Abstract

We propose and experimentally demonstrate a distributed directional torsion sensor based on an optical frequency domain reflectometer (OFDR) using a helical multicore fiber (MCF). A theoretical model is first established to reveal that the ability of the torsion direction discrimination stems from the fiber design of the central-offset cores with helical structure and the shorter helical pitch holds higher sensitivity. Such a distributed torsion sensor is then experimentally realized by using an OFDR system with an adjacent sensing distance of 9.4 mm. Comparative experiments with three different MCFs fully prove the theoretical predication. Finally, a distributed directional torsion sensor is realized with a linear sensitivity of 1.9 pm/(rad/m) by using the helical MCF with a helical pitch of 6 mm. Such a torsion sensing system would find potential applications in the fields of bionic robotics, 3-D shape sensing, oil drilling and so on.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Distributed torsion sensing is of great interest in various industrial applications including the bionic robot [1,2], 3-D shape sensing [3,4], and structural health monitoring [5,6]. For instance, oil drilling requires the distributed torsion measurement in the bit and in the pipe to improve drilling efficiency and avoid drill pipe twist-off [7]. Medical image guidance requires distributed torsion information for finally reconstructing the 3-D shape of the medical catheters and surgical instruments in minimally invasive surgery [8]. Fiber-optic sensors have the advantages of strong electromagnetic interference resistance, small size, lightweight, and corrosion resistance, and thus measurements of twist/torsion have been topics of intense research within the fiber-optic sensor community. Up to now, various optical fiber torsion sensors have been investigated, for example, specially designed gratings [911] and in-line fiber interferometers [1214]. Optical fibers with helical structure were usually employed to identify the torsion direction. For instance, helical Bragg grating waveguides can be directly written in the fiber by using the femtosecond-laser processing technology [10]. With the help the CO2 laser scanning, helical structures in photonic crystal fiber also has been realized for torsion direction discrimination [11]. The helical structure also can be fabricated into the multi-core fiber (MCF) by pre-twisting and then heating with a CO2 laser splicing system [13]. The interference between the lights guided in the multi-cores were employed for monitoring the torsion. However, these sensors based on gratings and interfeometers are usually developed for torsion sensing at a single portion, few works were reported to realize the distributed or quasi-distributed torsion sensing [15,16].

In this work, our idea is to develop a distributed torsion sensor with the capability of torsion direction discrimination. Optical frequency domain reflectometry (OFDR) is promising distributed sensing technology with a unique advantage of high spatial resolution. The measurement principle of the OFDR is based on measuring the wavelength shift of the Rayleigh scattering spectrum. Rayleigh backscattering lights at multiple locations along the optical fiber will generate an interference spectrum. The distributed Rayleigh scattering in the fiber is regarded as random weak gratings. The wavelength shift induced by strain/temperature can be obtained by implementing the cross-correlation of the reference and measurement signals in spectrum domain. OFDR has been developed for distributed strain sensing [17], temperature sensing [18], shape sensing [19,20], and polarization analysis [21]. In this work, we present a distributed directional torsion sensing system by using the OFDR technology and the helical multicore fiber (MCF). Theoretical and experimental studies are successively implemented for investigating the capability of the torsion direction discrimination and the dependence of the torsion sensitivity.

2. Working principle

To understand the working principle of our proposed torsion sensor, we build up a theoretical model of the helical fiber under external torsion. In our design, the fiber needs at least one eccentric core with helical structure, and the helical MCF used in out experiment is just an example to demonstrate our idea. The helical core is flattened into a triangle on a plane as shown in Fig. 1. The width equals the circumference at radius r, where r represents the radial offset from the helical core to the fiber center and the height equals to the helical pitch h.

 figure: Fig. 1.

Fig. 1. The theoretical model of the helical fiber under torsion.

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The length of the helical core can be calculated as:

$$l = \sqrt {{{({2\pi r} )}^2} + {h^2}} $$

When the helical fiber experiences an external torsion with a twist rate of γ, the helical core has a shear strain ɛs = rγ, and the width of the triangle will become (2πr+ɛsh). Here, the sign of external torsion (±) represents the clockwise (CW) and counterclockwise (CCW) direction, respectively. After applying the external torsion, the length of the strained core changes to be

$${l_\varepsilon } = \sqrt {{{({2\pi r + {\varepsilon_s}h} )}^2} + {h^2}} $$
Consequently, the final strain in the helical core is defined as follows:
$$\varepsilon = ({{l_\varepsilon } - l} )/l$$
To simplify the above equation, both the numerator and denominator terms are multiplied by (lɛ + l). Considering that the lɛl in the denominator term, the upper equation can be approximately written as
$$\varepsilon = \frac{{4\pi r{\varepsilon _s}h + {{({{\varepsilon_s}h} )}^2}}}{{2[{{{({2\pi r} )}^2} + {h^2}} ]}}$$
Here, fiber initial helical pitch, h is inversely proportional to the initial bias torsion γ0, i.e. h = 2π/γ0. Substituting it into the equation above, the torsion induced strain in the fiber core can be finally expressed as
$$\varepsilon = \frac{{{r^2}}}{{{{({r{\gamma_0}} )}^2} + 1}}\left( {{\gamma_0}\gamma + \frac{1}{2}{\gamma^2}} \right)$$
Thereby, the strain induced wavelength shift can be written as [22]
$$\Delta \lambda = {\lambda _c}\left( {1 - {P_e}} \right)\varepsilon$$
where Pe = 0.1994 is the effective elastic-optic coefficient of the optical fiber, and λc = 1550 nm is the central wavelength of the wavelength sweeping range in the OFDR system. Generally, the theoretical sensitivity before approximation can be expressed as
$$S = \left( {1 - {P_e}} \right){\lambda _c}\frac{{{r^2}}}{{{{\left( {r{\gamma _0}} \right)}^2} + 1}}\left( {{\gamma _0} + \gamma/2 } \right)$$
which means the sensitivity is depended on the torsion applied on the fiber, and the wavelength shift has a nonlinear response to torsion due to the quadratic term in Eq. (5). There are two special cases to obtain a relatively simpler solutions of the wavelength shift as the function of the external torsion.

