Laser ranging measurements of turbulent water surface

Gašper Rak1, Franc Steinman1, Marko Hočevar2*, Matevž Dular2, Matija Jezeršek2 and Urban Pavlovčič2

ISIMet 2

International

Symposium on

Image based

Metrology

Hawaii, Maui

December 16-21 2017

Abstract

Laser ranging is a measurement method, applied in wide range of applications. We use

laser ranging for measurements of water surface of turbulent flows. Measurements were

performed in three cross sections on the confluence of the main and tributary flow. Both

flows exhibited high Reynolds and Froude numbers where free - water surface profiles were

turbulent, non - stationary and non - homogeneous. A commercial LIDAR and fast camera

were used for measurements. The fast camera operated on the principle of laser

triangulation, using only illumination provided by the LIDAR laser beam. While we cannot

compare measurement results with state of the art measurements, results show a good

agreement between both methods. Average turbulent water profile measurements agree

very well, instantaneous profiles however agree less. We present an explanation for the

agreement of measurement results.

Keywords

LIDAR — laser triangulation — free surface — turbulence — non-stationary surface — fast camera

1 University of Ljubljana, Faculty of civil and geodetic engineering, Ljubljana,Slovenia 2 University of Ljubljana, Faculty of mechanical engineering, Ljubljana,Slovenia

*Corresponding author: gasper.rak@fgg.uni-lj.si

1. Introduction

Water surface measurements of turbulent free surface

flows are an important part of hydraulic measurements.

Such flows are encountered in a wide range of applications

in civil, chemical, environmental, mechanical, mining and

nuclear engineering etc. In turbulent free surface flows, air

bubble entrainment is the result of the deformation of the

surface. When the turbulent shear stress is greater than the

surface tension stress that resists the interfacial breakup,

bubble entrainment is possible. Water - air interface has

been studied experimentally, numerically and theoretically

over last few decades, overview was provided by H.

Chanson in [1].

Distance measurements to various objects are often

conducted with one of laser ranging methods due to their

inherent noncontacts and high speed capabilities. Laser

ranging is based on principles of interferometry,

triangulation or time of flight [2]. We will consider here the

latter two principles. Within time of flight measurements

principle, LIDAR technology is one of the most widely used

and promising remote sensing technologies [3; 4]. LIDAR is

composed of a laser source, scanner, optics, photodetector

and receiver electronics. LIDAR robustness and versatile

use is reflected in a number of completely different fields of

science and engineering, among them agriculture,

archaeology, surveying, autonomous vehicles, robotics,

military, atmospheric remote sensing, meteorology, etc.

LIDAR method is however not often used for water surface

measurements. Blenkinsopp et al. [5] used LIDAR to

measure the time - varying free - surface profile across the

swash zone. The water surface in the swash zone’s area

exhibit bubbles on the surface, which increases the

probability of diffuse reflections and hence measurements

with LIDAR. The results show good agreement with

measurements using ultrasonic sensors. Allis and

Blenkinsopp have independently applied the method for

laboratory profile measurements of time - varying free -

surface of propagating waves [6; 7]. Some authors use

LIDAR for to measure water surface in connection with the

bathymetry. Recently the influence of ocean wave patterns

on 3D underwater point coordinate accuracy for LIDAR

bathymetry was investigated by Westfeld et al. [8].

Laser triangulation measurements of surfaces are common

[2], but work related to triangulation measurements of water

surface is limited and almost non - existing for highly

turbulent and aerated flows. Mulsow et al. [9; 10] system

used a modified triangulation method capable of

measurement of reflected laser line projection. Other optical

methods [11] such as particle image velocimetry and stereo

vision photogrammetry or acoustic methods like Doppler

velocimetry are more common, but fail to provide

measurements for highly aerated turbulent flows.

Conventional methods are still most commonly used for

measuring of water levels, such as resistance - type probes

[12], U - manometers [13], point gauges, ultrasonic sensors

[14], etc.

In this study we are presenting measurements of turbulent

water surface of highly aerated flows at high Reynolds and

Froude numbers. By simultaneous using of LIDAR and laser

triangulation fast camera we provide deeper understanding

of how the laser beam is scattered on the turbulent and

aerated open surface and how both methods interpret the

data. Comparison of both measurement results is shown

while also interpretation of measurement results is provided

in the discussion section.

