System and method for overlaying and integrating a fluid model with a computer-aided design (CAD) drawing

Abstract

A technique is provided to facilitate overlaying and integrating a computer-aided design drawing with a fluid model. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates a three-dimensional model of a mechanical device based on a stored data element library. 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 machine learning component also generates physical modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component generates a graphical user interface for a display device, which displays the three-dimensional model and presents the physical modeling data on the three-dimensional model.

Description

[0001] This application is a divisional application of application number: 201810195151.5, and the invention name is: System and method for overlapping and integrating fluid models with computer-aided design (CAD) drawings.

[0002] Cross-references

[0003] This application claims priority to U.S. Provisional Patent Application No. 62 / 469,953, filed on March 10, 2017, and entitled “A MULTIPLE FLUID MODEL TOOL FOR INTERDISCIPLINARY FLUID MODELING,” the entire contents of which are incorporated herein by reference. Technical Field

[0004] The present disclosure relates generally to three-dimensional modeling systems and, more particularly, to modeling of fluid systems and / or fluid system design tools. Background Art

[0005] During the design phase of a device or product associated with a fluid system, it is often desirable to determine the impact of the fluid on the device or product associated with the fluid system. In order to determine the impact of the fluid on the design, numerical analysis of two-dimensional (2D) data associated with computational fluid dynamics can be used to analyze the fluid flow through the device or product. For example, the color of a 2D surface associated with a device or product can represent the degree of fluid flow. However, analyzing the impact of the fluid on the design of a device or product typically involves human interpretation of the 2D data, which may lead to human trial and error for the fluid system. In addition, human interpretation of 2D data and / or the use of multi-fluid model tools to determine the impact of the fluid on the design of a device or product may be cumbersome for costs, redundancy and / or maintenance associated with the device or product. Summary of the invention

[0006] The following is a simplified summary of the specification in order to provide a basic understanding of some aspects of the specification. This summary is not an in-depth review of the specification. Its only purpose is to present some concepts of the specification in a simplified form as a prelude to a more detailed description presented later.

[0007] According to one embodiment, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates a three-dimensional model of a mechanical device based on a stored data element library. The machine learning component predicts one or more characteristics of the mechanical device based on a machine learning process associated with the three-dimensional model. The machine learning component also generates physical modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component generates a graphical user interface for a display device that displays the three-dimensional model and presents the physical modeling data based on the three-dimensional model.

[0008] According to another embodiment, a method provides for generating, by a system including a processor, a three-dimensional model of a mechanical device based on a stored library of data elements. The method also provides for performing, by the system, a machine learning process associated with the three-dimensional model to predict one or more characteristics of the mechanical device. In addition, the method provides for generating, by the system, physical modeling data of the mechanical device based on one or more characteristics of the mechanical device. The method also provides for generating, by the system, a graphical user interface that presents the three-dimensional model via a display device and presents the physical modeling data on the three-dimensional model.

[0009] According to yet another embodiment, a computer-readable storage device includes instructions that, in response to being run, cause a system including a processor to perform operations including: generating a three-dimensional model of a mechanical device based on a stored library of data elements, executing a machine learning process associated with the three-dimensional model to predict one or more characteristics of the mechanical device, determining physical modeling data for the mechanical device based on the one or more characteristics of the mechanical device, and providing a graphical user interface that presents the three-dimensional model via a display device and overlays the physical modeling data on the three-dimensional model.

[0010] The following description and the accompanying drawings set forth certain illustrative aspects of the specification. However, these aspects indicate only a small portion of the various ways in which the principles of the specification can be employed. Other advantages and novel features of the specification will become apparent from the following detailed description of the specification when considered in conjunction with the accompanying drawings.

[0011] The present invention provides a set of technical solutions, as follows.

[0012] 1. A system comprising:

[0013] memory that stores computer executable components; and

[0014] a processor configured to execute the following computer executable components stored in the memory:

[0015] a modeling component that generates a three-dimensional model of the mechanical device based on a stored library of data elements;

[0016] a machine learning component that predicts one or more characteristics of the mechanical device based on a machine learning process associated with the three-dimensional model and generates physical modeling data for the mechanical device based on the one or more characteristics of the mechanical device; and

[0017] A graphical user interface component generates a graphical user interface for a display device, the graphical user interface presenting the three-dimensional model and rendering the physical modeling data on the three-dimensional model.

[0018] 2. A system according to technical solution 1, wherein the graphical user interface component displays one or more mechanical components associated with the stored data element library via the display device.

[0019] 3. A system according to technical solution 1, wherein the graphical user interface component selects one or more mechanical components associated with the stored data element library based on input data received from the display device.

[0020] 4. A system according to technical solution 1, wherein the graphical user interface component determines the computational geometry of one or more mechanical components associated with the stored data element library based on input data received from the display device.

[0021] 5. A system according to technical solution 1, wherein the graphical user interface component determines the computational geometry of the control volume associated with the three-dimensional model based on input data received from the display device.

[0022] 6. A system according to technical solution 1, wherein the graphical user interface component receives image data indicating an image of the mechanical device, and wherein the modeling component generates the three-dimensional model based on the image data.

[0023] 7. A system according to technical solution 1, wherein the graphical user interface component receives a set of mechanical components via the display device, and wherein the modeling component converts the set of mechanical components into computer-aided design data.

[0024] 8. A system according to technical solution 7, wherein the modeling component generates the three-dimensional model based on the computer-aided design data.

[0025] 9. A system according to technical solution 8, wherein the machine learning component performs the machine learning process based on the computer-aided design data associated with the set of mechanical components received via the display device.

[0026] 10. A system according to technical solution 1, wherein the machine learning component generates the physical modeling data of the mechanical device based on the one or more characteristics of the mechanical device and fluid data indicating the fluid received by the mechanical device.

[0027] 11. A method comprising:

[0028] generating, by a system including a processor, a three-dimensional model of a mechanical device based on a stored library of data elements;

[0029] executing, by the system, a machine learning process associated with the three-dimensional model to predict one or more characteristics of the mechanical device;

[0030] generating, by the system, physical modeling data for the mechanical device based on the one or more characteristics of the mechanical device; and

[0031] A graphical user interface is generated by the system, which presents the three-dimensional model via a display device and renders the physical modeling data on the three-dimensional model.

[0032] 12. The method according to technical solution 11 further comprises:

[0033] Information associated with one or more elements of the mechanism is displayed by the system on the display device.

[0034] 13. The method according to technical solution 11 further comprises:

[0035] One or more elements of the mechanical device are selected by the system from the stored library of data elements based on input data received from the display device.

[0036] 14. The method according to technical solution 11 further comprises:

[0037] A computational geometry of one or more elements of the mechanical device is determined by the system based on input data received from the display device.

[0038] 15. The method according to technical solution 11 further comprises:

[0039] A computational geometry of a control volume associated with one or more elements of the mechanical device is determined by the system based on input data received from the display device.

[0040] 16. The method according to technical solution 11 further comprises:

[0041] Image data indicative of an image of one or more elements of the mechanical device is received by the system, wherein generating the three-dimensional model includes generating the three-dimensional model based on the image data.

[0042] 17. A computer-readable storage device comprising instructions that, in response to being executed, cause a system comprising a processor to perform operations comprising:

[0043] generating a three-dimensional model of the mechanical device based on the stored data element library;

[0044] executing a machine learning process associated with the three-dimensional model to predict one or more characteristics of the mechanical device;

[0045] determining physical modeling data for the mechanical device based on the one or more characteristics of the mechanical device; and

[0046] A graphical user interface is provided that presents the three-dimensional model via a display device and overlays the physical modeling data on the three-dimensional model.

[0047] 18. The computer-readable storage device according to technical solution 17, wherein the operation further comprises:

[0048] A portion of the mechanical device is selected from the stored library of data elements based on input data received from the display device.

[0049] 19. The computer-readable storage device according to technical solution 17, wherein the operation further comprises:

[0050] A computational geometry of a portion of the mechanical device is determined based on input data received from the display device.

[0051] 20. The computer-readable storage device according to technical solution 17, wherein the operation further comprises:

[0052] Image data indicative of an image of a portion of the mechanical device is received, wherein generating the three-dimensional model includes generating the three-dimensional model based on the image data. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. The numerous aspects, implementations, objects and advantages of the present invention will become apparent upon consideration of the following detailed description in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout, and in which:

[0054] Figure 1 illustrates a high-level block diagram of an example fluid model tool component in accordance with various aspects and implementations described herein;

[0055] Figure 2 illustrates a high-level block diagram of another example fluid model tool assembly in accordance with various aspects and implementations described herein;

[0056] Figure 3 illustrates a high-level block diagram of yet another example fluid model tool component in accordance with various aspects and implementations described herein;

[0057] Figure 4illustrates a high-level block diagram of an example fluid model tool component in communication with a user display device in accordance with various aspects and implementations described herein;

[0058] Figure 5 illustrates an example system that provides for overlaying and integrating a fluid model with a computer-aided design drawing in accordance with various aspects and implementations described herein;

[0059] Figure 6 illustrates an example graphical user interface in accordance with various aspects and implementations described herein;

[0060] Figure 7 illustrates another example graphical user interface in accordance with various aspects and implementations described herein;

[0061] Figure 8 illustrates yet another example graphical user interface in accordance with various aspects and implementations described herein;

[0062] Fig. 9 illustrates yet another example graphical user interface in accordance with various aspects and implementations described herein;

[0063] Fig.10 illustrates an example 3D model according to various aspects and implementations described herein;

[0064] Fig.11 illustrating another example 3D model according to various aspects and implementations described herein;

[0065] Fig.12 illustrating yet another example 3D model according to various aspects and implementations described herein;

[0066] Fig.13 illustrating yet another example 3D model according to various aspects and implementations described herein;

[0067] Fig.14 A flow chart illustrating an example method for providing interdisciplinary fluid modeling according to various aspects and implementations described herein;

[0068] Fig.15 illustrating a flow chart of another example method for providing interdisciplinary fluid modeling in accordance with various aspects and implementations described herein;

[0069] Fig.16 is a schematic block diagram illustrating a suitable operating environment; and

[0070] Fig.17 is a schematic block diagram of a sample computing environment. DETAILED DESCRIPTION

[0071] Various aspects of the present disclosure are now described with reference to the accompanying drawings, wherein like reference numerals are used throughout to refer to like elements. In the following description, for the purpose of explanation, many specific details are set forth to provide a thorough understanding of one or more aspects. However, it should be understood that certain aspects of the present disclosure may be practiced without these specific details or with other methods, components, materials, etc. In other examples, well-known structures and devices are shown in block diagram form to facilitate description of one or more aspects.

