An Rapid Prediction Method for Propeller Hydrodynamic Performance Based on Deep Learning

doi:10.16183/j.cnki.jsjtu.2022.331

Abstract

Abstract:

In order to achieve rapid and accurate prediction of the hydrodynamic performance of propellers, a set of propeller hydrodynamic performance prediction model was established based on the improved residual connection network. The residual connection method greatly improves the depth of the model. In combination with the Inception structure to simultaneously extract data features from different scales, the depthwise separable convolution reduces the model parameters. The sample space for training the deep neural network is built based on the propeller geometric parameters and model test results. An improved beetle swarm antennae search algorithm is proposed to optimize the initial weights and thresholds of the model to further improve the prediction accuracy of the model. The research results indicate that the improved beetles swarm antennae algorithm significantly improves the accuracy of the model and solves the problem of overfitting of it. The prediction results of the model are in good agreement with the experimental values, and its prediction performance for the propellers which are not in the dataset is basically the same as that of the CFD method. The model has an excellent universality and its calculation period is extremely short, which can meet the requirements of real-time and accurate prediction of propeller open water performance.

Key words: propeller, hydrodynamic performance, residual neural network, Inception structure, the improved beetle swarm antennae search algorithm

CLC Number:

U664.33

GAO Nan, HU Ankang, HOU Lixun, CHANG Xin. An Rapid Prediction Method for Propeller Hydrodynamic Performance Based on Deep Learning[J]. Journal of Shanghai Jiao Tong University, 2024, 58(2): 188-200.

Figures/Tables 19

Fig.1

Fig.2

Fig.3

Fig.4

Tab.1

Tab.2

Tab.3

Test functions and their value range

测试函数	公式	搜索维度	取值区间
f₁	$f (x) = ∑ i = 1 n \| x i + 1 \|$	30	[-100, 100]
f₂	$f (x) = ∑ i = 1 n [100 (x i + 1 - x i 2) 2 + (x i - 1) 2]$	30	[-30, 30]
f₃	$f (x) = ∑ i = 1 n \| x i \| + ∏ i = 1 n \| x i \|$	30	[-10, 10]
f₄	$f (x) = ∑ i = 1 n \| x i + 0.5 \| 2$	30	[-100, 100]
f₅	$f (x) = ∑ i = 1 n [x i 2 - 10 c o s (2 π x i) + 10]$	30	[-5.12, 5.12]
f₆	$f (x) = ∑ i = 1 n \| x i s i n x i + 0.1 x i \|$	30	[-10, 10]
f₇	$f (x) = 418.9829 n - ∑ i = 1 n x i s i n \| x i \|$	30	[-500, 500]
f₈	$f (x) = ∑ i = 1 n i x i 4 + r a n d o m [0,1]$	30	[-1.28, 1.28]

Tab.3

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Tab.4

Tab.5

Fig.11

Fig.12

Fig.13

Fig.14

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参数	特征集
桨叶片数,Z	FS1
桨直径,D/m	FS1
毂径比,d/D	FS1
盘面比,D_r	FS1
无因次半径,r/R	FS2
螺距比,P/D	FS2
各桨叶剖面的无因次弦长,b/D	FS2
各桨叶剖面的最大厚度比,t_m/b	FS2
各桨叶剖面的拱度比,f_m/b	FS2
桨叶侧斜角,S/(°)	FS2
桨叶纵倾角,r_a/(°)	FS2
推力系数,K_T	OPS
转矩系数,K_Q	OPS
进速系数,J	FS1

型号	Z	D/m	D_r	P/D₍₀_.₇_R₎	S/(°)	r_a/(°)
HSP	5	0.220	0.700	0.944	45.00	-3.03
P4119	3	0.305	0.600	1.084	0.00	0.00
MAU5-65	5	0.250	0.650	1.000	50.00	10.00
KP458	4	0.204	0.431	0.721	13.15	0.00
DTRC 4118	3	0.305	0.600	1.077	0.00	0.00
E779A	4	0.240	0.550	1.100	0.00	4.05
P4381	5	0.305	0.725	1.210	0.00	0.00
KP068	5	0.250	0.725	1.210	0.00	0.00
KP069	5	0.250	0.725	1.191	31.58	0.00
KP070	5	0.250	0.725	1.184	63.10	0.00
B4-55	4	0.240	0.550	1.000	0.00	0.00
B4-40	4	0.300	0.400	1.300	0.00	15.00
P4382	5	0.305	0.725	1.200	36.00	5.38
DTRC 4497	5	0.240	0.700	1.200	36.00	0.00
DTRC 4383	5	0.305	0.725	1.098	72.00	10.15
VP1304	5	0.250	0.779	1.635	18.84	-7.50
DTRC4384	5	0.305	0.725	1.199	108.00	14.46
DTRC4679	3	0.607	0.755	1.572	51.00	0.00

模型	E_train	E_test
IDRN	0.004 85	0.005 95
DRN	0.061 6	0.080 1
DCNN	0.114 2	0.164 1

型号	Z	D/m	D_r	d/D	P/D₍₀_.₇_R₎	S₍₀_.₇_R₎/(°)	r_a/(°)	b/D₍₀_.₇_R₎	t_m/b₍₀_.₇_R₎	f_m/b₍₀_.₇_R₎
KP505	5	0.25	0.80	0.18	0.996 7	32	0.00	0.359 0	0.014 92	0.014 039
B4-70	4	0.3418	0.7	0.2	1.0	1.38	9.21	0.375 2	0.033 07	0.015 602