Power Prediction Method for Ships Using Data Regression Models
Abstract
:1. Introduction
2. Methods
2.1. Hull Geometry Representation
2.2. Model Construction
2.2.1. CNN Model
2.2.2. MLP Model
2.3. Training Data
3. Prediction Results Regarding the Ship Hydrodynamic Characteristics
3.1. Residuary Resistance Coefficient
3.1.1. CNN Model
3.1.2. MLP Model
3.2. Wake and Thrust Deduction Fractions
3.2.1. CNN Model
3.2.2. MLP Model
3.3. POW Characteristics
3.4. Summary of the Final Models
4. Power Prediction
4.1. Performance Prediction Method
4.2. Case Study
5. Concluding Remarks
- Development of MLP models for , , and t
- Development of CNN models for , , and t
- -
- Image-based hull form representation using a signed distance function
- Development of MLP models for and
- Development of power prediction method using the developed regression models
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
L | L indicates (Length between perpendiculars) |
B | Breadth |
T | Draught |
Block coefficient | |
Block coefficient of after body | |
Longitudinal center of buoyancy | |
∇ | Displacement volume |
Bulbous bow length | |
Propeller diameter | |
Z | Number of propeller blades |
Expanded blade area ratio | |
Pitch ratio at 0.7R | |
Mean pitch ratio | |
Wetted surface area of bare hull | |
Bilge keel area | |
Projected area of ship above the water line to the transverse plane | |
Mass density of air | |
Mass density of water | |
Froude number | |
Reynolds number | |
J | Propeller advance ratio |
Residuary resistance coefficient | |
Wake fraction | |
t | Thrust deduction fraction |
Thrust coefficient | |
Torque coefficient | |
Total resistance | |
Total resistance coefficient | |
Frictional resistance coefficient | |
Incremental resistance coefficient for model ship correlation | |
Air or wind resistance coefficient | |
Brake power | |
Relative rotative efficiency | |
Transmission efficiency | |
n | Propeller frequency of revolution |
Symbols for subscript | M: model, S: ship |
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Residuary | Wake & Thrust Deduction | POW | |||
---|---|---|---|---|---|
Resistance Coeff. | Fraction | Characteristics | |||
CNN | MLP | CNN | MLP | MLP | |
Hull geom. | 23 stations w/ | 12 stations w/ | |||
96 × 96 image | - | 96 × 96 image | - | - | |
(23 × 96 × 96) | (12 × 96 × 96) | ||||
Input var. | , , , , | J, Z, | |||
, | , | , | |||
CNN layer | [12-8-8] w/ | - | [8-8-4] w/ | - | - |
kernel size 3 | kernel size 3 | ||||
MLP layer for input var. | [64] | [128-64 | [64] | [128-64 | [48] |
-32] | -32] | ||||
MLP layer for concat. | [32-32] | - | [64-32] | - | - |
Bulk | Container | LPGC | Tanker | ||
---|---|---|---|---|---|
L (m) | 176.00 | 206.55 | 165.00 | 320.00 | |
B (m) | 30.0 | 30.6 | 28.0 | 60.0 | |
T (m) | 9.5 | 10.2 | 10.4 | 21.0 | |
(%) | +2.4 | −1.5 | +0.75 | +3.5 | |
Hull | (-) | 0.798 | 0.650 | 0.75 | 0.825 |
Scale ratio (-) | 24.4 | 30.0 | 26.0 | 39.6 | |
(m) | 4.3 | 6.2 | 5.7 | 6.5 | |
() | 7362.1 | 8165.0 | 6750.7 | 28,873.0 | |
() | 574.4 | 900.4 | 524.8 | 1200.0 | |
(m) | 6.1 | 7.5 | 6.5 | 9.9 | |
Z (-) | 4 | 5 | 4 | 4 | |
Propeller | (-) | 0.486 | 0.710 | 0.525 | 0.485 |
(-) | 0.768 | 0.971 | 0.897 | 0.743 | |
(-) | 0.755 | 0.943 | 0.850 | 0.727 |
Model | Bulk | Container | LPGC | Tanker |
---|---|---|---|---|
MLP | 1.43% | 1.38% | 2.97% | 0.31% |
CNN | 0.64% | 0.69% | 0.71% | 1.12% |
Exp | 14.02 kts. | 21.71 kts. | 16.82 kts. | 16.11 kts. |
Model | Bulk | Container | LPGC | Tanker |
---|---|---|---|---|
MLP | 5.5% | 4.9% | 9.3% | 1.2% |
CNN | 3.5% | 1.9% | 2.6% | 2.7% |
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Kim, Y.-C.; Kim, K.-S.; Yeon, S.; Lee, Y.-Y.; Kim, G.-D.; Kim, M. Power Prediction Method for Ships Using Data Regression Models. J. Mar. Sci. Eng. 2023, 11, 1961. https://doi.org/10.3390/jmse11101961
Kim Y-C, Kim K-S, Yeon S, Lee Y-Y, Kim G-D, Kim M. Power Prediction Method for Ships Using Data Regression Models. Journal of Marine Science and Engineering. 2023; 11(10):1961. https://doi.org/10.3390/jmse11101961
Chicago/Turabian StyleKim, Yoo-Chul, Kwang-Soo Kim, Seongmo Yeon, Young-Yeon Lee, Gun-Do Kim, and Myoungsoo Kim. 2023. "Power Prediction Method for Ships Using Data Regression Models" Journal of Marine Science and Engineering 11, no. 10: 1961. https://doi.org/10.3390/jmse11101961
APA StyleKim, Y.-C., Kim, K.-S., Yeon, S., Lee, Y.-Y., Kim, G.-D., & Kim, M. (2023). Power Prediction Method for Ships Using Data Regression Models. Journal of Marine Science and Engineering, 11(10), 1961. https://doi.org/10.3390/jmse11101961