[0072] Systems and techniques that facilitate overlapping and integrating fluid models with computer-aided design drawings are proposed. For example, compared to conventional analysis of fluid systems involving two-dimensional (2D) data about fluid systems and / or human interpretation of human trial and error, the innovation of the present invention provides for a three-dimensional (3D) design environment that can be generated by integrating fluid models with computer-aided design drawings. In aspects, physical modeling data associated with the degree of fluid flow can be presented on a 3D model of a device by integrating a fluid model with a computer-aided design drawing. In one example, the visual characteristics of the physical modeling data can be dynamic based on the degree of fluid flow. Various systems and techniques disclosed herein can be related to cloud-based services, heating, ventilation and air conditioning (HVAC) systems, medical systems, automobiles, aircraft, watercraft, water filtration systems, cooling systems, pumps, engines, diagnosis, prediction, optimized machine design considering material costs in real time, explicit and / or implicit training models by real-time aggregation of data, etc. In an embodiment, a graphical user interface can provide a platform for interdisciplinary fluid modeling. For example, a graphical user interface may allow for the input of a computer-aided design (CAD) drawing and the overlay (e.g., integration) of a fluid model (e.g., a fluid computational model, a thermal computational model, a combustion computational model, etc.) with CAD data. Another embodiment provides the ability to trace an image and / or a CAD drawing via a graphical user interface to create computational geometry, which may be employed to construct a control volume for a multi-fluid modeling tool, which may be employed to identify correlations associated with the model, including previously unknown correlations for the model and / or interdependencies of components of the model. In one example, a 3D model and / or a computational domain may be generated from an image. In another example, a 3D model and / or a computational domain may be generated from a depicted image. Therefore, a 3D model associated with physical modeling may be generated more efficiently, and / or data provided by a 3D model associated with physical modeling may be more accurate. In addition, by replacing human trial and error for analyzing one or more characteristics associated with a 3D model of a device, machine, and / or component, damage to the device, machine, and / or component associated with the 3D model may be minimized.

[0073] First reference Figure 1, there is illustrated an exemplary system 100 for providing a multi-fluid model tool for interdisciplinary fluid modeling according to aspects of the present disclosure. The system 100 may be employed by various systems such as, but not limited to, modeling systems, aviation systems, power systems, distributed power systems, energy management systems, thermal management systems, transportation systems, oil and gas systems, mechanical systems, machine systems, device systems, cloud-based systems, heating systems, HVAC systems, medical systems, automotive systems, aircraft systems, watercraft systems, water filtration systems, cooling systems, pump systems, engine systems, diagnostic systems, predictive systems, machine design systems, medical device systems, medical imaging systems, medical modeling systems, simulation systems, enterprise systems, enterprise imaging solutions systems, advanced diagnostic tool systems, image management platform systems, artificial intelligence systems, machine learning systems, neural network systems, etc. In one example, the system 100 may be associated with a graphical user interface system to facilitate visualization and / or interpretation of 3D data. Furthermore, system 100 and / or components of system 100 may be employed to solve problems that are highly technical in nature (e.g., involving processing 3D data, involving modeling 3D data, involving artificial intelligence, etc.) using hardware and / or software, which are not abstract and cannot be performed as mental actions by a group of people.

[0074] The system 100 may include a fluid model tool component 102, which may include a modeling component 104, a machine learning component 106, and / or a graphical user interface (GUI) component 107. In an embodiment, the GUI component 107 may include a 3D design component 108. In aspects, the modeling performed by the fluid model tool component 102 may be associated with a flow synthesis design environment, a heat transfer design environment, and / or a combustion design environment. Aspects of the systems, devices, or processes explained in the present disclosure may constitute one or more machine-executable components embodied in one or more machines, such as embodied in one or more computer-readable media (or multiple media) associated with one or more machines. When executed by one or more machines (e.g., computers, computing devices, virtual machines, etc.), such components may cause one or more machines to perform the described operations. The system 100 (e.g., the fluid model tool component 102) may include a memory 110 for storing computer-executable components and instructions. The system 100 (e.g., the fluid model tool component 102) can further include a processor 112 to facilitate operation of instructions (e.g., computer executable components and instructions) by the system 100 (e.g., the fluid model tool component 102). In certain embodiments, the system 100 can further include a data element library 114. The data element library 114 can be a library of stored data elements.

[0075] The modeling component 104 may generate a 3D model of the device. The 3D model may be a 3D representation of the device for presentation via a 3D design environment. In an embodiment, the modeling component 104 may generate a 3D model of a mechanical device and / or an electronic device. The modeling component 104 may generate a 3D model of the device based on, for example, a data element library 114. The data element library 114 may include a set of data elements for mechanical components and / or electronic components. In addition, the set of data elements may include, for example, geometric data and / or texture data. The geometric data may indicate the geometry of the device. In an aspect, the geometric data may include 3D data points (e.g., 3D vertices) that form a shape, structure, and / or a set of surfaces of the device via a 3D coordinate system. The geometric data may also include a set of polygons (e.g., a set of geometric faces) based on the 3D data points. In an embodiment, the geometric data may include mesh data associated with a set of 3D data points and / or polygons. In another embodiment, the geometric data may include non-uniform rational basis spline (NURBS) data. The NURBS data may include NURBS surface data, which represents the geometry and / or surface of the 3D model based on a set of parameters of the surface mapped in the 3D coordinate system. The NURBS data may also include a set of control points, which form the shape of the surface associated with the NURBS surface data. In a non-limiting example, the data element library 114 may include data elements for fluid sources, fuel sources, flow elements, piping systems, sealing systems, pressure drop components (such as holes, valves, accessories, joints, converters, etc.), diffusers, heat exchangers, controllers, pumps, fans, compressors, chambers, vortices and / or other components. Additionally or alternatively, the data element library 114 may include experimental data associated with the device (e.g., experimental test data). For example, the data element library 114 may include one or more properties of the device determined via one or more experiments and / or one or more research processes. One or more experiments and / or one or more research processes may include determining and / or capturing one or more properties via the physical representation of the device associated with the 3D model. The one or more properties of the device may include, for example, one or more physical properties of the device, one or more mechanical properties of the device, one or more measurements of the device, one or more material properties of the device, one or more electrical properties of the device, one or more thermal properties of the device, and / or one or more other properties of the device.

[0076] In some embodiments, the modeling component 104 may perform modeling of one or more mechanical elements of a device (e.g., a mechanical device and / or an electronic device). For example, the modeling component 104 may determine a set of boundaries of features of a mechanical element of the device. In addition, the modeling component 104 may determine a set of physical properties of the mechanical element. In a non-limiting example, the modeling component 104 may determine one or more chambers of the device. For example, the modeling component 104 may determine a set of boundaries that define one or more chambers. The modeling component 104 may also determine a set of physical properties of one or more chambers, such as, for example, the size of one or more chambers, the shape of one or more chambers, the volume of one or more chambers, and / or another physical property of one or more chambers. In an aspect, the modeling component 104 may calculate one or more mechanical elements of the device. In order to calculate one or more mechanical elements, the modeling component 104 may employ one or more modeling techniques. Therefore, one or more mechanical elements may be one or more computationally derived elements. In another aspect, the modeling component 104 may perform a modeling process associated with one or more modeling techniques to facilitate the design of a system associated with the device, wherein the system includes a set of mechanical elements that are combined to form the device.

[0077] In an embodiment, the modeling component 104 can determine a set of control volumes associated with the device. For example, the modeling component 104 can overlap a set of control volumes on the device. The control volume can be an abstract concept of the device area through which a fluid (e.g., liquid or gas) and / or current flows. In one example, the control volume can correspond to the chamber of the device. The modeling component 104 can determine the geometric characteristics of the group of control volumes. For example, the modeling component 104 can determine the geometric characteristics of the calculation control volume (e.g., chamber) and / or the calculation control volume. The control volume can be connected via various types of pre-configured elements and / or pre-configured components to build an analytical calculation model extending from supply to sink condition. The control volume can also simulate the operating conditions of the system associated with the pre-configured element, pre-configured component and / or the 3D model. For example, the pre-configured element and / or pre-configured component can be included in the data element library 114. For example, the data element library 114 may include an extended library of pre-configured elements and / or pre-configured components that can be employed by the modeling component 104 to facilitate modeling and / or simulation of a wide range of physical phenomena, including compressible / incompressible fluid flow, buoyancy-driven flow, rotating cavity system flow, conduction / convection / radiation heat transfer, combustion equilibrium chemistry, material transport, etc. The physical configuration of the pre-configured elements and / or pre-configured components can vary based on the complexity of the physical phenomenon to be simulated. In one aspect, the physical configuration of the pre-configured elements and / or pre-configured components can be classified as machine learning-based elements (e.g., seals, leaks, compressors, fans, joints, elbows, valves, holes, pipes, etc.). Additionally or alternatively, the physical configuration of the pre-configured elements and / or pre-configured components can be classified as computationally derived elements (e.g., a modeling approach that utilizes a combination of analytical modeling techniques and experimental test data). Combinations of pre-configured elements and / or pre-configured components may be employed by modeling component 104 to construct a 3D model that may be further employed (eg, by machine learning component 106 ) to simulate and / or predict steady-state or transient responses of a machine.

[0078] In another embodiment, the modeling component 104 can use 3D computer-aided design (CAD) data to automatically create a computational domain and / or control volume (e.g., chamber / component / assembly) for a 3D model, which can be used (e.g., by the machine learning component 106) to generate predictions of simulated machine conditions for a device associated with the 3D model. Automation of computational model creation can significantly reduce the cycle time of analysis setup. In addition, the computational domain can be bidirectionally linked to the 3D CAD via geometry tags, CAD curve parameter expressions, surface parameter tags, etc. For example, when the CAD data is updated, the computational domain can be automatically updated. In yet another embodiment, the modeling component 104 can integrate sub-assemblies of a device (e.g., a mechanical device and / or an electronic device) and / or sub-models of a device (e.g., a mechanical device and / or an electronic device) to form, for example, a sub-assembly and / or model of an entire machine. In aspects, the modeling component 104 can integrate a first flow network of a first sub-assembly with a second flow network of a second sub-assembly. Additionally or alternatively, the modeling component 104 can integrate a first heat transfer of the entire first sub-assembly with a second heat transfer of the entire second sub-assembly. Additionally or alternatively, the modeling component 104 can integrate the first multiphase flow of the entire first subassembly with the second multiphase flow of the entire second subassembly.

