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Abstract

The downhole temperature and drilling fluid pressure have a significant influence on the contact pressure and leakage rate of the dynamic seal. This article attempts to improve the seal performance using response surface method and multi-objective optimization. The width of the sealing interface, the pressure difference, the hardness of the O-ring and the hardness of the support ring are defined as independent variables, while the outer contact pressure and the leakage rate of the seal are chosen as objective functions. The response surface method has been employed to construct mathematical models combining both the independent variables and the objective functions, and the accuracy of the mathematical models has been proved. Furthermore, the leakage rate was regarded as a constraint function; hence, the minimum outer contact pressure and corresponding design parameters were obtained. The outer contact pressure of the seal increases with the increase in the drilling pressure difference, but decreases with the width of the sealing interface; the relationships between the leakage rate and the independent variables have also been obtained. According to the multi-objective optimization, the leakage rate of the improved seal decreased by more than 10% compared to the previous seal.

Keywords

Parameter optimization sealing performance response surface method cone bit finite element analysis

Introduction

With the increase in the downhole temperature and drilling fluid pressure, the life of a conventional rubber seal is greatly challenged in abrasive environment. Hence, the metal dynamic seal has been widely applied in the cone bit to prevent the drilling fluid from flowing into lubricant chamber of the cone bit.¹ The double metal floating seal was used in cone bit bearing sealing system. In 1987, Luo et al.² designed a single metal floating seal with the characteristics of simple structure, high temperature resistance and high pressure resistance to promote the seal performance, and the seal was widely used in the field of drilling. On this basis, the newest generation bearing seal was improved by Baker Hughes in 2003, as shown in Figure 1. The stator, the rubber support ring and the O-ring are relatively stationary; the rotor rotates together with the shaft. Meanwhile, the O-ring and the rubber support ring supply the axial compressive force for the seal interface between the rotor and the stator.³

Figure 1.

Schematic illustration of the bit seal.

Due to the complex downhole conditions and the non-linear seal force of rubber material, the research on the downhole seal is relatively few. Based on the elasto-hydrodynamic lubrication theory, Xiong and Salant^4,5 presented a numerical model of the first-generation metal dynamic seal to predict the contact pressure distributions and the leakage rate. As for the wear of seal interface, the performance of the dynamic seal was improved by Zhang et al.,^6,7 and the failure cause of the seal was discussed. As to the reliability and the parameter optimization of the dynamic seal, the influence of the hardness of the support ring and the O-ring on the seal contact pressure was studied using finite element analysis (FEA).⁸ Although these scholars have investigated the performance of bit seal, there are few descriptions about the bit seal optimization under drilling condition.

For the seal optimization, conventional experiment optimization and FEA are time-consuming when the experiment involves many parameters. To overcome this limitation, response surface method (RSM) is widely used in the engineering research field for its higher optimization efficiency to evaluate the response of the parameters using fewer FEA results.^9–11

Therefore, this article aimed to prolong the seal life through the numerical simulation and the RSM methodology. The article is organized as follows. Section “Problem description” introduces the problem of the seal failure. On the basis of the constitutive parameter of rubber, the distributions of seal contact pressure and the leakage rate were obtained using numerical calculation in section “Methods.” In section “Results and discussions,” the RSM approach was applied to obtain the better parameter combination of the seal, and the results were confirmed by the numerical simulations and experiments.

Problem description

For the cone bit, the bearing life is largely determined by the seal. To increase the seal life and provide a superior performance seal, the seal wear resistance and the rubber part fatigue resistance should be improved as much as possible. The material of the stator and rotor is YG8. The O-ring and rubber support ring are made of hydrogenated nitrile butadiene rubber (HNBR) with a hardness of about 80 international rubber hardness degree (IRHD), a material that ensures high temperature resistance together with high compatibility for lubrication. Figure 2 shows the wear condition of seal components, which clearly demonstrates that only the outer seal surface is worn out. The wear of the O-ring is very serious due to the slippage between the O-ring and the stator. However, the fundamental reason is that the hardness of the rubber is too low.

Figure 2.

Wear condition of the seal components.

