# Artificial Atmospheric Chamber Design to Mimic Turbulence Effect on Optical Wireless Communication Links

Kexuan Zhang, Mojtaba Mansour Abadi

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Kexuan Zhang, Mojtaba Mansour Abadi. Artificial Atmospheric Chamber Design to Mimic Turbulence Effect on Optical Wireless Communication Links. COAT-2023 - workshop (Communications and Observations through Atmospheric Turbulence), Durham University, UK, Mar 2023, Durham, United Kingdom. 10.34693/COAT2023-001 . hal-04217846

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Submitted on 26 Sep 2023

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Artificial Atmospheric Chamber Design to Mimic Turbulence Effect on Optical
Wireless Communication Links

Kexuan Zhang¹, Mojtaba Mansour Abadi²

¹Department of Electrical and Automation Engineering, Nanjing Normal University, 1 Wen Yuan Road
Xian Lin University City, Nanjing, China

²Department of Mathematics, Physics, and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, Newcastle, United Kingdom, email: [mojtaba.mansour@northumbria.ac.uk](mailto:mojtaba.mansour@northumbria.ac.uk)

**Abstract:** In this report, we will present the results of our research on design and simulation of an artificial atmospheric chamber in laboratory environment. The capability to test a free-space optical (FSO) link in a controlled laboratory environment under various atmospheric conditions including turbulence is a valuable asset that both academic and industrial researchers can benefit from. One of the methods that has been used over the last decade to approach that is to create a turbulence effect for terrestrial links by blowing both hot and cold air into a confined environment. The purpose of the chamber is to create turbulence eddies and effectively produce temperature gradient. The gradient will result in variation in refractive index, which affects the beam propagating through the it. One of the key missing aspects of this method is the scalability and reliability which has not been studied before. For instance, there is no clear instructions on how to adjust such a chamber to represent the turbulence in a ground to space communication, or how to achieve stronger turbulence without increasing the length of the chamber. In this report, we will present the methodology we used to design and simulate the turbulence inside the chamber structure using computational fluid dynamic. We will also look at two chamber arrangements to see the effect of chamber structure on produced turbulence.

Introduction

The use of Internet and data network, as well as concept
of being connected to the network have become crucial
part of life and businesses. There is a growing number
of applications, which require access quality to the data
services at any given location and time under all
possible circumstances. The number of smart devices is
increasing fast and the radio frequency (RF) spectrum,
which is already being stretched too thinly, is
experiencing congestion at a global level. Ideally, all
end users should have access to the optical fibre-based
backbone network with an ultra-high capacity, to
benefit from truly high-speed data communications
with an extremely low end-to-end transmission latency.
But it is hard to achieve with the current congested RF
spectrum. Therefore, there is a need to introduce
alternative technologies to complement or replace RF
connections due to its limited bandwidth and the high
cost [1]. At the moment, free-space optical (FSO)
communication has been used to provide high-speed
data service for aforementioned applications [2]. The
FSO technology has advantages of high-data rate,

license-free spectrum, low-power consumption, low
cost, high security, and backbone network compatibility.

However, FSO cannot provide 100% link availability
and reliability under turbulence induced fading,
atmospheric loss, pointing errors induced fading, link
blockage or ambient noise.[3] The turbulence caused by
temperature gradient, long distance, air pressure
variation, and wind will generate swirling regions called
eddies that affect the refractive index of the
communication channel [4]. Due to random nature of
the turbulence, the refractive index along the
propagation path changes randomly, which
consequently causes the beam to experience random
reflection and refraction [5]. This results in a beam
which does not have a well-defined phase profile as the
original beam.

Apart from actual field measurement, one way of
understanding turbulence is to create it in the laboratory
environment and study its effect on any given optical
link. There are three common ways to generate artificial
turbulence in the laboratories including turbulence
phase plates [6], spatial-light modulator-based
turbulence [7], and atmospheric chamber [8]. Out of
these three methods, the atmospheric chamber option is
<figure><img src="image_2.png" /><figcaption>Figure 1: Block diagram of a FSO system being tested in the atmospheric chamber.</figcaption></figure>

the most cost effective and flexible method. It requires cheap and easily accessible hardware and can be adjusted based on the given specification and requirements. However, the atmospheric chamber method offers the minimum control over the generated turbulence and the strength of the turbulence is decided by the channel state measurements and estimation or assumption made based on the available turbulence models. Therefore, there is a need to decide the properties of generated turbulence in an atmospheric chamber and investigate the possible methods to adjust them. The turbulence phenomena are a part of fluid dynamics and particularly they are governed by Navier–Stokes equations, which have no exact analytical solution. Thus, computation fluid dynamics (CFD) is used instead to study the fluid mechanics [9].

In this report, we intend to look at simulating an atmospheric chamber using OpenFOAM package tool and investigate two possible arrangements of the chamber and their turbulence performances [10].

## Atmospheric Chamber

The typical setup of a FSO system being tested in an atmospheric chamber is illustrated in Figure 1. The diagram shows a chamber which is based on the existing chamber in Optical Communication Research Group (OCRG) laboratory at Northumbria University. The total length of the chamber is 7.2 meters and is made of 6 smaller sections with length of 1.2 meters. The chamber is used for both turbulence and fog/smoke tests. The details of the test and measurement techniques can be found in [11, 12].

