Predicting turbofan engine properties

Abstract

Embodiments determine a physical property of a turbofan engine. Embodiments obtain in memory: (i) a computer-aided design (CAD) model representing a turbofan engine and (ii) an indication of a flow condition. The solver input file is then automatically determined based on the CAD model and the indication of the flow condition. In response thereto, a simulation of the turbofan engine is performed under flow condition constraints using the determined solver input file. A result of the simulation indicates a physical property of the turbofan engine.

Description

Background Art

[0001] Numerous products and simulation systems are available on the market for designing and simulating objects (e.g., vehicles). Such systems typically employ computer-aided design (CAD) and computer-aided engineering (CAE) programs. These systems allow users to construct, manipulate, and simulate complex three-dimensional models of objects or object assemblies. These CAD and CAE systems provide model representations of objects (e.g., real-world objects) using edges or lines (and in some cases, faces). Lines, edges, faces, or polygons can be represented in various ways, such as using non-uniform rational basis splines (NURBS).

[0002] These systems manage parts or assemblies of parts of modeled objects, which are primarily specifications of geometric shapes. Specifically, a CAD file contains specifications from which geometric shapes are generated. A three-dimensional CAD model or model representation is generated from these geometric shapes. The specifications, geometric shapes, and CAD models / representations can be stored in a single CAD file or in multiple CAD files. CAD or other such CAE systems include graphical tools for visually representing modeled objects in three-dimensional space to the designer; these tools are specifically designed for displaying complex real-world objects. For example, a single assembly can contain thousands of parts.

[0003] The advent of CAD and CAE systems has provided various representation possibilities for objects, such as CAD models. Computer-based models can be programmed in a way that gives the model the properties (e.g., physical, material, or other physics-based properties) of one or more underlying real-world objects represented by the model. Exemplary properties include hardness (the ratio of force to displacement), plasticity (irreversible strain), and viscosity (the resistance of one layer to flow relative to an adjacent layer), among others. When a CAD or other such computer-based model as known in the art is programmed in this manner, it can be used to perform simulations of the objects represented by the model. For example, a grid-based model can be used to represent the interior of a vehicle, an acoustic fluid surrounding a structure, or any number of real-world objects / systems. In addition, CAD and CAE systems and computer-based models can be used to simulate engineering systems, such as real-world physical systems, such as cars, airplanes, buildings, and bridges, among other examples. In addition, CAE systems can be used to simulate the behavior of any type and combination of these physics-based systems, such as noise and vibration. Summary of the Invention

[0004] As the population continues to grow, transportation and commuting are a burgeoning area of ​​interest. One solution to modernizing transportation is the use of turbofans, for example, in aircraft. While turbofans offer a promising solution to modern transportation problems, improved methods are needed to accurately determine their properties (e.g., physical and operational properties) in order to realize their full potential and optimize their design. Embodiments provide a solution to this problem.

[0005] An exemplary embodiment relates to a computer-implemented method for determining physical properties of a turbofan engine. According to an embodiment, the method begins by obtaining, by a processor, in a memory coupled to the processor, (i) a computer-aided design (CAD) model representing the turbofan engine and (ii) an indication of flow conditions. A solver input file is then automatically determined based on the CAD model and the indication of flow conditions. In response, a simulation of the turbofan engine is performed using the determined solver input file, subject to flow condition constraints. Results of the simulation indicate physical properties of the turbofan engine.

[0006] In one embodiment, the processor implementing the method is one of a variety of processors supporting a global network platform service. In another embodiment, the CAD model and the indication of the flow condition are obtained in response to user input via a user interface of the platform service. According to an embodiment, the flow condition includes at least one of the following: free stream air velocity, fan speed, and air temperature.

[0007] In another embodiment, the determined solver input file includes at least one of the following: a surface mesh, a measured surface, and an indication of flow conditions. According to an embodiment, determining the solver input file includes at least one of the following: creating a surface mesh; and creating a measured surface in the surface mesh. In an embodiment, creating the surface mesh includes analyzing the CAD model to determine geometric parameters of at least one of the following: engine bounding box size, nacelle leading and trailing edge position and shape, fan diameter, number of fan blades, fan tip gap size, fan blade leading and trailing edge shape, outlet guide vane position, outlet guide vane leading and trailing edge shape, low-pressure compressor stage position (if present), low-pressure compressor stage blade number (if present), low-pressure compressor stage blade leading and trailing edge shape (if present), and low-pressure compressor rotor stage fan tip gap size (if present). As part of creating the surface mesh, an embodiment may also identify parts of the turbofan engine based on the corresponding names of the components of the CAD model representing the identified parts. An embodiment may create a surface mesh based on at least one of the following: the determined geometric parameters and the identified parts.

[0008] According to an embodiment, performing the simulation includes generating a volume mesh based on the surface mesh, and performing the simulation using the generated volume mesh. In addition, an embodiment may set a local mesh resolution for at least a portion of the volume mesh, and set a volume mesh resolution region based on the size and shape of elements in the surface mesh.

[0009] In another aspect, performing the simulation includes determining simulation conditions. In one such embodiment, determining the simulation conditions includes at least one of: (i) determining a location of a measurement surface based on an indication of flow conditions and a size and shape of elements in a surface mesh; (ii) setting a length of the simulation based on a minimum frequency of interest; and (iii) setting a sampling rate based on a maximum frequency of interest.

[0010] According to an embodiment, performing the simulation includes at least one of the following: performing a computational fluid dynamics (CFD) simulation (e.g., a 3D CFD simulation) and a lattice Boltzmann method (LBM) simulation. Furthermore, in an embodiment, the determined physical properties include at least one of the following: aerodynamic properties, thermal properties, and acoustic properties.

[0011] An embodiment may also include: (i) generating a two-dimensional (2D) mesh from a solver input file; and (ii) using the results of the simulation and the generated 2D mesh, performing multiple finite element method (FEM) simulations to determine the noise reduction properties of each corresponding liner in the generated 2D mesh, each FEM simulation being performed using a corresponding flow condition and a representation of the corresponding liner.

[0012] Another embodiment relates to a system for determining a physical property of a turbofan engine. In an embodiment, the system includes a processor and a memory having computer code instructions stored thereon, wherein the processor and the memory are configured with the computer code instructions to cause the system to implement any embodiment or combination of embodiments described herein.

[0013] Yet another embodiment relates to a system for determining physical properties of a turbofan engine, wherein the system comprises a processor and a memory having computer code instructions stored thereon, the processor and the memory being configured with the computer code instructions to cause the system to implement a platform service configured to perform any embodiment or combination of embodiments described herein.

[0014] In another embodiment, a computer program product includes one or more non-transitory computer-readable storage devices having computer-readable program instructions stored thereon. The instructions, when executed by a processor, cause a device associated with the processor to implement any embodiment or combination of embodiments described herein.

