Automatic calculation method for the circular opening size of axial cooling fan guard

Abstract

The present application relates to a method for automatically calculating the size of a circular opening in an axial cooling fan shroud. Techniques for determining fan shroud dimensions are disclosed. These techniques receive, via a computer processing system, digital data representing a three-dimensional representation of an axial fan shroud, partition the received data into a first partition corresponding to a shroud segment and a second partition corresponding to a fan segment, determine a shroud boundary ring for the shroud segment and a viewing angle of the shroud boundary ring, apply a beamcasting process to an image of the first partition to determine a shroud diameter, determine whether pixels in the image have values that generate a signal indicating that the pixel coincides with a portion of the shroud, and, when the signal is detected, calculate the shroud diameter. One aspect includes performing a flow simulation using the determined shroud size opening.

Description

[0001] Priority claim

[0002] This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Serial No. 62 / 916,317, filed on October 17, 2019, and entitled “Method for Automatic Calculation of Circular Opening Sizes of Axial Cooling Fan Shrouds,” the entire contents of which are incorporated herein by reference. Background Art

[0003] This specification relates to computer simulations of physical processes, such as physical fluid flow.

[0004] High-Reynolds-number flows are simulated by generating discrete solutions to the Navier-Stokes differential equations by performing high-precision floating-point arithmetic operations on variables representing macroscopic quantities (e.g., density, temperature, flow rate) at each of many discrete spatial locations. Another approach replaces the differential equations with what are often called lattice gas (or cellular) automata, in which the macroscopic-level simulation provided by solving the Navier-Stokes equations is replaced by a microscopic-level model that operates on particles moving between sites on the lattice.

[0005] Some fluid simulations involve simulating the fluid flow caused by an axial cooling fan with blades, a motor, and a shroud. When simulating axial cooling fans using a rotating mesh or local reference frame in computational fluid dynamics (CFD) simulations, accurately defining the mesh rotation region around the fan is crucial for simulation accuracy and cost. To achieve accuracy at a low computational cost, the size of the circular opening in the axial fan shroud is often required to determine the shroud opening size. Determining the shroud opening size for numerical simulations can be a nontrivial process.

[0006] Typically, measuring the size of a circular shroud opening requires using geometry tools and manually selecting three points. The accuracy of the opening size measurement is highly dependent on the selection and placement of these three points, which can introduce errors. These errors can become even more significant when the shroud is digitally represented in a discretized 3D representation and a mesh is applied in which the shroud opening becomes less circular.

[0007] Although some tools provide the ability to indirectly obtain the size of the circular opening of the fan guard (for example, see the SIMULIA PowerDELTA tool called "Finding Grid Holes") function, Dassault Systèmes SIMULIA Corp., RisingSun Mills Providence, RI, USA), but significant errors may occur when the shield has some special structures that interrupt the circular contour along the circular opening (such as auxiliary attachments, holes, supports). Summary of the Invention

[0008] According to one aspect, a computer-implemented method for determining a shroud size for a fan includes receiving, by a computer processing system, digital data of a three-dimensional representation of a shroud of an axial flow fan, dividing the received data into a first partition corresponding to a shroud segment and a second partition corresponding to a fan segment, determining a shroud boundary ring of the shroud segment and a viewing angle of the shroud boundary ring, applying, by the computing system, a beam shooting process to an image of the first partition to determine a shroud diameter, determining, by the computing system, whether there are pixels in the image having values that generate a signal indicating that the pixel coincides with a portion of the shroud, and calculating the shroud diameter when the signal is detected.

[0009] The following are some embodiments disclosed herein that are within the scope of the above aspects.

[0010] The method also includes determining a viewing angle of a shroud boundary ring. The method also includes repeating the beam emission and determination until a signal is detected. Calculating the shroud diameter further includes determining a two-dimensional projection of the shroud based on data of a three-dimensional representation of the axial fan to determine the shroud ring. The calculated shroud diameter is determined in pixels using a digital image of a characteristic line of the fan, thereby obtaining a conversion ratio between physical units and pixels.

[0011] The method also includes calculating a value corresponding to the center of the fan using the fan image and passing the calculated center value to the shroud image analysis to reduce computational cost.

[0012] View vector and depth of field, as well as setting image resolution, are based on fan diameter, so fan and shroud size do not affect measurement accuracy.

[0013] When multiple concentric circles exist, the beam launching process launches multiple beams from the fan edge in multiple directions to search for the inner shroud circle, thereby reducing computational cost and increasing signal-to-noise ratio. The method also includes excluding certain directions and applying high-pass filtering to the signal to improve measurement accuracy for structures located along the opening or when the shroud opening is less circular after discretization. The method also includes searching for the target pixel among neighboring pixels along the beam movement direction.

[0014] According to an additional aspect, a computer system includes one or more processors, a memory operably coupled to the one or more processors, and a computer storage device storing a computer program for determining a shroud size of a fan, and the computer program includes instructions that cause the computer system to: receive digital data of a three-dimensional representation of a shroud of an axial flow fan, divide the received data into a first partition corresponding to a shroud segment and a second partition corresponding to a fan segment, determine a shroud boundary ring of the shroud segment and a viewing angle of the shroud boundary ring, apply a beam shooting process to an image of the first partition to determine a shroud diameter, determine whether there are pixels in the image having values that produce a signal indicating that the pixel coincides with a portion of the shroud, and calculate the shroud diameter when the signal is detected.

[0015] The following are some embodiments disclosed herein that are within the scope of the above aspects.

[0016] The system determines the viewing angle of the shroud boundary ring and repeats the beam launch and determination until a signal is detected. The system determines a two-dimensional projection of the shroud based on data from a three-dimensional representation of the axial fan to determine the shroud ring. The calculated shroud diameter is determined in pixels using a digital image of the fan's characteristic lines, thereby obtaining a conversion ratio between physical units and pixels.

[0017] According to another aspect, a computer program product stored on a non-transitory computer-readable medium for determining a shroud size of a fan includes instructions for causing a system including one or more processors and memory to: receive digital data of a three-dimensional representation of a shroud of an axial flow fan, partition the received data into a first partition corresponding to a shroud segment and a second partition corresponding to a fan segment, determine a shroud boundary ring of the shroud segment and a viewing angle of the shroud boundary ring, apply a beam shooting process to an image of the first partition to determine a shroud diameter, determine whether there are pixels in the image having values that generate a signal indicating that the pixel coincides with a portion of the shroud, and calculate the shroud diameter when the signal is detected.

[0018] The following are some embodiments disclosed herein that are within the scope of the above aspects.

[0019] The product also includes instructions for determining a viewing angle of a shroud boundary ring and repeating the beam launch and determination until a signal is detected. The instructions for calculating the shroud diameter also include instructions for determining a two-dimensional projection of the shroud based on data representing a three-dimensional representation of the axial fan to determine the shroud ring. The calculated shroud diameter is determined in pixels using a digital image of the fan's characteristic lines, thereby obtaining a conversion ratio between physical units and pixels.

[0020] One or more of these aspects can include one or more of the following advantages.

