Systems and methods for utilizing a 3D CAD point-cloud to automatically create a fluid model

• US 10,650,114 B2
• Filed: 07/19/2019
• Issued: 05/12/2020
• Est. Priority Date: 03/10/2017
• Status: Active Grant

• Current Assignee: Altair Engineering Incorporated
• Original Assignee: GE Aviation Systems LLC (GE Aerospace)
• Inventors: Dweik, Zain S.
• Primary Examiner(s): Flora, Nurun N
• Application Number:
  US16/517,154
• Publication Number:
  US 20190340331A1
• Time in Patent Office: 298 Days
• Field of Search: None
• CPC Class Codes: G06F 2111/10 Numerical modelling
• CPC Class Codes: G06F 2113/08 Fluids
• CPC Class Codes: G06F 2119/18 Manufacturability analysis or optimisation for manufacturability
• CPC Class Codes: G06F 30/17 Mechanical parametric or variational design
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06F 30/27 using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• CPC Class Codes: G06F 30/28 using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06T 15/005 General purpose rendering architectures
• CPC Class Codes: G06T 17/05 Geographic models

Abstract

A multiple fluid model tool for utilizing a 3D CAD point-cloud to automatically create a fluid model is presented. For example, a system includes a modeling component, a machine learning component, and a three-dimensional design component. The modeling component generates a three-dimensional model of a mechanical device based on point cloud data indicative of information for a set of data values associated with a three-dimensional coordinate system. The machine learning component predicts one or more characteristics of the mechanical device based on input data and a machine learning process associated with the three-dimensional model. The three-dimensional design component that provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the mechanical device based on the input data and the one or more characteristics of the mechanical device on the three-dimensional model.

Claims

1. A system, comprising:

a memory storing computer executable components; and

a processor configured to execute the following computer executable components stored in the memory;

a modeling component that determines a control volume associated with a three-dimensional model of a mechanical device based on point cloud data indicative of information for a set of data values associated with a three-dimensional coordinate system, wherein the modeling component updates the control volume in response to modification of computer aided design data associated with the mechanical device; and

a three-dimensional design component that provides a three-dimensional design environment associated with the three-dimensional model, wherein the three-dimensional design environment renders physics modeling data of the mechanical device based on a machine learning process associated with the three-dimensional model and fluid data indicative of a fluid received by the mechanical device.

2. The system of claim 1, wherein the modeling component generates identification data indicative of an identifier for the control volume.

3. The system of claim 2, wherein the modeling component updates, based on the identification data, the control volume in response to the modification of the computer aided design data associated with the mechanical device.

4. The system of claim 1, wherein the modeling component determines a computational geometry for a control volume associated with the three-dimensional model based on the point cloud data.

5. The system of claim 1, wherein the computer executable components further comprise:

a machine learning component that predicts one or more characteristics of the mechanical device based on the fluid data.

6. The system of claim 5, wherein the three-dimensional design component renders the physics modeling data via the three-dimensional design environment based on the fluid data and the one or more characteristics of the mechanical device.

7. The system of claim 5, wherein the machine learning component performs the machine learning process based on the control volume associated with the three-dimensional model.

8. The system of claim 5, wherein the machine learning generates the physics modeling data based on the control volume associated with the three-dimensional model.

9. The system of claim 1, wherein the modeling component determines geometric data indicative of geometric features of the control volume and surface data indicative of surface information for the control volume.

10. The system of claim 1, wherein the three-dimensional design component renders the physics modeling data on the three-dimensional model.

11. A method, comprising:

determining, by a system comprising a processor, a control volume associated with a three-dimensional model of a mechanical device based on point cloud data indicative of information for a set of data values associated with a three-dimensional coordinate system;

updating, by the system, the control volume in response to modification of computer aided design data associated with the mechanical device;

predicting, by the system, fluid flow and physics behavior associated with the three-dimensional model based on input data and a machine learning process associated with the three-dimensional model; and

rendering, by the system, physics modeling data of the mechanical device based on the fluid flow and the physics behavior.

12. The method of claim 11, further comprising:

determining, by the system, a computational geometry for the mechanical device based on the point cloud data.

13. The method of claim 11, further comprising:

determining, by the system, a computational geometry for the control volume associated with the three-dimensional model based on the point cloud data.

14. The method of claim 11, further comprising:

generating, by the system, identification data indicative of an identifier for the control volume.

15. The method of claim 14, further comprising:

in response to the modification of the computer aided design data associated with the mechanical device, updating, by the system, the control volume based on the identification data.

16. The method of claim 11, further comprising:

generating, by the system, the physics modeling data based on the control volume associated with the three-dimensional model.

17. A non-transitory computer readable storage device comprising instructions that, in response to execution, cause a system comprising a processor to perform operations, comprising:

determining a control volume associated with a three-dimensional model of a mechanical device based on point cloud data indicative of information for a set of data values associated with a three-dimensional coordinate system;

updating the control volume in response to modification of computer aided design data associated with the mechanical device;

performing a machine learning process associated with the three-dimensional model to predict one or more characteristics of the mechanical device; and

providing a three-dimensional design environment associated with the three-dimensional model that renders physics modeling data of the mechanical device based on the machine learning process.

18. The non-transitory computer readable storage device of claim 17, wherein the operations further comprise:

determining a computational geometry for the control volume associated with the three-dimensional model based on the point cloud data.

19. The non-transitory computer readable storage device of claim 17, wherein the operations further comprise:

generating identification data indicative of an identifier for the control volume.

20. The non-transitory computer readable storage device of claim 19, wherein the operations further comprise:

updating, based on the identification data, the control volume in response to the modification of the computer aided design data associated with the mechanical device.