Methods

Material constitutive of rubber

In this study, the material of the O-ring and rubber support ring is NBR with a hardness of 70 shore A, which exhibits high non-linear elasticity and high temperature resistance. There are many constitutive models to describe the mechanical properties of rubber. The Mooney–Rivlin strain energy function was selected to describe the non-linear and incompressible behaviours of rubber, and the stress can be written as the derivative of the energy function to the strain,¹² as given by

σ_{ij} = \frac{\partial W}{\partial ε_{ij}}

(1)

The strain energy is expressed by the following formula

W = \sum_{k + m = 1}^{n} C_{km} {(I_{1} - 3)}^{k} + (I_{2} - 3)^{m} + \frac{1}{2} K (I_{3} - 1)^{2}

(2)

where I₁, I₂ and I₃ are the strain invariants; C_km and K are the constant and bulk modulus of the Mooney–Rivlin model, respectively. I₃ is equal to 1 due to the incompressibility of rubber. Thus, only two Mooney–Rivlin parameters C₁₀ and C₀₁ need to be considered when n = 1. As to the incompressible material, the relationship between the elastic modulus E and the shear modulus G of rubber is¹³

G = \frac{E}{2 (1 + v)}

(3)

where $v \approx 0.5$ and $G = 2 (C_{1} + C_{2})$ ; the relationship between shore hardness H_r and elastic modulus E is¹⁴

\lg E = 0.0198 H_{r} - 0.5432

(4)

To get the ratio of C₁ and C₂ for different hardness of rubber, the curves of the rubber characteristics under different hardness can be obtained by changing the value of C₁/C₂. Numerical simulations indicate that C₂/C₁ is 0.1, 0.05 and 0.02 when the rubber hardness is 40, 60 and 70, respectively.¹⁴ The relationship between C₂/C₁ and shore hardness H_r can be obtained by numerical fitting and can be written as

\frac{C_{2}}{C_{1}} = - 0.0026 H_{r} + 0.2064

(5)

According to equation (5), the rubber constitutive parameters for different hardness are summarized in Table 1.

Table 1.

Parameters of rubber material for different hardness.

Hardness (HA)	E (MPa)	Mooney–Rivlin
Hardness (HA)	E (MPa)	C ₁	C ₂
70	6.96	1.137	0.023
75	8.74	1.444	0.0165
80	10.98	1.833	−0.003
85	13.80	2.334	−0.034
90	17.33	2.972	−0.082

Contact pressure

The contact pressure has an important influence on the seal surface wear. For this analysis, the seal structure and load condition can be regarded as an axial symmetry model, so the contact pressure can be obtained using ANSYS based on the two-dimensional finite element model. The seal assembly process with a 4-mm displacement loaded on the stator was completed in the first step, and then the ambient loads, including the inner lubricant pressure and the outer fluid pressure, were applied to the inner and the outer surface of the seal. The pressure difference ΔP between the lubricant and the drilling fluid ranges from 0.3 to 0.7 MPa and has a great impact on the contact pressure of seal interface during drilling.⁷ When the drilling fluid pressure is 3 MPa, the distributions of contact pressure under different pressure difference are shown in Figure 3. It can be seen that the maximum contact pressure is located on the outer surface, and the contact pressure distribution on the seal interface is uneven. Meanwhile, the outer contact pressure increases with the increase in drilling pressure difference.

Figure 3.

Contact pressure under different drilling pressure difference.

Seal leakage rate analysis

The leakage rate of the seal can be calculated by numerical simulation model. To simplify the numerical computation, the inverse method was used to solve the film thickness of the seal interface. Furthermore, the seal geometry, the loads and the flow field of seal interface were considered to be axisymmetric. Based on these assumptions, the leakage rate can be obtained by the one-dimensional Reynolds equation, which is shown as

\frac{1}{r} \frac{\partial}{\partial r} (\frac{ϕ_{r} r h^{3}}{12 μ} \frac{\partial P}{\partial r}) = 0

(6)

The boundary conditions are as follows: P = P₁ = 0.7 MPa (lubricant), at r = r₀ = 25 mm (inner radius of the seal); P = P₂ = 0.3 MPa (drilling fluid), at r = r₁ = 28.5 mm (outer radius of the seal); and μ and h are the lubricant dynamic viscosity and film thickness, respectively. The density and viscosity of the lubricant can be expressed as¹⁵