For the first case of the fiber without a helical structure, i.e. γ0 = 0, only the quadratic term in Eq. (5) is retained, and the wavelength shift is simplified to be

$$\Delta \lambda = {\lambda _c}({1 - {P_e}} )\frac{{{r^2}}}{2}{\gamma ^2}$$
which means a stretched strain is always induced in the initial straight fiber core whatever the torsion direction is. In other words, the straight core is unable to recognize the torsion direction.

For the second case of the fiber core with a relatively large twist bias, it is reasonable to neglect the quadratic term in Eq. (5), and the wavelength shift can be linearized with respect to the external torsion. Since the helical redial is in the order of micrometer, the term (0)2 is minuteness to be neglected. After the above approximation, Eq. (6) can be simplified as:

$${\Delta }\lambda \textrm{ = }S\gamma$$
with the torsion sensitivity
$${S_{\textrm{approximation}}} = \frac{{2\pi {r^2}}}{h}\left( {1 - {P_e}} \right){\lambda _c}$$
Here, the theoretical torsion sensitivity in Eq. (10) is obtain when the fiber has a relatively large twist bias, i.e. shorter helical pitch. Equation (9) means the external torsion with CW and CCW direction can induce stretched and compressed strain, and hence give a positive and negative wavelength shift, respectively. In other words, central-offset fiber core with helical structure provides the capability to realize directional torsion sensing. Furthermore, a linear response to both CW and CCW torsions is expected for a fiber with a small helical pitch.

We conducted a theoretical calculation according to Eq. (6) to discuss the torsion sensitivity, as shown in Fig. 2. If the helical pitch of h is sufficiently large, i.e. larger than approximately 9 mm, the quadratic term of the shear strain in Eq. (5) cannot be neglected, and hence the wavelength shift shows a nonlinear relationship with the twist rate (Fig. 2(b)), and the theoretical sensitivity is lower than 1.06 pm/(rad/m) when the helical pitch is larger than 9 mm. The nonlinear phenomenon is not obvious, but can be observed by comparing the wavelength shift at ±100 rad/m. For example, the wavelength shift for helical pitch of 9 mm are 100 and 125 pm, respectively. This nonlinear phenomenon was verified in our later experiment. If the helical pitch of h is small enough, it makes sense to retain only the linear term of the shear strain. Therefore, linear response between the wavelength shift and the twist rate is obtained when h is smaller than 8 mm (Fig. 2(a)), and the sensitivity is expected to be 4.8 pm/(rad/m) with the helical pitch of 2 mm. In consideration of the theoretical sensitivity discussion above, it is expected to design a distributed torsion sensor with high sensitivity and linear response in both CW and CCW direction by employing a fiber with shorter helical pitch.

 figure: Fig. 2.

Fig. 2. Simulated wavelength shift as a function of the torsion (a) linear response of the helical fiber with small pitches, (b) nonlinear response of the helical fibers with large pitches.

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3. Experimental setup

Figure 3 illustrates the OFDR system established in our lab for distributed torsion sensing. A tunable laser (Santec, TSL-550) with a linewidth of 400 kHz is linearly swept, and the output light is divided into two parts by a 1:99 coupler. The small fraction of the 1% light is then further split by a 50/50 coupler for constructing a Mach-Zehnder interferometer with a 10 m delay in one arm. This interferometer functions as an auxiliary interferometer for obtaining the instantaneous frequency of the sweep light source and then compensating the nonlinearity of the sweep process. Because of the frequency tuning nonlinearity of the tunable laser, no linear relationship exists between the beat frequency and the time. An effective interpolation algorithm is usually used for compensating the nonlinearity of the sweep process [18]. First, we obtained the instantaneous frequency of the sweep light source thought the auxiliary interferometer and then resampled the beat signal obtained from the main interferometer. The 99% light from C1 is launched to a main interferometer with a local arm and a measurement arm. The measurement arm is used to collect the Rayleigh backscattering light in the FUT through a circulator. In order to suppress the polarization fading effect in optical fibers, a polarization diversity receiver is employed to receive the Rayleigh backscattering light in two orthogonal states of polarization (SOP), i.e. s and p states. The light from the local arm is split evenly between two orthogonal SOPs by adjusting the polarization controller in front of the polarization beam splitter (PBS). All data from both the auxiliary and main interferometers are detected by the photoelectric detectors (Thorlabs, PDB470C), and then acquired by a four-channel data acquisition card (ATS9440) at a sampling rate of 20 MS/s.

 figure: Fig. 3.

Fig. 3. Configuration of the distributed torsion sensing based on the OFDR system. TLS: tunable laser source; C1, C2: 1:99 couplers; C3-C6: 50:50 couplers; VOA: variable optical attenuator; PC: polarization controller; PBS: polarization beam splitter; BPD: balanced photodetector; DAQ: data acquisition; OS: optical switch; FS: Fan-in spatial Mux coupler; FUT: fiber under test. Insert: schematic of the helical MCF with pitch of 15.4 mm, side view and cross views of the optical microscopy images of the helical MCF with pitch of 6 mm.

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To demonstrate the proposed sensor, we employed three types of multicore fibers (MCFs) for the torsion test. Both the first and second are commercially available seven-core fibers from FIBERCORE. The first one has a helical structure with a pitch of approximately 15.4mm according to the data sheet. This fiber has a central core, and 6 outer cores arranged in a hexagonal array, as shown in Fig. 3. The core-core separation is approximately 35 µm, and the NA of each core is approximately 0.2. In addition to the non-existence of helical structure, the second fiber has similar parameter equivalent to the first one. The third one is homemade seven-core helical fiber. This fiber was fabricated by means of twisting the second fiber during hydrogen-oxygen flame heating [23]. This post-processing method can be used to fabricate the fiber with a designed helical pitch. In order to balance sensing performance and the additional insertion loss induced by the post-processing, the third fiber was designed and fabricated with a helical pitch of 6mm, as shown in inset of Fig. 3. In order to sequentially interrogating each core of the helical MCF, the three types of fibers are spliced to a fan-in spatial Mux coupler, and a 1×7 optical switch is connected to the seven pigtails of the fan-in coupler. In order to reduce the reflection at the end of the fiber, a knot was tied at the end of the fiber during the experiment.