[0021] The circular opening dimensions of an axial cooling fan shroud are measured without manual input or manipulation. Image processing methods analyze 3D geometry in the 2D domain to reduce computational cost and improve accuracy. This process uses characteristic lines of the 3D geometry of the fan and shroud rather than digital images of the geometry's edges, eliminating the need for image preprocessing or edge detection.

[0022] The techniques disclosed herein can be used, for example, to accurately measure the circular opening dimensions of an axial fan shroud when a rotating mesh or local reference frame is used in computational fluid dynamics (CFD) simulations to simulate axial cooling fans. These techniques accurately define the mesh rotation region around the fan, which is critical to both simulation accuracy and cost.

[0023] These aspects involve an automated process that eliminates human-introduced errors. These aspects provide a circular sizing technique that is significantly faster (e.g., 10-20 times faster) than conventional circular Hough transform methods. Compared to directly analyzing the 3D geometry, these aspects can produce more accurate results when special structures are present on the shield opening, if these structures interrupt the circular contour. When the shield opening is not so circular due to distortions in the geometric discretization process, the standard deviation can be lower compared to the three-point method, and the training cost can be significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A system for fluid flow simulation including a process for determining fan shroud size is depicted, wherein the simulation example uses a turbulent boundary layer model for compressible flow.

[0025] Figure 2 A flow chart illustrating the operation of forming a lattice Boltzmann model simulation using determined fan shroud dimensions and a turbulent boundary layer model is depicted.

[0026] FIG3 illustrates the change in flow direction when experiencing a shock front (prior art).

[0027] FIG. 4 shows the pressure gradient decomposed into components along three orthogonal directions (prior art).

[0028] FIG5 depicts a flow chart illustrating aspects of a turbulent boundary layer model.

[0029] 6 and 7 show the velocity components of two LBM models (prior art).

[0030] Figure 8 It is a flow chart of the process followed by the physical process simulation system.

[0031] Figure 9 It is a perspective view of a microblock (prior art).

[0032] Figure 10A and Figure 10B is a diagram of a lattice structure (prior art).

[0033] 11 and 12 illustrate variable resolution technology (prior art).

[0034] FIG. 13 shows the area affected by bins of a surface (prior art).

[0035] Figure 14 is a flow chart of an automated process for sizing the axial fan shroud opening, which can be used in various applications such as flow simulations.

[0036] Figures 15A-15C 、 Figure 15F and Figure 15G It helps to understand Figure 14 Schematic diagram of various aspects of the automation process.

[0037] Figure 15D Is a Figure 15D-1 to Figure 15D -3 Components diagram, which helps to understand the use Figure 14 Automation of all aspects of the beam launch process.

[0038] Figure 15E Structures such as cutouts or holes are shown.

[0039] Below is an example application of the automated process for determining axial fan shroud opening size, as applied in an LBM fluid simulation using a turbulent boundary layer model for compressible flow. The use of an LBM fluid simulation and the use of a turbulent boundary layer model are merely illustrative examples of the results of using the described automated process for determining axial fan shroud opening size. DETAILED DESCRIPTION

[0040] The automated process for correctly determining fan shroud opening sizes may be used in fluid flow simulations performed by simulation engine 34, for example, as described in U.S. Patent Application No. 11 / 463,673, entitled “Computer Simulation of Physical Processes” (now issued as U.S. Patent No. 7,558,714), the entire contents of which are incorporated herein by reference.

[0041] In the following Figure 8 In the following, the flow simulation process is described using the results of an automated process for correctly sizing the fan shroud opening to configure the simulation space. Figure 9-Figure 1 3, each of these figures is marked as prior art because these figures appear in the above-referenced patents.

[0042] However, the figures appearing in the above-referenced patents do not take into account any modifications to the flow simulations using an automated process for correctively sizing the fan shroud openings to configure the simulation space, as the automated process described therein is not described in the above-referenced patents.

[0043] In the physical process simulation system based on LBM, the fluid flow is determined by a set of discrete velocity c i The distribution function value f evaluated under i The dynamics of the distribution function is determined by the following equation, where f i (0) is called the equilibrium distribution function, which is defined as:

[0044]

[0045] This equation is well known to describe the distribution function f i The time evolution of the lattice Boltzmann equation for . The left-hand side shows the change in distribution due to the so-called "fluidization process." Fluidization occurs when a fluid block starts at a grid location and moves to the next grid location along one of the velocity vectors. At this point, the "collision factor" is calculated—the influence of the nearby fluid block on the starting block. Fluid can only move to one grid location, so it is necessary to choose the velocity vectors so that all components of all velocities are multiples of a common velocity.

[0046] The right side of the first equation is the aforementioned "collision operator," which represents the change in the distribution function due to collisions between fluid parcels. The specific form of the collision operator used here is due to Bhatnagar, Gross, and Crocker (BGK). It forces the distribution function to reach the prescribed value given by the second equation, which is the "equilibrium" form.

[0047] From this simulation, conventional fluid variables, such as mass ρ and fluid velocity u, are obtained as simple sums. Here, c i and w i The collective value of defines the LBM model. The LBM model can be efficiently implemented on a scalable computer platform and operates with strong robustness to time-unsteady flows and complex boundary conditions.

[0048] The standard technique for obtaining the macroscopic equations of motion for a fluid system from the Boltzmann equation is the Chapman-Enskog method, in which successive approximations of the full Boltzmann equation are employed.

[0049] In fluid systems, small perturbations in density travel at the speed of sound. In gas systems, the speed of sound is often determined by temperature. The importance of the effects of compressibility in a flow is measured by the ratio of the characteristic velocity to the speed of sound, which is called the Mach number.

[0050] Now refer to Figure 1 , describes a system 10 including a turbulent boundary layer model that incorporates pressure gradient directional effects 34b for high-speed and compressible flows. The system 10 in this implementation is based on a client-server or cloud-based architecture and includes a server system 12 and a client system 14 implemented as a (standalone or cloud-based) massively parallel computing system 12. The server system 12 includes a memory 18, a bus system 11, an interface 20 (e.g., a user interface / network interface / display or monitor interface, etc.), and a processing device 24. Within the memory 18 are a grid preparation engine 32 and a simulation engine 34.

[0051] although Figure 1 A mesh preparation engine 32 is shown in memory 18, but the mesh preparation engine can be a third-party application executed on a system different from the server 12. Regardless of whether the mesh preparation engine 32 is executed in memory 18 or on a system different from the server 12, the mesh preparation engine 32 receives a user-provided mesh definition 30 and prepares a mesh based on the physical object being modeled for simulation by the simulation engine 34 and sends (and / or stores) the prepared mesh to the simulation engine 34. The simulation engine 34 includes a collision interaction module 34a, a boundary module 34b, and an advection particle collision interaction module 34c. The system 10 accesses a data repository 38 that stores 2D and / or 3D meshes (Cartesian and / or curvilinear), coordinate systems, and libraries.

[0052] In the example to be discussed here, the physical object is a ventilation system comprising an axial fan having a shroud having a shroud opening. It is desirable to accurately measure the dimensions of the shroud opening of the axial fan. However, the use of a ventilation system is merely illustrative, as the physical object can have any shape, and in particular can have (one or more) flat and / or curved surfaces. Furthermore, in some embodiments, the fluid flow can be in a fluid environment in which the axial fan is located. The system 10 accesses a data repository 38, which stores 2D and / or 3D grids (Cartesian and / or curvilinear) (e.g., 32a for the ventilation system and 32b for the axial fan), coordinate systems, and libraries.