[0079] The machine learning component 106 can perform learning (e.g., explicit learning and / or implicit learning) and / or can generate inferences about the one or more 3D models generated by the modeling component 104. The learned and / or generated inferences by the machine learning component 106 can facilitate determining one or more characteristics associated with the one or more 3D models generated by the modeling component 104. The learned and / or generated inferences can be determined by the machine learning component 106 via one or more machine learning processes associated with the one or more 3D models. The one or more characteristics determined by the machine learning component 106 can include, for example, one or more fluid characteristics associated with the one or more 3D models generated by the modeling component 104, one or more thermal characteristics associated with the one or more 3D models generated by the modeling component 104, one or more combustion characteristics associated with the one or more 3D models generated by the modeling component 104, one or more electrical characteristics associated with the one or more 3D models generated by the modeling component 104, and / or one or more other characteristics associated with the one or more 3D models generated by the modeling component 104. In aspects, the machine learning component 106 can predict and / or model flow networks of mechanical elements associated with one or more 3D models, heat transfer throughout mechanical elements associated with one or more 3D models, combustion associated with mechanical elements associated with one or more 3D models, multiphase flow through mechanical elements associated with one or more 3D models, and / or other characteristics of mechanical elements associated with one or more 3D models.

[0080] In an embodiment, the machine learning component 106 can predict one or more characteristics associated with one or more 3D models based on input data and one or more machine learning processes associated with one or more 3D models. The input data can be, for example, a set of parameters for a fluid that can flow through one or more 3D models, a set of parameters for thermal energy that can flow through one or more 3D models, a set of parameters for a combustion chemical reaction that can flow through one or more 3D models, a set of parameters for power that flows through one or more 3D models, and / or another set of parameters provided to one or more 3D models for input. One or more characteristics associated with one or more 3D models can correspond to one or more characteristics of a device (e.g., a mechanical device and / or an electronic device). In one example, different types of control volumes (e.g., chambers), simulated memories, volumetric hybrid dynamics, volumetric inertial dynamics, volumetric pumping dynamics, and / or volumetric gravity dynamics can be used by the machine learning component 106 to model and / or simulate various fluid flow conditions associated with one or more 3D models. In aspects, the machine learning component 106 can also use measured data and / or streaming data to set boundary conditions for one or more machine learning processes. For example, the machine learning component 106 may also employ measurement data and / or streaming data to set boundary conditions for supply and sink chambers and / or establish driving forces for simulating physical phenomena (e.g., fluid dynamics, thermodynamics, combustion dynamics, angular momentum, etc.).

[0081] Additionally or alternatively, the machine learning component 106 can perform a probability-based utility analysis that weighs the costs and benefits associated with one or more 3D models generated by the modeling component 104. The machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can also employ an automated classification system and / or automated classification process to facilitate learning and / or generating inferences about one or more 3D models generated by the modeling component 104. For example, the machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can employ a probability-based and / or statistical analysis (e.g., incorporating analytical utilities and costs) to learn and / or generate inferences about one or more 3D models generated by the modeling component 104. The machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can employ, for example, a support vector machine (SVM) classifier to learn and / or generate inferences about one or more 3D models generated by the modeling component 104. Additionally or alternatively, the machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can employ other classification techniques associated with Bayesian networks, decision trees, and / or probabilistic classification models. The classifier employed by the machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can be explicitly trained (e.g., via common training data) as well as implicitly trained (e.g., via receiving external information). For example, for a well-understood SVM, the SVM is configured via a learning or training phase within a classifier constructor and a feature selection module. A classifier is a function that maps an input attribute vector x = (x1, x2, x3, x4, xn) to a confidence that the input belongs to a certain class (i.e., f(x) = confidence(class)).

[0082] In an aspect, the machine learning component 106 may include an inference component that may utilize a partially inference-based approach to further enhance the automated aspects of the machine learning component 106 to facilitate learning and / or generating inferences about one or more 3D models generated by the modeling component 104. The machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) may employ any suitable machine learning-based, statistical-based, and / or probabilistic-based techniques. For example, the machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) may employ expert systems, fuzzy logic, SVMs, hidden Markov models (HMMs), greedy search algorithms, rule-based systems, Bayesian models (e.g., Bayesian networks), neural networks, other nonlinear training techniques, data fusion, utility-based analysis systems, systems employing Bayesian models, and the like. In another aspect, the machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) may perform a set of machine learning calculations associated with one or more 3D models generated by the modeling component 104. For example, the machine learning component 106 (e.g., one or more machine learning processes executed by the machine learning component 106) can perform a set of clustering machine learning computations, a set of decision tree machine learning computations, a set of instance-based machine learning computations, a set of regression machine learning computations, a set of regularization machine learning computations, a set of rule learning machine learning computations, a set of Bayesian machine learning computations, a set of deep Boltzmann machine computations, a set of deep belief network computations, a set of convolutional neural network computations, a set of stacked autoencoder computations, and / or a different set of machine learning computations.

[0083] In an embodiment, the machine learning component 106 may predict fluid flow and physical behavior associated with the 3D model. For example, the machine learning component 106 may perform a machine learning process associated with fluid flow through the 3D model. The machine learning component 106 may perform a machine learning process based on input data indicating an input received by a device associated with the 3D model. For example, the input data may include fluid data indicating a fluid provided to a device associated with the 3D model. The fluid data may include one or more properties of the fluid, such as a fluid type of the fluid, a density of the fluid, a viscosity of the fluid, a volume of the fluid, a weight of the fluid, a temperature of the fluid, and / or another property of the fluid. The input data may be used by the machine learning component 106 to predict fluid flow. The fluid flow may, for example, be the flow of the input data (e.g., fluid) through a device associated with the 3D model. The physical behavior may be the physical behavior of the fluid flow. For example, the physical behavior may be a simulated physics and / or change in the fluid flow. In addition, the physical behavior may be a simulated fluid flow condition associated with the 3D model. The physical behavior may also include correlations and / or behaviors determined based on one or more mathematical equations associated with fluid flow, such as a mass conservation equation associated with the fluid, a momentum conservation equation associated with the fluid, an energy conservation equation associated with the fluid, an angular momentum conservation equation associated with the fluid, and / or another mathematical equation associated with fluid flow.

[0084] Additionally or alternatively, the machine learning component 106 can predict thermal characteristics and physical behaviors associated with the 3D model. For example, the machine learning component 106 can perform a machine learning process associated with thermal characteristics associated with the 3D model. The machine learning component 106 can perform a machine learning process based on input data indicating input received by a device associated with the 3D model. For example, the input data can include fluid data indicating a fluid provided to a device associated with the 3D model. Additionally or alternatively, the input data can include electrical data indicating a voltage and / or current provided to a device associated with the 3D model. The input data can be used by the machine learning component 106 to predict thermal characteristics. The thermal characteristics can be, for example, a temperature associated with one or more regions of the 3D model, a heat capacity associated with one or more regions of the 3D model, a thermal expansion associated with one or more regions of the 3D model, a thermal conductivity associated with one or more regions of the 3D model, a thermal stress associated with one or more regions of the 3D model, and / or another thermal characteristic associated with one or more regions of the 3D model. The physical behavior can be a physical behavior of the thermal characteristic. For example, the physical behavior can be a simulated physics and / or change of the thermal characteristic. Additionally, the physical behavior may be a simulated thermal condition associated with the 3D model. The physical behavior may also include correlations and / or behaviors determined based on one or more mathematical equations associated with the thermal characteristic, such as a mass conservation equation associated with the thermal characteristic, a momentum conservation equation associated with the thermal characteristic, an energy conservation equation associated with the thermal characteristic, an angular momentum conservation equation associated with the thermal characteristic, and / or another mathematical equation associated with the thermal characteristic.

[0085] Additionally or alternatively, the machine learning component 106 may predict combustion characteristics and physical behaviors associated with the 3D model. For example, the machine learning component 106 may perform a machine learning process associated with the combustion characteristics associated with the 3D model. The machine learning component 106 may perform a machine learning process based on input data indicating input received by a device associated with the 3D model. For example, the input data may include fluid data indicating a fluid provided to a device associated with the 3D model. Additionally or alternatively, the input data may include electrical data indicating a voltage and / or current provided to a device associated with the 3D model. Additionally or alternatively, the input data may include chemical data indicating a chemical element provided to a device associated with the 3D model. The input data may be used by the machine learning component 106 to predict combustion characteristics. The combustion characteristics may be, for example, information related to a chemical reaction associated with one or more regions of the 3D model, such as, for example, temperature measurements, calorific value, elemental composition, moisture content, density, acoustic measurements associated with one or more regions of the 3D model, and / or another combustion characteristic. The physical behavior may be a physical behavior of the combustion characteristics. For example, the physical behavior may be a simulated physics and / or change of the combustion characteristics. In addition, the physical behavior can be a simulated combustion condition associated with the 3D model. The physical behavior can also include correlations and / or behaviors determined based on one or more mathematical equations associated with the combustion characteristics, such as a mass conservation equation associated with the combustion characteristics, a momentum conservation equation associated with the combustion characteristics, an energy conservation equation associated with the combustion characteristics, an angular momentum conservation equation associated with the combustion characteristics, and / or another mathematical equation associated with the combustion characteristics.

[0086] In an embodiment, the modeling component 104 may integrate a first 3D model associated with a first device (e.g., a first mechanical device and / or a first electronic device) and a second 3D model associated with a second device (e.g., a second mechanical device and / or a second electronic device) to generate a 3D model of the device. For example, the 3D model generated by the modeling component 104 may be a combination of two or more 3D models. In one aspect, a first geometric feature of the first 3D model may be combined with a second geometric feature of the second 3D model. The first geometric feature of the first 3D model may include, for example, a chamber, cavity, channel, and / or other geometric feature with the first 3D model. Similarly, the second geometric feature of the second 3D model may include, for example, a chamber, cavity, channel, and / or other geometric feature with the second 3D model. Thus, the chambers, cavities, channels, and / or other geometric features of the first 3D model and the second 3D model may be combined. In another embodiment, the first 3D model may include a first set of supply nodes and a first set of sink nodes forming a first flow network for the characteristics of the first 3D model. For example, a fluid provided by the first 3D model may flow from a supply node to a sink node of the first 3D model. In addition, the second 3D model may include a second set of supply nodes and a second set of sink nodes forming a second flow network for the characteristics of the second 3D model. For example, the fluid provided by the second 3D model may flow from the supply node to the sink node of the second 3D model. The modeling component 104 may combine the first flow network of the first 3D model with the second flow network of the second 3D model. For example, the first set of supply nodes of the first 3D model may be combined with the second set of supply nodes of the second 3D model. In addition, the first set of sink nodes of the first 3D model may be combined with the second set of sink nodes of the second 3D model.

