chosen operational breaks with
observed wind direction towards station Franz-Neumann-Platz, (b) towards Residenzstraße.
Correlations of measurements
Figure provides an overview of air temperature trends during the
measurement period for both used cables types. The graph shows clearly that,
using OFDR and newly installed fiber-optic cables, measuring small-scale
changes in the air temperature leads to results that are quality-wise comparable
to stand-alone logger measurements. Due to the much larger diameter, a higher
attenuation would be expected using the pre-installed cable, but the results
are promising. Table summarizes the correlation between the
stand-alone loggers and the temperature readings from DTS sensing. The
weakest correlation coefficient was found with R2=0.978, so it can be
assumed that using pre-installed cables for OFDR temperature sensing produces
the same results as the new sensor cable does, but it has to be mentioned
that the cable length does not exceed 2 km.
Deriving airflow from temperature profiles
The temperature distribution alongside the OFDR-DTS temperature measurements
should be related to the airflow in a certain tunnel section. The expectations
are that temperature anomalies, which occur naturally inside the subway
tunnels, should be shifted into the direction of airflow. The patterns
observed for the considered section shown in Fig. meet these
expectations. Cooler air falls through the emergency exit D190, located at
cable meter 1490 m, into to the subway tunnels. A number of 50 randomly
chosen situations of airflow direction with increasing and decreasing cable
meters showed a clear shift of the thermal depression towards the direction
of airflow. Furthermore, if an outflow from station Franz-Neumann-Platz is
observed, the steady flow of warmer air can heat up the tunnel until the
emergency exit D190 is reached. If dealing with airflow in the opposite
direction, inflowing cold air moves from D190 to the station, causing an
observable cooling effect.
Conclusions
The comparison of the temperature data from the dedicated fiber-optic sensor
cable and the existing communication cable showed very high correlation with
R2>0.97, thus confirming the eligibility of communication cables for
temperature monitoring (see Fig. 4). Therefore using pre-installed optical
fiber-optic cables for temperature sensing is possible at least up to cable
lengths of some kilometers. Available OFDR devices are able to monitor cable
lengths of up to 30 km, but it remains to be proven which cable lengths can
be reached with pre-installed fiber optics. In addition, future work has to
be done to show which temperature resolution is needed in order to derive the
airflow inside subway tunnels, in particular extending the cable lengths up
to their maxima. Finally, the OFDR temperature sensing using pre-installed
communication cables has the potential to derive airflow in subway systems,
thus generating realistic margin conditions for many stations, which can be
used in numeric simulation for fire scenarios .
We cannot provide any data at this stage. This is due to the research contract with the
subway operator as we are dealing with safety issues. The project is also still running, and we are still analyzing the data for other
research questions, which are quiet sensitive.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Sensor/IRS2 2017”. It is a result of the AMA Conferences, Nuremberg, Germany,
30 May–1 June 2017.
Acknowledgements
This work is part of the research project “Optimization of smoke exhaust and pedestrian management in underground stations: experiments and simulation”
(ORPHEUS) funded by the Federal Ministry of Education and Research (BMBF) – Germany.
We are grateful to the Berlin subway operator Berliner Verkehrsbetriebe (BVG) for supporting our research and allowing us to use their infrastructure.
Edited by: Werner Daum
Reviewed by: three anonymous referees
References
Albani, J. R.: Structure and dynamics of macromolecules: Absorption and
fluorescence studies, edited by: Albani, J. R., Elsevier, Amsterdam,
ISBN 978-0-444-51449-3, 2004.
Brüne, M., Pflitsch, A., Agnew, B., and Spiegel, J.: Dynamics of natural
air flow inside subway tunnels, in: Proceedings from the Fifth
International Symposium on Tunnel Safety and Security, edited by:
Lönnermark, A. and Ingason, H., 1, 329–337, SP Technical
Research Institute of Sweden, Borås and Sweden, 2012.Dakin, J. P., Pratt, D. J., Bibby, G. W., and Ross, J. N.: Distributed optical
fibre Raman temperature sensor using a semiconductor light source and
detector, Electron. Lett., 21, 569–570, 10.1049/el:19850402, 10.1049/el:19850402, 1985.Ghafoori-Shiraz, H. and Okoshi, T.: Optical frequency-domain reflectometery,
Opt. Quant. Electron., 18, 265–272, 10.1007/BF02029871,
1986.Hill, W., Kübler, J., and Fromme, M.: Single-mode distributed temperature
sensing using OFDR, in: (EWOFS'10) Fourth European Workshop on Optical
Fibre Sensors, edited by: Santos, J. L., Culshaw, B., López-Higuera,
J. M., and MacPherson, W. N., SPIE Proceedings, p. 765342, SPIE,
10.1117/12.866246, 2010.Li, W. and Bao, X.: High spatial resolution distributed fiber optic technique
for strain and temperature measurement in concrete structures, in:
International Workshop on Smart Materials and Structures, SHM and NDT for
the Energy Industry,
available at: http://www.ndt.net/article/ndt-canada2013/presentations/26_Li.pdf (last access: 12 February 2018), 2013.
LIOS Technology: DTS Systems – Controller Data Sheet, Datasheet Edn.: 18 July 2014, LIOS Technology
GmbH, 2008.LIOS Technology: Distributed Temperature Sensing,
available at: http://www.lios-tech.com/Menu/Technology/Distributed+Temperature+Sensing (last access: 3 April 2017), 2016.
Schröder, B., Arnold, L., Schmidt, S., Brüne, M., and Meunders, A.:
High parametric CFD-analysis of fire scenarios in underground train stations
using statistical methods and climate modelling, in: 10th International
Conference on Performance-Based Codes and Fire Safety Design Methods, edited
by: SFPE, 2014.Tanner, M. G., Dyer, S. D., Baek, B., Hadfield, R. H., and Nam, S. W.:
High-resolution single-mode fiber-optic distributed Raman sensor for
absolute temperature measurement using superconducting nanowire single-photon
detectors), in: Applied Physics Letters, Melville, NY,
10.1063/1.3656702,
2011.
Valensi, J.: Piston Effects in the Underground Stations of Marseille Metro,
in: The Third International Symposium on the Aerodynamics and Ventilation of
Vehicle Tunnels, edited by: BHRA Fluid Engineering, 1, 47–56,
Cranfield, 1979.