[0053] A process for determining the shield opening size is also included (process 55). Process 55 can be part of the mesh preparation process, a separate process, or included in the simulation process. A full discussion of process 55 is available at Figures 15A-15GAs with the grid preparation engine 32, the process 55 may be executed in the memory 18, or may be executed on a system different from the server 12, with the server receiving (and / or storing) the results of the process 55 for use by the simulation engine 34 (or other applications that may be used to determine shield opening size). Use with the simulation engine 34 is merely an example.

[0054] Now refer to Figure 2 , a process 40 for simulating fluid flow around a representation of a physical object is shown. The process 40 receives 42 a mesh 32a (or grid) for a physical object being simulated (e.g., a ventilation system) from, for example, a client system 14 or retrieves it from a data repository 38. In other embodiments, an external system or server 12 generates the mesh 32a for the physical object being simulated based on user input. Applying a mesh to an axial flow fan can complicate the simulation, as the fan's simulation physically rotates during the simulation. Therefore, it is necessary to accurately resolve the gap area between the fan and the shroud.

[0055] Process 40 also receives 42 a three-dimensional representation of the axial fan, for example, from client system 14, or retrieves the three-dimensional representation from data repository 38. Process 40 calls 41 process 55, or the calculated axial fan shroud opening size is provided to process 40 from another system / process executing process 55. That is, in other embodiments, the external system or server 12 executes process 55 to determine the axial fan shroud opening size and provides it as input to simulation process 40.

[0056] The simulation process 46 simulates the evolution of the particle distribution according to the Lattice Boltzmann equation (LBE). The process pre-computes 44 geometric quantities based on the retrieved grid and uses the pre-computed geometric quantities corresponding to the retrieved grid to perform a dynamic Lattice Boltzmann model simulation 46. The simulation process 46 simulates the evolution of the particle distribution according to the Lattice Boltzmann equation (LBE). The process 46 performs a collision operation 46a (and, according to the collision operation, collects the incoming distribution set from the neighboring grid locations), evaluates 46b the flow at the physical boundary according to boundary modeling, performs scalar processing when the flow hits the physical surface by applying a scalar solver 46c, and performs advection 46d of the particle to the next cell in the LBM grid.

[0057] Boundary Modeling

[0058] Reference is now made to Figure 3, which illustrates the change in flow direction when experiencing a shock front. In order to correctly simulate the interaction with the surface, each panel element satisfies four boundary conditions. First, the combined mass of the particles received by the panel element should equal the combined mass of the particles delivered by the panel element (i.e., the net mass flux of the panel element must be equal to zero). Second, the combined energy of the particles received by the panel element should equal the combined energy of the particles delivered by the panel element (i.e., the net energy flux of the panel element is equal to zero). These two conditions can be met by requiring the net mass flux of each energy level (i.e., energy levels 1 and 2) to be equal to zero.

[0059] Boundary layer models can model the wall shear stress (friction) corresponding to the usual no-slip boundary condition, which determines the momentum flux occurring at the solid wall, such as,

[0060]

[0061] The gradient value is taken from the wall (y=0), u * is the so-called friction velocity (= square root of the wall shear stress, and ρ—the fluid mass density), and v0 is the molecular kinematic viscosity of the flow. Accurately computing this gradient requires decomposing the velocity field down to very small scales, down to the walls, which is impractical. A central task in turbulence modeling is to approximate the wall shear stress without directly computing the velocity gradient at the wall. This is known in the fields of turbulence and computational fluid dynamics as turbulent boundary layer modeling (or wall modeling).

[0062] The formulation of turbulent boundary layer models is based on a fundamental turbulence phenomenon known as the "wall law." That is, if the solid wall is sufficiently flat and the turbulent flow adheres entirely to the wall, then the time-averaged velocity distribution of the turbulent flow has a known, specific, "universal" form over a large range of locations measured in terms of distance from the wall.

[0063] This "universal" form is preserved under scaling by certain local intrinsic physical properties (such as wall shear stress). Thus, the following expression can be used for the velocity profile,

[0064]

[0065] where U(y) is the average velocity of the fluid along the solid wall measured at a distance y from the wall, and B is a constant (which has been found empirically to be approximately 5). + is the dimensionless distance from the wall, defined as:

[0066]

[0067] The constant κ is the so-called von Karman constant (which has been empirically found to be about 0.41). The logarithmic functional form is good for a large range of y, from about 50 to several hundred or more. + The basic wall model functional form (Equation 1) can be extended to cover a larger range of y including the viscous and transition sublayers. + Value, 0 <y + <50. The expanded form is given as follows,

[0068] U(y)=u * F(y + ) (Equation 2)

[0069] It is generally believed that: <y + ≤5, F(y + )=y + ; for y + ≥50, Transition distribution form for 5 <y + <50.

[0070] However, this "wall law" is generally only applicable to the case where the boundary layer flow is completely adhered along a perfectly flat solid wall, so that the velocity changes parallel to the wall are negligible compared to the velocity changes perpendicular to the wall, which is known as the equilibrium condition. An equation (Equation 1) defines the relationship between the velocity profile (velocity as a function of distance from the wall) and the surface skin friction. This provides a basis for determining the skin friction without requiring information about the (unresolvable) velocity gradient at the wall, which is an observation relevant to the physics of modeling turbulent boundary layers. The wall shear stress vector defines the effective force exerted by the solid surface on the fluid in a direction opposite to the direction of the flow velocity.

[0071]

[0072] in, is the unit vector in the direction of flow velocity 900.

[0073] However, a solid wall (shock front 902) is generally not flat. Therefore, it is desirable to extend the "wall law" to the non-equilibrium case where there are flow variations in the streamwise direction caused by, for example, wall curvature. It is well known that the main effect of curvature on the profile of a turbulent boundary layer is the presence of a pressure gradient. Various extensions have been made to the basic wall model, which generally involve modifications to the equation (Equation 1) to include a term proportional to the pressure gradient.

[0074] One such extension is described in U.S. Pat. No. 5,910,902 A, which is incorporated herein by reference in its entirety, which describes an advanced extension of the basic wall model (Equation 1) to include the effects of pressure gradients using a specific approach based on the self-similarity argument of the boundary layer profile under pressure gradient influence. The general form of this extension is written as:

[0075]

[0076] where ξ(x) is a dimensionless positive function of x. dp / ds represents the pressure gradient component in the streamwise direction (parallel to the local fluid velocity),

[0077]

[0078] in is a unit vector in the streamwise direction. This approach enables accurate simulation of flows around arbitrarily shaped objects, including accurate prediction of boundary layer flow separation.

