Wind speed measurements using distributed fiber optics: a windtunnel study

Ramshorst, Justus van; Coenders-Gerrits, Miriam; Schilperoort, Bart; Wiel, B.J.H. van de; Izett, Jonathan G.; Selker, John S.; Higgins, C. W.; Savenije, Hubert; Giesen, Nick van de

doi:10.5194/amt-2019-63

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…Convolving the FODS wind direction observations along orthogonal directions into a fully three dimensional wind field is a substantial challenge and beyond the scope of this proof-of-concept study. The angle of attack of the mean wind direction along the fiber will influence of the wind direction signal, similar to issues with observing wind speed with FODS (Sayde et al, 2015;Pfister et al, 2019;Ramshorst et al, 2019). Exploring the effect of wind attack angle was not possible given the size and aspect ratio of the wind tunnel used in this study.…”

Section: Remaining Questions and Future Workmentioning

confidence: 99%

“…This temperature dependency can be used to observe air temperature directly. Wind magnitude orthogonal to the cable can be observed using the temperature difference between an active, resistively-heated cable and a paired, unheated cable, similar to the principle of hot-wire anemometery (Sayde et al, 2015;Ramshorst et al, 2019). Actively heating the cable causes it to be warmer than the atmosphere, thus the convective heat flux cools the heated cable: stronger winds cause 2 https://doi.org/10.5194/amt-2019-188 Preprint.…”

Section: Motivating the Microstructure Approachmentioning

confidence: 99%

“…The ability to observe spatially-distributed wind direction, in addition to wind speed and temperature, at a fine spatial and temporal scale near the surface, would be a powerful technique for studying atmospheric turbulence. However, prior work has only been able to observe the magnitude of wind speed normal to the fiber, not the direction (Pfister et al, 2019;Sayde et al, 2015;Ramshorst et al, 2019). The approach for observing flow direction with FODS explored in this study is based upon the hypothesis that microstructures with opposite orientations placed on paired, actively-heated fiber-optic cables impart a directionally-sensitive convective heat loss (section 2.1).…”

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confidence: 99%

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Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables

Lapo¹,

Freundorfer²,

Pfister³

et al. 2019

Preprint

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Abstract. The weak-wind boundary layer is characterized by turbulent and submeso-scale motions that break the assumptions necessary for using traditional eddy covariance observations such as horizontal homogeneity and stationarity, motivating the need for an observational system that allows spatially resolving measurements of atmospheric flows near the surface. Fiber-Optic Distributed Sensing (FODS) potentially opens the door to observing a wide-range of atmospheric processes on a spatially distributed basis and to date has been used to resolve the turbulent fields of air temperature and wind speed on scales of second and decimeters. Here we report on progress developing a FODS technique for observing spatially distributed wind direction. We affixed microstructures shaped as cones to actively-heated fiber-optic cables with opposing orientations to impose directionally-sensitive convective heat fluxes from the fiber-optic cable to the air, leading to a difference in sensed temperature that depends on the wind direction. We demonstrate the behavior of a range of microstructure parameters including aspect ratio, spacing, and size and develop a simple deterministic model to explain the temperature differences as a function of wind speed. The mechanism behind the directionally-sensitive heat loss is explored using Computational Fluid Dynamics simulations and infrared images of the cone-fiber system. While the results presented here are only relevant for observing wind direction along one dimension it is an important step towards the ultimate goal of a full three-dimensional, distributed flow sensor.

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Section: Remaining Questions and Future Workmentioning

confidence: 99%

Section: Motivating the Microstructure Approachmentioning

confidence: 99%

mentioning

confidence: 99%

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Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables

Lapo¹,

Freundorfer²,

Pfister³

et al. 2019

Preprint

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“…The application of DTS is of great value to characterize thermal dynamics at a scale that corresponds to, for example, many geophysical processes. Heat tracer tests with DTS have been used to estimate wind speed [3,4], evaporation [5,6], soil moisture [7], groundwater-surface water interaction [1,8], and groundwater flow [9,10]. The specification sheets of DTS measurement systems list the uncertainty in the temperature estimated under ideal conditions.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing

Tombe

Schilperoort

Bakker

2020

Sensors

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Distributed temperature sensing (DTS) systems can be used to estimate the temperature along optic fibers of several kilometers at a sub-meter interval. DTS systems function by shooting laser pulses through a fiber and measuring its backscatter intensity at two distinct wavelengths in the Raman spectrum. The scattering-loss coefficients for these wavelengths are temperature-dependent, so that the temperature along the fiber can be estimated using calibration to fiber sections with a known temperature. A new calibration approach is developed that allows for an estimate of the uncertainty of the estimated temperature, which varies along the fiber and with time. The uncertainty is a result of the noise from the detectors and the uncertainty in the calibrated parameters that relate the backscatter intensity to temperature. Estimation of the confidence interval of the temperature requires an estimate of the distribution of the noise from the detectors and an estimate of the multi-variate distribution of the parameters. Both distributions are propagated with Monte Carlo sampling to approximate the probability density function of the estimated temperature, which is different at each point along the fiber and varies over time. Various summarizing statistics are computed from the approximate probability density function, such as the confidence intervals and the standard uncertainty (the estimated standard deviation) of the estimated temperature. An example is presented to demonstrate the approach and to assess the reasonableness of the estimated confidence intervals. The approach is implemented in the open-source Python package "dtscalibration".Sensors 2020, 20, 2235 2 of 21 scattering), but a small fraction has different wavelengths (Raman scattering). The detectors in DTS systems measure the intensity of the backscatter at two distinct wavelengths: Stokes (-Raman) and anti-Stokes (-Raman) scatter. The temperature at the point of reflection is estimated from these two types of scatter. Stokes scatter has a longer wavelength than the laser and its intensity does not vary much with temperature, while anti-Stokes scatter has a shorter wavelength than the laser and its intensity varies significantly with temperature. The location of the measurement along the fiber is estimated from the time between sending the laser pulse and receiving the scatter. Temperature along the fiber is estimated from the measured intensities of the Stokes and anti-Stokes scatter by calibrating to reference sections with a known temperature. In practice, these fiber sections are submerged in water baths of which the temperature is continuously measured with a separate temperature sensor. The water can be mixed with small pumps in an attempt to equalize the temperature of the water in the baths. Sequential temperature measurements require continuous calibration due to varying gains and losses in the DTS system. Detailed calibration procedures are available in the literature [11][12][13][14]. The uncertainty in the temperature estimates is strongly aff...

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“…2015; van Ramshorst et al. 2019). It has even been combined with unmanned aerial vehicle technology to observe the morning boundary-layer transition from stable to unstable conditions (Higgins et al.…”

Section: Introductionmentioning

confidence: 99%

Missed Fog?

Izett

Schilperoort

Coenders-Gerrits

et al. 2019

Boundary-Layer Meteorol

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Conventional in situ observations of meteorological variables are restricted to a limited number of levels near the surface, with the lowest observation often made around 1-m height. This can result in missed observations of both shallow fog, and the initial growth stage of thicker fog layers. At the same time, numerical experiments have demonstrated the need for high vertical grid resolution in the near-surface layer to accurately simulate the onset of fog; this requires correspondingly high-resolution observational data for validation. A two-week field campaign was conducted in November 2017 at the Cabauw Experimental Site for Atmospheric Research (CESAR) in the Netherlands. The aim was to observe the growth of shallow fog layers and assess the possibility of obtaining very high-resolution observations near the surface during fog events. Temperature and relative humidity were measured at centimetre resolution in the lowest 7 m using distributed temperature sensing. Further, a novel approach was employed to estimate visibility in the lowest 2.5 m using a camera and an extended light source. These observations were supplemented by the existing conventional sensors at the site, including those along a 200-m tall tower. Comparison between the increased-resolution observations and their conventional counterparts show the errors to be small, giving confidence in the reliability of the techniques. The increased resolution of the observations subsequently allows for detailed investigations of fog growth and evolution. This includes the observation of large temperature inversions in the lowest metre (up to 5 K) and corresponding regions of (super)saturation where the fog formed. Throughout the two-week observation period, fog was observed twice at the conventional sensor height of 2.0 m. Two additional low-visibility events were observed in the lowest 0–0.5 m using the camera-based observations, but were missed by the conventional sensors. The camera observations also showed the growth of shallow radiation fog, forming in the lowest 0.5 m as early as two hours before it was observed at the conventional height of 2 m.

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Wind speed measurements using distributed fiber optics: a windtunnel study

Cited by 7 publications

References 25 publications

Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables

Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables

Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing

Missed Fog?

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