1.1 Flow at the confluence

The confluences occur in streams (natural and artificial river

channels, torrents, etc.), as well as in various types of

facilities and infrastructure (fish ways, drainage of surface

water from impervious surfaces, etc.). At confluences,

especially ones with incoming supercritical flows, a

distinctively three - dimensional flow of water is formed.

Surface profiles exhibit an non - stationary and non -

homogeneous profile of turbulent free - water surface. The

non - stationary structure of the water flow forms in the

transversal and longitudinal directions. Previous studies

[12; 15; 16] have largely neglected measurements of

turbulent water surface profiles in the transversal direction

of a supercritical flow channel’s confluence. One of the

reasons for this are limitations of conventional

measurement methods (piezometers, ultrasonic sensors,

point gauges, etc.), which do not allow for dynamic

measurements with high spatial resolution.

1.2 Laser ranging measurements of turbulent water

surface

In comparison with other measurement methods, the laser

ranging of turbulent water surfaces is limited due to

occurrences of specular reflections. In such cases the laser

beam is in most cases reflected away from the sensor and

measurement fails. Contrary the diffuse surface reflects at

least some laser light back to the photodetector and

distance can be measured provided that the intensity of

reflected laser light is above the threshold.

The use of laser ranging methods for measurements of non

- stationary and non - homogeneous open surface flows

presents several challenges; among them are successive

specular reflections, non - equidistant sampling,

thresholding of returned laser light, problems at channel

walls, etc. We estimate that the specular reflection of the

LIDAR beam from the air/water interface represents the

most important issue of the laser ranging measurements.

Refraction effects of the LIDAR pulse passing the air/water

and water/air interfaces have to be taken into account. This

includes the consideration of the reduced velocity of laser

light in water, being approximately 75 % of the velocity in

air. The water surface of highly turbulent open surface

flows, similar to the confluence flow used as an example in

this study, is difficult to determine (Figure 1). It is abundant

with droplets flying above the surface (overestimating the

surface height), numerous steep waves (preventing

measurements of concave surfaces), entrapped bubbles

below the surface (underestimating the surface height),

presence of foam (increasing the measurement

uncertainty), etc. The surface is also spatially very non -

homogenous and non - stationary in time.

In our experiment, besides being essential for LIDAR

measurements, the same LIDAR beam was used for laser

triangulation measurements using fast camera. This in part

explains the very good agreement between both laser

ranging methods, as we will show later. We assume that in

clean tap water of the confluence all laser beam reflections

form on the water/air interface and that they are all

specular. This assumption is justified by a very limited

number of impurities in the clean tap water. Due to a very

high turbulent nature of the confluence open surface flow,

the laser beam may be reflected many times, before it

reaches laser scanner or fast camera. Fast camera images

show regions of such successive reflections as bright pixels.

Although individual reflections are all specular and laser light

reflected to the fast camera clearly cannot reach laser

scanner receiver (and vice versa), we propose to look into

multiple consecutive reflections as a statistical process. In

such way, a high number of consecutive specular reflections

may be regarded as if they would exhibit behaviour of near -

diffuse reflections. The near - diffuse reflection is similar to

real diffuse reflections, although on a much larger spatial

scale [17]. For real diffuse reflections, the most general

mechanism by which a surface gives diffuse reflection, most

of the scattered light is contributed by scattering centres

beneath the surface [17], involving series of consecutive

multiple partial reflections. The near - diffuse reflections are

in our experimental configuration detected by LIDAR and

laser triangulation fast camera, although both sensors are

located in separate locations.

2. Measurements

Measurements were performed on the measuring station

shown in Figure 2. The measuring station is an open channel

confluence, arranged as usual for hydraulic model

experiments [12; 15]. Main flow and tributary flow are

arranged at an angle 90 ° using sharp edges. The length of

the main flow channel is 6 m, while the lengths of main and

tributary channel upstream of the confluence are 1 m.