[0079] Existing turbulent boundary layer modeling (including the turbulent boundary layer modeling described in the aforementioned U.S. Patent US5910902A) assumes that the direction of the pressure gradient is parallel to the velocity direction in the boundary layer. That is, the extension of equation (2), such as equation (4), only considers the contribution of the pressure gradient component in the flow direction, while ignoring the perpendicular pressure component. Although this is reasonable to address the effects of geometric curvature in the flow direction, the flow along the solid surface is not always in the same direction as the curvature direction. For example, consider a cylinder whose main axis forms an angle (0<θ<90) with respect to the flow direction. Due to this geometric shape, the resulting pressure gradient is neither parallel nor perpendicular to the flow direction. Therefore, it is necessary to generalize the existing turbulent boundary layer modeling to correctly capture the effects of curvature on non-parallel boundary layer flows.

[0080] 4, as described above, the pressure gradient can be decomposed into components along three orthogonal directions 70, one perpendicular to the wall, two tangential to the wall, but one "stream-wise" parallel to the average velocity in the boundary layer, and one "span-wise" perpendicular to the wall. Generally, in conventional extended wall models, the streamwise pressure gradient component is included, while the span-wise component is ignored or not identified.

[0081] The turbulent boundary layer modeling described in this paper begins with a different way of dealing with the relationship between the direction of the pressure gradient and the direction of the flow velocity. Instead of decomposing the pressure gradient into the three directions mentioned above (normal to the wall and two tangential directions, namely "streamwise" and "spanwise"), the boundary layer flow velocity is decomposed into three directions.

[0082] Since the velocity is tangential to the wall, the velocity component perpendicular to the wall is zero, so there are actually only two velocity directions, namely the first direction parallel to the wall-cut portion of the pressure gradient and the second direction perpendicular to the wall-cut portion of the pressure gradient.

[0083] Therefore, the velocity vector U can be expressed as:

[0084]

[0085] in and are the wall shear unit vectors of the wall shear parts parallel to and perpendicular to the pressure gradient direction, respectively. The velocity components are expressed by the following equations:

[0086]

[0087] After decomposing the boundary layer velocity into these two components, it is straightforward to apply appropriate wall modeling based on their two different directions. For the velocity component perpendicular to the pressure gradient, the basic law of the wall model as in equation (2) is used, i.e.:

[0088] U b (y)=u *b F(y + ) (Equation 7a)

[0089] The friction speed u *b corresponds to the skin friction perpendicular to the pressure gradient. In contrast, the extended wall model form (Eq. (4)) is used for the velocity component parallel to the pressure gradient:

[0090]

[0091] Therefore, the pressure gradient effect only applies to the parallel component of the boundary layer velocity. *p Corresponds to skin friction parallel to the direction of pressure gradient.

[0092] In addition, a more detailed definition of the streamwise pressure gradient dp / ds 904 is provided than has been previously defined and understood. As described above, in conventional understanding, dp / ds is the component of the pressure gradient in the streamwise direction, i.e., the projection of the pressure gradient in the direction of the boundary layer velocity:

[0093]

[0094] Contrary to the conventional understanding, dp / ds is defined here as the component of the pressure gradient tangential to the solid surface, which is usually in a different direction than the velocity. Specifically, dp / ds is defined according to this interpretation as:

[0095]

[0096] in is a unit vector perpendicular to the solid surface, and the unit vector is the direction of the projected pressure gradient tangent to the surface (equivalent to the unit vector defined in Equation 5 ).

[0097] The absolute value of the new dp / ds is usually larger than the absolute value of the traditional definition because

[0098]

[0099] Therefore, the resulting pressure gradient effect is slightly stronger in the new extended wall model. Most importantly, since the boundary layer velocity is usually not parallel to (the tangential part of) the pressure gradient, the resulting skin friction is no longer parallel to the velocity direction.

[0100] Combining all the above factors, a new expression of wall shear stress is obtained as follows:

[0101]

[0102] It can be seen that due to u *p Generally not equal to u *b , so the wall shear stress direction is not parallel to the flow velocity direction. This feature is believed to be lacking in all previous turbulent boundary layer models. Therefore, it is expected that the described extended wall model will show substantial improvement for non-flat solid wall surfaces, thereby extending the "wall law" to non-equilibrium cases in the presence of flow variations in streamwise direction caused by, for example, wall curvature, compared to conventional wall models. The non-parallel skin friction effects of the disclosed wall model can provide more accurate predictions of boundary layer turning phenomena due to the presence of near-wall shock waves on curved surfaces.

[0103] Referring to FIG5 , a turbulent boundary layer model 46 b is evaluated. The turbulent boundary layer model determines 82 the boundary layer velocity. Although there are three directions, the velocity component perpendicular to the wall is assumed to be zero, so only two velocity directions are actually determined: a first direction parallel to the wall cut portion of the pressure gradient and a second direction perpendicular to the wall cut portion of the pressure gradient, see Equations 6 a and 6 b (above).

[0104] By using the two components of the boundary layer velocity equations 6a and 6b (above), the turbulent boundary layer model calculates 84 the pressure gradient based on these velocity components by applying the velocity component parallel to the pressure gradient given in the above equation 9 as the wall shear stress in the extended wall model, where the wall shear stress direction is not parallel to the flow velocity direction.

[0105] 6 , a first model (2D-1) 100 is a two-dimensional model including 21 velocities. Of the 21 velocities, one velocity (105) represents a particle that does not move; three sets of four velocities represent particles that move at a normalized velocity (r) (110-113), twice the normalized velocity (2r) (120-123), or three times the normalized velocity (3r) (130-133) in the positive or negative direction along the x or y axis of the lattice; and two sets of four velocities represent particles that move at a normalized velocity (r) (140-143) or twice the normalized velocity (2r) (150-153) relative to both the x and y lattice axes.

[0106] As also shown in Figure 7, the second model (3D-1) 200 is a three-dimensional model including 39 velocities, each of which is represented by one of the arrows in Figure 7. Of these 39 velocities, one velocity represents a particle that does not move; three sets of six velocities represent particles that move at a normalized velocity (r), twice the normalized velocity (2r), or three times the normalized velocity (3r) in the positive or negative direction along the x, y, or z axis of the lattice; eight velocities represent particles that move at a normalized velocity (r) relative to all three of the x, y, and z lattice axes; and twelve velocities represent particles that move at twice the normalized velocity (2r) relative to two of the x, y, and z lattice axes.

[0107] More complex models may also be used, such as a 3D-2 model including 101 velocities and a 2D-2 model including 37 velocities.

[0108] For the three-dimensional model 3D-2, among the 101 velocities, one velocity represents a particle that is not moving (Group 1); three groups of six velocities represent particles that move at a normalized velocity (r), twice the normalized velocity (2r), or three times the normalized velocity (3r) in the positive or negative direction along the x, y, or z axis of the lattice (Groups 2, 4, and 7); and three groups of eight velocities represent particles that move at a normalized velocity (r), twice the normalized velocity (2r), or three times the normalized velocity (3r) relative to all three of the x, y, and z lattice axes (Groups 3, 8, and 10). ; Twelve velocities represent particles moving at twice the normalized velocity (2r) relative to two of the x, y, z lattice axes (Group 6); twenty-four velocities represent particles moving at a normalized velocity (r) and twice the normalized velocity (2r) relative to two of the x, y, z lattice axes and not moving relative to the remaining axes (Group 5); and twenty-four velocities represent particles moving at a normalized velocity (r) relative to two of the x, y, z lattice axes and at three times the normalized velocity (3r) relative to the remaining axes (Group 9).