In our experiment, the tunable laser is swept from 1545 to 1555nm at a rate of 16nm/s for 0.2048 s acquisition time, which gives a determined wavelength range of 3.278nm. In order to ensure the enough wavelength range, the sweep rang is usually larger than the really used wavelength range. The determined wavelength range of 3.278nm gives a theoretical spatial resolution of 235 µm in the distance domain. Two hundred data samples are selected for the cross correlation, giving a gauge spatial resolution of 4.7cm. In order to obtain sufficient sensing points and mitigate spectral leakage when implementing the short-time fast Fourier transformation, we selected the hamming window with 80% overlapping between adjacent windows. Hence, the distance between adjacent sensing points is 9.4mm.

Figure 4 illustrates the Rayleigh backscattering signal as a function of the fiber length. Five distinct strong reflection points are detected along the sensing fiber link. The first four reflection points are enlarged in the inset. The first one is located at approximately 1 m which indicates the APC-APC connector between the pigtail of the second port of the optical circulator and the input pigtail of the optical switch. The second one represents the internal reflection of optical switch. The third one is the APC-APC connector between one of seven output pigtails of the optical switch and the input pigtail of the Fan-in coupler. The fourth one indicates the strong reflection inside the Fan-in coupler. The last one is located around 60 m indicating the reflection at the end of the fiber.

 figure: Fig. 4.

Fig. 4. Rayleigh backscattered signal as a function of the distance. Inset: the enlarge image of the first four reflection points.

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Our signal processing procedure is the same as the commonly used OFDR system [24]. Firstly, both reference and measurement signals before and after torsion are converted to the spatial domain via fast Fourier transform. Secondly, a certain location signal is selected via a hamming window and converted to the spectrum domain via the short-time fast Fourier transform. Finally, cross-correlation is performed between the reference and measurement signals in spectrum domain to obtain the spectral shift. The cross-correlation peak is spectral shift at the selected location, which reflects the torsion variation. It is worth noting that the local spatial signal after applying the window function is zero-padded to increase the frequency resolution of the cross-correlation and hence improve the torsion measurement accuracy. The total time taken to make a complete measurement in the six out cores of the MCF is around 30 s which including the date acquisition and processing in each core, and this time can be shorten by using the parallel processing algorithm.

Figure 5(a) illustrates the normalized Rayleigh backscattering spectrum (RBS) before and after torsion. Here, the measurement spectrum was collected with the torsion of 15.7 rad/m. After implementing the cross-correlation of the measurement and reference Rayleigh backscattering spectrum, and a clear local spectral shift of 10.6 pm can be found, as shown in Fig. 5(b).

 figure: Fig. 5.

Fig. 5. (a) Rayleigh backscattering spectrum of the helical MCF before (reference) and after (measurement) twisting. (b) Cross-correlation of refence and measured Rayleigh backscattering spectrum when the torsion is 15.7 rad/m.

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4. Experimental results

Firstly, the multi-core fiber with a helical pitch of 15.4 mm is employed to implement the torsion measurement. A segment of 1.2 m long helical MCF is fixed between two rotators, as shown in Fig. 6(a). Prior to the fiber torsion, the reference signals of the 7 cores are collected and stored. In the torsion experiment, one of the rotators is gradually rotated to 18 turns with a step of 3 turns in both CW and CCW direction, thereby yielding a torsion range of 94.2 rad/m with a step of 15.7 rad/m, and the measurement signals are collected after each rotation.

 figure: Fig. 6.

Fig. 6. Twist setup of the helical MCF (a) one segment of torsion; (b) three segments of distributed torsions.

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Figure 7(a) shows the wavelength shift results when the torsion changed from −94.2 to 94.2 rad/m in an interval of 15.7 rad/m in the 1.2 m twist segment between 58.0 and 59.2 m along the helical MCF. When the helical fiber is additionally twisted in the same direction as the initial twist bias of the helical fiber, i.e. CW in our experiment, the additionally applied torsion will elongate the outer helical cores; the additional torsion in the opposite sense compresses these outer cores. In a nutshell, CW and CCW torsions in our experiment will equivalently stretch and compress the outer helical cores, respectably. Finally, the peak wavelengths of the cross-correlation exhibit a redshift and blue shift, as shown in Fig. 7(a). Such opposite phenomenon of wavelength shift can be used for distinguishing the torsion direction. The capability of directional torsion sensing is the result of the initial twist bias of the helical fiber. Figure 7(b) illustrates the wavelength shift as a function of the torsion. It is evident that torsion sensitivity is different between CCW (∼0.604 pm/(rad/m)) and CW (∼1.096 pm/(rad/m)) directions. Such nonlinear trend is proved by the theoretical simulation according the Eq. (7).

 figure: Fig. 7.

Fig. 7. (a) Measured wavelength shifts of the helical MCF with a helical pitch of 15.4 mm. (b) Wavelength shifts as a function of the torsion.

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Figure 6(b) illustrates the mechanical configuration for implementing the distributed torsions measurement. A 1.2 m long helical MCF is equally divided into three segments by four rotators, namely A, B, C, and D. To apply the distributed torsions, Rotator A and C were fixed and did not rotate. Only Rotator B and D were rotated to different angles in both CW and CCW directions, and the experimentally applied torsions of all segments are listed in Table 1. In case 1, only Rotator B is rotated by 720 degrees in CCW direction, and hence the 1st segment suffered a torsion of −31.4 rad/m in CCW direction, while the 2nd segment was exposed to the oppositely signed rotations, i.e. 2nd segment in a CW direction of 31.4 rad/m. In cases of 2, 3, and 4, the rotator B was rotated by 1080 degrees in CCW direction, and hence the 1st and 2nd segments were twisted by a torsion of −47.1 rad/m and 47.1 rad/m in CCW and CW directions, respectively. In later 3 cases, rotator D was sequentially rotated by 720, 1080, and 1800 degrees in a CW direction, giving a torsion of 31.4 rad/m, 47.1 rad/m and 78.5 rad/m in 3rd segment, respectively.

