	US 11,953,023 B2
	Two-vane pump and design method of two-vane pump for wastewater using machine learning
Jin Hyuk Kim, Incheon (KR); Sung Kim, Cheonan-si (KR); and Sang Bum Ma, Incheon (KR)
Assigned to Korea Institute of Industrial Technology, Cheonan-si (KR)
Filed by KOREA INSTITUTE OF INDUSTRIAL TECHNOLOGY, Cheonan-si (KR)
Filed on Sep. 3, 2021, as Appl. No. 17/466,755.
Claims priority of application No. 10-2020-0112912 (KR), filed on Sep. 4, 2020.
Prior Publication US 2022/0074427 A1, Mar. 10, 2022
Int. Cl. G06F 30/27 (2020.01); F04D 1/06 (2006.01); F04D 7/04 (2006.01); F04D 29/22 (2006.01); F04D 29/30 (2006.01); F04D 29/44 (2006.01); G06F 30/17 (2020.01); G06N 3/086 (2023.01)

CPC F04D 29/445 (2013.01) [F04D 1/06 (2013.01); F04D 7/04 (2013.01); F04D 29/225 (2013.01); F04D 29/30 (2013.01); G06F 30/17 (2020.01); G06F 30/27 (2020.01); G06N 3/086 (2013.01)]

6 Claims

1. A design method of two-vane pump for wastewater using machine learning, the design method comprising:

a) setting an objective function;

b) setting design variables of an impeller and a volute for deriving the set objective function and a range of each design variable;

c) deriving a plurality of experimental points including values of the design variables within a range of a design variable;

d) generating an input value by calculating a value of the objective function through numerical analysis of each of the derived experimental points;

e) constructing a surrogate model through machine learning for the input value;

f) Deriving an optimal design of the two-vane pump for wastewater from the constructed surrogate model; and

g) outputting at least one of a visual representation of the optimal design or a design value associated with the optimal design;

wherein the design variable includes:

a first variable which is a y-axis displacement of one end of the impeller located on an inlet side of a wastewater pump;

a second variable that is a y-axis displacement of another end of the impeller; and

a third variable that is a cross-sectional area according to a volute angle of the volute;

wherein in the e), the surrogate model is constituted by a radial basis neural network (RBNN) artificial neural network;

wherein the e) includes:

e1) deriving a variable value for constructing the surrogate model through machine learning for the input value; and

e2) constructing the surrogate model using the derived variable value; and

wherein the f) includes:

f1) predicting the objective function value by substituting a randomly selected design variable value in the surrogate model into a genetic algorithm;

f2) deriving a pareto-optimal front surface based on a correlation between predicted objective function values; and

f3) deriving an optimal design of the two-vane pump for wastewater that is a design variable value for a target design function value in the derived pareto-optimal front surface.