{\begin{matrix} ρ = ρ_{0} [1 + \frac{c_{a} P_{1}}{(1 + c_{b} P_{1})}] \\ η = η_{0} e^{α_{B} P_{1}} \end{matrix}

(7)

c_a and c_b are the lubricant constants; $ρ_{0}$ and $η_{0}$ are, respectively, the density and the viscosity of the lubricant when the lubricant pressure is equal to zero; and the pressure viscosity coefficient $α_{B}$ can be expressed as

α_{B} = 34.95 \times 10^{- 9} + 9.65 \times 10^{- 9} \log (η_{0})

(8)

Based on the contact pressure gradient distribution of the seal interface obtained by ANSYS, the film pressure distribution of the seal interface can be calculated according to equations (6)–(8), and the seal leakage rate Q can be calculated from equation (9)

Q = \int_{0}^{2 π} \frac{ρ ϕ_{r} r h^{3}}{12 μ} \frac{\partial p}{\partial r} d θ

(9)

In this article, the environmental temperature is T = 30°C, and the lubricant is lithium complex grease with a kinetic viscosity of 4.071 Pa·s at 30°C. The shaft rotating speed n = 200 r/min, and the pressure difference is 0.5 MPa. The result of leakage per second is presented in Figure 4. It is concluded that the leakage decreased first, and then increased with the increase in the drilling fluid pressure because the maximum contact pressure gradually moves to the inner surface.

Figure 4.

Effect of drilling fluid pressure on the leakage.

Results and discussions

Application of RSM

According to the contact pressure distributions and seal failure, the main focus of this section is to reduce the out contact pressure and leakage rate of the seal using multi-objective optimization as much as possible. The width of the sealing interface (W), the pressure difference (ΔP), the hardness of the O-ring (ShA) and the hardness of the support ring (ShB) were selected as four independent design factors that may affect sealing performance of the seal. The outer contact pressure and the seal leakage rate were regarded as the optimization targets. In this article, the RSM was employed to construct regression models for the multi-objective optimization, and the Design-Expert is the most commonly used analysis software for the RSM. The response surface design method in the Design-Expert includes the Box–Behnken and the Central Composite Design.¹⁶ The Central Composite Design method was adopted to obtain the better parameter combination of bit seal.

In this study, a prediction model between the optimization targets and the four individual factors denoted as x₁, x₂, x₃, x₄ can be obtained using the design of experiments (DOE) and the regression analysis.¹⁷ Furthermore, the impact of the individual factors and the interactions among these factors on the optimization targets also can be determined by the prediction model. The quality of the prediction model can be judged using the residual analysis and the statistical tests. The procedure of the RSM is shown in Figure 5.

Figure 5.

Multi-objective optimization procedure using RSM.

A second-order model was selected as a defined response using the least-squares method in RSM and can be expressed as¹⁷

y = a_{0} + \sum_{i = 1}^{n} a_{i} x_{i} + \sum_{i = 1}^{n} a_{ii} x_{ii} + \cdot \cdot \cdot + \sum_{i < j}^{n} a_{ij} x_{i} x_{j} \pm ε

(10)

where a₀ is a constant; a_i, a_ii and a_ij are the coefficients of linear, second degree and mixed terms of the model, respectively; and ϵ is the fitting error. The orthogonal design using Taguchi’s method can improve experimental efficiency and has been successfully used to solve the problem of optimal combination in quality improvement.¹⁸ As to the seal optimization, the three levels of the individual factors denoted as A, B, C and D were generated using an orthogonal array table, as shown in Table 2.

Table 2.

Impact factors and levels of seal.

Design parameters			Levels
Design parameters			Low−1	Medium0	High+1
Width of the sealing interface (A)	W	mm	2.5	3.5	4.5
Pressure difference (B)	ΔP	MPa	0.3	0.5	0.7
Hardness of support ring (C)	ShA	HA	70	80	90
Hardness of O-ring (D)	ShB	HA	70	80	90

Meanwhile, the outer contact pressure P_out and the seal leakage rate Q of the seal were used as the optimization targets, and 30-group simulations with different design parameters were conducted by FEA and MATLAB (the 15th–20th experimental simulations were the central experiments and were used to guarantee higher prediction), as seen in Table 3.

Table 3.

Results of the central experiments for the seal.