Tables Icon

Table 1. Distributed torsions of the helical MCF

Figure 8 illustrates the measured wavelength shifts along the 1.2 m long helical MCF using the OFDR system. Firstly, it is clear to identify the torsion direction of each sensing segment according to the sign of wavelength shift. The positive wavelength shift presents a torsion in CW direction, and vice versa. Secondly, we obtained the measured torsion by inserting the wavelength shift into Eq. (9), and the sensitivities of S are 1.096 and 0.604 pm/(rad/m) corresponding to CW and CCW directions. Finally, the calculated torsions are also listed in Table 1 for comparison with the prior imposed torsion values, which proves that the measured torsions are in a good agreement with the mechanically preset values, and the maximum fluctuation is 3.51 rad/m. Additionally, it is interesting to find that there is always a wavelength dip approaching to zero at the location of Rotator C. This phenomenon is in accordance with the fact that a section fiber around 1 cm long was fixed by glues at all rotators and did not produce any torsion. Therefore, the wavelength approaches to zero at the location of Rotator C. The discrimination of the short-untwisted section fiber at the Rotator C benefits from the high spatial resolution of the OFDR system, i.e. 9.4 mm separation between adjacent sensing points. It is expected to obtain a higher spatial resolution by increasing the wavelength sweeping range.

 figure: Fig. 8.

Fig. 8. Measured wavelength shifts of the helical MCF under distributed torsions.

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5. Discussion

As presented above, we have demonstrated a promising, fully distributed torsion sensing configuration with twist direction discrimination, even though the experimental torsion sensitivity is relatively low and nonlinear in the CW and CCW directions. In this section, we will discuss the sensitivity and the capability of recognizing the torsion direction. Firstly, the torsion sensitivity is directly related to the pitch of the helical MCF, according to Eq. (10). We tested the torsion sensing performance by using our post-processed fiber with helical pitch of 6 mm. Like the previous experimental results of the fiber with a helical pitch of 15.4 mm, we can identify the torsion direction through the direction of the wavelength shift. The important difference is that the wavelength shift of the fiber with a pitch of 6 mm exhibits a symmetrical shift between CW and CCW torsion directions, as shown in Fig. 9(a). Furthermore, the wavelength shift has a linear response in the whole torsion range from −36 to 36 rad/m. The sensitivity is around 1.9 pm/(rad/m), as shown in Fig. 9(b), agreeing well with the theoretical calculation of 1.6 pm/(rad/m) in Eq. (10).

 figure: Fig. 9.

Fig. 9. (a) Wavelength shift of the helical MCF with a helical pitch of 6 mm. (b) Wavelength shift as a function of the torsion.

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To discuss the capability of recognizing the torsion direction, we implemented a comparative experiment by using the fiber with straight fiber cores. The experimental results are shown in Fig. 10. It is found that a red wavelength shift is always observed whatever the torsion direction is. More importantly, the wavelength shift is parabolic with the torsion, which also proved our theoretical predict in Eq. (8). In short, the capability of recognizing the torsion direction stems from the fiber design with central-offset helical fiber cores. Furthermore, the shorter the helical pitch, the higher the sensitivity.

 figure: Fig. 10.

Fig. 10. (a) Wavelength shift of the MCF with straight fiber cores. (b) Wavelength shift as a function of the torsion.

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In our experiment, we used the multi-core fiber to experimentally demonstrate the proposed sensor scheme, but it is worth noting that the key idea of distributed torsion sensor with direction recognition ability is the optical fiber with eccentric spiral structure. Here, the use of multi-core fiber not only verifies our proposed sensors successfully but also provides a basic study and fiber design guidance for full 3-D optical fiber shape sensing. One problem for 3-D shape sensing is that the tether fiber is likely to experience not only bending, but also varying amounts of torsion along its length. A potential method to separate the bending and torsion is the use of helical multi-core fiber [25]. Our theoretical torsion model and the sensitivity study should be useful to design helical MCF for 3-D shape sensing.

In this paper, we just used the wavelength shift of the Rayleigh scattering spectrum to demonstrate the torsion sensing. Besides the wavelength shift induced by the shear strain, the state of the polarization undoubtedly changes and hence induces polarization fading effect to the Rayleigh scattering spectrum, which will influence the detection of the wavelength shift. In order to suppress this effect and ensure the measurement stability, we have used the polarization diversity receiver in the system as shown in Fig. 3. On the other hand, we also can measure the polarization variation by slightly changing our system to stablish a polarization-sensitive OFDR system [21].

6. Conclusion

In summary, we proposed and implemented a distributed directional torsion sensor system by using the OFDR technology and the helical MCF. Both theoretical and experimental results enforce that the central-offset fiber cores with a helical structure in the MCF endows the torsion sensor with the direction recognition ability. Furthermore, the shorter pitch gives higher sensitivity. By employing the OFDR technology, a distributed directional torsion sensor is experimentally demonstrated with a linear sensitivity of 1.9 pm/(rad/m) and an adjacent sensing points separation of 9.4 mm.

Funding

National Natural Science Foundation of China (61975022, 61905030); Fundamental Research Funds for the Central Universities (2019CDQYGD038).

Disclosures

The authors declare that there are no conflicts of interest related to this article.

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References

  • View by:

  1. P. E. Dupont, J. Lock, B. Itkowitz, and E. Butler, “Design and control of concentric-tube robots,” IEEE Trans. Robot. 26(2), 209–225 (2010).
    [Crossref]
  2. R. J. Webster, J. M. Romano, and N. J. Cowan, “Mechanics of precurved-tube continuum robots,” IEEE Trans. Robot. 25(1), 67–78 (2009).
    [Crossref]
  3. P. S. Westbrook, T. Kremp, K. S. Feder, W. Ko, E. M. Monberg, H. Wu, D. A. Simoff, T. F. Taunay, and R. M. Ortiz, “Continuous Multicore Optical Fiber Grating Arrays for Distributed Sensing Applications,” J. Lightwave Technol. 35(6), 1248–1252 (2017).
    [Crossref]
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  8. F. Parent, M. Gérard, F. Monet, S. Loranger, G. Soulez, R. Kashyap, and S. Kadoury, “Intra-Arterial Image Guidance With Optical Frequency Domain Reflectometry Shape Sensing,” IEEE Trans. Med. Imaging 38(2), 482–492 (2019).
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2019 (3)

F. Parent, M. Gérard, F. Monet, S. Loranger, G. Soulez, R. Kashyap, and S. Kadoury, “Intra-Arterial Image Guidance With Optical Frequency Domain Reflectometry Shape Sensing,” IEEE Trans. Med. Imaging 38(2), 482–492 (2019).
[Crossref]