[0109] For the two-dimensional model 2D-2, among the 37 velocities, one velocity represents a particle that does not move (Group 1); three groups of four velocities represent particles that move at a normalized velocity (r), twice the normalized velocity (2r), or three times the normalized velocity (3r) in the positive or negative direction along the x or y axis of the lattice (Groups 2, 4, and 7); two groups of four velocities represent particles that move at a normalized velocity (r) or twice the normalized velocity (2r) relative to both the x and y lattice axes; eight velocities represent particles that move at a normalized velocity (r) relative to one of the x and y lattice axes and at twice the normalized velocity (2r) relative to the other axis; and eight velocities represent particles that move at a normalized velocity (r) relative to one of the x and y lattice axes and at three times the normalized velocity (3r) relative to the other axis.

[0110] The LBM models described above provide a specific class of efficient and robust discrete velocity dynamics models for numerical simulation of flows in two and three dimensions. This type of model consists of a specific set of discrete velocities and weights associated with those velocities. These velocities correspond to a grid of Cartesian coordinates in velocity space, which facilitates accurate and efficient implementation of discrete velocity models, particularly the class known as lattice Boltzmann models. By utilizing such models, flows can be simulated with high fidelity.

[0111] refer to Figure 8 The physical process simulation system operates according to a procedure 300 to simulate a physical process such as fluid flow in which an axial fan is provided. Prior to the simulation, the axial fan shroud size (e.g., the size of the axial fan shroud determined by the process of automatically calculating the size of the axial fan shroud) is received 301. Figure 14 and Figures 15A-15G ) and applied to a fluid simulation 301. As part of the fluid simulation, the simulation space is modeled as a collection of voxels (step 302). Typically, the simulation space is generated using a computer-aided design (CAD) program. For example, a CAD program can be used to draw an axial fan in a ventilation system. Thereafter, the data generated by the CAD program is processed to add a lattice structure with appropriate resolution and to account for objects and surfaces within the simulation space.

[0112] The resolution of the lattice can be chosen based on the Reynolds number of the system being simulated. The Reynolds number is related to the viscosity of the flow (v), the characteristic length of objects in the flow (L), and the characteristic velocity of the flow (u):

[0113] Re=uL / v Equation (I-3)

[0114] The characteristic length of an object represents a large-scale feature of the object. For example, if the flow around a microdevice is being simulated, the height of the microdevice can be considered the characteristic length. For the flow around an axial fan, the diameter of the fan can be considered the characteristic length. When the flow around a small area of the object (e.g., the gap area between the fan and the shroud) is of interest, the resolution of the simulation can be increased, or a region of increased resolution can be used around the area of interest. The dimensionality of the voxel decreases as the resolution of the lattice increases.

[0115] The state space is represented as f i (x,t), where f i represents the number of elements or particles per unit volume in state i at the lattice site represented by the three-dimensional vector x at time t (i.e., the density of particles in state i). For a known time increment, the number of particles is simply referred to as f i (x). The combination of all states of the lattice site is denoted as f(x).

[0116] The number of states is determined by the number of possible velocity vectors within each energy level. A velocity vector consists of integer linear velocities in a space with three dimensions x, y, and z. For multi-genre simulations, the number of states increases.

[0117] Each state i represents a different velocity vector at a specific energy level (i.e., energy level zero, one, or two). The velocity c of each state i The "velocity" in each of the three dimensions is indicated as follows:

[0118] c i =(c i,x , c i,y , c i,z ). Equation (I-4)

[0119] The zero energy state represents a stationary particle that does not move in any dimension, i.e., c stopped =(0,0,0). The energy level one state represents a particle with a velocity of ±1 in one of the three dimensions and zero velocity in the other two. The energy level two state represents a particle with a velocity of ±1 in all three dimensions, or a velocity of ±2 in one of the three dimensions and zero velocity in the other two.

[0120] Generating all possible permutations of the three energy levels gives a total of 39 possible states (one energy zero state, 6 energy one states, 8 energy three states, 6 energy four states, 12 energy eight states, and 6 energy nine states).

[0121] Each voxel (i.e., each lattice site) is represented by a state vector f(x). This state vector completely defines the state of the voxel and includes 39 entries. These 39 entries correspond to one energy zero state, six energy one states, eight energy three states, six energy four states, 12 energy eight states, and six energy nine states. By using this set of velocities, the system can generate Maxwell-Boltzmann statistics for the achieved equilibrium state vector.

[0122] During the simulation, when the process encounters a location in the grid corresponding to a surface of a physical object or device, the process performs the above functions by evaluating it under a turbulent boundary layer model that decomposes the pressure gradient into boundary layer flow velocities, as described above.

[0123] Now refer to Figure 9 , showing a microblock. For processing efficiency, voxels are grouped in 2x2x2 volumes called microblocks. Microblocks are organized to allow parallel processing of voxels and minimize the overhead associated with data structures. The shorthand notation for voxels in a microblock is defined as N i (n), where n represents the relative position of the lattice site in the microscopic block and n∈{0,1,2,...,7}.

[0124] refer to Figure 10A and Figure 10B , surface S( Figure 10A ) in the simulation space ( Figure 10B ) is represented as a surface element F α A collection of:

[0125] S={F α} Equation (I-5) where α is an index that lists a particular facet. Facets are not limited to voxel boundaries, but typically have dimensions on the order of the dimensions of the voxels adjacent to the facet, or slightly smaller than the dimensions of the voxels adjacent to the facet, so that the facet affects a relatively small number of voxels. To implement surface dynamics, attributes are assigned to the facets. Specifically, each facet F α With unit normal (n α ), surface area (A α ), center position (x α ) and the surface element distribution function (f i (α)).

[0126] 11 , different resolution levels can be used in different regions of the simulation space to improve processing efficiency. Typically, the region 650 surrounding the object 655 is of greatest interest and is therefore simulated using the highest resolution. Because the effect of viscosity decreases with distance from the object, regions 660, 665 spaced at increasing distances from the object 655 are simulated using reduced resolution levels (i.e., enlarged voxel volumes).

[0127] 12 , lower resolution levels may be used to model regions 770 around less prominent features of object 775, while the highest resolution level is used to model regions 780 around the most prominent features (e.g., leading and trailing edge surfaces) of object 775. Outlying regions 785 are modeled using the lowest resolution level and the largest voxels.

[0128] Identify voxels affected by surfels

[0129] Reference again Figure 8 Once the simulation space has been modeled (step 302), the voxels that are affected by one or more bins are identified (step 304), taking into account the determined axial fan shroud dimensions. A voxel can be affected by a bin in a number of ways. First, a voxel that is intersected by one or more bins is affected in that the voxel has a reduced volume relative to a non-intersected voxel. This occurs because the bin and the material underlying the surface represented by the bin occupy a portion of the voxel. The fractional factor P is the number of bins that are affected by the bin. f (x) indicates the portion of the voxel that is not affected by the surface element (i.e., the portion that can be occupied by the fluid or other material for which the flow is simulated). For non-intersecting voxels, P f (x) is equal to 1.