[0015] Another embodiment includes an automated process for rapidly generating a digital twin of a 3D turbofan engine, performing fluid dynamics simulations under different operating conditions, and post-processing the results to evaluate the turbofan's aerodynamic performance and far-field / ground noise levels.

[0016] Another aspect involves automatically generating a 2D mesh from the turbofan's 3D engine geometry. This 2D mesh can then be used to perform a reduced-order acoustic evaluation of the nacelle in the presence of passive noise reduction devices like linings.

[0017] Yet another aspect includes extracting a selected data set (e.g., a transient pressure field) from a 3D turbofan engine simulation, and performing a series of reduced-order acoustic simulations using the selected data set along with liner impedance characteristics to quickly evaluate the noise reduction that can be achieved by placing one or more passive liners within a turbofan (e.g., a nacelle of a turbofan).

[0018] Embodiments may pre-process the engine geometry using a tool (i.e., a software component, an application, etc.) that can read and automatically extract several geometric properties of the engine. In one aspect, the tool is also configured to automatically generate measurement surfaces and other virtual entities for storing data for far-field noise assessment.

[0019] Embodiments may also implement and employ tools that can evaluate engine far-field noise using specific certified metrics and calculation methods.

[0020] Other aspects include a computer program product tangibly stored on a non-transitory computer-readable medium and a computing system such as a computer system and a computer server.

[0021] The exemplary embodiment implements a workflow in which, first, the user prepares the engine geometry (e.g., surface mesh) by following a set of guidelines that specify a specific nomenclature for naming relevant engine surfaces. This nomenclature allows such an embodiment to automatically identify each engine section and generate additional mesh entities to specify the volume mesh resolution and collect the results. Additionally, in yet another embodiment, the user provides one or more sets of flow conditions (operating conditions) for analyzing the turbofan of interest.

[0022] Using these two inputs (engine geometry with elements that conform to the nomenclature and flow conditions), the noise prediction process begins. In an embodiment, the process occurs in three stages: Stage 1: Analyze the engine model (surface mesh) and generate a solver input file along with the user-provided flow conditions; Stage 2: Automatically run simulations on a local or remote cluster, and multiple simulations can be run simultaneously (e.g., to evaluate different flow condition scenarios); and Stage 3: Process the simulation results obtained from Stage 2 and generate a dataset for each submitted case.

[0023] The implementation is performed on a cloud-based platform. In an embodiment, such a cloud-based platform includes an interactive environment for browsing and comparing data sets.

[0024] In another aspect, an embodiment may generate a simplified model for noise reduction analysis of selected cases.

[0025] It should be noted that the method, system, and computer program product embodiments can be configured to implement any embodiment or combination of embodiments described herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The foregoing will be apparent from the following more particular description of exemplary embodiments as illustrated in the accompanying drawings in which like reference numerals refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the embodiments.

[0027] Figure 1 is a flow chart of a method for determining physical properties of a turbofan according to an embodiment.

[0028] Figure 2 is a block diagram illustrating a Platform as a Service (PaaS) in which embodiments may be implemented.

[0029] Figure 3 is a flow chart illustrating a method for determining physical properties of a turbofan according to an embodiment.

[0030] Figure 4A -C shows a turbofan layout that can be analyzed using an embodiment.

[0031] Figure 5A -D shows a turbofan geometry that may be used in embodiments.

[0032] Figure 6A -D, 7A-C, 8A-B, 9A-B, and 10A-B show exemplary turbofan properties determined using the embodiments.

[0033] Figure 11 A noise analysis method according to an embodiment is shown.

[0034] Figure 12 is a simplified block diagram of an embodiment of a computer system for determining physical properties of a turbofan.

[0035] Figure 13 is a simplified block diagram of a computer network environment in which embodiments of the present invention may be implemented. DETAILED DESCRIPTION

[0036] The following is a description of exemplary embodiments.

[0037] Embodiments provide functionality to determine the physical properties of a turbofan.Among other examples, embodiments may provide a community noise assessment of a turbofan engine through a fully automated process running on a supporting service platform.

[0038] A turbofan is an aircraft engine designed to operate at low subsonic or transonic cruise speeds. A turbofan engine generates thrust by accelerating a certain amount of air through the engine. A portion of the inhaled air undergoes compression, combustion, and expansion (core airflow), while another portion of the inhaled air is accelerated by the engine fan without involving combustion (bypass airflow). Low emission requirements in recent years have driven new engine designs to increase the amount of bypass air, which has led to the use of larger fans. The shift to larger fans shifts the main source of noise from the jet to the engine fan and outlet guide vanes (OGVs), which are mounted directly downstream of the fan to straighten the cabin outlet airflow.

[0039] For this reason, along with other reasons such as the high cost and risk of physical testing and the long turnaround time (TAT) of design iterations, being able to capture the complex interaction between the fan and the OGV through simulation is currently crucial to accurately predicting the noise generated by the engine and transmitted to the ground. This is especially true during takeoff and landing, for which regulations set precise noise thresholds that, if not met, can severely limit the operability of the aircraft.

[0040] To accurately determine turbofan properties (e.g., noise), a simulation that reproduces the fan wake with full temporal and spatial complexity is required. Embodiments provide such functionality. In exemplary embodiments, a transient computational fluid dynamics (CFD) solver capable of handling realistic engine geometry is implemented. Furthermore, low numerical dissipation is desirable for aeroacoustic analysis in order to minimize numerical damping of pressure waves. Therefore, embodiments may employ a Lattice Boltzmann Method (LBM)-based solver for aeroacoustic analysis.

[0041] Figure 11 is a flow chart of an exemplary method 100 for determining physical properties of a turbofan. Method 100 begins at step 110, where a processor obtains (i) a computer-aided design (CAD) model representing a turbofan engine and (ii) an indication of flow conditions in a memory coupled to the processor. Then, at step 102, a solver input file is automatically determined based on the CAD model and the indication of flow conditions. In response, at step 103, a simulation of the turbofan engine is performed using the determined solver input file under flow condition constraints. In an embodiment, performing the simulation at step 103 includes submitting the simulation to a large data center using multiple processors organized as compute nodes, the large data center being capable of handling the required large memory and producing results within a reasonable time frame. The results of the simulation performed at step 103 indicate physical properties of the turbofan engine.

[0042] As described above, method 100 is computer-implemented, and therefore, the functions and effective operations, such as steps 101-103, can be automatically performed by one or more digital processors. In addition, method 100 can be implemented using any computer device or combination of computing devices known in the art. In other examples, method 100 can be implemented using the computer described herein below with respect to Figure 12 The computer system 1220 described herein and described below with respect to Figure 13 The described computer network environment 1330 is implemented.