No.	Design parameters				Objective functions
No.	W (A)	ΔP (B)	ShA (C)	ShB (D)	P_out (MPa)	Q× 10⁻² (mm³/s)
1	2.5	0.3	90	90	33.612	3.012
2	2.5	0.7	70	70	32.581	5.251
3	2.5	0.3	70	90	32.012	3.207
4	2.5	0.3	70	70	29.807	3.936
5	2.5	0.3	90	70	31.841	4.037
6	2.5	0.5	80	80	32.563	4.43
7	2.5	0.7	90	70	32.792	4.642
8	2.5	0.7	70	90	33.116	3.611
9	2.5	0.7	90	90	34.108	3.298
10	3.5	0.5	80	90	30.43	2.975
11	3.5	0.5	70	80	29.87	4.775
12	3.5	0.3	80	80	29.412	4.237
13	3.5	0.5	80	70	29.102	5.806
14	3.5	0.5	90	80	30.816	4.613
15	3.5	0.5	80	80	30.06	4.68
16	3.5	0.5	80	80	29.77	4.497
17	3.5	0.5	80	80	30.37	4.612
18	3.5	0.5	80	80	30.113	4.277
19	3.5	0.5	80	80	29.947	4.881
20	3.5	0.5	80	80	29.928	5.472
21	3.5	0.7	80	80	30.022	4.875
22	4.5	0.7	90	70	29.585	6.477
23	4.5	0.3	90	70	28.672	4.876
24	4.5	0.3	70	90	28.251	3.91
25	4.5	0.5	80	80	29.133	5.208
26	4.5	0.7	90	90	30.027	4.036
27	4.5	0.3	90	90	29.28	3.834
28	4.5	0.3	70	70	27.983	5.635
29	4.5	0.7	70	70	29.263	9.849
30	4.5	0.7	70	90	29.791	4.776

According to the results of the simulation analysis, a prediction model indicating the relationships between the objective functions and the design variables was established using the least-squares method and is given by

\begin{matrix} P_{out} = 5.91 - 0.072 X_{1} - 0.2 X_{2} + 0.11 X_{3} + 0.4 X_{4} \\ + 0.088 X_{1} X_{2} + 0.008 X_{1} X_{3} - 0.1 X_{1} X_{4} \\ - 0.053 X_{2} X_{3} - 0.077 X_{2} X_{4} + 0.016 X_{3} X_{4} + 0.047 X_{1}^{2} \\ - 0.068 X_{2}^{2} + 0.12 X_{3}^{2} - + 0.17 X_{4}^{2} \end{matrix}

(11)

\begin{matrix} Q = - 2.353 + 4.872 X_{1} + 23.838 X_{2} - 0.052 X_{3} \\ - 0.051 X_{4} + 1.363 X_{1} X_{2} \\ - 0.025 X_{1} X_{3} - 0.035 X_{1} X_{4} - 0.131 X_{2} X_{3} \\ - 0.189 X_{2} X_{4} + 2.128 \times 10^{- 3} X_{3} X_{4} \end{matrix}

(12)

The significance of the terms in the prediction model can be determined by the analysis of variance (ANOVA), and the results of the ANOVA for P_out and Q are shown in Tables 4 and 5, respectively.

Table 4.

ANOVA table for P_out.

Source	Sum of squares	df	Mean square	F-value	p-value	Significance
Model	14	4.77	0.34	19.6	<0.0001
A	1	0.094	0.094	5.4	0.0345
B	1	0.68	0.68	39.39	<0.0001
C	1	0.21	0.21	11.91	0.0036
D	1	2.86	2.86	164.37	<0.0001
AB	1	0.12	0.12	7.15	0.0173
AD	1	0.16	0.16	9.32	0.008
BC	1	0.045	0.045	2.6	0.1278
BD	1	0.095	0.095	5.44	0.034
Residual	15	0.26	0.017
Cor. total	29	5.03
Standard deviation		0.13	R ²			0.9482
Mean		6.08	Adjusted R²			0.9298
Coefficient of variation		2.17	Predicted R²			0.745
Predicted residual of sum of squares		1.28	Adequate precision			18.908

Table 5.

ANOVA table for Q.