Channel width is 0.5 m for both main and tributary flow. All

sides are rectangular and made from glass. The bottom of

the measuring station is horizontal. All joints were carefully

manufactured to avoid local flow separation.

A clean tap water was used for the experiment. Water was

provided by a centrifugal pump, filling the constant hydraulic

head reservoir. From the constant hydraulic head reservoir,

water flowed by gravity to the measuring station shown in

Figure 2. Water volume flow rate was measured for both

channels using electromagnetic flowmeters. ABB

Watermaster flowmeters were used. The selected volume

flow rates were set using valves while flaps were used to

select height of main and tributary flows. The flaps were

mounted at the outflow from pressure vessels. After the

outflow from the measuring station, water flowed into the

lower reservoir.

Unlike other authors [e.g. 6; 7], who applied laser scanning

to time - varying free - water surface profiles of wave

propagation in a wave flume, no additives were added to

improve reflection in our study.

1.1 Measuring equipment

Measuring equipment consisted of a commercial LIDAR

device, a fast laser triangulation camera and a secondary

camera. Measuring equipment installation is shown in Figure

2.

Laser ranging measurements of turbulent water surface— 2

Figure 1. Specular reflections at the turbulent water surface.

Figure 2. Measuring station

The LIDAR was mounted perpendicularly above and in the

middle of the measuring station channel 1 m above the

channel bottom wall. The LIDAR Sick LMS400 was used. It

operated with scanning frequency 270 Hz and angular

resolution 0.2 °. The LIDAR Sick LMS400 operates in

visible red light with wavelength λ = 650 nm. According to

the data provided by the producer, the systematic

measurement uncertainty is ± 4 mm and statistical

measurement uncertainty is ± 3 mm. The beam diameter is

1 mm.

The LIDAR data were transmitted to the measuring

computer by an Ethernet connection. A dedicated driver

and communication software was written in National

Instruments software package Labview, enabling reliable

LIDAR data acquisition and storage to local disk with no

lost measurement samples. The LIDAR was calibrated

before measurements with a strip of white paper, attached

to the bottom of the channel.

Laser triangulation method was composed of a fast camera

mounted on the downstream of the channel and a laser

scanning projector provided solely by the existing LIDAR

(Figure 2). The camera’s angle was set at 35 °, pointing

downwards and upstream. We have used the fast camera

Photron SA - Z. The camera was operating at a framerate

100000 frames/s at a resolution 640 x 280 pixels. Every

recorded fast camera image corresponded approximately to

a single LIDAR distance measurement with angular

resolution 0.2 °. The lens used for the fast camera was F -

mount 28 mm f / 1.8 G Nikkor. The red filter with cut - off

wavelength at 600 nm was mounted on the lens, with the

purpose to filter out the blue light used by secondary camera.

The calibration procedure for the triangulation method was

performed according to Jezeršek and Možina [18].

The secondary camera was oriented normal to the water

Laser ranging measurements of turbulent water surface— 3

surface in order to get visual information about the flow at

the measurement location. The secondary camera used

was a black and white Fastec Hispec 4. The diffuse

illumination for the secondary camera consisted of a large

number of LEDs with total power around 100 W at

wavelength of 465 nm. The secondary camera used C -

mount lens with 35 mm focal length and recorded images

with frequency 270 Hz, which was equal to the scanning

rate of LIDAR. The image resolution was 1696 x 360 pixel

and the exposure time was 3.7 µs. The secondary camera

detected both lights, i.e. blue LED and red LIDAR laser

beam.

Measurements duration in every measurement cross

section was 2 s. All measurement equipment was

synchronised using a mechanical shutter. The mechanical

shutter was mounted above the measuring station channel,

between the water surface and LIDAR with the secondary

camera. Before the acquisition, the shutter was closed,

such that the laser beam from LIDAR was cut and the laser

triangulation fast camera images were dark. Then, LIDAR,

fast camera and secondary camera were started. Shutter

opening enabled acquisition and recording of all three

sensors simultaneously.