[0043] Furthermore, the method 100 can be implemented in a Platform as a Service (PaaS) environment. PaaS environments are becoming increasingly popular as design spaces where products can be tracked and followed along their lifecycle. The platform is described herein as just one example of an environment that can provide data management and processing automation tools to implement embodiments (e.g., method 100). Using such an environment (PaaS) can be crucial for users to be able to browse the vast amounts of data that make up a digital twin of a given product. A digital twin can be a unified data set that includes geometry, materials, and ancillary data for a given product (e.g., a turbofan), and therefore, a PaaS can be used to store, manage, process, and evaluate the digital twin using method 100.

[0044] In an embodiment of a PaaS implementation of method 100, the processor implementing method 100 may be one of a plurality of processors supporting a global network (i.e., the Internet) platform service. It should be noted that the embodiments are not limited to implementation on a global network, and the embodiments may be implemented on any network known to those skilled in the art, including a local area network (LAN) and a wide area network (WAN). In addition, method 100 may be implemented on a Figure 2In addition, the embodiment utilizes SIMULIA To implement an automated workflow for turbofan property determination (e.g., noise assessment).

[0045] In an embodiment, the CAD model obtained at step 101 is a three-dimensional CAD model including all relevant parts of the turbofan. For example, the model obtained at step 101 may include the fan, outlet guide vanes (OGV), and engine nacelle mounted on the center part. Furthermore, in an embodiment, the CAD model obtained at step 101 may be any computer-based model known to those skilled in the art. Therefore, embodiments of method 100 may perform additional processing to place the model received at step 101 into a desired form, such as into the form of a 3D CAD model. Figure 5B An exemplary CAD model 550 is depicted that may be obtained at step 101. Furthermore, in an embodiment, each element of the CAD model received at step 101 is named according to a particular nomenclature. In such embodiments, utilizing this nomenclature enables automatic identification of each engine section.

[0046] The indication of the flow condition obtained at step 101 may be in any form that can be processed and recognized by a computing device implementing method 100. The indication received at step 101 may indicate any flow condition known to those skilled in the art. For example, according to an embodiment, the flow condition includes at least one of the following: free stream air velocity, fan speed, and air temperature.

[0047] Furthermore, in an embodiment, the CAD model and flow conditions are obtained at step 101 in response to user input via, for example, a user interface of a platform service in which the method 100 is being implemented.

[0048] To automatically determine the solver input file based on the CAD model and an indication of flow conditions at step 102, an embodiment determines a surface mesh of the CAD model (obtained at step 101) and determines the solver input file based on the determined surface mesh. In an embodiment, the surface mesh is generated almost automatically from the CAD model (the surface mesh is typically refined in the event of high curvature surfaces being found). The solver input file includes definitions of regions where a volume mesh (which may be generated at step 103 as part of performing the simulation) is refined based on the flow conditions and the engine part locations / sizes and shapes. In embodiments where a volume mesh is generated based on the solver input file at step 103, the volume mesh is automatically generated when the simulation is submitted.

[0049] According to an embodiment, at step 102, a solver input file is determined by generating a 3D computational mesh in a completely autonomous manner. In addition, in an embodiment, the solver input file contains data, including a turbofan surface mesh and specified flow conditions. An embodiment of method 100 determines the properties of the solver input file based on the characteristics of the turbofan engine model obtained at step 101. To achieve such functionality, an embodiment analyzes the geometry of the model, such as the gap between the cabin and the fan, and sets the parameters of the simulation, such as the minimum resolution, based on the analysis. The flow conditions can also be used to derive modifications to the model, for example, to analyze different operating conditions such as approach, takeoff, and cruise. In another embodiment, the received data (e.g., the data obtained at step 101) includes a frequency range of interest, and this data is used to set the length of the simulation in the solver input file. Similarly, a maximum frequency of interest can be used to specify the sampling frequency of the simulation results.

[0050] In yet another embodiment, the solver input file determined at step 102 includes at least one of the following: a surface mesh, a measured surface, and an indication of flow conditions. According to such embodiments, determining the solver input file at step 102 may include creating a surface mesh and / or creating a measured surface in the surface mesh. In an embodiment, creating the surface mesh includes analyzing the CAD model (obtained at step 101) to determine geometric parameters. According to an embodiment, the determined geometric parameters may include at least one of the following: engine bounding box size, nacelle leading and trailing edge position and shape, fan diameter, number of fan blades, fan tip gap size, fan blade leading and trailing edge shape, outlet guide vane position, outlet guide vane leading and trailing edge shape, low-pressure compressor stage position (if present), low-pressure compressor stage blade number (if present), low-pressure compressor stage blade leading and trailing edge shape (if present), and low-pressure compressor rotor stage fan tip gap size (if present). Based on the determined geometric parameters, several simulation and post-processing parameters can be defined. For example, the engine bounding box can define the size of the measurement surface used to calculate far-field noise, the fan tip clearance can set the minimum resolution of the 3D volume mesh (automatically generated from the input file when submitting the 3D simulation), the fan and outlet guide vane positions and shapes can define the measurement surface used to obtain stage performance and flow characteristics, the number of fan blades can define the blade pass frequency (BPF) of the turbofan, and the number of fan and compressor blades (if present) can define the time sampling used to generate phase-locked averages of flow properties. As part of creating the surface mesh, embodiments of method 100 can also identify turbofan engine parts based on the corresponding names of the components of the CAD model (received at step 101) representing the identified parts. A surface mesh is then generated based on these identified parts and / or the determined geometric parameters. This surface mesh can be generated by setting a spatial tolerance to be respected, which measures the distance from each surface mesh element to the initial CAD surface. This generates a surface mesh that is locally finer where high surface curvature is found. Once the solver input file is generated, the simulation is submitted and a 3D volume mesh can be automatically generated from the surface mesh and simulation settings.When creating the input file, embodiments of method 100 can also set a local mesh resolution for at least a portion of the computational domain.

[0051] According to an embodiment of the method 100 , performing the simulation at step 103 includes at least one of performing a computational fluid dynamics (CFD) simulation and a lattice Boltzmann method (LBM) simulation.

[0052] According to an embodiment, performing the simulation at step 103 includes generating a volume mesh based on the surface mesh and performing the simulation using the generated volume mesh. In addition, an embodiment may set a local mesh resolution for at least a portion of the volume mesh and set a volume mesh resolution region based on the size and shape of elements in the surface mesh.

[0053] In another aspect, performing the simulation at step 103 includes determining simulation conditions. In one such embodiment, determining the simulation conditions includes at least one of: determining the location of the measurement surface based on an indication of flow conditions and the size and shape of elements in the surface mesh, such as engine part size and shape (engine stage intake and exhaust cross sections may be defined based on fan and outlet guide vane locations and blade shapes); setting the length of the simulation based on a minimum frequency of interest (the simulation length may be defined by the number of cycles executed for the minimum frequency of interest); and setting the sampling rate based on a maximum frequency of interest (minimum number of points along the minimum wavelength of interest).

