The document discusses the optimization of weight design for a barge's deck, emphasizing the importance of minimizing structural weight to enhance production cost, vessel speed, and lifecycle cost. Various design optimization strategies are explored, with a focus on finite element analysis and sensitivity testing to determine the most effective parameters for design. The study concludes that optimization via genetic algorithms effectively reduced the deck weight by 9%, while highlighting the critical role of certain design parameters in impacting stress and overall performance.

1. Optimum Weight Design of
Ship’s Structures
with Application to a Barge’s Deck
By: Mohamed Moanes Abdel Salam
Prof. El-Sayed Hegazy
Professor of Ship’s Structures
Department of Marine Engineering
Port-Said University
Dr. Ahmed Naguib
Asst. Prof of Ship’s Structures
Department of Marine Engineering
Arab Academy (AAST)
Supervisors
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2. 2
Contents
• Chapter One: Introduction
• Chapter Two: Principles of Design Optimization
• Chapter Three: Reference Deck Stress Analysis
• Chapter Four: Optimum Design for Barge’s Deck
• Chapter Five: Conclusion

3. Chapter One: Introduction
Background and Motivation
3

5. Minimizing structural weight can have significant
impacts on;
• Production cost
• Vessel Speed
• Lifecycle cost
• Pollution
Design for Minimum Weight
Chapter One: Background and Motivation
5

6. Stiffened Panels
Stiffened panels are main member in marine structures
since half of twentieth century, as they are used in several
usages such as; bottom, side, deck and bulkhead
constructions etc.
Chapter One: Background and Motivation
6
They are more cost-effective by offering a desirable
strength/weight ratio.

7. Stiffened Panels
Chapter One: Background and Motivation
7

8. Chapter Two:
Principles of Design Optimization
8

9. Chapter Two: Design Optimization
Design Optimization Goal
Design Optimization is to design better, more efficient
and less expensive systems. By determining the best case
without actually testing all possible cases and identify
the relationship between the performance of the product
(Maximum stress, Deformation, Mass etc.) and the design
variables (Dimensions, Material, Loads etc.).
9

10. Chapter Two: Design Optimization
Strategy of Experimentation
10
 Best guess approach
 One-factor-at-a-time
 Factorial Approach

11. Chapter Two: Design Optimization
Strategy of Experimentation
Golf Example
11

13. Chapter Two: Design Optimization
Strategy of Experimentation
13
 Best guess approach (trial and error)
• Can continue indefinitely.
• Cannot guarantee that best solution has been found.

14. 14
One-factor-at-a-time (OFAT)
Chapter Two: Design Optimization
Strategy of Experimentation

15. Chapter Two: Design Optimization
Strategy of Experimentation
 One-factor-at-a-time (OFAT) approach
15
• Inefficient (requires many test runs).
• Fails to consider any possible interaction between factors.

16. 16
Factorial Experiment
Chapter Two: Design Optimization
Strategy of Experimentation
A =
40+50
2
−
20+30
2
= 20 B =
30+50
2
−
20+40
2
= 10
Factorial Sensitivity

17. 17
Strategy of Experimentation
Chapter Two: Design Optimization
Factorial Experiment

18. 18
Factorial Experiment
Strategy of Experimentation
Chapter Two: Design Optimization

19. 19
Fractional Factorial Experiment
Strategy of Experimentation
Chapter Two: Design Optimization

20. Chapter Two: Design Optimization
Design Of Experiment Techniques
Used to determine the Location of sampling points. The
goal is to get as Accurate Response Surface as possible
with as few input combinations as possible.
20

21. Chapter Two: Design Optimization
MCS Vs. LHS Vs. OSF
21

22. Chapter Two: Design Optimization
Multi-Objective Optimization
 Screening Optimization
 Multi-Objective Screening Optimization
22

24. Multi-Objective Genetic Algorithm
 Stopping rule
Process use some stopping rule, such as;
o Fixed number of iterations.
o Fixed amount of CPU time.
o Fixed number of consecutive iterations without any
improvement in the best trial solution found so far.
Chapter Two: Design Optimization
24

25. Chapter Three:
Finite Element Analysis for a
Barge’s Deck
25

26. A barge is a flat-Deck boat, built mainly to transport heavy goods
such as; containers, gravel and construction equipment.
Chapter Three: FEA for a Barge’s Deck
26

27. Reference Barge’s design
Length Overall 330 ft (100.58m)
Breadth Moulded 100 ft (30.48m)
Depth Moulded 20 ft (6.10m)
Design Draft 4.5m (Approx.)
Deadweight 10,500 Tons (Approx.)
Deck Strength 20 Ton/m2
Principal Dimensions
Chapter Three: FEA for a Barge’s Deck
27

28. 28

30. Deck Element Model
Name Dimensions (mm)
Deck Element Plate 12.8 m x 7.62 m x 18
Longitudinal Stiffeners 150 x 90 x 12
Transverse Webs 609 x 203 x 14
DK Side Girder 609 x 345 x 12
Spacing of Longitudinal Beams 508
Spacing of Transverse Webs 1829
Deck Element Weight is 22.647 T
Chapter Three: FEA for a Barge’s Deck
30

31. Applying Symmetry Boundary Conditions
Reduces the CPU time to the half, while delivering the same
level of accuracy in the results.
Chapter Three: FEA for a Barge’s Deck
31

