Abstract: This paper highlights the impact of dynamic amplification factors in remaining fatigue life assessment of ship unloaders. In practice, the widely accepted procedure for these structures is to carry out a fatigue life assessment envisages: (1) carrying out static analysis, (2) taking into account dynamics via the application of dynamic amplification factors, and (3) applying Miner’s rule. This factor, provided by the standard, is applied to the structure as a whole without considering the vibration of each structural member individually. This paper characterizes the dynamic behavior of each element using location-based dynamic amplification factors estimated from measurements. This caters for a more accurate assessment of the structure, whilst maintaining the simplicity of the standard procedure.

1. Field Characterization of
Location-specific Dynamic Amplification
Factors towards Fatigue Calculations in
Ship Unloaders
This project has received funding from the European Union’s Horizon 2020 research and
innovation programme under the Marie Skłodowska-Curie grant agreement No. 642453
ESREL2017 21st June 2017
G. Milana, K. Banisoleiman, A. Gonzalez
Lloyd’s Register/University College Dublin

2. STRUCTURAL ELEMENTS
2
UPPER
SUBSTRUCTURE
LOWER
SUBSTRUCTURE
WATERSIDE
TIES
REAR
TIES
FRONT
BOOM
REAR
BOOM
WATERSIDE
PORTAL
LANDSIDE
PORTAL

3. STANDARD PROCEDURE FOR ASSESSMENT
3
Ship Unloader
Modes
Measured ModesStrain Histories
Monitoring System Modal Testing
FEA Model
Stress
Modal
Analysis
Stress Ranges
+ Number Cycles
Cumulative Damage
Rainflow Counting
Method
Miner’s Rule
Reconciled Model
Critical Areas
Assessment
Static
Analysis

4. IDEA FOR IMPROVEMENTS
3
Ship Unloader
Modes
Measured ModesStrain Histories
Monitoring System Modal Testing
FEA Model
Stress
Modal
Analysis
Stress Ranges
+ Number Cycles
Cumulative Damage
Rainflow Counting
Method
Miner’s Rule
Reconciled Model
Critical Areas
Assessment
Static
Analysis
Location-based
DAFs

5. MONITORING SYSTEM
4
-48 channels of strain
-4 channels of temperature
transducers installed at 16 locations in
full bridge configuration
A1 L1
A2 L2
Axial
EX+
AI- AI+
EX-
L2 -vε
A2 +ε L1 -vε
A1 +ε T1 T2
C1 C2
Bending
EX+
AI- AI
+
EX-
C2 -ε
T2 +ε C1 -ε
T1 +ε

6. DATA PROCESSED
5
DATA
Strain-time histories
Dynamic Stresses
MATLAB
E=207 GPa
It can be assumed that:
• 400s the empty grab starts moving from the hopper to the boom
• 425s the grab starts to lift the coal
• then it starts to return to the hopper and drops the coal at 455s
Static Stresses
Cut off
frequency

7. ESTIMATED STATIC RESPONSE
6
FILTER
A low-pass filter was applied to the dynamic recorded data to
obtain a static response: 8th order Chebyshev Type I with cut-off
frequency (0.8*(Fs/2)/R)
*where fs is the sampling frequency (125 Hz) and R is the factor use for filtering
DYNAMIC AMPLIFICATION FACTOR
The estimated static stresses were then multiplied by a dynamic
amplification factor provided by the FEM 1.001
A: Overhead travelling cranes
B: Jib cranes
Y=1.3

8. DYNAMIC & 1.3*STATIC RESPONSE
7
For some locations, such as the lateral bending stress in the waterside ties the
dynamic stress is underestimated by the pseudo-static stress*, while for some
others, such as the vertical bending stress in the lifting boom, the pseudo-static
stress* turns out to be conservative.
*pseudo-static stress=DAF*estimated static stress

9. LOCATION-BASED DYNAMIC AMPLIFICATION FACTOR
8
𝐷𝐴𝐹𝑠 =
𝑑𝑦𝑛𝑎𝑚𝑖𝑐 𝑟𝑒𝑐𝑜𝑟𝑑𝑒𝑑 𝑑𝑎𝑡𝑎
𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑠𝑡𝑎𝑡𝑖𝑐 𝑠𝑡𝑟𝑒𝑠𝑠𝑒𝑠
Cut off frequency of 10 Hz to remove
noise from the measured signal
Cut off frequency of 0.4 Hz to
remove dynamics

10. IDENTIFY LOAD CYCLES
9
In order to define these DAFs as accurately as possible, several load cycles need
to be considered:
based on axial stress of the Inner ties and vertical bending stresses of the lifting
boom and lateral ties, 11 other files with dynamic recorded data have been
selected to identify a number of hoisting cycles.

11. DAFs AXIAL STRESS
10
Referring to axial stress, the lifting boom appears to have the biggest dispersion.
In fact, it has a wide range of values between 1.3 and 3.2.

12. DAFs
11
• The DAF provided by the standard was not considered
representative of the real behaviour for the majority of the
structural elements considered
• Some locations are more prone to dynamic amplification than
others. For example, the lifting boom and the waterside ties.

13. OUTER TIE
INNER TIE
LIFTING
BOOM
WATERSIDE
LEG
LANDSIDE
LEG
REAR
BOOM
REAR TIE
2D FINITE ELEMENT MODEL
12
• Primary elements modelled by beam elements
• Equivalent section for each member with ANSYS Workbench
• Modified densities to take into account non-structural members

14. STATIC RESULTS
83 Load cases have been considered:
-different position of the grab and shuttle trolley along the boom
13
INNER TIES LIFTING BOOM

15. STRESS RANGES FOR FATIGUE LIFE ASSESSMENT
14
• Axial and bending stresses were combined to evaluate the stress at each corner
• The maximum amplitude was evaluated to establish the stress range
• Location-based DAFs were applied to each component of stress

16. STRESS RANGES (Panamax range)
15

17. REMAINING LIFE
16
• The mean value of the stress ranges was evaluated for each location
• Miner’s rule was applied to evaluate the cumulative damage corresponding to a
single cycle at that stress range
• Remaining life for each location was evaluated

18. CONCLUSIONS
17
• For the scenario under investigation the DAF provided by the standard was
not considered representative of the real behaviour for the majority of the
structural elements considered.
• The remaining number of cycles can be extended or decreased with respect to
the standard by considering the unique dynamic features of each section
CONCLUSIONS
IMPROVEMENTS
• A more accurate 3D FE model will be built to model lateral bending stresses
and the characteristic behaviour of the waterside ties
• The FE model would need to be calibrated to gather DAF and remaining
number of cycles before fatigue failure, for locations with or without available
measurements.
• More sample will be taken into account, considering also different kind of
vessels unloaded

19. Thank you for your attention!
Lloyd’s Register University College Dublin
This project has received funding from the European Union’s Horizon 2020 research and
innovation programme under the Marie Skłodowska-Curie grant agreement No. 642453