[0130] Voxels that intersect one or more bins by either sending particles to them or receiving particles from them are also identified as voxels affected by the bins. All voxels intersected by a bin will include at least one state for receiving particles from the bin and at least one state for sending particles to the bin. In most cases, additional voxels will also include such states.

[0131] Referring to FIG13 , for a non-zero velocity vector c i For each state i, the surface element F α From the parallelepiped G iα The area defined by the particle receiving or transmitting is the parallelepiped G iα With the velocity vector c i and facets(|c i n i |)'s unit normal n α The height is defined by the magnitude of the vector dot product and the surface area A of the binα Define the base so that the parallelepiped G iα Volume V iα equal:

[0132] V ia =|c i n a |A a Equation (I-6)

[0133] When the velocity vector of the state points to the surface element (|c i n i |<0), panel F α From the volume V iα Receive particles, and when the velocity vector of the state points away from the surface element (|c i n i |>0), transmit particles to this area. As will be discussed below, when another surface element occupies the parallelepiped G iα This expression must be modified when θ is part of a convex feature, i.e., a condition that occurs near non-convex features such as interior corners.

[0134] Surf F α parallelepiped G iα Multiple voxels can be partially or completely overlapped. The number of voxels, or portions thereof, depends on the bin size relative to the voxel size, the energy of the state, and the orientation of the bin relative to the lattice structure. The number of voxels affected increases with the size of the bin. Thus, as noted above, the size of a bin is typically selected to be of the order of magnitude of, or smaller than, the size of voxels located near the bin.

[0135] Parallelepiped G iα The portion of the voxel N(x) that overlaps is defined as V iα (x). Using this terminology, between the volume element N(x) and the surface element F α The flux of particles moving between states i is Γ iα (x) is equal to the density of particles in state i in the element (N i (x)) multiplied by the volume of the region overlapping with the voxel (V iα (x)):

[0136] Γ iα (x) = N i (x)V iα (x) Equation (I-7)

[0137] When the parallelepiped G iα When intersected by one or more panels, the following conditions are true:

[0138] V iα=∑V α (x)+∑V iα (β) Equation (I-8)

[0139] The first summation is considered to be G iα All voxels that overlap and the second term considers G iα All the face elements intersecting. When the parallelepiped G iα When not intersected by another polygon, this expression simplifies to:

[0140] V iα =∑V iα (x) Equation (I-9)

[0141] Execute simulation

[0142] Once the voxels affected by one or more facets are identified (step 304), a timer is initialized to begin the simulation (step 306). During each time increment of the simulation, the movement of particles from voxel to voxel is simulated by an advection phase that takes into account the interaction of particles with surface facets (steps 308-316). Next, a collision phase (step 318) simulates the interaction of particles within each voxel. Thereafter, the timer is incremented (step 320). If the incremented timer does not indicate that the simulation is complete (step 322), the advection phase and collision phase (steps 308-320) are repeated. If the incremented timer indicates that the simulation is complete (step 322), the results of the simulation are stored and / or displayed (step 324).

[0143] Automatic measurement of circular opening size of shield

[0144] refer to Figure 14 , a process 55 for automatically determining the size of a circular opening of an axial fan shroud is shown. The process 55 is executed on a computer system. Each component feature of the process 55 will be combined below. Figures 15A to 15G The images are discussed. Figures 15A to 15G Various aspects of the constituent features of process 55 are described in detail.

[0145] Import

[0146] The process 55 imports 402 a 3D digital representation of an axial cooling fan and its corresponding shroud. Figure 15A A typical set 440 of axial cooling fans 442, enclosing shrouds 444, and blades 446 is shown. The import feature 402 of process 55 uses an image processing method that analyzes a complex 3D (three-dimensional) representation of the axial fan 442 ( Figure 15A), which is "projected" onto a 2D domain representation containing a contour drawing of the shroud 444. The shroud 444 typically has a circular opening on one side that encloses the fan 442, providing a smoother and more stable airflow through the blades 446. The digital format used to import the dataset can be a computer-aided design (CAD) format or a mesh format. Other digital formats can be used.

[0147] Division

[0148] Return Reference Figure 14 , process 55 divides 404 the digital representation of the axial cooling fan into the fan and the shroud. That is, the fan and the shroud are automatically divided or grouped so that these components can be analyzed and processed separately by process 55. These components, such as fan 442, shroud 444, blades 446 ( Figure 15A ) etc., such as motors, are labeled according to predefined rules before the data set with these data and labels is used by process 55, so that individual features can be automatically detected in process 55 and processed separately as needed.

[0149] Fan diameter calculation

[0150] The process 55 automatically calculates 406 the axial fan diameter and expresses the calculation in physical units, such as millimeters (mm) or inches (in). (In an alternative embodiment, this calculation 406 can be skipped if the fan size is obtained from user input, although this alternative embodiment reduces the level of automation of the process.)

[0151] The process 55 automatically calculates the axial fan diameter 406 by generating a set of vertices that follow the shape outline of the fan geometry in the radial direction. These vertices are connected by lines to form a polyline. The polyline is resolved along the axial fan rotation axis to form a cylindrical rotation body or fan boundary ring 410. Figure 15B A fan boundary ring 448 generated by process 55 is shown, which facilitates shroud circular opening size calculation 406. On the same plane as the generated fan boundary ring, the maximum distance in the radial direction is the diameter of the axial fan.

[0152] View vector calculations

[0153] continue Figure 14 , the calculated fan diameter 406 (or the fan diameter provided by the user) is used as input by process 55 to calculate 408 a view vector for generating an image corresponding to a snapshot of the target representation of the fan shroud and fan. Process 55 generates a fan boundary ring 448 ( Figure 15B ) output 410 to a module that calculates 412 the fan diameter and fan center in pixels. Process 55 also generates the fan boundary ring 448 ( Figure 15B) output 410 to a module that generates 414 a shield outline image 450 including a circular opening 451 and a frame 452, such as Figure 15C shown.

[0154] Fan diameter calculation

[0155] continue Figure 14 , the fan diameter (also the ring diameter) and the fan center are used by process 55 Figure 15B The fan boundary ring image shown is calculated in pixels 412. Together with the calculation of the fan diameter 406, the corresponding data between physical units and pixels is established. The calculated fan diameter, ring diameter and fan center data generated by the calculation 412 and the generated contour shield image 414 are input to the beam launch module 416.

[0156] Beam emission

[0157] continue Figure 14 , process 55 uses a beam launching method 416 to measure the shield circular opening size or shield diameter. The beam launching method used in the system is similar to the "ray tracing" used in the field of 3D computer graphics. The difference is that the beam launching method used in the system performs tracing in the 2D image domain. The beam launching method launches tracing rays in radial directions from the center. However, the actual starting position is located at 510h, the imaginary fan boundary, to save computing resources. The beam continuously checks the pixel value at each travel step in space until the beam intersects with the target object or reaches a predefined outer boundary.