The entire experiment was performed in dark environment. 1.2 Selection of operating point and

measuring positions

A single operating point was selected for analysis as shown

in Table 1. Measurements were performed in three

measurement cross sections (CS1 - CS3) as shown in

Figure 3. Measurement cross sections were located 900 mm

(CS1), 1100 mm (CS2) and 1300 mm (CS3) downstream

from the confluence.

Reynolds number evaluates the ratio of inertial forces to

viscous forces within a fluid. Reynolds numbers Re were

calculated as

vhRe

where is water density, v is water velocity, h is water

height and is water viscosity. Froude number evaluates

the ratio of the flow inertia to the external gravity field, Froude numbers Fr were calculated as

gh

vFr

where g is gravitational acceleration.

Table 1: Selection of the operating point.

variable value

main flow flow rate [l/s] 35.5

tributary flow flow rate [l/s] 26.6

main flow height [m] 0.02

tributary flow height [m] 0.02

main flow Re [-] 7.1x104

tributary flow Re [-] 5.3x104

main flow Fr [-] 8

tributary flow Fr [-] 6

Consecutive sample images of the confluence flow in the

selected operating point are shown in Figure 4. Images show a

very high turbulent nature of the flow.

Figure 3. Dimensions of the confluence and locations of measuring cross sections CS1, CS2 and CS3. All measurements

are in mm.

Images in Figure 4 were recorded in a separate experiment

with secondary camera, moved to an approximate location of

laser triangulation fast camera (Figure 2). For this separate

experiment framerate 50 Hz, shutter 500 µs and additional

blue LED illumination for the entire downstream section of

the confluence were used.

Figure 4. Consecutive sample images of the confluence flow recorded in a separate experiment with secondary camera, time interval

among consecutive images in the above case was 0.02 s.

Laser ranging measurements of turbulent water surface— 4

3. Measurements data analysis procedure The LIDAR measurements data consists of a set of

angle/distance pairs in a sensor’s radial coordinate system.

Each distance measurement was converted into coordinates

of the local two - dimensional Cartesian coordinate system.

To enable comparison with results of laser triangulation

measurements, a linear interpolation between consecutive

measurement points was performed in order to create an

equidistant grid of data in Cartesian coordinate system.

The laser triangulation fast camera images were analysed

using algorithms, developed in LabView and C++

programming languages. The images were first filtered by

Gaussian filter (Figure 5).

Figure 5. Image analysis for laser triangulation method.

Above: original image, below: processed image. Red square

marks the target detected by the laser triangulation method.

Dimensions of the filter kernel were 4.5 % of the image size

in horizontal and 1.7 % in the vertical direction. That is how the

intensities of specular reflections from multiple neighbouring

locations were merged together. Then the location of reflected

laser light was determined as the location of the maximal

intensity on the image. Finally, the location of the point in the

3D space was reconstructed by triangulation principle [18].

Measurements by LIDAR and laser triangulation were

synchronised in time by detecting and deletion of that signals,

where mechanical shutter was visible.

4. Results and discussion Results of measurements of average turbulent water surface

heights and corresponding standard deviations are shown in

Figure 6. Measurement results in CS2 and CS3 show

remarkably good agreement between both laser ranging

measurements. Both measurements agree to around ± 10 mm

in the channel cross section, except for the regions near the

walls. In average the LIDAR measures water level at slightly

deeper locations (see blue line on Figure 6). Such agreement

was achieved for every average point of the cross section.

Agreement between both methods is slightly worse for CS1,

where deviation is within ± 25 mm. This is emphasised on the

left side of the channel, approximately up to 300 mm from left

wall. Sample image of the secondary camera on the right of

Figure 6 for CS1 reveal high flow dynamics and turbulence in

this region.

Measurement results near the channel walls exhibit higher

measurement uncertainty in CS1 in comparison with central

region. The wall region, affected by such behaviour, extends

to around 50 mm from both walls. The localisation near walls

was better for cross sections CS2 and CS3, featuring less flow

dynamics than CS1. Due to poorer spatial localisation of

triangulation method in comparison with LIDAR method, we

have in Figure 6 omitted results near both walls for 50 mm

intervals for triangulation method and for 25 mm intervals for

LIDAR method. We will discuss localisation issues of both

methods later. Here, additional reflections from glass walls

were present, influencing performance of laser triangulation

fast camera detection, image analysis algorithm and LIDAR

internal algorithm.