[0087] In another embodiment, the machine learning component 106 may perform a first machine learning process associated with the first 3D model and a second machine learning process associated with the second 3D model. For example, the machine learning component 106 may perform learning (e.g., explicit learning and / or implicit learning) via the first machine learning process and / or may generate inferences about the first 3D model. In addition, the machine learning component 106 may perform learning (e.g., explicit learning and / or implicit learning) via the second machine learning process and / or may generate inferences about the second 3D model. The inferences and / or learning generated by the machine learning component 106 may facilitate determining one or more characteristics associated with one or more 3D models generated by the modeling component 104. In addition, the learned and / or generated inferences may be determined by the machine learning component 106 via one or more machine learning processes associated with one or more 3D models. In aspect, the machine learning component 106 may predict one or more characteristics of the device based on one or more first characteristics associated with the first 3D model and one or more second characteristics associated with the second 3D model. In one example, the machine learning component 106 may predict one or more characteristics of the device based on the one or more first characteristics and the one or more second characteristics. The one or more first characteristics may include a first fluid flow characteristic associated with the first 3D model, a first thermal characteristic associated with the first 3D model, a first combustion characteristic associated with the first 3D model, and / or a first physical behavior characteristic associated with the first 3D model. In addition, the one or more second characteristics may include a second fluid flow characteristic associated with the second 3D model, a second thermal characteristic associated with the second 3D model, a second combustion characteristic associated with the second 3D model, and / or a second physical behavior characteristic associated with the second 3D model. In an embodiment, the machine learning component 106 may facilitate interaction between the first 3D model and the second 3D model based on input data associated with the machine learning component 106. For example, the interaction of one or more first characteristics associated with the first 3D model and one or more second characteristics associated with the second 3D model may be determined by the machine learning component 106 based on the input data.

[0088] GUI component 107 may show a graphical user interface that presents a 3D model. The graphical user interface may be a graphical user interface for a display device. GUI component 107 may also present physical modeling data via a graphical user interface. For example, GUI component 107 may present physical modeling data on a 3D model. In aspect, GUI component 107 may present one or more mechanical components associated with data element library 114. One or more mechanical components associated with data element library 114 may be presented as a 3D model based on processing performed by modeling component 104 and / or machine learning component 106. In an embodiment, GUI component 107 may include 3D design component 108 to provide a 3D design environment associated with the 3D model. For example, 3D design component 108 may provide a 3D design environment associated with a mechanical element and / or 3D model generated by modeling component 104. The 3D design environment may be a single fluid system design tool. For example, a 3D design environment may be a tool that provides the functionality of multiple tools about a fluid system to provide a multi-disciplinary type of analysis. In one example, the 3D design environment may provide a flow synthesis design environment, a heat transfer design environment, and / or a combustion design environment. In another example, the 3D design environment can be a combustion design environment solver associated with the 3D design component 108. The 3D design environment associated with the 3D design component 108 can be employed to apply one or more numerical schemes to create predictions for machine simulation conditions. The predictions can be displayed and analyzed on a visual representation of the actual hardware using a post-processing module of a graphical user interface. In aspects, the 3D design environment associated with the 3D design component 108 can generate simulation predictions by saving the governing conservation equations of mass, momentum, energy, angular momentum, and / or species using a numerical analysis scheme. In certain embodiments, the fluid model tool component 102 can be used as a service. For example, the 3D model associated with the fluid model tool component 102 can be a generated computational model employed by the 3D design environment.

[0089] In an embodiment, the 3D design environment can present physical modeling data of the device based on the input data and one or more characteristics of the mechanical device on the 3D model. The physical modeling data can indicate a visual representation of fluid flow, thermal characteristics, combustion characteristics and / or physical behavior with respect to the 3D model. The physical modeling data can also be presented on the 3D model as one or more dynamic visual elements. In an aspect, the 3D design component 108 can change the visual characteristics (e.g., color, size, hue, shading, etc.) of at least a portion of the physical modeling data based on the fluid flow, thermal characteristics, combustion characteristics and / or physical behavior. For example, different degrees of fluid flow through the 3D model can be presented as different visual characteristics (e.g., color, size, hue or shading, etc.), different degrees of thermal characteristics relative to the 3D model can be presented as different visual characteristics (e.g., color, size, hue or shading, etc.), different degrees of combustion characteristics with respect to the 3D model can be presented as different visual characteristics (e.g., color, size, hue or shading, etc.), and different degrees of physical behavior with respect to the 3D model can be presented as different visual characteristics (e.g., color, size, hue or shading, etc.). In another aspect, the 3D design environment of the 3D model may allow a user to zoom in or out of the 3D model associated with the physical modeling data, rotate a view of the 3D model associated with the physical modeling data, etc. Thus, the user may view, analyze and / or interact with the 3D model associated with the physical modeling data to facilitate determining the impact of fluid flow, thermal properties, combustion properties, and / or physical behavior on the design of the device associated with the 3D model.

[0090] Reference now Figure 2 , there is illustrated an example system 200 providing a multi-fluid model tool for interdisciplinary fluid modeling according to aspects of the present disclosure. For the sake of brevity, repeated descriptions of similar elements employed in other embodiments described herein are omitted.

[0091] System 200 may include fluid model tool component 102 and / or data element library 114. Fluid model tool component 102 may include modeling component 104, machine learning component 106, GUI component 107, memory 110 and / or processor 112. Figure 2In the embodiment shown in , the GUI component 107 may include the 3D design component 108 and the selection component 202. The selection component 202 may receive input data indicating data selected via the graphical user interface generated by the GUI component 107. For example, the selection component 202 may receive input data including selecting one or more mechanical components and / or one or more electrical components associated with the data element library 114. In a non-limiting example, a user may use a display device presenting a graphical user interface to select one or more mechanical components and / or one or more electrical components from the data element library 114 accessed via the graphical user interface. Thus, the selection component 202 may select one or more mechanical components associated with the data element library 114 based on the input data received via the graphical user interface generated by the GUI component 107. In an embodiment, the selection component 202 may present information about one or more mechanical components and / or one or more electrical components associated with the data element library 114 on the graphical user interface generated by the GUI component 107. The one or more mechanical components and / or one or more electrical components associated with the data element library 114 may be presented, for example, as a list of one or more mechanical components and / or one or more electrical components via the graphical user interface. One or more mechanical components and / or one or more electrical components associated with the data element library 114 may also be presented along with corresponding textual data and / or visual data to facilitate identification and / or selection of one or more mechanical components associated with the data element library 114. Thus, a user may employ a graphical user interface generated by the GUI component 107 to select one or more mechanical components and / or one or more electrical components associated with the data element library 114 and facilitate generation of a 3D model associated with the one or more mechanical components.

[0092] In an embodiment, the selection component 202 may receive a set of mechanical components and / or a set of electrical components via a graphical user interface. In addition, the modeling component 104 may convert the set of mechanical components and / or the set of electrical components (e.g., the set of mechanical components and / or the set of electrical components received via a graphical user interface) into computer-aided design data. The computer-aided design data may be digital data configured in a vector format based on a file. In an example, the computer-aided design data may be formatted in a geometric description language so that the 2D features and / or 3D features of the components (e.g., mechanical components and / or electrical components) are connected. The computer-aided design data may include points, lines, curves, surfaces, and / or other data that form the components (e.g., mechanical components and / or electrical components). The computer-aided design data may also define one or more volumes of the components (e.g., mechanical components and / or electrical components). In addition, the computer-aided design data may be linked to a coordinate system to allow modification (e.g., movement, rotation, elongation, deformation, etc.) of the orientation of the components (e.g., mechanical components and / or electrical components) associated with the computer-aided design data. The computer-aided design data may include 2D data and / or 3D data. In aspects, the modeling component 104 can generate a 3D model based on computer-aided design data. In addition, the machine learning component 106 can perform a machine learning process based on the computer-aided design data associated with the set of mechanical components. In another embodiment, the selection component 202 can receive image data indicating an image including the set of mechanical components and / or the set of electrical components. For example, the image data can indicate a digitized image of a mechanical device and / or an electrical device. In certain embodiments, the image data can include one or more depicted portions that define portions of the image associated with the mechanical device. In aspects, the modeling component 104 can generate a 3D model based on the image data.

[0093] refer to Figure 3 , there is illustrated an example system 300 providing a multi-fluid model tool for interdisciplinary fluid modeling according to aspects of the present disclosure. For the sake of brevity, repeated descriptions of similar elements employed in other embodiments described herein are omitted.

[0094] System 300 may include fluid model tool component 102 and / or data element library 114. Fluid model tool component 102 may include modeling component 104, machine learning component 106, GUI component 107, memory 110 and / or processor 112. Figure 3In the embodiment shown in , the GUI component 107 may include the 3D design component 108 and the computational geometry component 302. The computational geometry component 302 may determine computational geometry for one or more mechanical components and / or one or more electrical components associated with the data element library 114. The computational geometry may, for example, correspond to a control volume for a component (e.g., a mechanical component and / or an electrical component). In aspects, the computational geometry component 302 may calculate computational geometry from computer-aided design data. For example, the computational geometry component 302 may calculate computational geometry such as surface area, volume, length, area change, and / or other geometric information for a component (e.g., a mechanical component and / or an electrical component).

[0095] In an embodiment, the computational geometry component 302 may determine computational geometry for one or more mechanical components and / or one or more electrical components associated with the data element library 114 based on input data received from a graphical user interface generated by the GUI component 107. For example, a user may define computational geometry for a component (e.g., a mechanical component and / or an electrical component) by depicting computer-aided design data presented via a graphical user interface. In another embodiment, the computational geometry component 302 may determine computational geometry for a control volume associated with a 3D model based on input data received via a graphical user interface generated by the GUI component 107. For example, a user may define computational geometry for a component (e.g., a mechanical component and / or an electrical component) by depicting image data of a digitized image of the component presented via a graphical user interface.

[0096] refer to Figure 4 , there is illustrated an example system 400 for providing a multi-fluid model tool for interdisciplinary fluid modeling according to aspects of the present disclosure. For the sake of brevity, repeated descriptions of similar elements employed in other embodiments described herein are omitted.

[0097] System 400 may include a fluid model tool component 102, a data element library 114, and a user display device 402. The user display device 402 may communicate with the fluid model tool component 102 via a network 404. The network 404 may be a wired network and / or a wireless network. The fluid model tool component 102 may include a modeling component 104, a machine learning component 106, a GUI component 107, a memory 110, and / or a processor 112. In some embodiments, the GUI component 107 may include a 3D design component 108, a selection component 202, and / or a computational geometry component 302. The user display device 402 may display a 3D model and / or a 3D design environment generated by the fluid model tool component 102. For example, a 3D model associated with physical modeling data may be presented on a graphical user interface associated with a display of the user display device 402. The user display device 402 may be a device having a display, such as, but not limited to, a computing device, a computer, a desktop computer, a laptop computer, a monitor device, a smart device, a smart phone, a mobile device, a handheld device, a tablet computer, a portable computing device, or another type of user device associated with a display.