[0054] Embodiments of method 100 may determine any physical property of the turbofan known to those skilled in the art at step 103. For example, according to an embodiment, the determined physical property includes at least one of the following: aerodynamic properties, thermal properties, and acoustic properties.

[0055] Embodiments of method 100 can also be used to evaluate the noise reduction achieved in turbofans using aeroacoustic liners. Aeroacoustic liners are passive devices primarily used to reduce noise generated by the engine fan. In its most basic layout, an aeroacoustic liner consists of a series of hollow cells covered by a perforated plate that absorbs and reduces the pressure fluctuations generated by the engine fan, which directly contribute to its noise footprint. The liner is typically placed along the nacelle air intake section (upstream of the fan) between the fan and the OGV, as well as downstream of the OGV. In one such exemplary embodiment of method 100, a two-dimensional (2D) mesh is generated from a solver input file (determined at step 102). Such embodiments can then utilize the simulation results (from step 103) and the generated 2D mesh to perform multiple finite element method (FEM) simulations. Each FEM simulation is performed using corresponding flow conditions and a representation of the corresponding liner in the generated 2D mesh. Thus, performing the FEM simulations determines the noise reduction properties of each corresponding liner under the constraints of the corresponding flow conditions. These results can then be used to manufacture turbofans and liners that meet, for example, noise requirements.

[0056] Figure 2is a block diagram illustrating a PaaS 220 in which embodiments (e.g., methods 100 and 330 described herein) may be implemented. Platform 220 is a cloud platform that may be used to perform simulations of turbofan engines as described herein. The description herein indicates that PaaS 220 is a Dassault Systèmes platform, but PaaS implementations are not limited to using Rather, the embodiments may be implemented using any PaaS known to those skilled in the art.

[0057] The platform (i.e., system 220) is based on a client-server or cloud-based architecture and includes a cloud server 221 connected to a massively parallel computing cluster 222 (which can be stand-alone or cloud-based) and a client system 223. The computing cluster 222 is communicatively coupled to platform storage 224, which is used to store data and software utilized by the cluster 222. In another example, the platform storage 224 includes CAD and 2D / 3D mesh data 225a, result data 225b, libraries 225c, and scripts 225d. Similarly, the client system 223 is communicatively coupled to client storage 226. The client storage 226 stores turbofan engine CAD data 227.

[0058] The cloud server 221 may include multiple instances 228a-n, each having a user interface 229, data / tools (CAD editor and mesher tools 230, digital twins (e.g., CFD solver inputs) 231, processed results 232), a bus system 233, and a processing unit and local memory (collectively 234). According to an embodiment, the tools and data 230-232 are made available to users of the client system 223 via the interface 229 and the bus 233. The processing and memory for providing access and implementation of the tools are provided by the processing unit 234.

[0059] In operation, a user at a client system 223 accesses a CAD model 227 (stored on storage device 226) of a turbofan of interest and provides the CAD model 227 to a cloud instance 228a via an interface 229. The user prepares and submits a simulation (e.g., an aeroacoustic simulation) in the form of a digital twin (i.e., a solver input file 231) via interface 229. In an embodiment, the simulation file 231 is prepared and submitted by modifying a series of flow parameters via interface 229. The flow parameters for the simulation can be set via interface 229 and stored as part of the digital twin (i.e., CFD input file) 231 via bus 233. In addition to these parameters, the engine digital twin 231 includes a CAD file containing a digital representation of the entire engine (from the CAD model 227) uploaded from the client 223 and processed within the instance 228a. This processing can include editing the CAD model 227 and / or creating a mesh for the CAD model 227 using editing and meshing tools 230. As part of preparing and submitting a simulation (e.g., during pre-processing, i.e., PHASE-1 as described herein), the engine model (227) can be analyzed by one or more geometry preparation scripts 225d, which extract a series of geometric parameters for the simulation setup (engine size, number of fan blades, fan tip / shroud gap size, etc.). Simultaneously, post-processing parameters can be prepared and set to default values. If desired, the user can modify these parameters via interface 229 to customize post-processing based on the user's needs.

[0060] Once the digital twin is prepared, the CFD input file 231 is provided to the cluster 222 that performs the simulation. To implement such functionality, the cluster 222 is driven by a cloud instance 228a and accesses a platform storage device 224 that stores 2D and / or 3D meshes 225a, coordinate systems, and libraries 225c to perform simulations using any known computational techniques such as CFD or LBM. The simulation (e.g., PHASE-2 as described herein) can be used for various purposes, such as aeroacoustics, estimating aerodynamic and thermal performance, acoustic noise footprint, or the effects of noise reduction devices installed in the engine nacelle. The results of the simulation 225b indicate the physical properties of the turbofan engine represented by the CAD model 227.

[0061] In an embodiment, once a simulation is run, a post-processing script 225d can be automatically launched from instance 228a (e.g., during PHASE-3 as described herein) to generate results 225b (e.g., charts and images) based on the selections made during pre-processing. The processed data set (results 225b) can be stored in platform storage 224. A user can browse the processed results via interface 229 using visualization tools implemented by a processed results browser 232.

[0062] Additionally, according to an embodiment, post-processing may include collecting pressure field data on the fan inlet and OGV outlet surfaces and storing the pressure field data as part of results 225b after projecting the time domain fields into corresponding radial and azimuthal modal components in the frequency domain.

[0063] Furthermore, in an embodiment of platform 220, script 225d is executed in processing unit 234, which accesses the geometric data in 231 to automatically generate a 2D mesh representing a vertical cross-section of the 3D engine (considered a semi-geometry here). In such an embodiment, a portion of the nacelle's interior surface may be provided as a lining. Such a 2D mesh is stored with data 225a and can be used to construct a simplified model to evaluate the noise reduction capabilities of the lining when installed in / on the engine. Such an embodiment can model the lining in a 2.5D simulation for pressure azimuthal modes in the frequency domain. This implementation can utilize a 2D mesh previously generated from the 3D engine mesh and an FEM solver (e.g., the OptydB_gfd FEM solver from applicant-assignee Dassault Systèmes USA, Inc.), which can set a pressure dataset previously collected from the 3D simulation as a forced term and calculate the pressure fluctuations in the far field generated by the engine with the lined nacelle. In one embodiment, an automated script 225d stored in the platform storage 224 is launched by instance 228a and runs multiple FEM simulations for different frequencies and fan pressure boundary conditions (azimuthal / radial modes). According to one embodiment, only engine-on modes are used. A second script 225d stored in the platform storage 224 is run by instances 228a-n and collects far-field noise results and generates a graph showing the noise level across the frequency domain of interest.

[0064] Figure 3 is a flow chart illustrating a method 330 for determining physical properties of a turbofan according to an embodiment. Figure 3 In FIG. 3 , a process 330 is presented with details of each processing step (e.g., steps 331-338). In addition to other examples, the process 330 may be described above with respect to Figure 2 The platform 220 is implemented.