Figure 6. Results of measurements of average turbulent water surface heights (left) and corresponding sample images of the

recorded sequence with secondary camera (right). Results are shown for LIDAR (blue) and triangulation method (red). Above: CS1, middle: CS2 and below: CS3.

Laser ranging measurements of turbulent water surface— 5

Figure 7. Samples of instantaneous turbulent water surface heights. Blue: LIDAR and red: triangulation method. Above: CS1,

middle: CS2 and below: CS3.

Samples of instantaneous turbulent water surface

profiles are shown in Figure 7. We can notice, that both

laser ranging methods failed to provide valid

measurements for all measurement positions as

described in section 3 "Analysis". Amount of rejected

measurements was 14.6 % for CS1, 14.9 % for CS2 and

13.2 % for CS3 for LIDAR and 12.2 % for CS1, 7.2 % for

CS2 and 9.2 % for CS3 for laser triangulation method.

The amount of rejected measurements provides us with

estimation, how the laser light originating from

successive reflections at different air / water boundaries

such as droplets, bubbles and waves is detected by

LIDAR and laser triangulation fast camera. In the

following we will provide explanation why these values

do not agree for both laser ranging methods, as one

may guess due to the fact, that both operate using the

same laser beam from the LIDAR.

The cross sections differ among each other on the

amount of the turbulence of the surface. The CS1 has

more turbulent surface, because it is located nearer to

the confluence as CS2 and CS3. The cross section CS3

is located the farthest downstream from the confluence

and features the least turbulent surface. We did not

notice a significant influence of the surface turbulence

on the amount of rejected measurements for both

methods. We have observed that LIDAR method

expressed more rejected measurements than laser

triangulation method. However, the number of rejected

measurements is heavily influenced by the settings of

the fast camera image processing algorithm, while we

were unable to influence the LIDAR algorithm.

The laser beam of LIDAR is sent and received at the

same angle of mirror rotation. Because of very short

time required for the laser pulse to travel from, reflect

and return back to the LIDAR, the turbulent water

surface is measured as perfectly stationary. Only the

laser light returning from the point of direct impact and

narrow spatial angular section on the radius vector from

the LIDAR to the impact point is detected by the

LIDAR’s photodetector (in Figure 1 shown as dark yellow

part of the LIDAR measurement cross section). The

reflected beams originating from other positions are

therefore rejected.

Reflections of the laser beam to the fast camera are

recorded in a different way. In a single recorded image,

fast camera lens collects all reflected beams, heading in

the direction of the lens. These reflected beams originate

from a larger volume, being illuminated through

successive specular reflections of the water surface.

Therefore the laser triangulation method offers slightly

poorer localisation in comparison with LIDAR ranging.

Fast camera ability to record weak reflected beams is

compromised by the need of high image acquisition

frequency and very short image integration time, leading

to high noise and low image quality.

Direct comparison of single measured points between

LIDAR and laser triangulation shows Figure 8. Three

manually selected typical images recorded by the fast camera

are shown. Red circles show points, detected by LIDAR and

green circles show points measured by laser triangulation

method. Successive specular reflections are clearly visible

on those images as a cloud of multiple dark spots. We can see

that laser triangulation detects approximate centre of brightest

point cloud. Notably, measurements provided by LIDAR are not

in exactly the same locations as the centres of intensities of

reflections (see also Figure 1). The difference is however not

large. We estimate that mismatch is caused by the

difference of observation positions. The reflected beams,

returning to the LIDAR are reflected from different

position than those reflected to the laser triangulation fast

camera. This observation confirms our assumption, that

specular reflections are the most important issue of the

laser ranging measurements.

Current algorithm of the laser triangulation turbulent water

surface detection is based on finding brightest area which

originates from laser beam scattering (see section 4

“Analysis”). Since the image smoothing (Gaussian filter)

is performed before maximum search, it’s filter width has

Laser ranging measurements of turbulent water surface— 6

a major impact on result of detected point. Wider the

filter is, more robust the detection is, but also the spatial

resolution is smaller. So, the selected parameters of the

laser triangulation method represent a compromise

between resolution and spatial noise reduction.