Source	Sum of squares	df	Mean square	F-value	p-value	Significance
Model	14	52.67	3.76	67.89	<0.0001
A	1	10.32	10.32	186.23	<0.0001
B	1	21.74	21.74	392.2	<0.0001
C	1	0.12	0.12	2.17	0.1617
D	1	1.55	1.55	27.95	<0.0001
AB	1	3.72	3.72	67.21	<0.0001
A²	1	0.31	0.31	5.54	0.0326
B²	1	1.16	1.16	20.95	0.0004
Residual	15	0.83	0.055
Cor. Total	53.5	29
Standard deviation		0.24	R ²			0.9845
Mean		4.55	Adjusted R²			0.97
Coefficient of variation		5.18	Predicted R²			0.9373
Predicted residual of sum of squares		3.35	Adequate precision			26.806

The confidence analysis of these factors was provided to verify the accuracy of the prediction model, including ‘squares’, ‘mean square’, ‘F–value’ and ‘p-value’. The factors are significant to the responses when the p-values are less than 0.05; hence, the four design parameters of the seal have a significant effect on the P_out and Q. Meanwhile, their interaction effect items AC, AD and BC, as well as the quadratic item A², are also significant for the P_out. Furthermore, the R² and the adjusted R² values are also the statistical criteria to evaluate the significance of the prediction model, and the values of P_out are 94.8% and 93.0%, while the two values of Q are 98.5% and 97.0%, respectively. Since the difference between the R² and the adjusted R² for the two optimization targets is very small, the prediction model can represent the relationships between the design parameters and the responses. Furthermore, Figure 6 shows the studentized residual of the outer contact pressure and leakage rate. It can be seen that the distribution of residual points almost along a straight line, which also indicates that the prediction model, can predict the objective functions accurately.

Figure 6.

Residual distribution of outer contact pressure and leakage rate.

The impacts of the design variables on the objective functions are shown in Figures 7 –10, where each contour represents the gradient value of the optimization targets in the contour diagrams. According to Figures 7 and 8, when the values of A are larger and those of B are smaller, the minimum outer contact pressure can be obtained to reduce the wear of sealing surface, and the outer contact pressure decreases with the decrease in C and D.

Figure 7.

Outer contact pressure versus width of the sealing interface and pressure difference.

Figure 8.

Outer contact pressure versus hardness of support ring and O-ring.

Figure 9.

Leakage rate versus width of the sealing interface and pressure difference.

Figure 10.

Leakage rate versus hardness of support ring and O-ring.

Moreover, it can be observed that A and D have greater effects on the outer contact pressure than B and C, respectively. For the leakage rate, Figure 9 shows that the leakage rate increases with the increase in the environmental pressure difference B and the width of the sealing interface A. In contrast, Figure 10 shows that the leakage rate is inversely proportional to the hardness of the O-ring and rubber support ring. It can also be seen that A and D have more impact on the leakage rate than B and C, respectively.

Multi-objective optimization

The commonly used multi-objective optimization methods include the linear weighted combination method, the least-squares method and the main target method. In this article, the main objective method was selected to solve the seal optimization, and the outer contact pressure and the leakage rate of the seal were deemed as a major design goal and a constraint function, respectively.¹⁹ For the eight-inch bit seal, the volume of lubricant is approximately 80 cm³. To prolong the life of the seal, the allowable leakage rate of the seal is approximately 0.045 mm³/s under normal working conditions. Therefore, the objective function of the seal can be expressed as

{\begin{matrix} Min F (X), X = (W, Δ P, ShA, ShB) \\ s . t . Q (X) ⩽ 0.045 m m^{3} / s \\ 2.5 mm ⩽ W ⩽ 4.5 mm \\ 0.3 MPa ⩽ Δ P ⩽ 0.7 MPa \\ 70 ⩽ ShA ⩽ 90 \\ 70 ⩽ ShB ⩽ 90 \end{matrix}

(13)

The out contact pressure has been obtained by the RSM in equation (11). Thus, the single-objective function F(X) can be solved by the optimization module in Design-Expert. The optimization values and corresponding design parameters were obtained after 54 iterative computations. For the improved and previous seal parameters, the outer contact pressure and leakage rate of the seal were calculated by the prediction model using the RSM, and the simulation results were calculated by FEA under the same condition as shown in Table 6.

Table 6.

Comparison of the results before and after optimization.