[0065] Process 330 begins at step 331 with geometry and input data. More specifically, at step 331, the geometry of the turbofan engine is obtained, for example via user input, and flow conditions are set. Table 1 below shows the input data that may be obtained at step 331 of process 330. These parameters may be set within a cloud platform graphical user interface (GUI).

[0066]

[0067]

[0068] Table 1: Input parameters

[0069] Continuing, at step 332, the geometry obtained at step 331 is processed and a surface mesh of the turbofan engine is created therefrom, and in the mesh, part names are assigned according to the guidelines. Figure 5A -D describes more details about meshing and naming. Continuing, at step 333, a solver input file is automatically generated using the flow conditions and the created surface mesh. According to an embodiment, the solver input file contains information about the flow conditions (e.g., all necessary information), the surface mesh of the engine, the measured surface, and volume mesh resolution control. Then, at step 334, one or more simulations of the turbofan engine are performed using the solver input file generated at step 333. In an embodiment, multiple simulations can be performed simultaneously to, for example, identify the characteristics of turbofans with different geometries and / or identify the characteristics of turbofans subject to different flow conditions. At step 335, post-processing is performed on the results from the (multiple) simulations performed at step 334. The post-processing at step 335 can include aerodynamic and thermal data processing as well as aeroacoustic processing. In other examples, the aeroacoustic processing can utilize specialized tools (e.g., software tools) for determining far-field / ground noise levels. Table 2 below lists possible input parameters for post-processing obtained at step 335. These parameters can be set by the user, for example, within the cloud platform GUI.

[0070]

[0071]

[0072] Table 2: Post-processing input parameters

[0073] At step 336, the user(s) may browse the results of the simulation (334) and post-processing (335). Browsing may be provided via an interactive environment within the cloud-based implementation that facilitates user data browsing and comparison.

[0074] Method 330 can implement additional functionality by first generating a simplified model at step 337. Specifically, at step 337, a 2D surface mesh (i.e., a simplified model mesh) is automatically generated using the engine surface mesh. At step 338, a 2.5D FEM simplified model of the original engine (from step 331) is used to evaluate different lining options and their effects on noise reduction.

[0075] Embodiments may be used to evaluate any of a variety of turbofan engines known to those skilled in the art. Figure 4A-C shows an exemplary turbofan engine layout that can be evaluated using an embodiment. Figure 4A The layout 440a shown in FIG. 4 is a bypass-only airflow layout and does not include core airflow. Figure 4B The layout 440b in FIG includes bypass and core flows. Note that in the embodiment, the core flow combustion, high pressure compressor (HPC), mixing, and combustor and turbine sections are not modeled because they are generally considered secondary to community noise assessments and are very expensive in terms of computational resources. Figure 4C Layout 440c in FIG is a variation of layout 440b in which the core flow is merged with the bypass flow. Layout 440c represents an engine prototype used for testing.

[0076] Figure 5A -D shows an exemplary engine CAD geometry used in the embodiment. For example, Figure 5A The CAD engine geometry shown in -D can be found in the previous section about Figure 1 Received at step 101 of the method 100 described above, and similarly, may be Figure 3 According to an embodiment, Figure 5A The CAD model shown in -D is prepared locally on the client side and then uploaded to the cloud platform for executing the embodiment.

[0077] Figure 5B The nacelle, bypass and core sections of a turbofan model 550 are shown. Figure 5A FIG5 is an enlarged view 552 of the core section 551 of the turbofan 550. Figure 5A and 5B In FIG, each part of the engine has a specific name 553a-p, specifically, *PRIMARY*_LE*(553a), *PRIMARY*_inner*(553b), *PRIMARY*_outer*(553c), *NACELLE*_LE*(553d), *NACELLE*_TE*(553e), *BIFURCATION*(553f), *exhaust*plug*(553g), *exhaust*duct*(553h), *exhaust*duct*(553i), *PRIMARY*_TE*(553j), *BYPASS*_inner*(553k), *BYPASS*_outer*(5531), *NACELLE*_outer*(553m), *SPINNER*(553n), *outlet_BC*(553o), and *inlet_BC*(553p). Similarly, Figure 5CTurbofan blades and exit guide vanes are shown, where the various parts of the blades are named 561a-f according to a predefined nomenclature, specifically, FAN_LE* or OGV_LE* (561a), FAN_TIP* or OGV_TIP* (561b), FAN_SS* or OGV_SS* (561c), FAN_root* or OGV_root* (561d), FAN_TE* or OGV_TE* (561e), and FAN_PS* or OGV_PS* (561f). Similarly, Figure 5D A low-pressure compressor (LPC) 570 is shown, with sections designated 571a-c (IGVX[X]) and 572a-b (BOOSTERX[X]). For the LPC 570, each stage is identified (named) with a different number, X[X], and the guide vane stage (stator) sections 571a-c are designated IGVX[X]*, while the rotor stage sections 572a-b are designated BOOSTERX[X]*. Furthermore, the nomenclature 561a-f (*_LE*, *_TE*, *_root*, *_TIP*, *_SS*, and *_PS*) used to designate the sections of the blade 560 may also be used to designate the sections of the blade 570.

[0078] exist Figure 5A -D, the parts of the turbofan engine are named 553a-p, 561a-f, 571a-c, and 572a-b according to a preset nomenclature specified by the computer code instructions executing the embodiment. In this way, the computer code instructions executing the embodiment can identify specific parts of the model and determine the characteristics of these parts for the purpose of executing the method, such as to automatically create a solver input file. Figure 5A In -D, the asterisk (metacharacter) specifies parts of the surface name that can be added / changed arbitrarily.

[0079] Figure 6A -D, 7A-C, 8A-B, 9A-B and 10A-B show exemplary results obtained by the Examples. According to the Examples, Figure 6A The results shown in FIG. 7A-D, 7A-C, 8A-B, 9A-B, and 10A-B were generated using corresponding post-processing tools, the names of which are provided below. In embodiments, these tools are used to post-process the results from the embodiments described herein, for example, to process the simulation results from step 103.

[0080] Figure 6A -D shows flow field visualizations 660a-d determined using the PowerVIZ tool. Figure 6A -D, use different gradient shading and / or color coding to indicate the value of the depicted data. Figure 6A and6B is a surface contour plot showing flow variables. Specifically, Figure 6A shows the flow field 660a through a turbofan engine, and Figure 6B An expansion field 660b is shown. Additionally, Figure 6C The processed results of the filtered pressure field 660c are shown to visually indicate the directionality of noise propagation for selected frequencies (typically the fan blade pass frequency and multiples thereof). Figure 6D is a visualization 660d of a body-fitting surface.

[0081] Embodiments generate radial and cross-sectional surfaces on which flow data can be displayed, for example, in PHASE-3. The cross-sectional surface shows the flow parameters on a plane perpendicular to the coordinate axis. The radial surface shows the flow parameters on a series of circular surfaces placed at radial positions and can be used to analyze the flow behavior through the engine stage at different span positions (specific span positions of interest can be specified by the user in PHASE-1).