Average intensities of recorded laser triangulation fast

camera images are shown in Figure 9 for all three

measured cross sections. Purely for the sake of clear

demonstration, the averaging was done over 700

acquired fast camera images, which correspond to two

LIDAR scans. Therefore results differ slightly from

results presented in Figure 6, due to much shorter

integration time. For instance, a local increase of water

level height for CS3 on the right is a consequence of

non - stationary local flow turbulence. Second important

information on Figure 9 is depiction of average laser

beam scattering through the droplets (see the middle

image), steep waves (left and right sides of middle and

below images) and entrapped bubbles below the

surface (centre top and middle image). All these

features may be regarded as a near - diffuse reflections

as discussed in Section 1.2., where the width of contour

on image corresponds to the average light penetration

depth into the measured turbulent water surface. Thus,

the wider contour corresponds to more dynamic water

behaviour.

Figure 8. Comparison of LIDAR and image triangulation

methods. Shown are three selected samples of instantaneous

fast camera images for the cross section CS2. Red squares

show LIDAR measurements. Blue squares show

measurement results of image triangulation method.

In contrast to triangulation method image analysis

algorithm we have no access to the algorithm of the

LIDAR signal processing, because we have used a

commercial LIDAR device. It is optimised for

measurements of solid surfaces with diffuse surfaces

where single high contrast laser pulse is usually

detected. For the understanding of LIDAR performance,

it would be necessary to evaluate the time series of

returned LIDAR beam intensity. Comparison with

recorded fast camera images would allow optimizing

LIDAR performance for the purpose of water surface

measurements of turbulent free surface flows.

The experimental setup allows comparison of every

single LIDAR measurement (for each scan and angular

position) with a corresponding laser triangulation fast

camera image, recorded at approximately the same time.

However phenomena comparison is limited because laser

triangulation fast camera images do not show the

turbulent water surface from the same location. The

secondary camera provides only qualitative data. To be

able to deeper understand the series of specular

reflections another fast camera should be mounted in the

same location. Without the use of red filter to screen the

blue diffuse illumination for the secondary camera, the

second fast camera would acquire instantaneous

turbulent water surface images, illuminated with the blue

light. In this new configuration, synchronising both fast

cameras and comparison with LIDAR should provide full

understanding of the scattering process and LIDAR

operation, how and when LIDAR detects turbulent water

surface air/water boundary and the roles of flying

droplets, foam or bubbles below surface for turbulent

open flow surface measurements.

Figure 9. Average intensities of acquired laser triangulation fast

camera images sequence. Above: CS1, middle: CS2 and below: CS3.

5. Conclusions

The paper presents the first to us known comparison of

two laser ranging methods for measurements of turbulent

open flows. We have compared LIDAR and laser

triangulation methods for the case of a 90 ° confluence

flow and high Reynolds and Froude numbers. Analysis

Laser ranging measurements of turbulent water surface— 7

was performed such that LIDAR laser beam was at the

same time used also for laser triangulation with the fast

camera and that both measurements methods were

synchronised in time. We have noticed that:

- performance of both laser ranging measurement

methods is largely comparable for measurements of

turbulent open surface height,

- both laser ranging methods agree well in estimation of

average turbulent open surface height,

- methods do not provide same instantaneous height

measurements for every position on the surface profile,

- LIDAR method feature more rejected measurements

for flows with less turbulent surface, while laser

triangulation method in current implementation provides

more rejected measurements for flows with highly

turbulent surface and

- LIDAR method is localized better than laser

triangulation method.

We used slow secondary camera, located near the

LIDAR, to provide qualitative data of the flow. We

propose to use an additional synchronised fast camera

in the future to better understand the roles of flying

droplets, foam and bubbles below surface for LIDAR

and laser triangulation method performance. Such

configuration would also allow for optimizing of LIDAR

algorithm for measurements of water surface

measurements for turbulent free surface flows.

Acknowledgments: The authors acknowledge the

financial support from the state budget by the Slovenian

Research Agency (Programmes No. P2 - 0392, P2 -

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