32. 32
Element Size
(mm)
No. of
Elements
Equ. Stress
Max. (MPa)
110 8130 221
100 11664 221.36
95 12426 222.47
90 14954 225.95
85 16556 226.38
80 17590 226.49
75 20622 226.57
More elements in a mesh might give more accurate results
but can significantly increase the computational time. 14954
elements (9mm quadratic) used to create a fine mesh.
Chapter Three: FEA for a Barge’s Deck
Meshing

33. Maximum Reported Stress is 226 MPa
Chapter Three: FEA for a Barge’s Deck
33

34. Chapter Four:
Optimum Weight Design for the
Barge’s Deck
34

35. ABS Barge Rules
The performance of the reference deck and the ABS
class minimum requirements for the deck dimensions.
Chapter Four: Optimum Weight Design
35
Parameter Reference Deck
ABS Minimum
Allowable Limit
Maximum Stress (MPa) 226 ≤ 235
Plate thickness (mm) 18 ≥ 17.55
Longitudinal Section Modulus L (mm^3) 54 ≥ 36.04
Transverse Section Modulus L (mm^3) 1144 ≥ 778

36. Input Parameters
Name Description
Initial Value
(mm)
P5 Flange width of Long. Stiffener 90
P4 Flange thickness of Long. 12
P6 Thickness of Long. Stiffener 12
P7 Height of Long. Stiffener 150
P8 Flange thickness of Trans. Web 14
P9 Flange Width of Trans. Web 203
P10 Height of Trans. Web 610
P11 Thickness of Trans. Web 14
Chapter Four: Optimum Weight Design
36

37. Upper & Lower Bounds
The output of the OSF was 82 Design Points.
Chapter Four: Optimum Weight Design
37
Initial Value Upper Bound Lower Bound
P4 Flange thickness of Longitudinal Stiffener 12 13 11
P5 Flange width of Longitudinal Stiffener 90 99 81
P6 Thickness of Longitudinal Stiffener 12 13 9
P7 Height of Longitudinal Stiffener 150 165 115
P8 Flange thickness of Transverse Web 14 15 11
P9 Flange Width of Transverse Web 203 224 156
P10 Height of Transverse Web 610 671 469
P11 Thickness of Transverse Web 14 15 11
Name Description
Input Parameters
(mm)

38. Response Surface
Chapter Four: Optimum Weight Design
38

39. Sensitivities Chart
Positive sensitivity occurs when increasing the input increases the
output, negative sensitivity occurs when increasing the input
decreases the output.
Chapter Four: Optimum Weight Design
39

40. Sensitivities Chart
The larger the change of the output parameters, the more
significant is the role of the input parameters that were varied.
Output Parameter – Equivalent stress max.
Chapter Four: Optimum Weight Design
40

41. Number Of Parameters Vs. Number of Design Points
Chapter Four: Optimum Weight Design
41
After disabling P4 & P5 the number of design
points that created by OSF method reduced from
82 design points for 8 input parameters to 45
design points for 6 parameters.

42. Sensitivities Chart
Objectives and Constrains
Chapter Four: Optimum Weight Design
42

43. 3D Tradeoff Chart
Chapter Four: Optimum Weight Design
43

44. MOGA Stopping rules (Ansys)
Chapter Four: Optimum Weight Design
44

45. Samples Chart for MOGA Optimization
Chapter Four: Optimum Weight Design
45

46. The Optimum Design of the Deck
Chapter Four: Optimum Weight Design
46
Initial Value Upper Bound Lower Bound Optimum Design
P5 Flange width of Longitudinal Stiffener 90 99 81 90 0
P4 Flange thickness of Longitudinal Stiffener 12 13 11 12 0
P6 Thickness of Longitudinal Stiffener 12 13 9 10 -20
P7 Height of Longitudinal Stiffener 150 165 115 121 -19
P8 Flange thickness of Transverse Web 14 15 11 11 -23
P9 Flange Width of Transverse Web 203 224 156 187 -8
P10 Height of Transverse Web 610 671 469 635 4
P11 Thickness of Transverse Web 14 15 11 11 -20
P1 Geometry Mass (kg) 22647 20680 -9
P14 Equ. Stress Max (MPa) 226 235 4
Change
Percentage
%
Input Parameters
Name Description
(mm)
Output Parameters

47. The Optimum Design Vs. ABS Criteria
Chapter Four: Optimum Weight Design
47
Parameter Reference Deck Optimized Design
ABS Minimum
Allowable Limit
Maximum Stress (MPa) 226 235 ≤ 235
Plate thickness (mm) 18 17.55 ≥ 17.55
Longitudinal Section Modulus L (mm^3) 54 38.05 ≥ 36.04
Transverse Section Modulus L (mm^3) 1144 976.05 ≥ 778

48. Conclusion
48

49.  Sensitivity tests used to explore the design and understand
how each output parameter is driven by input parameters and
how the design can be modified.
 “The Web Height” is playing a major role and has the
significant impact in changing the stress with relatively small
increase in weight.
 On the other hand, two parameters “Flange thickness of
Longitudinal Stiffener & Flange width of Longitudinal
Stiffener” have a very small impact on the output parameters.
MOGA optimization shows its effectiveness, as in the final
iteration, the weight of the deck was reduced by 9 percent.
Chapter Five: Conclusion
49

50.  Recommendations for Future Work
 All FE analyses in this study were made with constant
loads. In real life, however, marine structures are subjected
to cyclic loads arising from ship motions and encounter
with waves.
 It is suggested that a more sensitive simulation method
be adopted for this task, and assigning different boundary
conditions.
It is recommended to use different Design of Experiment
methods which could lead to better results.
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Chapter Five: Conclusion

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