P. Z. Jiang, Y. Ouyang, H. Y. Guo, and A. Zhou, “Highly sensitive torsion senor based on dual-side-hole fiber Mach-Zehnder interferometer,” Opt. Express 27(23), 33880–33888 (2019).
[Crossref]

C. Shao, G. Yin, L. Lv, M. Liu, I. P. Ikechukwu, H. N. Han, L. Zhou, J. D. Zhang, W. J. Zhai, S. Wang, and T. Zhu, “OFDR with local spectrum matching method for optical fiber shape sensing,” Appl. Phys. Express 12(8), 082010 (2019).
[Crossref]

2018 (5)

2017 (1)

2016 (4)

R. Xu, A. Yurkewich, and R. V. Patel, “Curvature, torsion, and force sensing in continuum robots using helically wrapped FBG sensors,” IEEE Robot. Autom. Lett. 1(2), 1052–1059 (2016).
[Crossref]

B. Huang and X. W. Shu, “Ultra-compact strain- and temperature-insensitive torsion sensor based on a line-by-line inscribed phase-shifted FBG,” Opt. Express 24(16), 17670–17679 (2016).
[Crossref]

V. Budinski and D. Donlagic, “Quasi-distributed twist/torsion sensor,” Opt. Express 24(23), 26282–26299 (2016).
[Crossref]

B. K. Cheng, W. G. Zhu, L. W. Hua, J. Liu, Y. R. Li, R. Nygaard, and H. Xiao, “Distributed torsion sensor based on cascaded coaxial cable fabry-perot interferometers,” Meas. Sci. Technol. 27(7), 075103 (2016).
[Crossref]

2015 (1)

2014 (2)

K. R. Henken, J. Dankelman, J. J. van den Dobbelsteen, L. K. Cheng, and M. S. van der Heiden, “Error Analysis of FBG-Based Shape Sensors for Medical Needle Tracking,” IEEE/ASME Trans. Mechatron. 19(5), 1523–1531 (2014).
[Crossref]

J. Song, W. H. Li, P. Lu, Y. P. Xu, L. Chen, and X. Y. Bao, “Long-range high spatial resolution distributed temperature and strain sensing based on optical frequency-domain reflectometry,” IEEE Photonics J. 6(3), 1–8 (2014).
[Crossref]

2013 (1)

2012 (1)

2011 (1)

A. L. Materazzi and F. Ubertini, “Eigenproperties of suspension bridges with damage,” J. Sound Vib. 330(26), 6420–6434 (2011).
[Crossref]

2010 (1)

P. E. Dupont, J. Lock, B. Itkowitz, and E. Butler, “Design and control of concentric-tube robots,” IEEE Trans. Robot. 26(2), 209–225 (2010).
[Crossref]

2009 (1)

R. J. Webster, J. M. Romano, and N. J. Cowan, “Mechanics of precurved-tube continuum robots,” IEEE Trans. Robot. 25(1), 67–78 (2009).
[Crossref]

2001 (1)

G. Wang, K. Pran, G. Sagvolden, G. B. Havsgard, A. E. Jensen, G. A. Johnson, and S. T. Vohra, “Ship hull structure monitoring using fibre optic sensors,” Smart Mater. Struct. 10(3), 472–478 (2001).
[Crossref]

1998 (1)

Aitchison, J. S.

Arkwright, J. W.

K. Hoehn, A. Olsson, and J. W. Arkwright, “High Capacity Torque and Compression Measurements Using Fibre Optic Sensors,” in 2017 2nd International Conference for Fibre-optic and Photonic Sensors for Industrial and Safety Applications (OFSIS) (2017), pp. 39–44.

Askins, C. G.

C. G. Askins, G. A. Miller, and E. J. Friebele, “Bend and twist sensing in a multiple-core optical fiber,” in 2008 Conference on Optical Fiber Communication/National Fiber Optic Engineers Conference (2008), pp. 1545–1547.

Bai, Z.

Bao, X. Y.

J. Song, W. H. Li, P. Lu, Y. P. Xu, L. Chen, and X. Y. Bao, “Long-range high spatial resolution distributed temperature and strain sensing based on optical frequency-domain reflectometry,” IEEE Photonics J. 6(3), 1–8 (2014).
[Crossref]

Budinski, V.

Butler, E.

P. E. Dupont, J. Lock, B. Itkowitz, and E. Butler, “Design and control of concentric-tube robots,” IEEE Trans. Robot. 26(2), 209–225 (2010).
[Crossref]

Chen, L.

J. Song, W. H. Li, P. Lu, Y. P. Xu, L. Chen, and X. Y. Bao, “Long-range high spatial resolution distributed temperature and strain sensing based on optical frequency-domain reflectometry,” IEEE Photonics J. 6(3), 1–8 (2014).
[Crossref]

Chen, X.

Cheng, B. K.

B. K. Cheng, W. G. Zhu, L. W. Hua, J. Liu, Y. R. Li, R. Nygaard, and H. Xiao, “Distributed torsion sensor based on cascaded coaxial cable fabry-perot interferometers,” Meas. Sci. Technol. 27(7), 075103 (2016).
[Crossref]

Cheng, L. K.

K. R. Henken, J. Dankelman, J. J. van den Dobbelsteen, L. K. Cheng, and M. S. van der Heiden, “Error Analysis of FBG-Based Shape Sensors for Medical Needle Tracking,” IEEE/ASME Trans. Mechatron. 19(5), 1523–1531 (2014).
[Crossref]

Cowan, N. J.

R. J. Webster, J. M. Romano, and N. J. Cowan, “Mechanics of precurved-tube continuum robots,” IEEE Trans. Robot. 25(1), 67–78 (2009).
[Crossref]

Cui, J.

Dankelman, J.

K. R. Henken, J. Dankelman, J. J. van den Dobbelsteen, L. K. Cheng, and M. S. van der Heiden, “Error Analysis of FBG-Based Shape Sensors for Medical Needle Tracking,” IEEE/ASME Trans. Mechatron. 19(5), 1523–1531 (2014).
[Crossref]

Ding, Z.

Dinh, X. Q.