[0082] Figure 7A -C shows bypass / core airflow phase lock data obtained using the OptydB_phaselock tool. Figure 7A -C, use different gradient shading and / or color coding to indicate the value of the depicted data. Figure 7A is a visualization 770a of the phase-locked average fan wake speed. Figure 7B is a visualization 770b of the phase-locked average fan wake speed fluctuation root mean square (RMS), and Figure 7A A visualization 770c of the phase-locked average blade pressure is presented. Visualizations 770a-c show the results for both the engine bypass and core sections, where the flow variables are time-averaged for the same fan angular position, providing an average wake image. This is available before and after each blade stage (fan, OGV, low-pressure compressor (LPC) rotor / stator stage, if present). Additionally, the blade surface data can be processed in a similar manner to provide time-averaged 3D blade load contours and 2D plots collected at different span positions. This allows analysis of each blade load distribution individually.

[0083] Figure 8A -B shows the noise footprint data obtained using the OptydB_footprint tool. Figure 8A is a graph 880 showing the effective perceived noise level (EPNL), where the color / gradient shading indicates the EPNL according to a legend 881 . Figure 8BGraph 890 shows the perceived noise level 891 on the ground during a flyover versus the flyover time 892. Graph 890 includes data for perceived noise level (PNL) 894 and PNLt (pitch-corrected PNL) 893. According to an embodiment, the far-field noise perceived on the ground is calculated by propagating near-field pressure fluctuations to the far field via the Ffowcs Williams-Hawking (FW-H) method (e.g., using the Farassat 1A protocol). From this, an image can be generated showing a 2D noise footprint (the perceived noise on the ground as a graph, e.g., 880) for a given flight maneuver (approach by default, but the user can specify a custom engine trajectory). When the generated noise map is overlaid on a satellite image of an actual airport of similar scale, this can provide direct information, for example, on the operability of the engine at a given location. In an embodiment, noise levels are provided in terms of total sound pressure level (OASPL), perceived noise level (PNL), and effective perceived noise level (EPNL).

[0084] Figure 9A -B shows the noise directivity data obtained using the OptydB_directivity tool. Figure 9A is a noise directionality polar plot 990 , where the color / gradient shading indicates the noise directionality according to a legend 991 . Figure 9B is a graph 993 of power watt levels 993 (i.e., source power levels) in dB / Hz versus frequency 994 in Hz. In an embodiment, the noise radiated by a turbofan engine is calculated and a polar directivity plot, such as 990, is generated. The polar directivity plot can highlight the specific angles from which the loudest noise components are propagated and help identify the area of ​​the engine that causes most of the noise. Contour plots can also be provided according to sound pressure levels (SPL) in two different frequency bands (3 / 8 and 1 / 12 octave bands). In addition, engine power watt levels (PWL) can also be provided to quickly compare different designs or flow conditions and rank them from quietest to loudest.

[0085] Figure 10A -B shows intake / exhaust bypass modal analysis data obtained using the OptydB_azi tool according to an embodiment. Figure 10A is a graph 1010 showing fan inlet conduction and cutoff modes in terms of frequency 1011 versus azimuthal mode number 1012 , with color / gradient shading provided according to a legend 1013 . Figure 10BA plot 1014 of the fan inlet azimuthal model shape in terms of acoustic pressure is shown, with the color / gradient shading according to the legend 1015 indicating the acoustic pressure. In one embodiment, the pressure fields collected at the fan inlet plane and the OGV exhaust plane are projected in the frequency domain onto their azimuthal and radial modes to obtain a database for analyzing nacelle acoustics in the presence of a liner. Furthermore, using this projected dataset, contour plots such as 1010 can be generated showing the engine cutoff / conduction mode range. This allows the user to quantitatively analyze the interaction between the fan wake and the OGV.

[0086] Figure 11 A method 1100 for noise reduction analysis according to an embodiment is shown. According to an embodiment, method 1100 is a simplified method because method 1100 is streamlined in size (e.g., the size of the grid) and the number of equations solved. This results in a reduction in both required memory and computational resources. In such embodiments, method 1100 is a simplification of methods 100 and 330, and method 1100 can provide a basic assessment of key advantages to drive design iterations, although it does not produce the same amount of detailed results as the original methods (e.g., 100 and 330).

[0087] Method 1100 begins at step 1101 by obtaining engine geometry 1120. At step 1103, a 3D simulation is performed using engine geometry 1120 and simulation results, such as plot 1121, are generated. Furthermore, at step 1102, engine geometry 1120 is meshed to create a 2D mesh 1122 of the turbofan engine. At step 1104, the results from the simulation performed at step 1103 are further processed to determine fan inlet characteristics, such as in the form of plot 1123. Method 1100 continues at step 1105 by performing a 2.5D FEM acoustic analysis using (i) 2D mesh 1122 and (ii) fan inlet data processing results (e.g., 1123). The results of the acoustic analysis at step 1105 can be plotted, such as plot 1124. Finally, at step 1106, far-field noise is determined. The determined far-field noise can be plotted (e.g., as shown in graph 1125) and provided to a user.

[0088] In an embodiment, method 1100 can prepare a 2.5D FEM simplified model for liner noise reduction assessment. According to an embodiment, a ducted modal analysis can be performed on the 3D simulation 1103 results to express the pressure field in the frequency domain in terms of azimuthal modes. This pressure field data can be used to directly set a simplified 2.5D model of the engine for further analysis. This simulation can generate 2D contours of the pressure wave for a specified frequency. Several frequencies can be analyzed over multiple rounds, and the results can be collected and combined to generate far-field power watt level (PWL) curves, such as chart 1125. In addition, in an embodiment, the user can submit the process for different liner properties or spatial extensions. This allows the user to quickly make trade-offs to identify the configuration that is most likely to produce the maximum noise reduction for a given frequency range of interest.

[0089] Embodiments provide a fully automated process for community noise assessment of turbofan engines and a supporting infrastructure for executing the process. In embodiments, the process is divided into three phases: 1) generating a digital twin of the real engine; 2) running a 3D high-fidelity CFD analysis; and 3) processing the simulation results. According to embodiments, the proposed method and infrastructure allow for fully automated execution of the three phases. Embodiments can run multiple simulations simultaneously. Each simulation can include modified engine geometry or the same engine for multiple operating conditions. Once the 3D simulation is complete, the results can be automatically processed and a readable dataset can be generated that can be interactively accessed within the system. This allows users to quickly assess the noise generated by a given engine and also allows for the identification of possible design improvements, for example by conducting trade-off studies. Furthermore, a smaller 2D model can be generated from a vertical cross-section of the original engine geometry, and a simplified model can be generated for nacelle aeroacoustic analysis. This simplified model can be used to quickly analyze the potential noise reduction that can be achieved through passive noise reduction devices, such as linings installed within the nacelle. Benefits of this approach include the ability to run shorter simulations, thus analyzing many liner placements and impedance values ​​in a very short time, which would not be feasible through physical testing.

