###### Abstract  
Ship resistance has a very important value in the determination of ship power and the design of emission standards. In this paper, a ship resistance model with different displacement, speed, and attachment under the condition of a fixed scale ratio is tested by means of experimental research, which is used to analyze the change law of ship resistance under the condition of a single factor. The coupling effects of multiple factors on the actual ship power are studied after the establishment of a mathematical relationship between the actual ship power and resistance on the basis of the response surface method. The research results show that: (1) there is an obvious positive correlation between ship resistance and speed, which matches the change law of the exponential equation. Compared with ship appendages, displacement and speed have the greatest influence on resistance. (2) According to the correlation analysis, the maximum correlation coefficient between ship speed/resistance and power is 0.99, and the correlation coefficients between displacement/resistance and power are 0.93 and 0.88, respectively. However, the correlation coefficients between ship appendages and resistance and power are only 0.23 and 0.14, respectively. (3) The actual ship power and speed, displacement, and appendages form a quadratic polynomial relationship. The multi-factor interaction analysis results show that speed and displacement have the greatest influence on the actual ship power. The research results have a certain guiding significance for ship design. ship resistance; speed; displacement and appendages; response surface; quadratic multinomial +
Footnote †: journal:  
0000-0002-4313-2223-2233-2  
[PERSON] et al. [9] proposed a new prediction method for added resistance due to heterogeneous hull roughness after incorporating the similarity law scaling and the roughness impact factor to consider the relative impacts of hull roughness in different regions, which showed better prediction performance compared to the conventional method after it was assessed through experimental fluid dynamics (EFD) and computational fluid dynamics (CFD) results. [PERSON] et al. [10] studied the combined effects of shallow water and roughness on ship resistance through an unsteady Reynolds mean Navier-Stokes model to verify the hypothesis that ship resistance in shallow water was quite different from that in deep water. [PERSON] et al. [11] used computational fluid dynamics to explore and demonstrate the scale effect of ship resistance components, and the results show that the scale effect is more obvious in wave resistance, while the free surface effect was displayed in friction resistance. [PERSON] et al. [12] proposed an explicit and easy-to-use mathematical expression by analyzing the resistance of ships with extreme size ratios using the optimized semi-empirical SNNM(SHOPERA-NTUA-NTU-MARIC) method, which proved that the improved formula has a higher correlation coefficient and a smaller average percentage error through resistance verification experiments of the 11 different types of ships with extreme size ratios. [PERSON] [13] established an artificial neural network model to calculate the weight of each external environmental condition on ship resistance with nine external environmental conditions, such as speed, navigation direction, surge height, surge direction, wave direction, effective wave height, wind Angle, and wind speed, as the input and ship resistance as the output. [PERSON] et al. [14] used machine learning methods, such as random forest, extreme gradient enhancer, and multi-layer perceptron, to analyze the additional wave resistance of a ship in head to beam waves of 18 hulls and demonstrated that the established prediction technology had a good generalization effect based on Bayesian optimization of hyperparameters. [PERSON] et al. [15] used the unsteady RANSE method in combination with the towing cabin test to study the influence of ship trim degree on ship resistance under three different influent and two Froude numbers.