Donlagic, D.

Dupont, P. E.

P. E. Dupont, J. Lock, B. Itkowitz, and E. Butler, “Design and control of concentric-tube robots,” IEEE Trans. Robot. 26(2), 209–225 (2010).
[Crossref]

Feder, K. S.

Feng, T.

Fernandes, L. A.

Friebele, E. J.

C. G. Askins, G. A. Miller, and E. J. Friebele, “Bend and twist sensing in a multiple-core optical fiber,” in 2008 Conference on Optical Fiber Communication/National Fiber Optic Engineers Conference (2008), pp. 1545–1547.

Froggatt, M.

Fu, C.

Fu, Q. J.

Fu, S. N.

Gérard, M.

F. Parent, M. Gérard, F. Monet, S. Loranger, G. Soulez, R. Kashyap, and S. Kadoury, “Intra-Arterial Image Guidance With Optical Frequency Domain Reflectometry Shape Sensing,” IEEE Trans. Med. Imaging 38(2), 482–492 (2019).
[Crossref]

Grenier, J. R.

Guo, H. Y.

Han, H. N.

C. Shao, G. Yin, L. Lv, M. Liu, I. P. Ikechukwu, H. N. Han, L. Zhou, J. D. Zhang, W. J. Zhai, S. Wang, and T. Zhu, “OFDR with local spectrum matching method for optical fiber shape sensing,” Appl. Phys. Express 12(8), 082010 (2019).
[Crossref]

Havsgard, G. B.

G. Wang, K. Pran, G. Sagvolden, G. B. Havsgard, A. E. Jensen, G. A. Johnson, and S. T. Vohra, “Ship hull structure monitoring using fibre optic sensors,” Smart Mater. Struct. 10(3), 472–478 (2001).
[Crossref]

He, J.

Henken, K. R.

K. R. Henken, J. Dankelman, J. J. van den Dobbelsteen, L. K. Cheng, and M. S. van der Heiden, “Error Analysis of FBG-Based Shape Sensors for Medical Needle Tracking,” IEEE/ASME Trans. Mechatron. 19(5), 1523–1531 (2014).
[Crossref]

Herman, P. R.

Hoehn, K.

K. Hoehn, A. Olsson, and J. W. Arkwright, “High Capacity Torque and Compression Measurements Using Fibre Optic Sensors,” in 2017 2nd International Conference for Fibre-optic and Photonic Sensors for Industrial and Safety Applications (OFSIS) (2017), pp. 39–44.

Hua, L. W.

B. K. Cheng, W. G. Zhu, L. W. Hua, J. Liu, Y. R. Li, R. Nygaard, and H. Xiao, “Distributed torsion sensor based on cascaded coaxial cable fabry-perot interferometers,” Meas. Sci. Technol. 27(7), 075103 (2016).
[Crossref]

Huang, B.

Ikechukwu, I. P.

C. Shao, G. Yin, L. Lv, M. Liu, I. P. Ikechukwu, H. N. Han, L. Zhou, J. D. Zhang, W. J. Zhai, S. Wang, and T. Zhu, “OFDR with local spectrum matching method for optical fiber shape sensing,” Appl. Phys. Express 12(8), 082010 (2019).
[Crossref]

Q. J. Fu, J. D. Zhang, C. C. Liang, I. P. Ikechukwu, G. Yin, L. Lu, Y. F. Shao, L. Liu, and T. Zhu, “Intensity-modulated directional torsion sensor based on in-line optical fiber Mach-Zehnder interferometer,” Opt. Lett. 43(10), 2414–2417 (2018).
[Crossref]

Itkowitz, B.

P. E. Dupont, J. Lock, B. Itkowitz, and E. Butler, “Design and control of concentric-tube robots,” IEEE Trans. Robot. 26(2), 209–225 (2010).
[Crossref]

Jensen, A. E.

G. Wang, K. Pran, G. Sagvolden, G. B. Havsgard, A. E. Jensen, G. A. Johnson, and S. T. Vohra, “Ship hull structure monitoring using fibre optic sensors,” Smart Mater. Struct. 10(3), 472–478 (2001).
[Crossref]

Jiang, P. Z.

Johnson, G. A.

G. Wang, K. Pran, G. Sagvolden, G. B. Havsgard, A. E. Jensen, G. A. Johnson, and S. T. Vohra, “Ship hull structure monitoring using fibre optic sensors,” Smart Mater. Struct. 10(3), 472–478 (2001).
[Crossref]

Kadoury, S.

F. Parent, M. Gérard, F. Monet, S. Loranger, G. Soulez, R. Kashyap, and S. Kadoury, “Intra-Arterial Image Guidance With Optical Frequency Domain Reflectometry Shape Sensing,” IEEE Trans. Med. Imaging 38(2), 482–492 (2019).
[Crossref]

Kashyap, R.

F. Parent, M. Gérard, F. Monet, S. Loranger, G. Soulez, R. Kashyap, and S. Kadoury, “Intra-Arterial Image Guidance With Optical Frequency Domain Reflectometry Shape Sensing,” IEEE Trans. Med. Imaging 38(2), 482–492 (2019).
[Crossref]

Khomenko, A.

Ko, W.

Kremp, T.

Li, W. H.

J. Song, W. H. Li, P. Lu, Y. P. Xu, L. Chen, and X. Y. Bao, “Long-range high spatial resolution distributed temperature and strain sensing based on optical frequency-domain reflectometry,” IEEE Photonics J. 6(3), 1–8 (2014).
[Crossref]

Li, Y. R.

B. K. Cheng, W. G. Zhu, L. W. Hua, J. Liu, Y. R. Li, R. Nygaard, and H. Xiao, “Distributed torsion sensor based on cascaded coaxial cable fabry-perot interferometers,” Meas. Sci. Technol. 27(7), 075103 (2016).
[Crossref]

Li, Z.

Liang, C. C.

Liao, C.

Liu, J.

B. K. Cheng, W. G. Zhu, L. W. Hua, J. Liu, Y. R. Li, R. Nygaard, and H. Xiao, “Distributed torsion sensor based on cascaded coaxial cable fabry-perot interferometers,” Meas. Sci. Technol. 27(7), 075103 (2016).
[Crossref]

Liu, L.

Liu, M.