[0090] An embodiment provides a computer-implemented, automated method for flow simulation and community noise assessment of a turbofan engine in a PaaS environment. Such an embodiment imports a file containing a digital representation of the three-dimensional engine geometry, automatically identifies the various engine parts, and generates a digital twin that can be simulated using CFD software. The embodiment performs high-fidelity CFD simulations to calculate the aerodynamic and far-field noise characteristics of the turbofan engine. A data set is generated by processing the results of the CFD simulations. The data indicates key design strengths and weaknesses and pinpoints areas of the model that need to be modified or redesigned. These identified problem areas can be redesigned, and an improved real-world turbofan can then be manufactured.

[0091] Embodiments may also generate a 2D simplified model of the original 3D engine to perform a trade-off analysis of the benefits of a liner mounted on the nacelle in terms of the noise level generated in the far field.

[0092] One embodiment creates a digital twin by reading the native CAD engine geometry, generating a surface mesh, and calculating the size and position of several engine parts, as well as characteristic geometric parameters such as fan diameter, number of blades, and fan tip gap size. Starting from scratch, this embodiment generates supporting entities that define the local size of the computational volume mesh, measurement surfaces, and so on. It then imports user-specified input parameters and manages the solver pre-processing software by assigning input parameters and launching and generating solver input files.

[0093] Another exemplary embodiment performs CFD analysis using a scheduling system to submit single or multiple runs on a remote or local cluster, and using centralized storage to store data generated by 3D engine simulation for subsequent post-processing.

[0094] In an embodiment, performing post-processing includes running a series of post-processing tools to generate comprehensive acoustic and aerodynamic datasets for each 3D simulation that has been previously run, and providing interactive access to all datasets via a single interface in an easy-to-navigate environment that allows a user to analyze simulation results and compare different runs.

[0095] In yet another embodiment, generating a 2D simplified model and performing a trade-off analysis includes extracting vertical 2D cross-sections of the original engine 3D geometry and generating a 2D mesh for simplified model analysis. Such an embodiment extracts the projected pressure field on the fan inlet and OGV outlet sections from the processed 3D engine data set, and then reads the simulation parameter list, the projected pressure field, and the user-specified liner impedance, and generates a simplified 2.5D FEM model for the cabin aeroacoustic analysis. Continuing, a series of single-frequency calculations can be run on the simplified model to cover a given frequency range of interest specified by the user. The generated noise is collected via a FW-H method on a series of microphones placed in the far field, and the generated data is collected and evaluated to determine the far-field noise level.

[0096] Exemplary Advantages

[0097] Embodiments provide the ability to perform the simulation and noise assessment process in an automated manner. Such functionality can be provided by the tools that have been selected to build this process and from the proposed methods presented herein for combining different data sets. In contrast, existing methods require the user to (i) manually modify engine model regions to have specific mesh refinements (e.g., changing the mesh resolution of specific parts), modify simulation characteristics (e.g., based on the sampling rate of the frequency of interest), and manually create a series of stages and surfaces to determine flow and measure flow properties.

[0098] The embodiments also provide a reduction in the cost and time required to obtain accurate engine noise predictions. This allows the embodiments to identify design flaws. In addition, the embodiments can be used to evaluate the noise reduction that can be achieved by installing passive noise reduction devices (liners).

[0099] Being able to identify design flaws in the early stages of turbofan engine design can avoid time-consuming and expensive physical testing and reduce the risk of later-stage fixes. Furthermore, early assessment of the engine's aerodynamic performance and estimation of its noise footprint can lead to better aircraft designs with more efficient engines and maximized maneuverability. Implementing noise reduction devices like liners can further extend engine maneuverability without limiting its performance (thrust). Due to the large number of parameters in the liner design space, reduced-order models are very useful because they can be used to quickly iterate multiple solutions.

[0100] Embodiments can be used to digitally determine the properties of real-world turbofans, for example, for regulatory compliance purposes. Furthermore, embodiments can be used to determine optimized turbofan designs, which can then be manufactured for real-world applications. As an example, embodiments can identify design flaws in a turbofan, and in response to such identified flaws, the turbofan can be redesigned and manufactured for real-world use.

[0101] Furthermore, embodiments can be used to improve existing turbofans. For example, it can be determined that an existing turbofan does not comply with noise regulations, and a CAD model of the turbofan can then be created (e.g., by measuring a real-world turbofan). This CAD model can be used in embodiments to determine design changes or implement improvements, such as an improved liner, that can be used in the real world to bring the turbofan into compliance.

[0102] Computer Support

[0103] Figure 121 is a simplified block diagram of a computer-based system 1220 that can be used to implement any of the various embodiments of the present invention described herein. System 1220 includes a bus 1223. Bus 1223 serves as an interconnect between the various components of system 1220. Connected to bus 1223 is an input / output device interface 1226 for connecting various input and output devices (e.g., keyboard, mouse, display, speakers, etc.) to system 1220. Central processing unit (CPU) 1222 is connected to bus 1223 and provides execution of computer instructions that implement embodiments such as methods 100, 330, 1100, and system 220. Memory 1225 provides volatile storage for data used to execute computer instructions that implement embodiments described herein, such as those previously described above. Storage device 1224 provides non-volatile storage for software instructions such as an operating system (not shown) and embodiment configurations. The system 1220 also includes a network interface 1221 for connecting to any of various networks known in the art, including a wide area network (WAN) and a local area network (LAN).

[0104] It should be understood that the exemplary embodiments described herein can be implemented in many different ways. In some cases, the various methods and systems described herein can each be implemented by a physical, virtual, or hybrid general-purpose computer such as computer system 1220 or a computer such as described below. Figure 13 The computer network environment 1330 described herein is implemented in the computer environment 1330. The computer system 1220 can be transformed into a system that performs the methods described herein (e.g., 100, 330, 1100), for example, by loading software instructions into the memory 1225 or non-volatile storage device 1224 for execution by the CPU 1222. It should be further understood by those skilled in the art that the system 1220 and its various components can be configured to perform any embodiment or combination of embodiments described herein. Furthermore, the system 1220 can implement the various embodiments described herein using any combination of hardware, software, and firmware modules operatively coupled internally or externally to the system 1220.

[0105] Figure 13 A computer network environment 1330 is shown in which embodiments may be implemented. In the computer network environment 1330, a server 1331 is linked to clients 1333a-n via a communication network 1332. The environment 1330, alone or in combination with the server 1331, can be used to enable the clients 1333a-n to perform any of the embodiments described herein. For non-limiting examples, the computer network environment 1330 provides cloud computing embodiments, software as a service (SAAS) embodiments, and the like.

