Apparatus for optimizing flow analysis and method therefor

Patent metadata

Field	Value
Publication number	US-11531795-B2
Application number	US-201916420130-A
Country	US
Kind code	B2
Filing date	May 22, 2019
Priority date	Aug 21, 2018
Publication date	Dec 20, 2022
Grant date	Dec 20, 2022

Abstract

A flow analysis apparatus is provided. The flow analysis apparatus includes a flow analyzer configured to derive a plurality of output signals by performing flow analysis for a plurality of cells by using a flow analytic model for simulating numerical analysis by Computational Fluid Dynamics (CFD) with respect to a plurality of cells that divide a space around a component, and an analysis optimizer configured to perform optimization for the plurality of output signals.

First claim

What is claimed is:

1. A flow analysis apparatus comprising:
a processor and a memory storing computer program commands, the computer program commands when executed by the processor implement the steps of:
(a) acquiring training data for an artificial neural network comprising;
storing, in the memory, a plurality of input signals with respect to a plurality of cells that divide a space around a structural component wherein the plurality of input signals includes a laminar flow viscosity or a turbulent conduction and;
producing, by the processor, a plurality of output signals at an initial stage of fluid flow corresponding to each of the plurality of input signals by performing Computational Fluid Dynamics (CFD) numerical analysis a predetermined number of times, wherein the plurality of output signals includes a density, a momentum, or an internal energy;
(b) training, by the processor, parameters of a first artificial neural network model by inputting the plurality of input signals and the plurality of output signals to the first artificial neural network model, wherein the first artificial neural network model predicts an input signal at a steady state of the fluid flow;
(c) generating, by the processor, a plurality of predicted input signals using the first artificial neural network model;
(d) training, by the processor, parameters of a second artificial neural network model by inputting the plurality of output signals and the plurality of predicted input signals to the second artificial neural network model, wherein the second artificial neural network model predicts an output signal at the steady state of the fluid flow;
(e) generating, by the processor, a plurality of predicted output signals using the second artificial neural network model; and
(f) performing optimization for the plurality of predicted output signals.

2. The flow analysis apparatus of claim 1 , wherein the performing of the optimization includes a primary optimization generating primary optimization data from the plurality of predicted output signals through an Equation

Y
^

nf
1

⁡

(

k
+

T
1

)

=

1

s
1

+
1

⁢

∑

k
s

=
0

s
1

⁢

⁢

[

Y
^

1

⁡

(

k
+

T
1

-

k
s

)

]

,

l
=
1

,
⋯
⁢

,
g

wherein the k+T l refers to the number of times of the CFD numerical analysis,
wherein the l refers to a cell to be analyzed and has 1 to g cells (g is a natural number),
wherein the s 1 +1 refers to the number of the plurality of predicted output signals used for the primary optimization,
wherein the Ŷ l refers to the plurality of predicted output signals, and
wherein the Ŷ nf l refers to the primary optimization data.

3. The flow analysis apparatus of claim 2 , wherein the performing of the optimization further includes a secondary optimization generating secondary optimization data from the primary optimization data through an Equation,

Y
^

f
1

⁡

(

k
+

T
1

)

=

1

s
2

+
1

⁢

∑

k
l

=
0

s
2

⁢

⁢

[

Y
^

nf
1

⁢

(

k
+

T
1

-

k
s

)

]

,

Assignees

• Doosan Enerbility Co Ltd
• Dosan Enerbility Co Ltd

Inventors

• Park Jeehun
• Kim Hyunsik
• Park Jaehyeon
• Lee Sangjin

Classifications

• G06F30/27

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• G06F30/28

  using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD] · CPC title

• G06F2111/10

  Numerical modelling · CPC title

• G06F30/20 Primary

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G06F17/13 Primary

  Differential equations (using digital differential analysers G06F7/64) · CPC title