	Parameters				P_out (MPa)			q× 10⁻² (mm³/s)
	L (mm)	ΔP (MPa)	ShA (HA)	ShB (HA)	Predicted	Simulation	Error (%)	Predicted	Simulation	Error (%)
Previous	3.5	0.7	80	70	29.634	30.572	3.17	5.286	4.992	5.56
Improved	4.28	0.3	73.63	81.04	28.147	28.906	2.70	4.50	4.278	4.93

Fortunately, the improved seal has an outer contact pressure of 28.147 MPa and a leakage rate of 0.045 mm³/s, compared to 29.634 MPa and 0.0528 mm³/s, respectively, for the previous seal. Meanwhile, Table 6 also compares the relative error for the outer contact pressure and the leakage rate using RSM and FEA to verify the accuracy of the regression model. For the improved seal, the error of the outer contact pressure is only 2.7%, and the error of leakage rate is 4.93%; thus, the RSM can be applied to the optimization design of the bit seal to extend the life of the seal.

Experimental verification

To verify the analysis results, a seal test rig, shown in Figure 11, was set up to evaluate the performance of the seal.²⁰ The sealing cavity was filled with lubricant, and the outer drilling fluid was replaced by water. To simulate the pressure difference more accurately, a hydro-cylinder with a spring was used to ensure that the lubricant pressure is higher than that of the water. The seal leakage rate can be calculated using the piston area and the piston displacement. It is difficult to obtain the seal contact pressure through experiments. However, a higher contact pressure results in more friction heat, and thus, the contact pressure distribution follows the same trend as the temperature distributions. The outer surface temperature of the stator can be measured using a thermocouple.

Figure 11.

Schematic diagram of a seal tester.

In this trial, the pressure difference is 0.7 and 0.3 MPa for the previous seal and the improved seal, respectively. The environmental temperature is T = 30°C, the shaft speed n = 200 r/min and the water pressure is 3 MPa. The temperature of the outer seal interface and the leakage rate for the previous and improved seal with the lubricant pressure are presented in Figure 12.

Figure 12.

Comparison of the surface temperature and leakage rate before and after optimization.

Figure 12 shows the comparison of the surface temperature and leakage rate before and after optimization, where the temperature increases with the increase in the lubricant pressure from 0.5 to 2.5 MPa, and the error bars represent the standard deviations of the three experiments. The experimental results show that the temperature of the improved seal interface is lower than the previous, which means that the contact pressure of the outer seal is reduced after optimization, so the wear of the improved sealing surface can be reduced effectively. Meanwhile, the outer contact pressure increases rapidly with the increase in the lubricant pressure; thus, the wear of seal interface will be further aggravated during ultra-high pressure drilling. For the seal leakage rate, Figure 12 shows that the trend of the effect of lubricant pressure on seal leakage rate is opposite to the temperature. Therefore, the contact pressure should be reduced as much as possible when the leakage rate is allowed. The results of the experiment are consistent with the RSM and the FEA on the whole, even though there are some errors.

Conclusion

In this article, a study of parameter optimization of the cone bit seal has been conducted to find the optimum values of the design parameters through RSM and numerical simulation. The relations between the objective targets and four design parameters were set up, and a multi-objective optimization was studied to determine the optimal parameter combination to extend the life of the cone bit. The main results can be summarized as follows.

The ANOVA results show that the prediction model for the objective targets adequately represents the numerical analysis results because the F-values are less than 0.05, and the difference between the R² and adjusted R² for the objective targets is very small. Based on the RSM, the regression equations relating to the objective functions and the design parameters were established.

The error of the objective functions for the regression model and simulation is small enough and falls in the acceptable range. The leakage rate of the optimized seal gets an improvement of more than 10% compared to the previous seal.

The results of the confirmation experiments basically coincide with the the numerical simulation. Thus, the optimization method in this article can be applied to the optimization design of the seal more effectively.

Footnotes

Handling Editor: Davood Younesian

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Yi Zhang

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Optimization design for downhole dynamic seal based on response surface method

Abstract

Keywords

Introduction

Problem description

Methods

Material constitutive of rubber

Contact pressure

Seal leakage rate analysis

Results and discussions

Application of RSM

Multi-objective optimization

Experimental verification

Conclusion

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References