[0098] refer to Figure 5 , there is illustrated an example system 500 that facilitates overlaying and / or integrating a fluid model with a computer-aided design drawing according to aspects of the present disclosure. For the sake of brevity, repeated descriptions of similar elements employed in other embodiments described herein are omitted.

[0099] System 500 may include 3D model 502. 3D model 502 may be generated by modeling component 104. In one aspect, 3D model 502 may be based on computer-aided design data (e.g., Figure 5 ). The computer-aided design data may be configured in a file-based vector format. In one example, the computer-aided design data may be associated with a computer-aided design drawing presented via a graphical user interface generated by the GUI component 107. In another aspect, the 3D model 502 may be associated with a device (e.g., a mechanical device and / or an electrical device). In certain embodiments, the 3D model 502 may be generated additionally or alternatively based on the data element library 114. The system 500 may also include a machine learning process 504. The machine learning process 504 may be performed by the machine learning component 106. In addition, the machine learning process 504 may be a machine learning process associated with the 3D model 502. In aspect, the machine learning component 106 may generate a 3D model 502 based on the 3D model data (e.g., Figure 5502). The machine learning component 106 may also perform the machine learning process 504 based on the learned and / or generated inferences associated with the 3D model 502. In addition, the system 500 may include a physical 3D model 506. The physical 3D model 506 may be associated with the 3D model 502. The physical 3D model 506 may also include physical modeling data generated by the machine learning process 504 (e.g., Figure 6 ). The physical modeling data may indicate information associated with fluid dynamics, thermodynamics, and / or combustion dynamics. For example, the physical modeling data may be presented on the physical 3D model 506 to represent fluid flow, thermal properties, combustion properties, and / or physical behavior of a device associated with the physical 3D model 506. In one example, the physical modeling data may simulate physical phenomena such as, but not limited to, compressible fluid flow, incompressible fluid flow, buoyancy-driven flow, rotating cavity system flow, conduction heat transfer, convection heat transfer, radiation heat transfer, combustion equilibrium chemistry, species transport, and / or other physical behaviors.

[0100] Figure 6 An example graphical user interface 600 is illustrated according to various aspects and implementations described herein. The graphical user interface 600 may include a depiction 602 for an element 604. The element 604 may be an element of a device (e.g., a mechanical device and / or an electrical device). In addition, the element 604 may be presented as computer-aided design data via the graphical user interface 600. The depiction 602 may create a boundary (e.g., a boundary line) around the element 604 to indicate the geometry of the element 604. In one example, the depiction 602 may be formed by the user display device 402 via input data provided to the graphical user interface 600. For example, a user may click on the screen of the user display device 402 using an input device (e.g., a mouse) and form the depiction 602. Alternatively, a user may touch the screen of the user display device 402 using pressure (e.g., a user's finger) and form the depiction 602. In one example, the element 604 may be added to the data element library 114 after the depiction 602 creates a boundary (e.g., a boundary line) around the element 604.

[0101] Figure 7An example graphical user interface 700 is illustrated according to various aspects and implementations described herein. The graphical user interface 700 may include a depiction 702 for an element 704. The element 704 may be an element of a device (e.g., a mechanical device and / or an electrical device). In addition, the element 704 may be presented as image data via the graphical user interface 700. The depiction 702 may create a boundary (e.g., a boundary line) around the element 704 to indicate the geometry of the element 704. In one example, the depiction 702 may be formed by the user display device 402 via input data provided to the graphical user interface 700. For example, the user may click on the screen of the user display device 402 using an input device (e.g., a mouse) and form the depiction 702. Alternatively, the user may touch the screen of the user display device 402 using pressure (e.g., a user's finger) and form the depiction 702. In one example, the element 704 may be added to the data element library 114 after the depiction 702 creates a boundary (e.g., a boundary line) around the element 704.

[0102] Figure 8 An example graphical user interface 800 is illustrated in accordance with various aspects and implementations described herein. The graphical user interface 800 may include an input component 802. The input component 802 may allow a user to input data to facilitate the generation of a 3D model and / or physical modeling data. In one aspect, the input component 802 may include a model data component 804 to allow input parameters for displaying physical modeling data and or running a machine learning process. In another aspect, the input component 802 may include a selection component 806 to allow selection of control parameters for a fluid model tool assembly 102, etc.

[0103] Fig. 9 An example graphical user interface 900 is illustrated in accordance with various aspects and implementations described herein. The graphical user interface 900 may include an input component 902. The input component 902 may allow a user to input data to facilitate the generation of a 3D model and / or physical modeling data. In aspects, the input component 902 may include a model run component 904 to allow the start of a machine learning process associated with the 3D model and / or the generation of physical modeling data.

[0104] Fig.10An example 3D model 1000 is illustrated in accordance with various aspects and implementations described herein. The 3D model 1000 may, for example, correspond to the 3D model 506 and / or a physical 3D model generated by the fluid model tool component 102. In one example, the 3D model 1000 may be generated in response to input received by the model running component 904. The 3D model 1000 may illustrate fluid dynamics, thermal dynamics, and / or combustion dynamics regarding the design of the device. For example, the 3D model 1000 may be a 3D model in which physical modeling data associated with the fluid dynamics, thermal dynamics, and / or combustion dynamics is presented on the device. In aspects, the 3D model 1000 may include a device portion 1002 of the 3D model 1000 and physical modeling data 1004 presented on the device portion 1002. Based on the value of the physical modeling data 1004, the visual characteristics (e.g., color, size, hue, shading, etc.) of the physical modeling data 1004 may be dynamic. For example, a first portion of the physical modeling data 1004 associated with the first physical modeling information may include a first visual characteristic and a second portion of the physical modeling data 1004 associated with the second physical modeling information may include a second visual characteristic. In an embodiment, the physical modeling data 1004 may be determined by the machine learning component 106. In one example, the physical modeling data 1004 may be associated with a set of control volumes and / or a flow network regarding fluid dynamics, thermal dynamics, and / or combustion dynamics. In one embodiment, a 3D design environment associated with the 3D model 1000 may include a heating bar 1006. The heating bar 1006 may include a set of colors corresponding to different values ​​of the physical modeling data 1004. For example, a first color (e.g., red) in the heating bar 1006 may correspond to a first value of the physical modeling data 1004, and a second color (e.g., blue) in the heating bar 1006 may correspond to a second value of the physical modeling data 1004. In another embodiment, the 3D design environment associated with the 3D model 1000 may include a side bar 1008. The side bar 1008 may include information used to facilitate generating the 3D model 1000 and / or the physical modeling data 1004. For example, the side bar 1008 may facilitate selecting one or more subcomponents (e.g., a flow element, a tube, an orifice, a bend valve, a joint, a fan, a compressor, another other component, etc.) of the device portion 1002 that forms the 3D model 1000. In another example, the side bar 1008 may facilitate selecting the type of physical modeling data provided by the physical modeling data 1004 (e.g., flow dynamics, thermal dynamics, combustion dynamics, etc.).

[0105] Fig.11An example 3D model 1100 is illustrated in accordance with various aspects and implementations described herein. The 3D model 1100 may, for example, correspond to the physical 3D model 506 and / or a 3D model generated by the fluid model tool component 102. In one example, the 3D model 1100 may be generated in response to input received by the model running component 904. The 3D model 1100 may illustrate fluid dynamics, thermal dynamics, and / or combustion dynamics regarding a device design. For example, the 3D model 1100 may be a 3D model in which physical modeling data associated with the fluid dynamics, thermal dynamics, and / or combustion dynamics is presented on the device. In aspects, the 3D model 1100 may include a device portion 1102 of the 3D model 1100 and physical modeling data 1104 presented on the device portion 1102. Based on the value of the physical modeling data 1104, the visual characteristics (e.g., color, size, hue, shading, etc.) of the physical modeling data 1104 may be dynamic. For example, a first portion of the physical modeling data 1104 associated with the first physical modeling information may include a first visual characteristic and a second portion of the physical modeling data 1104 associated with the second physical modeling information may include a second visual characteristic. In an embodiment, the physical modeling data 1104 may be determined by the machine learning component 106. In one example, the physical modeling data 1104 may be associated with a set of control volumes and / or a flow network regarding fluid dynamics, thermal dynamics, and / or combustion dynamics. In an embodiment, a 3D design environment associated with the 3D model 1100 may include a heating bar 1106. The heating bar 1106 may include a set of colors corresponding to different values ​​of the physical modeling data 1104. For example, a first color (e.g., red) in the heating bar 1106 may correspond to a first value of the physical modeling data 1104, and a second color (e.g., blue) in the heating bar 1106 may correspond to a second value of the physical modeling data 1104. In another embodiment, the 3D design environment associated with the 3D model 1100 may include a side bar 1108. The side bar 1108 may include information used to facilitate generating the 3D model 1100 and / or the physical modeling data 1104. For example, the side bar 1108 may facilitate selecting one or more subcomponents (e.g., a flow element, a tube, an orifice, a bend valve, a joint, a fan, a compressor, another other component, etc.) of the device portion 1102 that forms the 3D model 1100. In another example, the side bar 1108 may facilitate selecting the type of physical modeling data provided by the physical modeling data 1104 (e.g., flow dynamics, thermal dynamics, combustion dynamics, etc.).