[0158] Now also refer to Figure 15D , which shows the beginning of the beam launching process 500 ( Figure 15D-1 ), intermediate stage ( Figure 15D-2 ) and the end phase ( Figure 15D -3). According to an embodiment, beam emission is used to measure the shroud diameter, i.e., the shroud opening size, wherein the search process begins with an imaginary fan boundary ring. Circle 502, shown as a dashed line, represents the calculated fan boundary ring. Outer circle 504, shown as a solid line, represents the shroud opening. These two circles 502 and 504 are concentric. The area between them represents the fan clearance region. The beam emission measures the boundary of this fan clearance region. The stop limit is slightly outside of shroud opening 504 to ensure coverage of the entire fan clearance region.

[0159] Figure 15D-1 to Figure 15D Each of FIG. 3 shows eight lines 510a-510h. Figure 15D-1 These lines are shown where the arrows touch circle 502, Figure 15D-2 These lines are shown with the arrows terminating in the gap between circle 502 and circle 504. Figure 15D3 shows these lines, where the arrows terminate at circle 504. These arrowed lines 510a-510h are "beams" emanating from the centers of the concentric circles 502, 504. Note that the number of beams presented in this figure is for demonstration purposes only and may not represent actual values, as fewer or more beams may be used in any embodiment.

[0160] View in order Figure 15D-1 to Figure 15D -3 shows the beam trajectory, where Figure 15D-1 The view represents the starting stage, Figure 15D-2 The view represents the intermediate stage, Figure 15D The view at -3 represents the final stage.

[0161] Lines 510a-510g represent the same beam, but at different stages in time. The search begins at fan boundary ring 502 and terminates whenever one or more of the lines 510a-510g representing the beams hit an object. A signal is generated when one or more of the lines 510a-510g representing the beams hit an object, or the search terminates before one of the lines 510a-510g representing the beams reaches a predefined upper limit. The signal includes information such as the beam launch angle and the length the beam traveled before it terminated. For the purposes of this description, this length is referred to as the "free path length."

[0162] Figure 15D-2 An intermediate stage in the beam launch process is shown, where lines 510a-510g representing the beams reach the middle of the fan gap region. This is the case with some special fitting structure along the circular shroud opening. Figure 15D -3 shows a stage in the beam emission process where the beam hits the inner boundary of the shield and produces a valid signal.

[0163] Figure 15E As depicted, structures such as cutouts or holes are shown, where lines 510a-510g representing the beam will leak from these cutouts 460 and 462, resulting in either no signal or some noise. This noise can affect the accuracy of the results when lines 510a-510g representing the beam hit other objects near the cutout or hole. To address this, the process ignores signals from specific directions, based on the specific orientations in which these structures, such as real axial cooling fan shrouds used in industry, are typically located.

[0164] The signal check step 418 in process 55 checks the value of the generated signal to detect a non-zero value and passes the non-zero value signal to the analysis module 420 for further analysis. However, if a "zero signal value" is detected in a particular direction, the process will move back to 416, where beam transmission will continue in the other direction.

[0165] In step 420, the shield diameter is calculated in physical units based on the received signal. To improve accuracy, the process uses the symmetry of the ray pairs to calculate the shield diameter. That is, the shield diameter calculated from a specific emission direction is equal to the sum of the free path lengths of the two collinear beams.

[0166] For example, Figure 15D-1 to Figure 15D The beams 504 and 506 shown in Figure 3 are collinear and their free path lengths are added together to obtain the shield diameter for that particular direction. These shield diameters are then averaged for all emission directions.

[0167] Under normal circumstances, the fan gap area in the shroud image should be clean and free of any other objects, so the noise value ratio is very low. If the user mistakenly names or assigns the wrong part identifier (PID) to other parts of the axial fan besides the shroud, the noise may increase.

[0168] Figure 15F An example is shown in which the radiator inlet hose 701, cooling assembly seal 703, bottom coolant tank 704, top coolant tank 705, radiator outlet hose 707, and another hose 708 are all incorrectly labeled with the PID of the shroud, even though only portion 702 is the actual shroud. When this happens, when the beam hits an object whose projection falls into the fan gap area in the image, the beam will hit the surface earlier than it should. As a result, the measured shroud diameter will be lower than the true value.

[0169] Process 55 uses a high pass filter to filter 420 to help resolve Figure 15F The signal received from the high-pass filter 420 significantly improves the accuracy of process 55. In addition, process 55 performs a reversible Boolean operation on the input shroud geometry in the three normal directions of the fan bounding box before generating the image to reduce noise.

[0170] Figure 15G A special case is described in which beam leakage occurs and can lead to severe errors. Figure 15G The black squares in represent the non-zero pixels in the generated shield outline image. In some locations, two adjacent square pixels share only one vertex, and the beam will not intersect any target pixel when passing through this vertex. Figure 15E This is another form of beam leakage outside of the case shown.

[0171] Pixels are represented as squares. The pixels representing the concentric shield outline are pixel group 801. The pixels in pixel group 801 are the actual shield opening inner boundaries, while the pixels in pixel group 802 are noise. Ideally, beam 809 will hit pixel 808 and produce a valid signal.

[0172] However, due to the uncertainty in pixel arrangement or position when representing a circle or any other shape, beam 807 may penetrate a pixel pair, such as pixels 805 and 806, or even further penetrate another outer pixel pair, such as pixels 803 and 804. This introduces significant error, which can be addressed by including the nearest neighbors of the target pixel in the search.

[0173] exist Figure 15G In the specific example shown, either 805 or 806 would be returned as the target pixel to generate a valid signal. To calculate the distance, the system determines the difference between the original pixel (0.0.0) and the position of pixel 808 (xyz) (e.g., for 3D, or (0,0) to (x,y) for 2D).

[0174] The disclosed embodiments may provide one or more of the following advantages over existing solutions: The process is automated, whereas existing solutions are typically manual. It is anticipated that the automated process will provide relatively high accuracy compared to visual inspection, which is highly dependent on the skill of the person performing the inspection. The process can determine the direction of rotation relatively quickly compared to manual processes, which may take longer depending on the inspector's expertise and the complexity of the fan. The disclosed embodiments can automatically calculate the airflow direction in a local coordinate system and pass this information to numerical simulations, whereas existing processes are manual.

[0175] Embodiments of the subject matter and functional operations described in this specification may be implemented in digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware (including the structures disclosed in this specification and their structural equivalents), or a combination of one or more of them. Embodiments of the subject matter described in this specification may be implemented as one or more computer programs (i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus). A computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

[0176] The term "data processing apparatus" refers to data processing hardware and includes all kinds of devices, equipment, and machines for processing data, including, for example, a programmable processor, a computer, or multiple processors or computers. The apparatus may also be or further include special-purpose logic circuitry (e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit)). In addition to hardware, the apparatus may optionally include code that creates an execution environment for a computer program (e.g., code constituting processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these).

[0177] A computer program, which may also be referred to or described as a program, software, software application, module, software module, script, or code, may be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinating files (e.g., files storing one or more modules, subroutines, or portions of code)). A computer program may be deployed so that the program is executed on a single computer or on multiple computers located in one location or distributed across multiple locations and interconnected by a data communications network.

