Experimental validation - Validation with model of bottom fixed offshore structure

IV. NUMERICAL AND EXPERIMENTAL VALIDATION

4.2 Validation with model of bottom fixed offshore structure

4.2.2 Experimental validation

This section experimentally validates the algorithm with a model of offshore structure. The experiment is conducted in a flume with controlled water current. Measured responses are used to validate the response estimation algorithm.

4.2.2.1 Experimental setup

Experiment was carried out in a circulating water channel as shown in Figure. 4.18. Length, width, and height of the circulating water channel are 24 m, 480 mm, and 900 mm respectively. Specimen was installed 9 meters away from the sluice gate to avoid the effect of reflected water current from sluice gate and also to ensure a uniform flow from source.

Fig.

Figure 4.18. Circulating water channel setup

Figure. 4.19 shows the dimension of specimen and the sensors deployed on it. The selected accelerometers are integrated circuit piezoelectric (ICP) Type 355B33, PCB Piezotronics, Inc. Strain gauges were deployed on each column at 150 mm from the root of the specimen. As the velocity of water current is lower at the root of column, Strain gauges are installed near the root to avoid getting damaged. Also, waterproof material was applied to avoid distortion of the strain response. Strain gauges are configured with half-bridge to improve its sensitivity. The acceleration and strain responses were measured with a sampling rate of 200

Hz by data acquisition devices (DAQ), produced by HBM, Inc., QuantumX 1601b and 1615b (Operation manual).

Fig. 4.19. Specimen with deployed strain gauge and accelerometer

Figure 4.20. Shows the specimen with and without water current. It can be observed that the upstream water level is higher than the downstream. Also the columns in the downstream experience some turbulence compared to other columns. Thus columns in upstream and downstream will experience different force.

x

x y

z

Figure. 4.20. Specimen with and without water current

Table 4.2 shows material properties and the dimension for the top plate and columns which are parts of the experimental specimen.

TABLE 4.2. Material properties and the dimension for the experimental specimen

Properties Top plate Columns

Material Steel HDPE (high-density polyethylene)

Young’s modulus 210 GPa 1000 MPa

Shear modulus 79.3 GPa 800 MPa

Density 7850 Kg/m³ 953 Kg/m³

Size 430mm x 350mm x 9t D60mm x 5t height : 1000mm

Weight 10.63kg

(without accessories)

0.823kg/each column (without connector)

The experiment is carried out in 3 steps: Step 1: Initially the flume is filled with a water level of 400mm by shutting the sluice gate at the end of the water channel (see Figure. 4.18 ). Step 2: Now the pump is turned

on and also the sluice gate is controlled to maintain the same water level throughout the experiment. Step 3: after few minutes pump is turned off and simultaneously sluice gate is controlled to maintain the same water level.

4.2.2.2 Estimation results

Figure. 4.21 compares the experimental and estimated strain response at each column. Validation cases here are similar to the cases seen in numerical validation. From the experimental strain response it can be observed that first 60 sec the water is still and after 60sec current velocity increases rapidly until 130 sec.

the water velocity is maintained constantly for about 100 sec and gradually to zero. Response estimation algorithm uses the same FE model used in numerical validation. The estimation consists of two steps. Step 1: estimate the input time history on each column with limited responses (Eq. 4.2), strain at unmeasured location is assumed to be same as nearest available strain response. Step 2: Calculate input covariance with available input time history and use limited response in Kalman filter based response estimation algorithm to estimate unmeasured responses. Overall estimation from Figure. 4.21 is in good agreement with measured responses. As it is difficult to see the dynamic component of estimation from Figure. 4.21, Figure.

4.22 shows the estimation between 130 and 140 sec.

(a) Case 1 (b) Case 2

Figure. 4.21 Estimated and experimental strain response in time domain

Figure. 4.22 Estimated and experimental strain response in column 4 (between 130 and 140 sec ) Unlike the reference strain from numerical model the strain data from experiment is subjected to small electrical noise (see Figure. 4.23), 60Hz peak can be observed. Where else in the estimated response the 60Hz peak is relatively smaller than the experimental response. The reason is multi-metric fusion of acceleration with strain helps to reduce the noise component from strain gauge.

Figure. 4.23. Estimated and experimental strain response in frequency domain

Figure. 4.24 shows the RMSE error in estimated strain at each column. Strain estimation in Column 1 and 2 has higher error % which could arise based on strain deployment skills. The strain gauges should be deployed perpendicular to the plane of bending else the measured responses may not be accurate. Since the model and estimator assumes the strain measured are purely in x direction, the higher errors in column 1 and 2 could be due to improper sensor deployment. Column 3 and 4 has about 1% error.

Fig. 4.24 Error in estimated strain at each column

Chapter 4 CONCLUSION, LIMITATION AND FUTURE SCOPE

The virtual sensing strategy tailored to SHM of offshore structures was proposed in this study. As the important structural members of the offshore structures are located under water, the virtual sensing strategy can be a powerful alternative to the direct measurement, particularly when the structural responses are desired at locations of unavailable sensors. The problem formulation for the virtual sensing based on the Kalman filtering was provided with how the non-zero mean input excitation can be properly handled for accurate response estimation. Different types of responses, viz., acceleration, tilt and strain, were employed as the input to Kalman filter. The strain and tilt response contributes to the low frequency, large amplitude trend in the estimation, while the acceleration with good high frequency information is capable of reducing the random noise. The numerical simulation was conducted with the finite element model of the small scale offshore structure. The non-stationary random input excitation varying depending on time and height is introduced to simulate the tidal current. The simulation result from offshore model showed that the estimation was accurate with errors all less than 1%. The laboratory experiment is subsequently conducted with the small scale offshore structure installed in the water channel. The virtual sensing strategy was shown to successfully find a strain response using the other three strains and the acceleration on the top plate. The numerical analysis and the experiment lead to the following conclusions:

 The redesigned Kalman state estimator has successfully estimated the strain responses at the unmeasured locations excited by the non-stationary random inputs in the numerical and experimental validation tests, even with the erroneous model used in the Kalman state estimator.

 The acceleration has been figured out to be improper to estimate the quasi-static trend of non-zero mean strain response excited by the non-zero mean input due to lack of accuracy in low frequency measurement near 0 Hz.

 The virtual sensing strategy has the potential to be able to capture structural responses of the bottom-fixed offshore structures under the non-stationary random tidal current.

 The fused use of the different types of measurements (i.e., strain and acceleration) can help to improve the estimation in lower and higher frequency regions.

This virtual sensing technique has general limitations and also under special conditions.

 When there is a damage at the location of estimation, the estimated response may not be accurate.

If the damage can make a significant change in other measured sensors, in such a case estimation shall be accurate.

 The assumption of Gaussian distribution for random variables is also one of the limitation of this virtual sensing technique. Under some cases process noise distribution is not Gaussian.

 Since virtual sensing algorithm is executed at every timestamp, computationally overall SHM system becomes heavy and thus it needs a better processing hardware.

The virtual sensing strategy is seen to be quite useful for monitoring the offshore structures. Based on the findings in this dissertation, further study may include the use of the estimated response for the SHM purposes such as fatigue estimation and damage detection.

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