[0106] Fig.12An example 3D model 1200 is illustrated in accordance with various aspects and implementations described herein. The 3D model 1200 may, for example, correspond to the physical 3D model 506 and / or a 3D model generated by the fluid model tool component 102. In one example, the 3D model 1200 may be generated in response to input received by the model running component 904. The 3D model 1200 may illustrate fluid dynamics, thermal dynamics, and / or combustion dynamics regarding a device design. For example, the 3D model 1200 may be a 3D model in which physical modeling data associated with the fluid dynamics, thermal dynamics, and / or combustion dynamics is presented on the device. In aspects, the 3D model 1200 may include a device portion 1202 of the 3D model 1200 and physical modeling data 1204 presented on the device portion 1202. Based on the value of the physical modeling data 1204, the visual characteristics (e.g., color, size, hue, shading, etc.) of the physical modeling data 1204 may be dynamic. For example, a first portion of the physical modeling data 1204 associated with the first physical modeling information may include a first visual characteristic and a second portion of the physical modeling data 1204 associated with the second physical modeling information may include a second visual characteristic. In an embodiment, the physical modeling data 1204 may be determined by the machine learning component 106. In one example, the physical modeling data 1204 may be associated with a set of control volumes and / or a flow network regarding fluid dynamics, thermal dynamics, and / or combustion dynamics. In one embodiment, a 3D design environment associated with the 3D model 1200 may include a heating bar 1206. The heating bar 1206 may include a set of colors corresponding to different values ​​of the physical modeling data 1204. For example, a first color (e.g., red) in the heating bar 1206 may correspond to a first value of the physical modeling data 1204, and a second color (e.g., blue) in the heating bar 1206 may correspond to a second value of the physical modeling data 1204. In another embodiment, the 3D design environment associated with the 3D model 1200 may include a side bar 1208. The side bar 1208 may include information used to facilitate generating the 3D model 1200 and / or the physical modeling data 1204. For example, the side bar 1208 may facilitate selecting one or more subcomponents (e.g., a flow element, a tube, an orifice, a bend valve, a joint, a fan, a compressor, another other component, etc.) of the device portion 1202 that forms the 3D model 1200. In another example, the side bar 1208 may facilitate selecting the type of physical modeling data provided by the physical modeling data 1204 (e.g., flow dynamics, thermal dynamics, combustion dynamics, etc.).

[0107] Fig.13An example 3D model 1300 is illustrated in accordance with various aspects and implementations described herein. The 3D model 1300 may, for example, correspond to the 3D model 506 and / or a physical 3D model generated by the fluid model tool component 102. In one example, the 3D model 1300 may be generated in response to input received by the model running component 904. The 3D model 1300 may illustrate fluid dynamics, thermodynamics, and / or combustion dynamics regarding the design of the device. For example, the 3D model 1300 may be a 3D model in which physical modeling data associated with the fluid dynamics, thermodynamics, and / or combustion dynamics is presented on the device. In one aspect, the 3D model 1300 may include a device portion 1302 of the 3D model. The 3D model 1300 may also include first physical modeling data 1304a, second physical modeling data 1304b, and third physical modeling data 1304c presented on the device portion 1302. Based on the value of the physical modeling data 1304, the visual characteristics (e.g., color, size, hue, shade, etc.) of the first physical modeling data 1304a, the second physical modeling data 1304b, and the third physical modeling data 1304c can be dynamic. For example, the first physical modeling data 1304a can include a first visual characteristic (e.g., yellow) associated with the first physical modeling data value, the second physical modeling data 1304b can include a second visual characteristic (e.g., green) associated with the second physical modeling data value, and the third physical modeling data 1304c can include a third visual characteristic (e.g., blue) associated with the third physical modeling data value. In one embodiment, the first physical modeling data 1304a, the second physical modeling data 1304b, and the third physical modeling data 1304c can be determined by the machine learning component 106. In one example, the first physical modeling data 1304a, the second physical modeling data 1304b, and the third physical modeling data 1304c can be associated with a set of control volumes and / or flow networks related to fluid dynamics, thermal dynamics, and / or combustion dynamics. In one embodiment, the 3D design environment associated with the 3D model 1300 may include a heat bar 1306. The heat bar 1306 may include a set of colors corresponding to different values ​​of the first physical modeling data 1304a, the second physical modeling data 1304b, and the third physical modeling data 1304c. For example, a first color (e.g., yellow) in the heat bar 1306 may correspond to a first physical modeling data value associated with a first visual characteristic of the first physical modeling data 1304a, a second color (e.g., green) in the heat bar 1306 may correspond to a second physical modeling data value associated with a second visual characteristic of the second physical modeling data 1304b, and a third color (e.g., blue) in the heat bar 1306 may correspond to a third physical modeling data value associated with a third visual characteristic of the third physical modeling data 1304c.In another embodiment, the 3D design environment associated with the 3D model 1300 may include a side bar 1308. The side bar 1308 may include information used to facilitate generating the 3D model 1300, the first physical modeling data 1304a, the second physical modeling data 1304b, and / or the third physical modeling data 1304c. For example, the side bar 1308 may facilitate selecting one or more subcomponents (e.g., a flow element, a tube, an orifice, a bend valve, a joint, a fan, a compressor, another other component, etc.) of the device portion 1302 that forms the 3D model 1300. In another example, the side bar 1308 may facilitate selecting the type of physical modeling data (e.g., flow dynamics, thermal dynamics, combustion dynamics, etc.) provided by the first physical modeling data 1304a, the second physical modeling data 1304b, and the third physical modeling data 1304c.

[0108] The foregoing systems and / or devices have been described with respect to the interaction between several components. It should be appreciated that such systems and components may include those components or sub-components specified therein, some specified components or sub-components, and / or components of the aircraft. Sub-components may also be implemented as components that are communicatively coupled to other components rather than being included in a parent component. In addition, one or more components and / or sub-components may be combined into a single component that provides aggregate functionality. For the sake of brevity, these components may also interact with one or more other components that are not specifically described herein but are known to those skilled in the art.

[0109] Figure 14-15 The method and / or flow chart according to the disclosed subject matter are illustrated. In order to simplify the explanation, the method is depicted and described as a series of actions. It should be understood and appreciated that the innovation of the present invention is not limited by the illustrated actions and / or the order of the actions, for example, the actions can occur in various orders and / or simultaneously, and other actions are not proposed and described herein. In addition, not all illustrated actions may require the realization of these methods according to the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the method can alternatively be represented as a series of interrelated states via a state diagram or an event. In addition, it should be further appreciated that the methods disclosed below and in this entire specification can be stored on an article of manufacture so as to facilitate the transmission and transfer of these methods to a computer. The term "article of manufacture" as used herein is intended to encompass a computer program accessible from any computer-readable device or storage medium.

[0110] refer to Fig.14, a method 1400 for providing interdisciplinary fluid modeling according to aspects of the present invention is illustrated. As an example, the method 1400 can be used in various applications, such as but not limited to modeling systems, aviation systems, power systems, distributed power systems, energy management systems, thermal management systems, transportation systems, oil and gas systems, mechanical systems, machine systems, device systems, cloud-based systems, heating systems, HVAC systems, medical systems, automotive systems, aircraft systems, watercraft systems, water filtration systems, cooling systems, pump systems, engine systems, diagnostic systems, prediction systems, machine design systems, medical device systems, medical imaging systems, medical modeling systems, simulation systems, enterprise systems, enterprise imaging solutions systems, advanced diagnostic tool systems, image management platform systems, artificial intelligence systems, machine learning systems, neural network systems, etc. At 1402, a 3D model of a mechanical device is generated (e.g., by a modeling component 104) based on a stored data element library. The data element library 114 may include a set of data elements for electrical components of the mechanical device and / or mechanical components of the mechanical device. The set of data elements may include, for example, geometric data and / or texture data to facilitate the generation of the 3D model. In an embodiment, generating the 3D model may include integrating a first 3D model associated with the first mechanical device and a second 3D model associated with the second mechanical device.

[0111] At 1404, a machine learning process associated with the 3D model may be performed (e.g., by machine learning component 106) to predict one or more characteristics of the mechanical device. The one or more characteristics may include, for example, fluid flow, thermal properties, combustion properties, and / or physical behavior. For example, the machine learning process may perform learning and / or may generate inferences about fluid flow, thermal properties, combustion properties, and / or physical behavior associated with the 3D model. In aspects, the machine learning process may also be performed based on input data. The input data may include fluid data, electrical data, and / or chemical data associated with input provided to the device associated with the 3D model. The physical behavior may indicate behavior regarding fluid dynamics, thermal dynamics, and / or combustion dynamics in the entire device associated with the 3D model in response to the input data.

[0112] At 1406, physical modeling data of the mechanical device is generated (e.g., by machine learning component 106) based on one or more characteristics of the mechanical device. The physical modeling data may indicate a visual representation of fluid flow, thermal characteristics, combustion characteristics, and / or physical behavior with respect to the 3D model.

[0113] At 1408, it is determined (e.g., by the machine learning component 106) whether the machine learning process has produced new output. If so, the method 1400 returns to 1406 to update the physical modeling data based on the new output. If not, the method 1400 continues to 1410.

[0114] At 1410, a graphical user interface is generated (e.g., by GUI component 107) that presents the 3D model via a display device and presents the physical modeling data on the 3D model. In aspects, the physical modeling data may be presented on the 3D model as a dynamic visual element. In an embodiment, the generation of the graphical user interface may include providing a 3D design environment associated with the 3D model.

[0115] At 1412, it is determined whether input parameters for the physical modeling data have been changed (e.g., by selecting component 202). For example, it may be determined whether new input parameters for the machine learning process are provided via a graphical user interface. If so, method 1400 returns to 1404 to perform a new machine learning process based on the changed input parameters. If not, method 1400 ends. In an embodiment, method 1400 includes displaying display information associated with one or more elements of the mechanical device on a display device. In another embodiment, method 1400 may include selecting one or more elements of the mechanical device from a stored data element library based on input data received from the display device. In yet another embodiment, method 1400 may include determining computational geometry for one or more elements of the mechanical device based on input data received from the display device. Additionally or alternatively, method 1400 may include determining computational geometry for a control volume associated with one or more elements of the mechanical device based on input data received from the display device. In certain embodiments, method 1400 may include receiving image data indicating an image for one or more elements of the mechanical device, wherein generating the 3D model includes generating the 3D model based on the image data.

[0116] refer to Fig.15, a method 1500 for providing interdisciplinary fluid modeling according to aspects of the present invention is illustrated. As an example, the method 1500 can be used in various applications, such as, but not limited to, modeling systems, aviation systems, power systems, distributed power systems, energy management systems, thermal management systems, transportation systems, oil and gas systems, mechanical systems, machine systems, device systems, cloud-based systems, heating systems, HVAC systems, medical systems, automotive systems, aircraft systems, watercraft systems, water filtration systems, cooling systems, pump systems, engine systems, diagnostic systems, predictive systems, machine design systems, medical device systems, medical imaging systems, medical modeling systems, simulation systems, enterprise systems, enterprise imaging solutions systems, advanced diagnostic tool systems, image management platform systems, artificial intelligence systems, machine learning systems, neural network systems, etc. At 1502, computational geometry for one or more elements of a mechanical device and / or control volumes for one or more elements of a mechanical device are determined based on input data received by a graphical user interface (e.g., by a computational geometry component 302). For example, the input data may include delineation data that defines boundary lines for one or more elements and / or control volumes.