C. Shao, G. Yin, L. Lv, M. Liu, I. P. Ikechukwu, H. N. Han, L. Zhou, J. D. Zhang, W. J. Zhai, S. Wang, and T. Zhu, “OFDR with local spectrum matching method for optical fiber shape sensing,” Appl. Phys. Express 12(8), 082010 (2019).
[Crossref]

Liu, S.

Lock, J.

P. E. Dupont, J. Lock, B. Itkowitz, and E. Butler, “Design and control of concentric-tube robots,” IEEE Trans. Robot. 26(2), 209–225 (2010).
[Crossref]

Loranger, S.

F. Parent, M. Gérard, F. Monet, S. Loranger, G. Soulez, R. Kashyap, and S. Kadoury, “Intra-Arterial Image Guidance With Optical Frequency Domain Reflectometry Shape Sensing,” IEEE Trans. Med. Imaging 38(2), 482–492 (2019).
[Crossref]

Lu, L.

Lu, P.

J. Song, W. H. Li, P. Lu, Y. P. Xu, L. Chen, and X. Y. Bao, “Long-range high spatial resolution distributed temperature and strain sensing based on optical frequency-domain reflectometry,” IEEE Photonics J. 6(3), 1–8 (2014).
[Crossref]

Lv, L.

C. Shao, G. Yin, L. Lv, M. Liu, I. P. Ikechukwu, H. N. Han, L. Zhou, J. D. Zhang, W. J. Zhai, S. Wang, and T. Zhu, “OFDR with local spectrum matching method for optical fiber shape sensing,” Appl. Phys. Express 12(8), 082010 (2019).
[Crossref]

Materazzi, A. L.

A. L. Materazzi and F. Ubertini, “Eigenproperties of suspension bridges with damage,” J. Sound Vib. 330(26), 6420–6434 (2011).
[Crossref]

Miller, G. A.

C. G. Askins, G. A. Miller, and E. J. Friebele, “Bend and twist sensing in a multiple-core optical fiber,” in 2008 Conference on Optical Fiber Communication/National Fiber Optic Engineers Conference (2008), pp. 1545–1547.

Monberg, E. M.

Monet, F.

F. Parent, M. Gérard, F. Monet, S. Loranger, G. Soulez, R. Kashyap, and S. Kadoury, “Intra-Arterial Image Guidance With Optical Frequency Domain Reflectometry Shape Sensing,” IEEE Trans. Med. Imaging 38(2), 482–492 (2019).
[Crossref]

Moore, J.

Moore, J. P.

Nygaard, R.

B. K. Cheng, W. G. Zhu, L. W. Hua, J. Liu, Y. R. Li, R. Nygaard, and H. Xiao, “Distributed torsion sensor based on cascaded coaxial cable fabry-perot interferometers,” Meas. Sci. Technol. 27(7), 075103 (2016).
[Crossref]

Olsson, A.

K. Hoehn, A. Olsson, and J. W. Arkwright, “High Capacity Torque and Compression Measurements Using Fibre Optic Sensors,” in 2017 2nd International Conference for Fibre-optic and Photonic Sensors for Industrial and Safety Applications (OFSIS) (2017), pp. 39–44.

Ortiz, R. M.

Ouyang, Y.

Parent, F.

F. Parent, M. Gérard, F. Monet, S. Loranger, G. Soulez, R. Kashyap, and S. Kadoury, “Intra-Arterial Image Guidance With Optical Frequency Domain Reflectometry Shape Sensing,” IEEE Trans. Med. Imaging 38(2), 482–492 (2019).
[Crossref]

Patel, R. V.

R. Xu, A. Yurkewich, and R. V. Patel, “Curvature, torsion, and force sensing in continuum robots using helically wrapped FBG sensors,” IEEE Robot. Autom. Lett. 1(2), 1052–1059 (2016).
[Crossref]

Pran, K.

G. Wang, K. Pran, G. Sagvolden, G. B. Havsgard, A. E. Jensen, G. A. Johnson, and S. T. Vohra, “Ship hull structure monitoring using fibre optic sensors,” Smart Mater. Struct. 10(3), 472–478 (2001).
[Crossref]

Rogge, M. D.

Romano, J. M.

R. J. Webster, J. M. Romano, and N. J. Cowan, “Mechanics of precurved-tube continuum robots,” IEEE Trans. Robot. 25(1), 67–78 (2009).
[Crossref]

Russell, P. S. J.

Sagvolden, G.

G. Wang, K. Pran, G. Sagvolden, G. B. Havsgard, A. E. Jensen, G. A. Johnson, and S. T. Vohra, “Ship hull structure monitoring using fibre optic sensors,” Smart Mater. Struct. 10(3), 472–478 (2001).
[Crossref]

Shang, Y.

Shao, C.

C. Shao, G. Yin, L. Lv, M. Liu, I. P. Ikechukwu, H. N. Han, L. Zhou, J. D. Zhang, W. J. Zhai, S. Wang, and T. Zhu, “OFDR with local spectrum matching method for optical fiber shape sensing,” Appl. Phys. Express 12(8), 082010 (2019).
[Crossref]

Shao, X. G.

Shao, Y. F.

Shu, X. W.

Shum, P. P.

Simoff, D. A.

Song, J.

J. Song, W. H. Li, P. Lu, Y. P. Xu, L. Chen, and X. Y. Bao, “Long-range high spatial resolution distributed temperature and strain sensing based on optical frequency-domain reflectometry,” IEEE Photonics J. 6(3), 1–8 (2014).
[Crossref]

Soulez, G.

F. Parent, M. Gérard, F. Monet, S. Loranger, G. Soulez, R. Kashyap, and S. Kadoury, “Intra-Arterial Image Guidance With Optical Frequency Domain Reflectometry Shape Sensing,” IEEE Trans. Med. Imaging 38(2), 482–492 (2019).
[Crossref]

Steve Yao, X.

Tang, M.

Taunay, T. F.

Tong, W. J.

Ubertini, F.

A. L. Materazzi and F. Ubertini, “Eigenproperties of suspension bridges with damage,” J. Sound Vib. 330(26), 6420–6434 (2011).
[Crossref]

van den Dobbelsteen, J. J.