The strength of turbulence on the data signal is characterised based on a parameter called scintillation

index, which is the normalised variance of the received signal intensity I given by [13]:

$$ \sigma_1^2 = \frac{E[I^2] - E[I]^2}{E[I]^2}, \qquad (1) $$

where E[·] denotes the expected value. The variance of log-intensity signal fluctuation defined by Rytov variance $\sigma_R^2$ is given by [13]:

$$ \sigma_R^2 = 1.23 k_{FSO}^{\frac{7}{6}} C_n^2 L^{\frac{11}{6}}. \qquad (2) $$

where $k_{FSO}$, $C_n^2$, and L are laser wavenumber, the refractive index structure parameter, and link length, respectively. Assuming a plane wave model for the propagating laser, we can let $\sigma_I^2 = \sigma_R^2$ [5]. we also can classify the turbulence strength as [5]:

$$ \begin{aligned} \sigma_R^2 &\ll 1, & \text{for weak regime,} \\ \sigma_R^2 &\approx 1, & \text{for moderate regime,} \\ \sigma_R^2 &\gg 1, & \text{for strong regime.} \end{aligned} \qquad (3) $$

The intensity of an auxiliary signal is constantly sampled at the receiver side to estimate the turbulence strength based on (1) and (3). However, a backup method which uses (2) and $\sigma_I^2 = \sigma_R^2$ estimation is also implemented for increasing measurement accuracy. This is done by sampling the temperature across the chamber (temperature sensors $S_1$ to $S_n$ in Figure 1) and estimating $\sigma_R^2$ by (2) knowing that [5]:

$$ C_n^2|_i = (79 \times 10^{-6} \frac{P}{T_{avg}^2})^2 C_T^2|_i, \qquad (4) $$

where $P$, $T_{avg}$, and $C_T^2$ are the atmospheric pressure in millibar, average temperature, and the temperature structure constant, respectively. $C_T^2$ is given by [5]:

$$ C_T^2|_i = (T_i - T_{i+1})^2 / L_{p,i}^3. \qquad (5) $$

where $T_i$, and $L_{p,i}$ are the temperature recorded by sensor,
and distance between adjacent sensors of i and i+1.

Based on the discussion so far, temperature and pressure inside the chamber are the parameters that can indicate the strength of turbulence making them the main outcomes of the simulation.

## OpenFOAM Tool

OpenFOAM is a free and open-source tool that includes a comprehensive set of packages for simulating CFD problems [10]. The procedure of simulating a fluid dynamic problem structure in OpenFOAM is straightforward and starts with defining the geometry of the problem. In our case, the goal is to simulate the air flow inside the chamber modelled in Figure 2(a). To simplify the problem, we reduce 3D structure to a 2D one shown in Figure 2(b). Even though the 2D model will not results in the exact turbulence parameters, it is still a useful asset to investigate the significance of the chamber geometry and particularly the inlet air vents arrangements, see Figure 3(c). OpenFOAM like other computational techniques requires the problem to have well-defined boundary conditions. For our case study, the air vents are modelled as inlet while both open ends of the chamber section are modelled as outlets, see Figure 2(d). The other edges are defined as wall to simulate the confined environment of the chamber section. Since the built-in mesh generator tool of OpenFOAM is used, the whole structure is divided into 7 smaller rectangles, see Figure 2(e). the generated mesh is also depicted in Figure 2(f). The time $\Delta t$ and space $\Delta h$ cell divisions are set to make sure that Courant number C given by:

$$C = U \frac{\Delta t}{\Delta h}, \qquad (6)$$

is less than 1 provided that U is the speed of the fluid [10]. After defining the geometry, boundary conditions, and mesh parameters, the inlets are defined. The cold air inlet is set to have a temperature of 300° K, where the hot air inlet is 400° K hot. For both inlets, the air speed of 20 m/s is used. As the last step, the solver of buoyantBoussinesqPimpleFoam is picked to simulate

the chamber section. This solver provides solution for both turbulence and heat exchange terms.

## Results and Discussion

The simulation results for two different air inlets arrangements are given in Figure 3.

<figure><img src="image_3.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(d)</figcaption></figure>
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>Figure 2: (a) 3D model of a single section of the atmospheric chamber; (b) The simplified 2D model structure; (c) the dimensions of a single section; (d) boundary conditions of the simplified chamber section; (e) mesh regions; and (f) generated mesh.</figcaption></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>Figure 3: Pressure contour plots for two air inlets arrangements.</figcaption></figure>

As seen from the contour plots, both arrangements result in formation of eddy region in the middle of the chamber section. However, the size of the eddy is smaller for the case where the inlets are closer. However, for closer inlets, the pressure values as well as temperature gradient is larger, which leads to a larger variation in the refractive index. Therefore, one method that can be used to change the turbulence strength and properties, is to change the point where hot and cold air flows come in contact, which can be done easily by changing the direction of the inlet air flows.

## Conclusion

In this report we discussed the method to simulate the turbulence in an atmospheric chamber. The simulation results can be used to determine the design of the chamber and how to control the turbulence properties. Our findings show that for a chamber with hot and cold air inlets, the closer the inlets are the turbulence is stronger and the generated eddy is smaller.

## Acknowledgement

This report is based on a undergraduate student project carried out by Kexuan Zhang and supervised by Mojtaba Mansour Abadi.

## References

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[9] https://www.grc.nasa.gov/www/k-12/airplane/nseqs.html

[10] https://www.openfoam.com/

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