[0117] At 1504, a 3D model of the mechanical device is generated based on the computational geometry and / or the stored data element library (e.g., by the modeling component 104). In one example, the data elements from the data element library 114 can be defined by computational geometry. The data element library 114 can include a set of data elements for mechanical components of the mechanical device and / or electrical components of the mechanical device. The set of data elements can include, for example, geometric data and / or texture data to facilitate generation of the 3D model.

[0118] At 1506, physical modeling data of the mechanical device is generated by executing a machine learning process associated with the 3D model (e.g., by the machine learning component 106) to predict one or more characteristics of the mechanical device. One or more characteristics may include, for example, fluid flow, thermal characteristics, combustion characteristics, and / or physical behavior. For example, the machine learning process may perform learning and / or may generate inferences about fluid flow, thermal characteristics, combustion characteristics, and / or physical behavior associated with the 3D model. The physical modeling data may indicate a visual representation of the fluid flow, thermal characteristics, combustion characteristics, and / or physical behavior of the 3D model. In aspects, the machine learning process may also be performed based on input data. The input data may include fluid data, electrical data, and / or chemical data associated with input provided to the device associated with the 3D model. The physical behavior may indicate behavior regarding fluid dynamics, thermal dynamics, and / or combustion dynamics in the entire device associated with the 3D model in response to the input data.

[0119] At 1508, it is determined whether the machine learning process has generated new outputs. If so, method 1500 returns to 1506 to update the physical modeling data based on the new outputs. If not, method 1500 proceeds to 1510.

[0120] At 1510, the 3D model is presented via a graphical user interface (e.g., by GUI component 107) and the physical modeling data is rendered on the 3D model (e.g., by GUI component 107). In aspects, the physical modeling data may be rendered on the 3D model as dynamic visual elements. In embodiments, the generation of the graphical user interface may include providing a 3D design environment associated with the 3D model.

[0121] At 1512, it is determined whether the input parameters for the physical modeling data have changed. For example, it can be determined whether new input parameters for the machine learning process are determined via the graphical user interface. If so, method 1500 returns to 1506 to perform a new machine learning process based on the changed input parameters. If not, method 1500 can end.

[0122] To provide context for various aspects of the disclosed subject matter, Fig.16 and Fig.17 The following discussion is intended to provide a brief, general description of a suitable environment in which various aspects of the disclosed subject matter may be implemented.

[0123] refer to Fig.16 A suitable environment 1600 for implementing various aspects of the present disclosure includes a computer 1612. The computer 1612 includes a processing unit 1614, a system memory 1616, and a system bus 1618. The system bus 1618 couples system components including, but not limited to, the system memory 1616 to the processing unit 1614. The processing unit 1614 can be any of a variety of available processors. Dual microprocessors and other multi-processor architectures can also be used as the processing unit 1614.

[0124] The system bus 1618 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus or external bus and / or a local bus using any of a variety of available bus architectures including, but not limited to, Industry Standard Architecture (ISA), Micro Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), card bus, Universal Serial Bus (USB), Advanced Graphics Port (AGP), Personal Computer Memory Card International Association (PCMCIA), FireWire (IEEE 1394), and Small Computer System Interface (SCSI).

[0125] The system memory 1616 includes volatile memory 1620 and nonvolatile memory 1622. A basic input / output system (BIOS), containing basic routines to transfer information between elements within the computer 1612, such as during startup, is stored in the nonvolatile memory 1622. By way of illustration, and not limitation, the nonvolatile memory 1622 can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). The volatile memory 1620 includes random access memory (RAM) that acts as external cache memory. By way of illustration and not limitation, RAM may be available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM.

[0126] The computer 1612 also includes removable / non-removable, volatile / nonvolatile computer storage media. Fig.16 As shown, for example, disk storage device 1624 is shown. Disk storage device 1624 includes, but is not limited to, devices such as a disk drive, a floppy disk drive, a tape drive, a Jaz drive, a Zip drive, an LS-100 drive, a flash memory card, or a memory stick. Disk storage device 1624 may also include storage media alone or in combination with other storage media, including but not limited to optical drives such as a compact disk ROM device (CD-ROM), a CD recordable drive (CD-R drive), a CD rewritable drive (CD-RW drive), or a digital versatile disk ROM drive (DVD-ROM). In order to facilitate the connection of disk storage device 1624 to system bus 1618, a removable or non-removable interface, such as interface 1626, is typically used.

[0127] Fig.16Also depicted is software that acts as an intermediary between a user and the basic computer resources described in the appropriate operating environment 1600. Such software includes, for example, an operating system 1628. The operating system 1628, which may be stored on disk storage 1624, functions to control and allocate the resources of the computer system 1612. System applications 1630 utilize resource management through the operating system 1628, for example, to program modules 1632 and program data 1634 stored in system memory 1616 or on disk storage 1624. It should be appreciated that the present disclosure may be implemented with various operating systems or combinations of operating systems.

[0128] A user enters commands or information into the computer 1612 through one or more input devices 1636. Input devices 1636 include, but are not limited to, pointing devices such as a mouse, trackball, stylus, touchpad, keyboard, microphone, joystick, game controller, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, etc. These and other input devices are connected to the processing unit 1614 through the system bus 1618 via one or more interface ports 1638. One or more interface ports 1638 include, for example, serial ports, parallel ports, game ports, and universal serial bus (USB). One or more output devices 1640 use some of the same types of ports as one or more input devices 1636. Thus, for example, a USB port can be used to provide input to the computer 1612 and output information from the computer 1612 to the output device 1640. Output adapters 1642 are provided to illustrate that there are some output devices 1640, such as monitors, speakers, and printers along with other output devices 1640 that require special adapters. By way of illustration and not limitation, output adapters 1642 include video and sound cards that provide a means of connection between output devices 1640 and system bus 1618. It should be noted that other devices and / or device systems provide both input and output capabilities, such as one or more remote computers 1644.

[0129] The computer 1612 can operate in a networked environment using logical operations to one or more remote computers (e.g., one or more remote computers 1644). The one or more remote computers 1644 can be personal computers, servers, routers, network PCs, workstations, microprocessor-based devices, peer devices or other common network nodes, etc., and typically include many or all of the elements described relative to the computer 1612. For the sake of simplicity, only a memory storage device 1646 with one or more remote computers 1644 is illustrated. One or more remote computers 1644 are logically connected to the computer 1612 through a network interface 1648, and then physically connected via a communication connection 1650. The network interface 1648 encompasses wired and / or wireless communication networks such as local area networks (LANs), wide area networks (WANs), cellular networks, etc. LAN technologies include fiber distributed data interface (FDDI), copper distributed data interface (CDDI), Ethernet, token ring, etc. WAN technologies include, but are not limited to, point-to-point links, circuit switching networks such as integrated services digital networks (ISDN) and variations thereon, packet switching networks, and digital subscriber lines (DSL).

[0130] Communication connection(s) 1650 refers to the hardware / software used to connect the network interface 1648 to the system bus 1618. Although the communication connection 1650 is shown within the computer 1612 for clarity of illustration, it may also be external to the computer 1612. The hardware / software required to connect to the network interface 1648 is for exemplary purposes only and includes both internal and external technologies, such as modems including conventional telephone grade modems, cable and DSL modems, ISDN adapters, and Ethernet cards.

[0131] Fig.17 1 is a schematic block diagram of a sample computing environment 1700 with which the subject matter of the present disclosure can interact. System 1700 includes one or more clients 1710. Client 1710 can be hardware and / or software (e.g., thread, process, computing device). System 1700 also includes one or more servers 1730. Therefore, system 1700 can correspond to a two-tier client server model or a multi-tier model (e.g., client, middle tier server, data server) together with other models. Server 1730 can also be hardware and / or software (e.g., thread, process, computing device). For example, server 1730 can accommodate threads to perform transformations by adopting the present disclosure. One possible communication between client 1710 and server 1730 can take the form of data packets transmitted between two or more computer processes.

[0132] The system 1700 includes a communication framework 1750 that can be used to facilitate communication between one or more clients 1710 and one or more servers 1730. The one or more clients 1710 are operably connected to one or more client data storage devices 1720, which can be used to store information local to the clients 1710. Similarly, the one or more servers 1730 are operably connected to one or more server data storage devices 1740, which can be used to store information local to the servers 1730.

[0133] It should be noted that aspects or features of the present disclosure can be utilized with essentially any wireless telecommunication or radio technology, such as Wi-Fi; Bluetooth; Worldwide Interoperability for Microwave Access (WiMAX); Enhanced General Packet Radio Service (Enhanced GPRS); 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE); 3rd Generation Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB); 3GPP Universal Mobile Telecommunications System (UMTS); High Speed ​​Packet Access (HSPA); High Speed ​​Downlink Packet Access (HSDPA); High Speed ​​Uplink Packet Access (HSUPA); GSM (Global System for Mobile Communications) EDGE (Enhanced Data Rates for GSM Evolution) Radio Access Network (GERAN); UMTS Terrestrial Radio Access Network (UTRAN); Advanced LTE (LTE-A); etc. In addition, some or all aspects described herein can be used with traditional telecommunication technologies, such as GSM. In addition, mobile as well as non-mobile networks (e.g., the Internet, data service networks such as Internet Protocol Television (IPTV), etc.) can utilize aspects or features described herein.

[0134] Although the subject matter has been described above in the general context of computer executable instructions of a computer program running on one and / or multiple computers, those skilled in the art will recognize that the present disclosure can also be or can be implemented in combination with other program modules. Typically, program modules include routines, programs, components, data structures, etc. that perform specific tasks and / or implement specific abstract data types. In addition, those skilled in the art will appreciate that the method of the present invention can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, small computing devices, mainframe computers and personal computers, handheld computing devices (e.g., PDAs, phones), microprocessor-based or programmable consumer devices or industrial electronic devices, etc. The illustrated aspects can also be implemented in a distributed computing environment, where tasks are performed by remote processing devices linked through a communication network. However, some (if not all) of the present disclosure can be practiced on a stand-alone computer. In a distributed computing environment, program modules can be located in local and remote memory storage devices.

[0135] As used in this application, the terms "component", "system", "platform", "interface", etc. may refer to and / or may include computer-related entities or entities related to an operating machine having one or more specific functionalities. The entities disclosed herein may be hardware, a combination of hardware and software, software, or software in operation. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, a thread of operation, a program, and / or a computer. By way of illustration, both an application running on a server and a server may be a component. One or more components may be located within a process and / or thread of operation, and a component may be located on one computer and / or distributed between two or more computers.