[0106] The embodiments or aspects thereof may be implemented in the form of hardware, firmware, or software. If implemented in software, the software may be stored on any non-transitory computer-readable medium that is configured to enable a processor to load the software or a subset of its instructions. The processor then executes the instructions and is configured to operate or cause the device to operate in the manner described herein.

[0107] Further, firmware, software, routines, or instructions may be described herein as performing certain actions and / or functions of a data processor. However, it should be understood that such descriptions are included herein only for convenience and that such actions are actually caused by a computing device, processor, controller, or other device executing the firmware, software, routines, or instructions.

[0108] It should be understood that the flow charts, block diagrams, and network diagrams may include more or fewer elements, be arranged in different ways, or be presented in different ways. However, it should be further understood that certain embodiments may indicate the number of block diagrams and network diagrams and block diagrams that illustrate the execution of an embodiment implemented in a particular manner.

[0109] Thus, further embodiments may also be implemented using a variety of computer architectures, physical, virtual, cloud computers, and / or some combination thereof, and thus the data processors described herein are intended for illustration purposes only and not as limitations of the embodiments.

[0110] The teachings of all patents, published applications, and references cited herein are incorporated by reference in their entirety.

[0111] While exemplary embodiments have been particularly shown and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the embodiments as encompassed by the appended claims.

[0112] For example, the foregoing description and details of the embodiments in the figures refer to applicant-assignee (Dassault Systèmes USA, Inc.) and Dassault Systèmes, tools, and platforms for purposes of illustration and not limitation. Other similar tools and platforms are suitable.

Claims

1. A computer-implemented method for determining a physical property of a turbofan engine, the method comprising, by a processor: obtaining, in a memory coupled to the processor: (i) a computer-aided design (CAD) model representing a turbofan engine and (ii) an indication of a flow condition; automatically determining a solver input file based on the CAD model and the indication of the flow condition; and Using the determined solver input file, a simulation of the turbofan engine is responsively performed under the constraints of the flow conditions, wherein, Results of the simulation indicate physical properties of the turbofan engine.

2. The method according to claim 1, wherein The processor is one of a variety of processors that support global network platform services.

3. The method according to claim 2, wherein: The CAD model and an indication of a flow condition are obtained in response to user input via a user interface of the platform service.

4. The method according to claim 1, wherein The flow conditions include at least one of: free stream air velocity, fan speed, and air temperature.

5. The method according to claim 1, wherein The determined solver input file includes at least one of: a surface mesh, a measurement surface, and an indication of the flow condition.

6. The method according to claim 5, wherein: Determine that the solver input file includes at least one of the following: creating the surface mesh; and The measurement surface is created in the surface mesh.

7. The method according to claim 6, wherein: Creating the surface mesh includes at least one of the following: analyzing the CAD model to determine geometric parameters of at least one of: engine bounding box dimensions, nacelle leading and trailing edge locations and shapes, fan diameter, number of fan blades, fan tip clearance size, fan blade leading and trailing edge shapes, outlet guide vane location, outlet guide vane leading and trailing edge shapes, low-pressure compressor stage location, number of low-pressure compressor stage blades, low-pressure compressor stage blade leading and trailing edge shapes, and low-pressure compressor rotor stage fan tip clearance size; as well as Parts of the turbofan engine are identified based on corresponding names of components of the CAD model representing the identified parts.

8. The method according to claim 7, further comprising: The surface mesh is created based on at least one of: the determined geometric parameters and the identified parts.

9. The method according to claim 5, wherein: Executing the simulation includes: generating a volume mesh based on the surface mesh; and The simulation is performed using the generated volume mesh.

10. The method according to claim 9, further comprising at least one of the following: setting a local grid resolution of at least a portion of the volume grid; and The volume mesh resolution region is set based on the size and shape of the elements in the surface mesh.

11. The method according to claim 5, wherein: Executing the simulation includes: Determine the simulation conditions.

12. The method according to claim 11, wherein Determining the simulation condition includes at least one of the following: determining a position of the measurement surface based on the indication of the flow condition and a size and shape of elements in the surface mesh; setting a length of the simulation based on a minimum frequency of interest; as well as Set the sampling rate based on the maximum frequency of interest.

13. The method according to claim 1, wherein Performing the simulation includes at least one of performing a computational fluid dynamics (CFD) simulation and a lattice Boltzmann method (LBM) simulation.

14. The method according to claim 1, wherein The determined physical properties include at least one of: an aerodynamic property, a thermal property, and an acoustic property.

15. The method according to claim 1, further comprising: generating a two-dimensional (2D) mesh from the solver input file; as well as Using the results of the simulation and the generated 2D mesh, a plurality of finite element method (FEM) simulations are performed to determine noise reduction properties for each respective liner in the generated 2D mesh, each FEM simulation being performed using respective flow conditions and a representation of the respective liner.

16. A system for determining a physical property of a turbofan engine, the system comprising: processor; as well as a memory having computer code instructions stored thereon, the processor and the memory being configured with the computer code instructions to cause the system to: obtaining in the memory: (i) a computer-aided design (CAD) model representing a turbofan engine and (ii) an indication of a flow condition; automatically determining a solver input file based on the CAD model and the indication of the flow condition; as well as Using the determined solver input file, a simulation of the turbofan engine is responsively performed under the constraints of the flow conditions, wherein results of the simulation indicate physical properties of the turbofan engine.

17. The system according to claim 16, wherein: The determined solver input file includes at least one of: a surface mesh, a measurement surface, and an indication of the flow condition.

18. The system according to claim 17, wherein: Upon determining the solver input file, the processor and the memory are configured with the computer code instructions to cause the system to perform at least one of the following: creating the surface mesh; as well as The measurement surface is created in the surface mesh.

19. A system for determining a physical property of a turbofan engine, the system comprising: processor; as well as a memory having computer code instructions stored thereon, the processor and the memory being configured with the computer code instructions to cause the system to implement a platform service, the platform service being configured to: obtaining in the platform memory: (i) a computer-aided design (CAD) model representing a turbofan engine and (ii) an indication of flow conditions; determining a solver input file based on the CAD model and the indication of the flow condition; as well as Using the determined solver input file, a simulation of the turbofan engine is performed under the constraints of the flow conditions, wherein results of the simulation indicate physical properties of the turbofan engine.

20. A computer program product for determining a physical property of a turbofan engine, the computer program product comprising: one or more non-transitory computer-readable storage devices and program instructions stored on at least one of the one or more storage devices that, when loaded and executed by a processor, cause an apparatus associated with the processor to: obtaining: (i) a computer-aided design (CAD) model representing a turbofan engine and (ii) an indication of flow conditions; determining a solver input file based on the CAD model and the indication of the flow condition; as well as Using the determined solver input file, a simulation of the turbofan engine is performed under the constraints of the flow conditions, wherein results of the simulation indicate physical properties of the turbofan engine.

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