K. R. Henken, J. Dankelman, J. J. van den Dobbelsteen, L. K. Cheng, and M. S. van der Heiden, “Error Analysis of FBG-Based Shape Sensors for Medical Needle Tracking,” IEEE/ASME Trans. Mechatron. 19(5), 1523–1531 (2014).
[Crossref]

van der Heiden, M. S.

K. R. Henken, J. Dankelman, J. J. van den Dobbelsteen, L. K. Cheng, and M. S. van der Heiden, “Error Analysis of FBG-Based Shape Sensors for Medical Needle Tracking,” IEEE/ASME Trans. Mechatron. 19(5), 1523–1531 (2014).
[Crossref]

Vohra, S. T.

G. Wang, K. Pran, G. Sagvolden, G. B. Havsgard, A. E. Jensen, G. A. Johnson, and S. T. Vohra, “Ship hull structure monitoring using fibre optic sensors,” Smart Mater. Struct. 10(3), 472–478 (2001).
[Crossref]

Wang, G.

G. Wang, K. Pran, G. Sagvolden, G. B. Havsgard, A. E. Jensen, G. A. Johnson, and S. T. Vohra, “Ship hull structure monitoring using fibre optic sensors,” Smart Mater. Struct. 10(3), 472–478 (2001).
[Crossref]

Wang, R. X.

Wang, S.

C. Shao, G. Yin, L. Lv, M. Liu, I. P. Ikechukwu, H. N. Han, L. Zhou, J. D. Zhang, W. J. Zhai, S. Wang, and T. Zhu, “OFDR with local spectrum matching method for optical fiber shape sensing,” Appl. Phys. Express 12(8), 082010 (2019).
[Crossref]

Wang, X.

Wang, Y.

Webster, R. J.

R. J. Webster, J. M. Romano, and N. J. Cowan, “Mechanics of precurved-tube continuum robots,” IEEE Trans. Robot. 25(1), 67–78 (2009).
[Crossref]

Weiss, T.

Westbrook, P. S.

Wong, G. K. L.

Wu, H.

Wu, S.

Wu, Z. F.

Xi, X. M.

Xiao, H.

B. K. Cheng, W. G. Zhu, L. W. Hua, J. Liu, Y. R. Li, R. Nygaard, and H. Xiao, “Distributed torsion sensor based on cascaded coaxial cable fabry-perot interferometers,” Meas. Sci. Technol. 27(7), 075103 (2016).
[Crossref]

Xu, R.

R. Xu, A. Yurkewich, and R. V. Patel, “Curvature, torsion, and force sensing in continuum robots using helically wrapped FBG sensors,” IEEE Robot. Autom. Lett. 1(2), 1052–1059 (2016).
[Crossref]

Xu, Y. P.

J. Song, W. H. Li, P. Lu, Y. P. Xu, L. Chen, and X. Y. Bao, “Long-range high spatial resolution distributed temperature and strain sensing based on optical frequency-domain reflectometry,” IEEE Photonics J. 6(3), 1–8 (2014).
[Crossref]

Yang, D.

Yang, K.

Yin, G.

C. Shao, G. Yin, L. Lv, M. Liu, I. P. Ikechukwu, H. N. Han, L. Zhou, J. D. Zhang, W. J. Zhai, S. Wang, and T. Zhu, “OFDR with local spectrum matching method for optical fiber shape sensing,” Appl. Phys. Express 12(8), 082010 (2019).
[Crossref]

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Zhang, J. D.

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[Crossref]

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Figures (10)

Fig. 1.
Fig. 1. The theoretical model of the helical fiber under torsion.
Fig. 2.
Fig. 2. Simulated wavelength shift as a function of the torsion (a) linear response of the helical fiber with small pitches, (b) nonlinear response of the helical fibers with large pitches.
Fig. 3.
Fig. 3. Configuration of the distributed torsion sensing based on the OFDR system. TLS: tunable laser source; C1, C2: 1:99 couplers; C3-C6: 50:50 couplers; VOA: variable optical attenuator; PC: polarization controller; PBS: polarization beam splitter; BPD: balanced photodetector; DAQ: data acquisition; OS: optical switch; FS: Fan-in spatial Mux coupler; FUT: fiber under test. Insert: schematic of the helical MCF with pitch of 15.4 mm, side view and cross views of the optical microscopy images of the helical MCF with pitch of 6 mm.
Fig. 4.
Fig. 4. Rayleigh backscattered signal as a function of the distance. Inset: the enlarge image of the first four reflection points.
Fig. 5.
Fig. 5. (a) Rayleigh backscattering spectrum of the helical MCF before (reference) and after (measurement) twisting. (b) Cross-correlation of refence and measured Rayleigh backscattering spectrum when the torsion is 15.7 rad/m.
Fig. 6.
Fig. 6. Twist setup of the helical MCF (a) one segment of torsion; (b) three segments of distributed torsions.
Fig. 7.
Fig. 7. (a) Measured wavelength shifts of the helical MCF with a helical pitch of 15.4 mm. (b) Wavelength shifts as a function of the torsion.
Fig. 8.
Fig. 8. Measured wavelength shifts of the helical MCF under distributed torsions.
Fig. 9.
Fig. 9. (a) Wavelength shift of the helical MCF with a helical pitch of 6 mm. (b) Wavelength shift as a function of the torsion.
Fig. 10.
Fig. 10. (a) Wavelength shift of the MCF with straight fiber cores. (b) Wavelength shift as a function of the torsion.

Tables (1)

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Table 1. Distributed torsions of the helical MCF

Equations (10)

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l = ( 2 π r ) 2 + h 2
l ε = ( 2 π r + ε s h ) 2 + h 2
ε = ( l ε l ) / l
ε = 4 π r ε s h + ( ε s h ) 2 2 [ ( 2 π r ) 2 + h 2 ]
ε = r 2 ( r γ 0 ) 2 + 1 ( γ 0 γ + 1 2 γ 2 )
Δ λ = λ c ( 1 P e ) ε
S = ( 1 P e ) λ c r 2 ( r γ 0 ) 2 + 1 ( γ 0 + γ / 2 )
Δ λ = λ c ( 1 P e ) r 2 2 γ 2
Δ λ  =  S γ
S approximation = 2 π r 2 h ( 1 P e ) λ c

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