[0136] In another example, the corresponding components can be run from various computer-readable media on which various data structures are stored. The components can communicate via local and / or remote processes, for example, based on signals having one or more data packets (e.g., data from a component that interacts with other systems across a network such as the Internet via a signal). As another example, the component can be a device with a specific functionality provided by a mechanical component operated by an electrical or electronic circuit, which is operated by a software or firmware application program run by a processor. In this case, the processor can be inside or outside the device and can run at least a portion of the software or firmware application program. As yet another example, the component can be a device that provides a specific functionality by an electronic component that does not have a mechanical component, wherein the electronic component may include a processor or other device to run software or firmware that at least partially imparts the functionality of the electronic component. In aspect, the component can simulate the electronic component via a virtual machine, for example, within a cloud computing system.

[0137] Additionally, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless specified otherwise, or clear from the context, "X employs A or B" is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then "X employs A or B" is satisfied under any of the foregoing instances. Furthermore, the articles "a" and "an", as used in this specification and the drawings, should generally be construed to mean "one or more" unless specified otherwise or clear from the context to direct to a singular form.

[0138] As used herein, the terms "example" and / or "exemplary" are used to mean used as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited to these examples. In addition, any aspect or design described herein as "example" and / or "exemplary" is not necessarily to be construed as being preferred or advantageous over other aspects or designs, nor is it meant to exclude equivalent exemplary structures and techniques known to those of ordinary skill in the art.

[0139] The various aspects or features described herein may be implemented as methods, devices, systems or products using standard programming or engineering techniques. In addition, the various aspects or features disclosed in the present disclosure may be implemented by program modules that implement at least one or more methods disclosed herein, which are stored in a memory and run by at least a processor. Other combinations of hardware and software or hardware and firmware may implement or implement the aspects described herein, including one or more methods disclosed. The term "product" as used herein may encompass a computer program accessible from any computer-readable device, carrier or storage medium. For example, a computer-readable storage medium may include, but is not limited to, a magnetic storage device (e.g., a hard disk, a floppy disk, a magnetic strip...), an optical disk (e.g., a compact disc (CD), a digital versatile disc (DVD), a Blu-ray disc (BD)...), a smart card and a flash memory device (e.g., a card, a stick, a key drive...), etc.

[0140] As it is employed in this specification, the term "processor" may refer to substantially any computing processing unit or device, including but not limited to a single-core processor; a single processor with software multithreading capability; a multi-core processor; a multi-core processor with software multithreading capability; a multi-core processor with hardware multithreading technology; a parallel platform; and a parallel platform with distributed shared memory. In addition, a processor may refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. In addition, the processor may utilize nanoscale architectures, such as but not limited to molecular and quantum dot-based transistors, switches, and gates to optimize space usage or enhance the performance of user devices. The processor may also be implemented as a combination of computing processing units.

[0141] In this disclosure, terms such as "storage device," "storage means," "data storage device," "data storage means," "database," and substantially any other information storage component related to the operation and functionality of the component are used to refer to a "storage component," an entity embodied in a "memory," or a component containing a memory. It should be appreciated that the memory and / or storage components described herein may be volatile memory or non-volatile memory, or may include volatile and non-volatile memory.

[0142] By way of illustration and not limitation, nonvolatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM). For example, volatile memory may include RAM, which may act as an external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). In addition, the memory components of the systems or methods disclosed herein are intended to include, but are not limited to, including these and any other suitable types of memory.

[0143] It is to be appreciated and understood that components as described with respect to a particular system or method may include the same or similar functionality as corresponding components (eg, respectively named components or similarly named components) as described with respect to other systems or methods disclosed herein.

[0144] The above description includes examples of systems and methods that provide advantages of the present disclosure. Of course, it is not possible to describe every conceivable combination of components or methods for the purpose of describing the present disclosure, but one of ordinary skill in the art can recognize that many further combinations and arrangements of the present disclosure are possible.

Claims

1. A system comprising: a memory storing computer executable instructions; as well as a processor configured to execute computer executable instructions stored in the memory to cause the processor to: Generating a three-dimensional model of a mechanical device based on a stored library of data elements, wherein generating the three-dimensional model of the mechanical device comprises: receiving an image of the mechanical device, receiving a user input representing a tracing along the image of the mechanical device, wherein the tracing defines a portion of the image of the mechanical device, and generating the three-dimensional model of the mechanical device based on the image of the mechanical device and the user input, wherein generating the three-dimensional model includes determining a control volume associated with the three-dimensional model, including determining a boundary of a geometry of the control volume corresponding to the depiction, the control volume representing a chamber of the device through which a fluid flows through the device; predicting one or more characteristics of the mechanical device based on a machine learning process associated with the three-dimensional model, and generating physical modeling data for the mechanical device based on the one or more characteristics of the mechanical device, wherein the physical modeling data includes a fluid flow simulation associated with the mechanical device, a thermal simulation associated with the mechanical device, a combustion simulation associated with the mechanical device, or an electrical simulation associated with the mechanical device; and A graphical user interface is generated for a display device, the graphical user interface presenting the three-dimensional model and rendering the physical modeling data on the three-dimensional model.

2. The system according to claim 1, wherein: the processor presenting, via the display device, one or more mechanical components associated with the stored library of data elements, and The one or more properties of the mechanical device include thermal properties, the thermal properties including at least one of temperature, heat capacity, thermal expansion, thermal conductivity, or thermal stress associated with one or more regions of the three-dimensional model.

3. The system according to claim 1, wherein: The processor selects one or more mechanical components associated with the stored library of data elements based on input data received from the display device.

4. The system according to claim 1, wherein: The processor receives a set of mechanical components via the display device and converts the set of mechanical components into computer-aided design data.

5. The system according to claim 4, wherein: The processor generates the three-dimensional model based on the computer-aided design data.

6. The system according to claim 5, wherein: The processor performs the machine learning process based on the computer-aided design data associated with the set of mechanical components received via the display device.

7. The system according to claim 1, wherein: The processor generates the physical modeling data for the mechanical device based on the one or more characteristics of the mechanical device and fluid data indicative of a fluid received by the mechanical device.

8. The system of claim 1, wherein the physical modeling data determines the impact of fluid flow, thermal properties, combustion properties, and / or physical behavior on the design of a mechanical device associated with the three-dimensional model.

9. A method comprising: Generating, by a system including a processor, a three-dimensional model of a mechanical device based on a stored library of data elements, wherein generating the three-dimensional model of the mechanical device includes: receiving an image of the mechanical device, receiving a user input representing a tracing along the image of the mechanical device, wherein the tracing defines a portion of the image of the mechanical device, and generating the three-dimensional model of the mechanical device based on the image of the mechanical device and the user input, wherein generating the three-dimensional model includes determining a control volume associated with the three-dimensional model, including determining a boundary of a geometry of the control volume corresponding to the depiction, the control volume representing a chamber of the device through which a fluid flows through the device; executing, by the system, a machine learning process associated with the three-dimensional model to predict one or more characteristics of the mechanical device; generating, by the system, physical modeling data for the mechanical device based on the one or more characteristics of the mechanical device; and A graphical user interface is generated by the system, the graphical user interface presenting the three-dimensional model via a display device and rendering the physical modeling data on the three-dimensional model.

10. The method according to claim 9, further comprising: displaying, by the system, on the display device, information associated with one or more elements of the mechanical device, and The one or more properties of the mechanical device include thermal properties, the thermal properties including at least one of temperature, heat capacity, thermal expansion, thermal conductivity, or thermal stress associated with one or more regions of the three-dimensional model.

11. The method according to claim 9, further comprising: One or more elements of the mechanical device are selected by the system from the stored library of data elements based on input data received from the display device.

12. The method of claim 9, wherein the physical modeling data determines the impact of fluid flow, thermal properties, combustion properties, and / or physical behavior on the design of a mechanical device associated with the three-dimensional model.

13. A non-transitory computer-readable storage device comprising instructions that, in response to being executed, cause a system comprising a processor to perform operations comprising: Generating a three-dimensional model of a mechanical device based on a stored library of data elements, wherein generating the three-dimensional model of the mechanical device comprises: receiving an image of the mechanical device, receiving a user input representing a tracing along the image of the mechanical device, wherein the tracing defines a portion of the image of the mechanical device, and generating the three-dimensional model of the mechanical device based on the image of the mechanical device and the user input, wherein generating the three-dimensional model includes determining a control volume associated with the three-dimensional model, including determining a boundary of a geometry of the control volume corresponding to the depiction, the control volume representing a chamber of the device through which a fluid flows through the device; executing a machine learning process associated with the three-dimensional model to predict one or more characteristics of the mechanical device; determining physical modeling data for the mechanical device based on the one or more characteristics of the mechanical device; and A graphical user interface is provided, which presents the three-dimensional model via a display device and overlays the physical modeling data on the three-dimensional model.

14. The non-transitory computer readable storage device of claim 13, wherein: The operations further include selecting a portion of the mechanical device from the stored library of data elements based on input data received from the display device, and The one or more properties of the mechanical device include thermal properties, the thermal properties including at least one of temperature, heat capacity, thermal expansion, thermal conductivity, or thermal stress associated with one or more regions of the three-dimensional model.

15. The non-transitory computer readable storage device of claim 13, wherein the physical modeling data determines the impact of fluid flow, thermal properties, combustion properties, and / or physical behavior on the design of a mechanical device associated with the three-dimensional model.

16. A system comprising: a processor that executes computer executable instructions stored in a computer readable memory to cause the processor to: Generating a three-dimensional model of a mechanical device based on a stored library of data elements, wherein generating the three-dimensional model of the mechanical device comprises: receiving an image of the mechanical device, receiving a user input representing a tracing along the image of the mechanical device, wherein the tracing defines a portion of the image of the mechanical device, and generating the three-dimensional model of the mechanical device based on the image of the mechanical device and the user input, wherein generating the three-dimensional model includes determining a control volume associated with the three-dimensional model, including determining a boundary of a geometry of the control volume corresponding to the depiction, the control volume representing a chamber of the device through which a fluid flows through the device; predicting at least one characteristic of a mechanical device by running a machine learning process on the three-dimensional model of the mechanical device; and A graphical user interface is displayed that presents physical modeling data on the three-dimensional model based on the at least one characteristic.

17. The system of claim 16, wherein: The physical modeling data includes a fluid flow simulation associated with the mechanical device, a thermal simulation associated with the mechanical device, a combustion simulation associated with the mechanical device, or an electrical simulation associated with the mechanical device.

18. The system of claim 16, wherein: The computer executable instructions are further executable to cause a processor to: The three-dimensional model of the mechanical device is generated based on a stored library of data elements.

19. The system of claim 16, wherein: The at least one property includes a thermal property including at least one of temperature, heat capacity, thermal expansion, thermal conductivity, or thermal stress associated with one or more regions of the three-dimensional model.

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