[0178] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can be implemented as, special purpose logic circuitry, such as an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

[0179] A computer suitable for executing a computer program can be based on a general or special microprocessor or both, or any other type of central processing unit. Typically, a central processing unit will receive instructions and data from a read-only memory or random access memory or both. The basic elements of a computer are a central processing unit for executing or implementing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include or be operably coupled to one or more large-capacity storage devices (e.g., magnetic disks, magneto-optical disks, or optical disks) for storing data, to receive data from one or more large-capacity storage devices for storing data or to transmit data to one or more large-capacity storage devices for storing data, or both, however, a computer does not necessarily need to have such a device. In addition, a computer can be embedded in another device (e.g., a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name a few examples).

[0180] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory on media and memory devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0181] To provide for interaction with a user, embodiments of the subject matter described in this specification may be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user, as well as a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices may also be used to provide for interaction with the user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including sound, speech, or tactile input. In addition, a computer may interact with a user by sending documents to and receiving documents from a device used by the user, for example, by sending a web page to a web browser on a user's device in response to a request received from the web browser.

[0182] Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back-end component (e.g., as a data server), or includes a middleware component (e.g., an application server), or includes a front-end component (e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification), or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks (LANs) and wide area networks (WANs) (e.g., the Internet).

[0183] A computing system may include a client and a server. The client and the server are typically remote from each other and typically interact via a communication network. The relationship between the client and the server is generated by computer programs running on respective computers and having a client-server relationship with each other. In some embodiments, the server transmits data (e.g., an HTML page) to a user device acting as a client (e.g., for the purpose of displaying data to a user interacting with the user device and receiving user input from the user device). Data generated at the user device (e.g., a result of a user interaction) can be received from the user device at the server.

[0184] Although this specification contains many specific implementation details, these should not be interpreted as limitations on the scope of any invention or the scope that can be claimed, but rather as descriptions of features that are unique to specific embodiments of specific inventions. Certain features described in this specification in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment may also be implemented individually in multiple embodiments or in any suitable sub-combination. In addition, although features may be described above as working in certain combinations and even initially claimed as such, in some cases, one or more features from the claimed combination may be deleted from the combination, and the claimed combination may be directed to a sub-combination or a variation of the sub-combination.

[0185] Similarly, although operations are described in a particular order in the accompanying drawings, this should not be understood as requiring that the operations be performed in the particular order shown or in sequence, or that all illustrated operations be performed, in order to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0186] Specific embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve the desired results. As an example, the processes depicted in the accompanying drawings do not necessarily require the particular order or sequential sequence shown to achieve the desired results. In certain circumstances, multitasking and parallel processing may be advantageous.

Claims

1. A computer-implemented method for determining a shroud size for a fan, comprising: receiving, by a computer processing system, digital data of a three-dimensional representation of a shroud of an axial flow fan; dividing the received data into a first partition corresponding to the shroud segment and a second partition corresponding to the fan segment, determining a shroud boundary ring of the shroud segment; applying, by the computer processing system, a series of beams to the image of the first sector to determine a shroud diameter of the shroud in pixels; determining, by the computer processing system, a pixel in the image having a value that generates a signal indicating that the pixel coincides with a portion of the shield; as well as The shroud diameter is calculated by the computer processing system based on information from the series of beams, including travel lengths and beam angles of the series of beams when the signal is detected.

2. The method according to claim 1, further comprising: The viewing angle of the shroud boundary ring is determined.

3. The method according to claim 1, further comprising: Applying the beam and determining is repeated until the signal is detected.

4. The method of claim 1 , wherein calculating the shroud diameter further comprises: A two-dimensional projection of the shroud is determined based on the data of the three-dimensional representation of the axial fan to determine the shroud boundary ring.

5. The method according to claim 4, wherein The calculated shroud diameter is determined in pixels using a digital image of the fan's characteristic lines, thereby obtaining a conversion ratio between physical units and pixels.

6. The method according to claim 4, further comprising: Using the fan image, calculating a value corresponding to the center of the fan; and The calculated center value is passed to the shroud image analysis to reduce the computational cost.

7. The method according to claim 4, wherein: View vector and depth of field as well as setting the resolution of the image are based on the diameter of the fan so that the size of the fan and the shroud do not affect measurement accuracy.

8. The method according to claim 4, wherein When there are multiple concentric circles, the beam launching process launches multiple beams in multiple directions from the edge of the fan to search the inner circle of the shroud in order to reduce computational cost and increase signal-to-noise ratio.

9. The method according to claim 4, further comprising: Preventing the shield from interrupting the circular contour of the special structure in a specific direction, and High-pass filtering of the signal is applied to improve the measurement accuracy for structures present along the opening of the shield or when the opening of the shield has a distortion compared to a circular profile after discretization.

10. The method according to claim 4, further comprising: A target pixel is searched for among neighboring pixels along the moving direction of the beam.

11. A computer system comprising: one or more processors; a memory operatively coupled to the one or more processors, and A computer storage device storing a computer program for determining a shroud size for a fan, the computer program comprising instructions causing the computer system to: receiving digital data of a three-dimensional representation of a shroud of an axial fan; dividing the received data into a first partition corresponding to the shroud segment and a second partition corresponding to the fan segment, determining a shroud boundary ring of the shroud segment; applying a series of beams to the image of the first partition to determine a shroud diameter of the shroud in pixels; determining a pixel in the image having a value that generates a signal indicating that the pixel coincides with a portion of the shield; and The shroud diameter is calculated based on information from the series of beams, including travel lengths and beam angles of the series of beams when the signal is detected.

12. The system of claim 11, further comprising instructions for: determining a viewing angle of the shroud boundary ring; and Applying the beam and determining is repeated until the signal is detected.

13. The system of claim 11, further comprising instructions for: A two-dimensional projection of the shroud is determined based on the data of the three-dimensional representation of the axial fan to determine the shroud boundary ring.

14. The system according to claim 11, wherein: The calculated shroud diameter is determined in pixels using a digital image of the fan's characteristic lines, thereby obtaining a conversion ratio between physical units and pixels.

15. A computer program product stored on a non-transitory computer readable medium for determining a shroud size for a fan, the computer program product comprising instructions for causing a system comprising one or more processors and a memory to: receiving digital data of a three-dimensional representation of a shroud of an axial fan; dividing the received data into a first partition corresponding to the shroud segment and a second partition corresponding to the fan segment, determining a shroud boundary ring of the shroud segment; applying a series of beams to the image of the first partition to determine a shroud diameter of the shroud in pixels; determining a pixel in the image having a value that generates a signal indicating that the pixel coincides with a portion of the shield; and The shroud diameter is calculated based on information from the series of beams, including travel lengths and beam angles of the series of beams when the signal is detected.

16. The product of claim 15, further comprising instructions for: determining a viewing angle of the shroud boundary ring; and Applying the beam and determining is repeated until the signal is detected.

17. The product of claim 15, wherein the instructions for calculating the shroud diameter further comprise instructions for: A two-dimensional projection of the shroud is determined based on the data of the three-dimensional representation of the axial fan to determine the shroud boundary ring.

18. The product of claim 17, wherein The calculated shroud diameter is determined in pixels using a digital image of the fan's characteristic lines, thereby obtaining a conversion ratio between physical units and pixels.

Images